Music Therapy modulates Craving, Inhibitory Control, and Emotional Regulation: EEG, Psychometric, and Qualitative Findings from a Pilot RCT in a Community Outpatient Service

Music Therapy modulates Craving, Inhibitory Control, and Emotional Regulation: EEG, Psychometric, and Qualitative Findings from a Pilot RCT in a Community Outpatient Service | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Music Therapy modulates Craving, Inhibitory Control, and Emotional Regulation: EEG, Psychometric, and Qualitative Findings from a Pilot RCT in a Community Outpatient Service Filippo Pasqualitto, Clemens Maidhof, Daniel Murtagh, Devon Silva, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5837441/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Music therapy (MT) has been shown to be effective for multiple clinical endpoints in clients with Substance Use Disorder (SUD). However, a gap remains in understanding the impact of MT interventions in community services, primarily due to the lack of studies that combine neural measures (e.g., EEG), psychometric tests, and semi-structured interviews. Methods. This pilot study is a three-arm, non-blinded, mixed-methods randomized trial. Sixteen participants with Substance Use Disorder (SUD) were recruited from a community service in London. Ten of these participants received six weekly group or individual music therapy (MT) sessions in addition to the standard treatment (ST) provided by the community outpatient service. The remaining six participants received only the ST. Pre-/post-intervention as well as in-session measures have been collected utilizing EEG in addition to psychometric tests and semi-structured interviews addressing craving, depressive, and anxiety symptoms, inhibitory cognitive control, and participants’ perceptions on the music-therapeutic process. An intention-to-treat approach was employed. Results. Fourteen participants completed the study. Results showed (1) lower beta frequency band related to craving arousal post-MT intervention as compared to ST; (2) lower subjective evaluation of craving intensity after MT sessions; (3) different impact of MT and ST on frontal alpha asymmetry related to affective processing; (4) enhanced neural mechanisms (i.e., P3d in a Go/NoGo task) related to sensorimotor response inhibition following MT; (5) qualitative themes reflecting absence of craving, reluctance towards craving discussions, narratives on experiences, emotions, and the therapeutic process. Conclusions. MT might facilitate lower post-intervention arousal related to craving as compared to ST. While this effect is evident at the neural level, the conscious perception of the decrease emerges only after MT sessions and not after the entire intervention. The differential brain asymmetry may represent higher emotional regulation and introspection associated with MT compared to ST. MT may facilitate neuromodulation that boosts inhibitory cognitive control functions. Themes emerging from semi-structured interviews highlight the transformative potential of MT in alleviating craving and stimulating reflection. Findings from this pilot study are promising but further research through a larger clinical trial is necessary to confirm and expand upon this pilot. Trial registration. NCT05180617. Music therapy Substance Use Disorder community service electroencephalography psychometric tests semi-structured interviews Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Substance Use Disorder (SUD) has been defined as a chronic, and often relapsing disorder ( 1 – 4 ) targeting multiple interacting neural circuits in the brain ( 5 , 6 ). Craving is the key symptom of SUD ( 1 , 7 ), often accompanied by cognitive, physiological, and behavioural indicators that the individual continues to use the substance despite adverse consequences such as difficulties in emotional regulation and inhibitory cognitive control, as well as symptoms related to depression and anxiety ( 1 ). To date, interventions aimed at improving mental health symptoms, quality of life and reducing relapses in individuals with SUD show variable outcomes ( 1 , 8 , 9 ) with limited and inconsistent reports for cognitive-behavioural therapy ( 10 ), pharmacotherapy, and psychotherapy/psychosocial interventions ( 11 ). Despite inconsistent results that have also been noted in the field of music therapy (MT) for SUD ( 12 , 13 ), a recent Cochrane systematic review has identified MT as a valuable type of intervention for SUD in addition to standard treatment (ST) ( 12 ). A similar conclusion was suggested by a World Health Organization report, which examined over 3,000 studies on the impact of the arts and art-based interventions (including MT) on health and well-being ( 14 ). Those reviews analysed evidence from a broad spectrum of research designs, including uncontrolled pilot studies, case reports, small-scale cross-sectional surveys, nationally representative longitudinal cohort studies, community-wide ethnographies, and RCTs ( 12 , 14 ). While no previous research has specifically used neuroscientific methods, these studies have provided insights into the potential of MT for addressing SUD. Incorporating neuroscientific methods is recommended to enhance our understanding of the brain-based mechanisms underlying the effects of MT and art-based interventions on health and well-being ( 12 , 14 , 15 ). This pilot intended to lay the foundations for larger clinical trials, presenting, for the first time in a community setting, findings on craving and psychological dimensions examined through multiple lenses. Indeed, in the clinical trial protocol ( 16 ) we proposed a multi-domain approach within a mixed-methods design integrating neurophysiological data from electroencephalography (EEG) recordings with subjective assessments obtained via psychometric tests and semi-structured interviews. These assessments covered various aspects, including craving symptoms, depressive and anxiety symptoms, inhibitory control, emotional expression, and therapeutic alliance ( 16 ). MT is defined as a clinical intervention delivered by an accredited music therapist who adopts music as a therapeutic tool to accomplish individualized goals within a therapeutic relationship ( 17 , 18 ). It has also been defined as a psychological therapy that aims to create an interpersonal relationship between the client and the therapist to relieve symptoms and determine positive changes related to emotional regulation, motivation, and social engagement ( 12 , 19 – 21 ). Recognizing the distinction between therapeutic music interventions and the general use of music without therapeutic intent is crucial ( 22 ). Key elements of a therapeutic intervention include the involvement of a certified professional, a therapeutic environment, and an underlying framework guiding the intervention ( 23 ). There is extensive literature showing the emotional impact of music, even outside therapeutic settings, suggesting its potential for therapeutic applications ( 24 , 25 ). Emotional responses to music involve complex psycho-physiological mechanisms, including evocation of autobiographical memories, automatic physiological reactions, and visual imagery ( 24 – 26 ). Neuroimaging studies revealed that music appreciation is processed in analogous brain regions (i.e., the mesocorticolimbic system) as other intensely pleasurable and rewarding stimuli, such as psychoactive drugs ( 24 , 25 , 27 , 28 ). These findings led researchers and clinicians to consider that engaging with music might alleviate SUD-related symptoms opening various possibilities for clinical application ( 12 , 16 , 29 , 30 ). However, music can also represent a cue inducing the feeling of craving ( 31 , 32 ), underscoring the importance of investigating MT mechanisms of change ( 12 ). Although a definite neural mechanism of therapeutic change is still based on empirical speculations, it has been proposed that within a MT setting, emotional experiences can be retrained and reframed ( 30 ). Such a process would gradually mitigate and recalibrate the emotional impact related to music-induced memories ( 12 ) by targeting neurobiological elements that function as a common denominator in the neural underpinning of reward processing, addiction, and craving ( 33 ). Three systematic reviews ( 12 , 13 , 34 ) have explored the impact of MT on SUD, revealing positive impact on depressive ( 31 – 33 ) and anxiety symptoms ( 36 , 38 , 39 ), negative emotions ( 36 ), and craving ( 40 – 42 ). However, there is a need for rigorous evaluations of MT effectiveness within community services, particularly combining neuroscientific methods, such as EEG, with subjective evaluation instruments ( 12 , 16 ). As detailed in our clinical trial protocol paper ( 16 ), the current pilot study addresses this gap by combining EEG-based neurophysiological data with psychometric assessments to examine MT’s impact on craving, depression, and anxiety. Specifically, we aim: (a) to investigate whether MT is associated with a reduction of craving, depressive, and anxiety symptoms; (b) to investigate whether MT is associated with an improvement in inhibitory cognitive control; (c) to explore the subjective experiences of SUD outpatients regarding the music-therapeutic process, focusing on the influence of MT sessions on their craving sensations, emotional expression and the dynamics of the therapeutic relationship. Methods Design This pilot study is a part of a parent feasibility project for MT in community settings for SUD - ClinicalTrial.gov number: NCT05180617 (https://clinicaltrials.gov/ct2/show/NCT05180617). The study was designed as a three-arm non-blinded mixed-methods randomized trial. Participants were systematically randomized into three groups: individual-MT, group-MT, and ST. Outcome measures were taken at pre-, post-intervention stages and at in-session timepoints. The study commenced and concluded with pre- and post-intervention assessments, respectively in the first and eighth week. Measures included in these two stages were named "Assessment Module 1" (Figure 1). The intervention phase, from weeks 2 to 7, involved distinct protocols for group-MT and individual-MT groups, with the former undergoing "Assessment Module 2" and the latter completing both "Assessment Module 2" and an additional "Assessment Module 3" during specified sessions. [Figure 1] Settings An initial cohort of 18 service users from “V-i-a” community service (based in London, UK) were assessed for eligibility based on the inclusion/exclusion criteria defined beforehand in the protocol (16). The sample's heterogeneity is addressed in the limitations section, while participants' demographic information and medication details are provided in the supplementary materials (Additional file 1, A7). Recruitment and allocation After two participants declined to participate, a cohort of 16 SUD participants was formed in two recruitment phases (Figure 1). In an initial two-week recruitment phase, 6 participants voluntarily consented to join the study and subsequently underwent pre-intervention measurements (i.e., Assessment Module 1). Due to logistical considerations related to the scheduling of group-MT, a staggered random allocation approach was employed: (I) 4 participants out of the first 6 recruited were randomly assigned to group-MT arm utilizing the GraphPad software (www.graphpad.com); (II) the allocation of the remaining 2 participants to either the ST or the individual-MT group was determined taking into consideration the order of returned consent forms and a stochastic method: a random number between 0 and 1 was generated, where a result smaller than 0.5 led to the earlier respondent being placed in the ST, while the subsequent participant was assigned to individual-MT. To address recruitment challenges within the community service, a second two-week recruitment phase was added. Because the group-MT sessions could begin after the first recruitment phase, all incoming participants were randomly assigned either to individual-MT group or to the ST. Thus, a sample of 16 participants with SUD was formed: 4 participants allocated to the group-MT, 6 to the individual-MT and 6 to the ST. It's worth noting that the original, published recruitment strategy (16), required a modification due to complexities inherent to the clinical setting. Outcome measures EEG and self-reported assessment of craving To measure participants’ feeling of craving in the preceding 24 hours, the Brief Substance Craving Scale (BSCS) questionnaire was completed. The neurophysiological signature associated with craving, resting-state EEG beta frequency band, was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages. The BSCS is a validated questionnaire to assess craving in the SUD population over a period of 24 hours (43,44). It is a 4-item questionnaire with three major factors (i.e., duration, frequency, and intensity of craving) structured at 5-point Likert scale. Although the BSCS has initially been used and validated in cocaine studies, this questionnaire has been shown to be easy to use and reliable across different substances (43). The frontally-distributed EEG power in the beta frequency band is a neural EEG rhythm underlying both abstinence-related and cue-induced craving (45,46). Key studies found an increase in beta frequency band mean absolute power (μV 2 ) in a frontal region of interest (i.e., Fz, FP1, FP2, F3, F4, F7, F8 electrodes’ sites) during exposure to drug-related cues (45). These increased values in frontal beta mean absolute power were found both in ERPs (45) and resting-state EEG activity (46–49). Modulations in the frontally-distributed beta frequency bands have been proposed as a marker for craving-related arousal (46,50). For this outcome measure, population-normalized z-scores over a frontal region-of-interest (ROI), derived from the beta absolute power values, have been assessed. To measure instantaneous levels of craving intensity, an additional short self-report has been included. A “craving thermometer” (CT) - in the form of a visual analogue scale (VAS) - was completed before and after each MT session (group-MT and individual-MT sessions) by each participant. This measure has been adopted in SUD populations, both as a standalone subjective outcome (51) and combined with a neurophysiological assessment (52). The CT offers a quick-scan of craving intensity with one item: “please, rate how strong your drug craving is right now by putting a mark on the line going from 0 - not craving at all - to 10 - the most ever ”. EEG and self-reported assessment of depressive symptoms To assess frequency and severity of depressive symptoms in the preceding 2 weeks, the Patient Health Questionnaire – 9 (PHQ-9) was completed. The neural signature associated with depressive symptoms, resting-state EEG frontal alpha asymmetry (FAA), was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages. The PHQ-9, a widely recognized self-report tool for assessing depressive symptoms (53–55), has demonstrated a good internal consistency when administered to participants with SUD (Cronbach’s α=.90; (56)). It consists of one central question (“over the last two weeks, how often have you been bothered by any of the following problems”) to assess nine symptoms in nine items of depression on a 4-point Likert scale. The PHQ-9 total score, given by the sum of the nine items is ranked in one of the following severity categories: 1-4 – minimal depression; 5-9 – mild depression; 10-14 – moderate depression; 15-19 – moderately severe depression; 20-27 – severe depression) (53). The FAA is an EEG hemispheric asymmetry measure representing the difference between the left and right alpha activity over the frontal regions of the brain (57–62). Modulations of power imbalances between the left and right hemispheres in the alpha frequency band have been associated with the approach/withdrawal theory of emotions (58,60) and proposed as a marker of affective processing underlying symptoms of depression (58–60,63,64). FAA has also been analysed in cue-exposure paradigm as an indicator craving-related approach towards the substance (65,66). This signature is generally examined over homologous pairs of frontal electrodes, namely F3/F4, F7/F8 and FP1/FP2 (67–70). For this outcome measure, population-normalized z-scores on the above-cited electrode pairs, derived from the FAA absolute power values, have been assessed. EEG and self-reported assessment of anxiety symptoms To measure participants’ anxiety symptoms in the preceding 2 weeks, the Generalised Anxiety Disorder Assessment – 7 (GAD-7) questionnaire was completed. The neural signature associated with anxiety symptoms, resting-state frontal midline theta (FMT), was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages. The GAD-7 is a validated (71) and standardized (72) self-report to screen for anxiety symptoms showing a good internal consistency when completed by SUD participants (Cronbach’s α = .91; (72)). The 7-item GAD-7 has been identified a reliable screening tool for anxiety (71,72). It consists of one central question (“over the last two weeks, how often have you been bothered by any of the following problems”) to assess seven symptoms in seven items of anxiety on a 4-point Likert scale (or forced Likert scale). The GAD-7 total score, given by the sum of the seven Items, is ranked in one of the following categories: 0-4 – minimal anxiety; 5-9 – mild anxiety; 10-14 – moderate anxiety; 15-21 – severe anxiety. The FMT is an EEG measure that has been associated with anxiety (73). It has been suggested that modulations in resting-state FMT power (maximal at Fz electrode’s site) might represent a neural marker of cognitive and emotional demands underlying anxiety symptoms, where lower FMT values are associated with more severe anxiety symptoms, and higher values with less severe symptoms (67,68,73). For this outcome measure, population-normalized mean z-scores over Fz electrode’s site, derived from the FMT absolute power values, are the main outcome measure. Go/NoGo ERP task to assess inhibitory control After the 5-minute eyes-closed resting-state EEG recordings, participants completed a behavioural Go/No-Go task while event-related potentials (ERPs) were recorded to assess inhibitory cognitive control at pre- and post-intervention stages. This task required participants to respond with a button press when a set of stimuli was presented in a monitor (e.g., circles appearing at the top right and bottom left corners of the screen) and to withhold the response when another set of stimuli, with the same probability of occurrence, was presented (e.g., circles appearing at the top left and bottom right corners of the screen) (74,75). The Go/NoGo task serves as a tool for assessing the ability to execute or inhibit motor responses based on the nature of the presented stimuli and it is instrumental in examining sensorimotor response inhibition (76–80), a key aspect of cognitive control that is often compromised in individuals with SUD (81,82). The P3 is a positive ERP component, emerging approximately 300–800 ms after stimulus onset, and analysed primarily in the "oddball" and the "Go-NoGo" tasks (83–85). The P3d, or the difference wave obtained by subtracting Go-P3 amplitudes from NoGo-P3 amplitudes, offers a refined measure of the neural activity specifically associated with inhibitory control, by isolating the neural responses to inhibition from those related to general task engagement and response execution (78,80). The task was constituted of two types of visual stimuli: (I) a fixation cross and (II) a circle that, depending on the position on the screen, could be either a Go or a NoGo stimulus (Figure 2). Participants were instructed to press a button on a response box (The Black Box Toolkit Ltd., www.blackboxtoolkit.com) as quickly as possible, when a circle appeared at the top right or at the bottom left-hand corner of the screen (Figure 2.A) and to withhold the response any time a circle appeared at the top left and bottom right corners (Figure 2.B). Both Go and NoGo conditions had a fixed inter-trial interval (ITI) of 2000 ms and an inter-stimulus interval (ISI) of 1000 ms. The Go and NoGo stimuli occurred with an equal probability (100 trials evenly distributed in 50 Go and 50 NoGo) and their presentation order was randomized for each participant adhering to two restrictions: a stimulus type (i.e., Go or NoGo) (I) could not appear for more than 10 times in a row and (II) could not appear for more than 50 times (i.e., to guarantee equiprobability). The stimuli were presented for 100 ms and between the presentation of Go and NoGo stimuli, a fixation cross was presented (500 ms before a stimulus and 400 ms after the presentation of the stimulus). Any response that occurred outside a 400 ms window post-stimulus presentation was considered incorrect. The experimental design comprised two distinct phases: a practice session (40 trials) and a recording session (100 trials). The total duration of the task was approximately 5 minutes, ensuring it remains manageable for patient compliance. The task was designed with a custom-made script, developed, and displayed for participants using Presentation® software (Version 23.0, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com). Description of semi-structured interviews The semi-structured interview used in this study served as a qualitative outcome measure to explore participants’ perspectives on the music-therapeutic process by focusing on craving, emotional expression, and the therapeutic relationship. Three main topics were addressed with three open questions: “Was there any moment or moments in today’s session where you felt the urge for something?”, “Was there something particularly unpleasant or enjoyable that stood out?”, “Thinking about the relationship with your therapist, is there anything that you’d like to share about today’s session?”. These three questions represented the structured part and were followed (or not, depending on participants’ availability to share more information) by a non-structured part. In the non-structured part, participants could share more detailed responses regarding the three main topics or explore other elements that were relevant to them in their therapeutic journey. Measurement tools The instruments used for data collection encompassed a range of psychometric, neurophysiological, and qualitative tools to ensure a comprehensive assessment. Psychometric measures, including the BSCS, PHQ-9, GAD-7, and the CT, were administered in a traditional pencil-and-paper format, alongside a pre-EEG screening questionnaire completed by participants. The screening questionnaire and all the other measures were detailed in the clinical trial protocol paper (16) beforehand. Neurophysiological data were collected using EEG recordings, which included both 5-minutes resting-state and 5-minutes ERP measurements. The EEG system employed R-Net passive electrodes with Ag/AgCl pellets and sponges soaked in an electrolyte/saltwater solution. The data acquisition was managed through BrainVision Recorder software, and signals were amplified using the LiveAmp amplifier (Brain Products, GmbH, Gilching, Germany). A total of 32 channels were used (Fp1, Fp2, Fz, F3, F4, F7, F8, F9, F10, FC5, FC6, FC1, FC2, Cz, C3, C4, T7, T8, CP5, CP6, CP1, CP2, Pz, P3, P4, P7, P8, TP9, TP10, Oz, O1, O2), positioned according to the international 10/20 system. The ground electrode was placed at FPz, while the reference electrode at FCz. The amplifier’s sampling rate was set at 250 Hz, and electrode impedances were carefully adjusted between 0-100 kΩ to align with the amplifier’s input impedance, following recommendations from the literature (86) and the manufacturer’s guidelines for optimal data quality. EEG signals were inspected for quality and artifacts, with noisy segments excluded. Pre-intervention EEG resting-state signals were inspected (in terms of electrographic seizures, interictal epileptiform discharges, asymmetry, and slowing) by a clinical neurophysiologist (A.M.) who confirmed no clinically relevant incidental findings. For the ERP-Go/NoGo task, the LiveAmp amplifier received stimulus onset and response triggers via the Presentation software, interfaced through a Trigger Box (Brain Products, GmbH, Gilching, Germany). Stimulus and response markers were processed with a custom MATLAB (2022b) script, which re-coded markers in the EEG file to match the corresponding Presentation software log entries. Unique labels were assigned for Go stimuli (S_Go), NoGo stimuli (S_NoGo), and participant responses (R). The script also calculated behavioural performance metrics and cross-verified latencies between EEG markers and logfiles (details in Appendix A.3). Semi-structured interviews were conducted as part of the qualitative data collection, using a smartphone for recording. These recordings were promptly transferred to a password-protected computer at Anglia Ruskin University (ARU) and deleted from the smartphone to ensure data security. This comprehensive approach integrated psychometric assessments, advanced EEG recordings, and qualitative interviews to provide a robust dataset for analysis. [Figure 2] Types of intervention The MT intervention proposed in this study aligned with the integrative improvisational MT model, characterized by its integration of musical improvisation and verbal therapeutic techniques (19–21). This approach leverages the dynamic interplay between spontaneous musical creation and verbal reflection to facilitate therapeutic outcomes. In the present study, the MT sessions had the aim to support and encourage physical, mental, social, emotional, and spiritual wellbeing (16) and were delivered by a qualified music therapist registered with the UK Regulator for health and care professions (Health and Care Professions Council). The music therapist trained through an accredited training course, offered in the UK in the form of a master’s level training. The MT sessions were multifaceted, encompassing a range of musical activities such as improvisation, composition, music production, song writing, lyric analysis of preferred songs, singing, and dialogue. These sessions were delivered by a music therapist (who also was a music technology researcher), utilising music production technologies and mobile/smart technologies (e.g., iPads, MIDI [Musical Instrument Digital Interface] keyboards, Ableton Digital Audio Workstation) playing a supportive role in enhancing the therapeutic environment. A distinctive feature of these sessions, in line with the principles of improvisational MT, was that participants were not required to have any prior musical experience or skill level (16,20). During the sessions, a hybrid approach was employed, integrating digital and acoustic instruments to create a multifaceted therapeutic environment. Participants in the community service could be engaged in different treatment statuses which involved different types of ST received (see Additional file 1, A7). Participants engaged in structured treatment (tier 3) were offered 1:1 individual session with a keyworker, group-based activities, prescribing, detoxification, blood-borne virus testing and vaccination. Structured treatment could also include referrals to or help in accessing support from other services within the community, e.g., mental health services, social services, employment support. All service users in structured treatment are also provided a “Capital Card”, which acts as a form of contingency management (87). The “Capital Card” allows service users to accrue points when they engaged in structured treatment activities. Service users are then able to spend these points within Via's “Capital Card” shops and at local partner agencies such as cinemas and gyms. Service users who successfully completed the structured treatment (tier 3) stage could enter “tier 2” - recovery support. Recovery support included supporting service users with sustaining their recovery, determining what their future life will look like and reintegrating into the local community. The tier 2 stage included recovery check-ups and activities related to educational opportunities or to support in the search of vocational training or support in finding employment. This type of support was also associated with peer mentoring opportunities. Processing and analysis Resting-state EEG preprocessing EEG resting-state data were preprocessed and processed using Neuroguide software (88,89) (Applied Neuroscience; St. Petersburg, Florida, United States), which is an FDA-approved tool and incorporates a normative database from 625 healthy individuals aged 2 months to 82 years. The processing of resting-state EEG involved several steps. (I) Data Recording and Segmentation. Manual notes were taken during EEG recording to identify artifacts and disturbances. The raw data were visually inspected and segmented using BrainVision Analyzer (BrainVision Analyzer, Version 2.2.2, Brain Products GmbH, Gilching, Germany) to include only task-relevant resting-state sections (e.g., excluding moments at the beginning or at the end of the resting period). (II) Data Import and Re-referencing. Segmented resting-state recordings were exported in EDF+ format and imported into Neuroguide. Participant information (sex, age, recording date, etc.) was entered for normative comparison. The EEG data were re-referenced to a standard linked-ears montage. Neuroguide applied preprocessing steps: down-sampling to 128 Hz, and bandpass filtering (1-55 Hz). (III) Artifact Rejection. Neuroguide's semi-automatic artifact rejection procedure was employed, using a 10-second artifact-free data selection and a z-score pattern recognition routine to identify and exclude artifacts (drowsiness, eye movements, muscle activity). Sensitivity was set to "high" with a 2.0 SD z-score threshold. (IV) Reliability Estimation. Selected artifact-free data were assessed for split-half reliability (> .95) and test-retest reliability (> .90) for data segments longer than 60 seconds. Specifically, artifact-free recordings in the MT group ranged from 01:16 and 04:07 minutes (mean at pre-intervention stage of 2.1 minutes; mean at post-intervention stage of 2.2 minutes). Similarly, in the ST group, artifact-free lengths ranged from 01:14 and 03:27 minutes (mean at pre-intervention stage of 1:30 minutes; mean at post-intervention stage of 1:34 minutes). Resting-state EEG processing Neuroguide calculates the power spectrum for artifact-free EEG selections using Fast Fourier Transform (FFT). EEG data were divided into 2-second epochs at 128 samples/s, covering a frequency range of 0.5-55 Hz. Frequency ranges of interest were theta (4-8 Hz), alpha (8-12 Hz), and beta (12-25 Hz). For beta and theta power, the linked-ears montage was used, while a Laplacian re-referencing was applied for FAA power calculation as in previous MT studies exploring resting-state FAA power (90). The software log-transforms amplitudes within the frequency range for 19 channels to compute population-normalized z-scores. ERP – Go/NoGo task preprocessing and processing EEG datasets were imported into BrainVision Analyzer and re-referenced to linked-mastoids. A high-pass filter (0.1 Hz) was applied before Independent Component Analysis (ICA) to identify and remove ocular artifacts. ICA separates the EEG into independent components, aiding artifact detection(91) Components reflecting blinks and eye movements were identified through their topography, time course, and spectral distribution. On average, 1.6 components per participant were corrected to zero. Post-ICA, a low-pass filter (30 Hz) and a Notch filter (50 Hz; Butterworth 4 th order) were applied to correct for electromyogram-related artifacts and electrical noise. Data were segmented relative to Go and NoGo stimuli onsets (from -200 to +800 ms), and segments were baseline corrected (from -200 to 0 ms). Artifact rejection was performed based on predefined parameters (gradient: 50 μV; max/min 200 μV min allowed amplitude: -100 μV; max allowed amplitude: 100 μV) marking the data violating those parameters with a window going from -200 to 200 ms from the artifact. After that, flagged segments were visually inspected and only artifact-free epochs retained for analysis. Artifact-free EEG epochs were averaged to generate ERP waveforms for each subject, including Go and NoGo trials, and difference waves (NoGo-minus-Go). For statistical analyses, data were averaged separating conditions and timepoints (pre- and post-intervention) to examine effects within the 350-500 ms time window for Go-P3, NoGo-P3, and NoGo-minus-Go P3 (i.e., P3d) waveforms. A multi-layered approach has been used to determine the optimal time-window for P3 component analysis. Combining information from (a) previous literature employing similar designs (74), (b) visual inspection, and (c) latency analysis with Brain Vision Analyzer’s peak detection tool, a consistent 350-500 ms time window has been identified. The region-of-interest (ROI) for Go-P3, NoGo-P3, and P3d was based on existing literature (74,92,93), focusing on midline and frontocentral electrodes. Psychometric reports processing Data from pencil-and-paper psychometric reports were entered into Excel files for subsequent statistical analysis using JASP software (Version 0.19.0, https://jasp-stats.org/). Semi-structured interview processing A modified version of Reflexive Thematic Analysis was employed to analyse qualitative data from semi-structured interviews in six steps (94,95). The six-phase process included familiarization with data, systematic coding, initial theme generation, theme development and review, theme refinement and naming, and report writing. The adaptation lies in the fact that a Defense Mechanism Rating Scales framework was used to guide and enhance the coding procedure of the latent meanings (96,97). Variables The independent variable introduced in the current study is the type of intervention: MT or ST. MT included group-MT and individual-MT, which were treated as a unique group for quantitative data analysis. Constructs of interest were short-term and present-moment craving symptoms (as expressed with dependent variables related to BSCS, beta frequency band, and CT), depressive symptoms (as expressed with dependent variables related to PHQ-9 and FAA) and anxiety symptoms (as expressed with dependent variables related to GAD-7 and FMT), sensorimotor response inhibition (as expressed with dependent variables related to P3d ERP and behavioural indicators), and qualitative perceptions of the music-therapeutic process (as expressed with dependent variables related to the content of participants’ interviews). Statistics Approach The analysis strategy for pre-/post-intervention outcome measures involved the use of a mixed-design ANCOVA to examine within-subjects (Timepoint: pre-intervention, post-intervention) and between-subjects (Type of intervention: MT, ST) changes, adjusting for baseline differences (i.e., using baseline scores as a covariate) (98,99). Parametric assumptions (i.e., normality of residuals, linearity of relationships, sphericity, homogeneity of variance, and independence of observations) were checked, and because of the small sample size, non-parametric alternatives were considered when necessary. This involved the use of mean ranks as a substitute for parametric tests to yield comparable outcomes (100,101). Concerning relational analyses, Spearman’s rho correlations explored relationships between EEG z-scores and questionnaire scores. Concerning the CT, administered pre-/post-MT sessions, the Wilcoxon signed-rank test was adopted. Overall, the study utilized a comprehensive data analysis strategy incorporating both quantitative and qualitative methods. Results Pre-/post-intervention craving symptoms Results from beta mean z-scored absolute power Beta power decreased after MT from 11.7 μV 2 (SD = 4.8, SE = .6) to 10.7 μV 2 (SD = 5.0, SE=.7) and this is reflected in beta mean z-scores trajectory, i.e., from .63 to .44. Similarly, beta power decreased after ST from 25.5 μV 2 (SD = 18.2, SE = 3.3) to 22.2 μV 2 (SD = 14.3, SE = 2.6) and this is reflected in beta mean z-scores trajectory, i.e., from 1.66 to 1.58. Beta mean z-scores, both at pre-intervention and post-intervention stages, from MT and ST, stayed within the ±1.96 standard deviation range, indicating no significant deviations from the normative population benchmarks (88,89). Non-parametric mixed-design ANCOVA: Within-subjects effects. The main effect of “Timepoint” did not reach significance levels ( F( 1,13) = 1.45, p = .232). However, a statistically significant interaction with a small effect size has been observed between “Timepoint” and “Type of intervention” ( F( 1,13) = 9.80, p = .002, ω² = .049). Non-parametric mixed-design ANCOVA: Between-subjects effects. The model indicated a significant main effect of “Type of intervention” with a medium effect size ( F( 1,13) = 12.27, p = .001, ω² = .059). Post-hoc comparisons. Bonferroni’s pairwise post-hoc comparisons were conducted. At post-intervention stage, participants in the MT group had significantly lower beta mean z-scores than participants in the ST group ( t = -4.68; Cohen’s d = -1.06; p = .00003). This significant difference with a large effect size explained the main effect of “Type of intervention” suggesting a different neurophysiological state, in the beta frequency band, at post-intervention stage (Figure 3.A). This effect can be discerned in Figure 3.B, which depicts a comparative beta power scalp topography within the frontal ROI for the MT group versus the ST group at post-intervention stage. The topographical plot illustrates the differences in power values, which are significantly smaller in the MT group at post-intervention stage. Importantly, this effect is not driven by baseline differences: in fact, post-hoc tests revealed no significant difference between the MT and ST groups at the pre-intervention stage ( t = .14; p = 1.0). Results from the Brief Substance Craving Scale At pre-intervention stage, the MT group exhibited a mean total score of 2.6 (SD = 3.1, SE = 1.0), in contrast to the ST group displaying a higher mean score of 5.8 (SD = 3.4, SE = 1.4). At post-intervention stage, the mean total score in the MT group increased to 4.2 (SD = 4.1, SE = 1.4), whereas in the ST group, it decreased to 2.5 (SD = 2.3, SE = .9). Non-parametric mixed-design ANCOVA: Within-subjects effects. The analysis revealed that the main effect of “Timepoint” on BSCS total scores did not reach statistical significance ( F( 1, 13) = .11, p = .743). The interaction between “Timepoint” and “Type of intervention” was also not statistically significant ( F( 1, 13) = 2.81, p = .11). Non-parametric mixed-design ANCOVA: Between-subjects effects. The effect of the “Type of intervention” while approaching significance levels was not significant ( F( 1, 13) = 3.18, p = .097, ω² = .072). Relational results: beta mean z-scored and Brief Substance Craving Scale No significant correlations have been found between beta z-scores and BSCS total scores. Results from the Craving Thermometer All the participants assigned to individual-MT and group-MT (i.e., 10) compiled the CT, however not all participants compiled it for all the 6 sessions. Indeed, we collected 31 CTs pre-MT and post-MT sessions out of 60 (i.e., 29 pre-MT and post-MT sessions missing) with a mean of 3.1 CTs completed per participant. As shown in Figure 4, the CT mean score prior to MT sessions was 1.8 (SD = 1.8, SE = .3), with a median score of 1.3. Following the MT sessions, the CT mean score decreased to .8 (SD = 1.2, SE = .2), with a median score of .0. Non-parametric Wilcoxon test: Within-subjects effects. The results of the Wilcoxon signed-rank test (Figure 4) indicated a significant difference between Pre-MT (Median = 1.3) and Post-MT (Median = .00) scores on the subjective evaluation of instantaneous levels of craving ( z = 3.83, p = .0001). [Figure 3] On average, participants showed a significantly lower evaluation of instantaneous craving following the MT session (M=.8, SD=1.2, SE=.2) than before the MT session (M=1.8, SD=1.8, SE=.33) (Figure 4). [Figure 4] Pre-/post-intervention depressive symptoms/emotional regulation Results from FAA mean z-scored absolute power While the analysis of FAA was conducted on the F7/F8, F3/F4, and FP1/FP2 electrode pairs as typically investigated in the literature (67–70), we have chosen to report only the results for F7/F8 in the main text. The findings at F3/F4 showed the same picture but no significant changes, significant and consistent changes were observed at FP1/FP2 (Additional file 1, A3.1. and A3.2.). FAA mean z-scores have been measured at F7/F8 electrode pair within the alpha band (8.0-12.0 Hz). At pre-intervention stage, the MT group showed an FAA mean of -.3 μV 2 (SD = 52.9; SE=17.7) and a mean z-score of -.02 while post-intervention the mean FAA power increased to 10.7 μV 2 (SD = 42.7; SE=14.4) and a mean z-score increased to .27 suggesting a change in the hemispheric asymmetry activity. Specifically, this increase indicated higher alpha power in the left frontal hemisphere relative to the right frontal hemisphere after MT, signifying less brain activity in the left hemisphere as alpha power is inversely related to brain activity (102,103). A different pattern has been displayed by the ST group which showed a mean FAA of -19.5 μV 2 (SD = 35.6; SE=17.8) with a mean z-score of -.28 at pre-intervention stage, while a mean FAA of -45.7 μV 2 (SD = 10.8; SE=5.4) and a mean z-score of -.93 after the intervention. This suggests an opposite trajectory compared to the MT group. Indeed, the ST group showed a decreasing alpha power (more negative values) in the left hemisphere relative to the right hemisphere after the intervention, signifying more brain activity in the left compared to the right hemisphere. Non-parametric mixed-design ANCOVA: Within-subjects effects. The main effect of “Timepoint” did not reach significance levels ( F (1,11) = 2.36; p = .153). However, a statistically significant interaction with a medium effect size has been observed between “Timepoint” and “Type of Intervention” on the FAA mean z-scores ( F (1,11) = 15.95, p = .002, ω² = .381). Non-parametric mixed-design ANCOVA: Between-subjects effects. Concerning between-subject effects, the model indicated a significant difference, with a medium effect size, between groups receiving MT and ST showing a main effect of “Type of intervention” ( F (1,11) = 13.09, p = .004, ω² = .335). Post-hoc comparisons. To understand which comparison was responsible for within- and between-subjects effects, Bonferroni’s pairwise post-hoc comparisons were run. Two significant results emerged (Figure 5). Firstly, within the ST group, there was a significant, with a large effect size, change in FAA mean z-scores from pre- to post-intervention (t = 3.32; Cohen’s d = 2.34; p = .041). This result explained the significant interaction between “Timepoint” and “Type of Intervention” and suggested a decrease in FAA mean z-scores after ST (from pre-ST (-.28) to post-ST (-.93)). In other words, there was a significant neurophysiological modulation of the ST intervention on the FAA at F7/F8. Secondly, a comparison between the two groups (MT and ST) at the post-intervention stage revealed a significant difference, with a large effect size, in FAA mean z-scores ( t = 5.38; Cohen’s d = 3.23; p = .0001). The FAA mean z-score increased in MT (from -.02 to .27) and decreased in ST (from -.28 to -.93). Post-hoc analysis suggested that the main between-subjects effect comes from the mean z-score difference at post-intervention stage MT (.27) and ST (-.93). The decreased FAA mean z-scores after ST suggested a lower left-sided alpha power (i.e., higher left-sided brain activity) while the increase after MT suggested a higher left-sided alpha power (i.e., lower left-sided brain activity). In other words, the main effect of “Type of intervention” suggested a differential neurophysiological state, in FAA, at post-intervention stage. [Figure 5] Results from the Patient Health Questionnaire-9 The evaluation of PHQ-9 mean total scores indicated no significant within- ( F( 1,14) = .03, p = .862) nor between-subjects ( F( 1,14) = .63, p = .441) differences in the effects of MT and ST on self-reported depressive symptoms. Descriptive statistics showed a slight increase in mean total scores for both groups. Relational results: FAA mean z-scored and Patient Health Questionnaire-9 No significant correlations have been found between FAA z-scores and PHQ-9 total scores. Pre-/post-intervention anxiety symptoms Results from FMT mean z-scores and GAD-7 Anxiety symptoms before and after the intervention were assessed using z-score values from resting-state FMT frequency bands, derived from absolute power values, and the GAD-7 questionnaire. No significant changes were observed within or between groups at pre- and post-intervention stages in either measure (see Additional file 1, A1.1. and A1.5) Pre-/post-intervention inhibitory cognitive control Due to the findings of a noise quantification analysis (see Additional file 1, A4), we excluded data from the ST group from further analysis. This decision was made following previous studies (104) to ensure the reliability of the results, as the ST group exhibited significantly higher noise levels in their ERP waveforms, rendering them unsuitable for accurate comparison with the MT group. Evoked P3d (NoGo-minus-Go P3) At pre-intervention stage, the mean P3d amplitude was .27 μV (SD = 4.39, SE = 1.03). At post-intervention stage, there was an increase in mean amplitude to 1.07 μV (SD = 3.49, SE = .82). A repeated measures ANOVA was utilized to analyse the P3d in the MT group with the factor “Timepoint” (pre- and post-intervention) as within-subjects factor. The results (Figure 6) indicated a main effect of “Timepoint” indicating a significant increase in P3d mean amplitude following MT with a small effect size ( F (1, 17) = 4.87, p = .041, ω² = .00798). Figure 7 provides a comparative analysis of the P3d component in SUD participants before and after MT showing increased P3d component in the 350-500 ms time range following MT in the time-domain (Figure 7.A) and scalp topography (Figure 7.B). This effect has been observed over fronto-central electrodes (Fz, FCz, Cz) and reflects a stronger neural response post-intervention, indicating the potential impact of MT on neural activity. [Figure 6] Behavioural indicators Behavioural results from the Go/NoGo task showed stable patterns at pre- and post-intervention stages (see Additional file 1, A2). Hit rates numerically increased marginally in both the MT (from .79 to .82) and ST groups (from .85 to .88), while miss rates decreased (MT: .21 to .17; ST: .15 to .11). False alarm rates numerically increased post-MT (.05 to .07) and post-ST (.03 to .12), with corresponding decreases in correct rejection rates (MT: .96 to .94; ST: .97 to .89). Reaction times remained stable (MT: from 299.74 ms to 301.70 ms; ST: from 292.12 ms to 286.91 ms). Lastly, d'prime decreased slightly post-MT (2.84 to 2.64) and ST (3.42 to 3.07), but none of these changes reached statistical significance. [Figure 7] Results from semi-structured interviews: participants perceptions on the music-therapeutic process. [Figure 8] Data from the semi-structured interviews were collected following the second or third and fifth sessions of individual-MT. This section presents the results of the qualitative analysis, which utilized a modified reflexive thematic analysis to identify themes, subthemes, and sub-subthemes. Theme 1. Outcomes and impacts on craving. This theme explores participants' perceptions and insights on the outcomes and the impacts that MT sessions had on the feeling of craving. Subtheme 1.1. Absence of craving in music therapy sessions . This subtheme reflects interview data indicating that participants typically do not experience craving while they were in their individual-MT sessions. When asked about sensations potentially associated with cravings during the semi-structured interviews, participants uniformly reported a lack of such feelings. Subtheme 1.2. Resistance to craving conversations. This subtheme emerged from an examination of the participants' apparent resistance to discussing cravings. It delves into the observed reluctance and opposition among participants when addressing the topic of craving. Typically, participants would answer the questions but limit their responses to one or two words, often simply saying, “No, No,” before either remaining silent or steering the conversation away from the topic. Subtheme 1.3. Reduced craving after music therapy sessions. This subtheme examines the reports from participants on reduced feeling of craving following their MT sessions. Common expressions from participants after the sessions included statements such as, “I don’t feel the urge to consume a substance”. Theme 2. Outcomes and impacts on experience and emotions. This theme examines the impact of individual-MT on participants' emotional expression and their perception of past life experiences. Subtheme 2.1. Echoes from the past. This subtheme describes the interplay between past experiences and current behaviour in the context of emotional expression felt by participants that have a history of SUD with craving symptoms. For instance, when one participant was asked about positive emotions S/He was experiencing during the MT, S/He spontaneously began sharing their past struggles with substance use: "Yes, I am always happy... I was sent into rehab 7 times... I stopped in 2012... this year I said no, enough is enough, so I just stopped and started coming here regularly." Subtheme 2.2. Emotional transformation. focuses on the quality and nature of emotional expression displayed by participants. Within this subtheme, two additional layers are present: "Active engagement and hope in therapeutic journey" (Subsubtheme 2.2.1) and "Perception of therapeutic change" (Subsubtheme 2.2.2). These explore, respectively, the level of active participation in therapy and how participants perceive their own emotional and therapeutic changes over time. Overall, this theme serves to explore the broader emotional dimensions that shaped participants' experiences throughout their therapeutic journey. Theme 3. Process dynamics in music therapy. The theme captures participants’ perception on the multi-dimensional aspects of music therapy, ranging from the individual's interaction with the music itself to the interpersonal therapeutic relationship that contributed to therapeutic success. Subtheme 3.1. Positive influence from music. The subtheme "Positive influence from music" explores participants' experiences with music as an effective therapeutic resource for emotional exploration and expression. For instance, one participant commented "I can get out of my comfort zone; that’s why I was more comfortable playing in the central part [of the piano keyboard] , but now and then I did go on the black buttons, and I did go on the sides." This remark not only suggested the participant's growing comfort level and willingness to explore different emotional terrains through music but also highlighted how music facilitated a mature articulation of wishes and feelings, serving as a bridge to deeper emotional expression. Subtheme 3.2. Therapeutic connection. The subtheme "Therapeutic connection" emphasizes the crucial role that the relationship between the therapist and the participant played in the therapeutic process. For example, one participant stated, "I was looking at Paul [The music therapist] and trying to mimic him…" and added, "I feel connected; he understands where I come from". These remarks effectively illustrate the importance of a strong therapeutic alliance in enhancing the outcomes of therapy. Another participant reported a transition in the musical interaction with the therapist from playing "aggressive music" to adopting a "calm and calm and just going through it" approach. This further underlined the impact of therapeutic relationship on facilitating change and fostering personal growth within a music therapeutic setting. Discussion Impact of music therapy on craving symptoms There is an extensive body of literature exploring the link between the beta frequency band in neurophysiological findings and the experience of craving. Increased power in the beta frequency band has been associated with the feeling of craving in resting-state EEG studies with SUD participants undergoing periods of abstinence (46–49,105–107) and in task-based cue-reactivity paradigms with SUD participants exposed to drug-related stimuli (45,52). The increased beta power has been interpreted as indicating heightened craving arousal (46,50,106,107). Therefore, it has been suggested that increased power in the frontally-distributed beta frequency band could serve as a biomarker for hyperarousal related to craving intensity (46,50,106). In light of existing literature, the neuromodulation that MT might facilitate in the beta frequency band could potentially be associated with lower post-intervention levels of arousal related to craving compared to the ST group. The lower level of frontally-distributed beta power in the MT group at the post-intervention stage suggests that MT and ST may be more effective than ST alone in mitigating the arousal linked to the craving's motivational drive or in enhancing the mechanisms that suppress this drive. The neurophysiological findings presented here indicate that MT may facilitate neuromodulation within the beta frequency band, which is a biomarker of craving sensations, i.e., indicating less craving after MT. This is suggested by the lower beta mean z-score values in the MT group compared to the ST group. However, the conscious perception (i.e., subjective evaluation) of this change is only noticeable in the CT administered before and after MT sessions and not in the BSCS administered before and after the interventions. Differences in Neurophysiological and Self-Reported Craving Measures The divergence between the intrinsic neurophysiological activity related to craving and the psychometric assessment underscores the multifaceted nature of craving: while the EEG measurements suggested a non-significant pre-post intervention decrease in arousal related to craving symptoms following MT, self-reported data from the BSCS depicted a different picture, indicating a non-significant pre-post intervention increase in self-reported craving post-MT. While measures showed a consistent stable pattern (i.e., non-significant differences), the direction of those patterns is opposite highlighting the challenge of capturing the multi-dimensional construct of craving (51,108) and emphasizing the intricate interplay between the brain's intrinsic activity – also called "interoceptive signals" related to craving (108) – and an individual's subjective experience over time (51,108). Importantly, the translation of interoceptive signals of craving into a conscious craving experience does not always occur (109,110). In other words, craving processes are not consistently and invariably subject to conscious awareness (109,110). An important aspect to consider is that, although BSCS and EEG measurements were conducted at the same points in time, they provide information on different timescales of craving: EEG offers an instantaneous measure of intrinsic brain activity (without any instructed mental activity), while BSCS requires participants to reflect on their state over the previous 24 hours. Thus, psychometric tests, compared to resting-state EEG recordings, rely on higher-order cognitive functions such as introspection and retrospective thinking, encompassing an assessment of one's mental state that may be influenced by subjective interpretation, social desirability, and limited self-awareness – especially during a vulnerable period (111). The Role of Interventions in Modulating Resting-State Beta Frequency Band The neurophysiological results from this pilot investigation contribute to the existing body of literature in a new setting: a clinical trial involving MT for SUD outpatients engaged in a CSMTS. These findings suggest that MT may significantly facilitate neuromodulation in the beta frequency band, which could potentially reflect a reduction in craving arousal. However, the conscious perception of this neuromodulation may differ depending on the measures used to assess craving. Present-moment craving symptoms – craving thermometer The CT, a single-item psychometric tool presented in the form of a VAS, was utilized to capture instantaneous or present-moment self-evaluations of craving intensity exclusively among MT participants before and after each session. Unlike the subjective evaluation through the BSCS (showing a non-significant increase), the analysis of the CT revealed a significant reduction in self-reported craving levels post-MT session. The consistency across individual responses (Additional file 1, A5), underscores the uniform effectiveness of MT sessions in reducing immediate craving conscious feelings. These findings are consistent with recent studies conducted on a larger sample size in a detoxification unit showing present-moment self-reported reduction in craving symptoms following a single MT session (112,113). The reduction in craving levels as measured by the CT was in line with the pre- to post-intervention neurophysiological changes observed in the beta EEG biomarker analysis, indicative of a neurophysiological response to MT associated with reduced craving symptoms. When comparing the CT findings with the pre- to post- BSCS changes, a contrasting picture emerged. While the CT indicated a significant reduction in craving levels immediately following MT sessions, the BSCS results, which measured self-reported craving before and after a 6-week MT intervention, showed an increase in craving scores post-MT intervention. This instability aligns with the literature (52,114) on the fluctuations of craving symptoms, suggesting that as a multi-dimensional construct (51,108), craving may manifest different patterns at various timepoints and with different measures (109,111), especially if an interpretation of the concept of craving is required (109,110). The findings from the CT, have completed neurophysiological and self-reported findings on short-term craving symptoms highlighting the importance of considering the temporal dynamics of craving. Furthermore, they suggested the potential for MT to have varying impacts on immediate (when administered before and after single MT sessions) and short-term (when administered before and after the 6-week intervention) craving experiences, especially regarding a conscious evaluation of the feelings of craving. Impact of music therapy on depressive symptoms The analysis of FAA at the F7/F8 homologous pairs revealed differences in the neurophysiological correlates of depressive symptoms related to emotional and affective processing. The ST group showed a significant increase in left-sided frontal brain activity from pre- to post-intervention. In contrast, the MT group exhibited a slight, non-significant decrease in left-sided frontal activity, indicating stable frontal asymmetry. Notably, a significant post-intervention difference between groups was observed: the MT group had higher FAA mean z-scores, suggesting lower left frontal activity compared to the right, while the ST group showed lower z-scores, indicating higher left frontal activity. These results imply that the two interventions may differentially affect a neurophysiological marker associated with affective processing underlying depressive symptoms. Concerning the PHQ-9 questionnaire, no significant differences have been found. The role of interventions in modulating frontal alpha asymmetry Numerous studies have demonstrated a link between functional hemispheric asymmetry and positive/negative emotions (valence-based distinction) as well as approach/withdrawal emotional dichotomy. Initially, it has been noted that a damage to the left hemisphere affected the perception of positive emotions, while an injury to the right hemisphere impaired the recognition of negative emotions (101,102). Contextually, efforts have increased to clarify the role of frontal hemisphere asymmetry through EEG alpha wave pattern analysis. The aim in those studies was to understand how individual differences in emotional processing relate to approach and withdrawal behaviours (57–62). An approach system has been proposed to facilitate appetitive behaviour and generate positive emotions, such as enjoyment, in the context of a goal-oriented motivational processing. Contrarily, a withdrawal system has been associated with aversive or defensive behaviours and negative emotions like fear, sadness, or disgust. The concept of affective style, as proposed by Davidson and colleagues (58,59), is linked to the lateralization of the approach and withdrawal systems, as measured by EEG alpha asymmetry. Research indicates that the approach system is primarily associated with increased left frontal activity, while the withdrawal system is linked to increased right frontal activity (57–59,61,62). FAA serves as a measure of the balance between these systems, with variations in FAA correlating with changes in affective style and depressive symptoms (67,70,117). This positions FAA as a potential biomarker, though not a diagnostic marker, for depression (118). The significant post-intervention difference between the MT and ST groups indicates lower left-sided frontal activity in the MT group and higher left-sided activity in the ST group. Notably, the MT group's z-scores were closer to normative values, suggesting a more balanced, though right-lateralized, frontal activity. This result may imply that MT enhances the withdrawal system, potentially reducing approach-related positive affect and impacting emotional and motivational regulation. Conversely, the ST group’s increased left-sided frontal activity suggests an improvement in approach-related processing, potentially enhancing positive emotional engagement and goal-directed behaviours. Addressing and experiencing negative emotions in therapy can strengthen the therapist-patient relationship, increasing the therapy’s effectiveness (119,120). Accepting and being aware of negative and mixed emotions, rather than focusing solely on positive emotions can lead to psychological health benefits (121,122). Comparing ST and MT intervention in an RCT with 79 depressed patients indicated comparable F7/F8 asymmetry patterns, i.e. less left hemispheric activation after music therapy (67). The authors discussed the involvement of the right inferior frontal gyrus (homologous to Broca’s area in the left hemisphere) in processing and expressing negative emotions and related prosodic aspects of verbal expression in MT (67). Supporting this, a recent review highlighted prosodic expression as a critical indicator of depression severity, suggesting an additional avenue for exploring the therapeutic potential of music therapy in addressing the emotional and communicative dimensions of depression (123). Depressive symptoms and craving in approach/withdrawal theory The approach/withdrawal theory is particularly interesting because it does not exclude or overlap with the valence theory, which divides positive and negative emotions (61). Rather, it complements the valence theory by emphasizing the motivational and behavioural aspects of emotional states, for example claiming that negative but approach-related emotions, such as anger, would be lateralized over the left hemisphere like positive approach-related emotions (61,124). This element of the theoretical framework is important because it allows the distinction between a valence-based dissociation and an action-tendency dissociation in the understanding of affective states. Craving has been defined as a combination of negative emotions and an approach bias towards substances and related stimuli (51,125,126). Studies on individuals with SUD have examined FAA in response to cannabis-related cues, revealing greater left frontal activation to both drug-related and neutral cues compared to healthy controls, suggesting a generalized approach motivation (66). Furthermore, it has been noted how addiction may reshape brain functionality and behaviour, leading to an imbalance in the left hemisphere associated with reward and approach behaviours (65). This imbalance, particularly the increased left frontal activation in the alpha band, may serve as a marker for compulsive behaviours, impaired decision-making, and approach motivation towards addictive substances (65,66). In this study, changes in FAA are interpreted as markers of affective processing within the framework of approach/withdrawal theory, underlying depressive symptoms (58–60,63,64). Additionally, FAA changes may also indicate the neural basis of craving-related approach tendencies (65,66). Introspection and emotional regulation in MT and ST Here, we suggest a complex impact of MT and ST interventions on depressive symptoms and affective processing. Although psychometric values did not significantly correlate with FAA values, the PHQ-9 results showed a non-significant increase in self-reported depressive symptoms in MT and ST, that potentially involves different affective states represented by FAA lateralization patterns. Post-intervention, the MT group’s decrease in left-sided frontal activity may indicate deeper introspection and more stable emotional regulation, contrasting with the ST group’s higher left-sided activity. This introspective state in the MT group might also explain the mismatch between reduced brain-related (i.e., beta activity) craving arousal and the non-significant conscious (i.e., subjective evaluation from questionnaire) increases in craving symptoms. According to a systematic (Cochrane) review, MT is associated with higher motivation to change in participants with SUD (12). The introspective state combined with potentially more frequent thoughts about craving (as suggested by BSCS results and qualitative findings in theme 2: Outcomes and impacts on experience and emotions), could represent a predisposition achieved after MT. In contrast, the approach-related affective state observed in the ST group appears to align with heightened post-intervention craving arousal, reflected in increased beta activity. Impact of music therapy on inhibitory cognitive control Discussion on the noise quantification analysis The exclusion of ERP data from the ST group was due to significantly higher noise levels in their ERP waveforms, making comparison with the MT group unfeasible. It is essential to clarify that this decision was not made to systematically exclude ST group data to artificially enhance the significance of the results, thereby contributing to publication bias (127). In fact, the P3d waveforms observed in the ST group were characterized by a negative polarity, which became more pronounced (i.e., more negative) following the ST intervention (see Additional file 1, A6). Because higher P3d amplitudes have been associated with improved inhibitory control (74,75,78,80), this pattern suggests that including the ST group data would likely have amplified, rather than diminished, the observed beneficial effects of the MT intervention. No differences in behavioural responses to Go and NoGo stimuli Regarding behavioural performance, neither MT nor ST seemed to modulate the participants' abilities to successfully discern between Go and NoGo stimuli, nor did they seem to impact the correct response to Go or NoGo stimuli, suggesting that the fundamental behaviour in responding to such stimuli remains unaffected by interventions. This result aligns with the understanding that SUD patients often show neurophysiological changes that may change beyond overt behavioural indicators (92,128). This could potentially indicate the presence of compensatory brain processes that mask the behavioural response (92,128). Here, it is possible that SUD participants maintained their level of behavioural performance even after expending significantly higher brain energy before MT and reduced effort afterward as shown by P3d changes. The role of music therapy in modulating P3d Modulations of P3d amplitudes have been associated with sensorimotor response inhibition capabilities in SUD and healthy participants (75,78,80). Relative to the higher amplitude values found in healthy controls, diminished P3d and NoGo-P3 amplitudes in individuals with SUD suggest a compromised sensorimotor response inhibition, potentially reflecting a neurophysiological underpinning of the unsuccessful effort to control an impulsive behaviour (74). In the current pilot study, there was a pre- to post-MT significant increase in P3d amplitude. This increase suggests that after undergoing MT, SUD participants demonstrated improved sensorimotor response inhibition capabilities, aligning with preliminary indications that music has the potential to modulate brain networks related to inhibitory cognitive control (14,129,130). In the current analysis, the exclusion of the ST group due to noise-related issues prevents us from fully ruling out potential practice effects on P3d amplitudes. However, previous research (131) on Go/NoGo P3 amplitudes in task repetition paradigms demonstrated a reduction in both Go-P3 and NoGo-P3 amplitudes with repeated task performance. This reduction reflects the automation of the stimulus discrimination process and a decreased reliance on attentional resources: two elements that contribute to the parent waveform of NoGo- and Go-P3 (whose difference computes the P3d). While acknowledging the limitation of not controlling for practice effect, this line of reasoning supports the notion that MT might play a role in improving neural mechanisms (i.e., increased P3d) linked to sensorimotor response inhibition in outpatients with SUD. In other words, MT might serve as a potential intervention to reverse the usual compromised inhibitory cognitive control (1,81,82) and support a recovery journey. Participants’ perceptions on music therapeutic process: discussion on themes from the semi-structured interview The qualitative analysis of semi-structured interviews conducted post individual-MT sessions provides a nuanced understanding of participants' experiences, focusing on craving, emotional expression, and the therapeutic relationship. This analysis offers insights that complement the quantitative data previously discussed. Absence of Craving During and After Individual-MT Sessions Participants consistently reported an absence of craving during individual-MT sessions, indicating that the immersive nature of MT may temporarily disrupt habitual craving patterns associated with SUD. This observation aligns with previous quantitative findings showing reduced craving post-MT sessions (132). Qualitative themes in MT for SUD include the emphasis on music and non-music related elements, such as using music intentionally to target therapeutic goals or fostering genuine therapeutic relationships (132). The use of music and the establishment of therapeutic relationships appear to contribute to this effect, providing a distraction from cravings and a coping mechanism during and after sessions. Resistance to craving conversations Some participants displayed instances of resistance, such as abrupt interruptions, when discussing cravings, suggesting discomfort or denial in acknowledging these feelings within a therapeutic setting. This resistance may explain the differences between neurophysiological craving biomarkers and self-reported craving scores, highlighting the challenges of introspection on sensitive issues. This theme resonates with prior research indicating a tendency among individuals with SUD to conceal aspects of themselves to protect their self-image during interventions (133,134). Echoes from the Past and Perception of Therapeutic Change The emergence of past experiences during MT sessions points to the reflective and integrative nature of the therapy. Recounting past struggles appears to facilitate a therapeutic re-contextualization of experiences, fostering resilience and a proactive approach to recovery. The increasing focus on past experiences in later sessions suggests a deepening therapeutic process that enhances engagement and hope. This dynamic aligns with existing literature that links the exploration of past experiences in therapy to a sense of empowerment and motivation for change (135). Process Dynamics in Music Therapy Concerning this main theme, two main sub-themes emerged: positive influence from music and therapeutic connection. Participants' experiences of music as a therapeutic tool for emotional exploration and expression, reinforce the core principles of music therapy. The reported shift towards more adventurous musical engagement and the positive impact on mood and self-awareness highlight music's capacity to facilitate emotional growth and transformation. This is in accordance with previous research showing how pivotal emotional experiences facilitated by music can lead to profound insights and emotional change (90,136). The emphasis on the therapeutic relationship as a foundational element of the therapeutic process underscores the relational dynamics integral to music therapy. Participants' reflections on their interactions with the therapist and the evolving nature of their musical collaboration reflect the therapeutic alliance's centrality in facilitating therapeutic change (136). We were also keen to investigate the mechanisms of therapeutic change and processes in MT using advanced neuroimaging techniques, such as EEG hyperscanning, and comprehensive behavioural assessments, including music technology. In this regard, EEG hyperscanning data during MT sessions for SUD have been collected (see study protocol (16)) to consider moment-to-moment analysis of brain oscillations during MT sessions that will contribute to the SUD literature as individual case studies and out of the scope of this pilot RCT. In this way, we capture the dynamic interplay of neural and emotional processes. This approach, exemplified by recent dual-EEG hyperscanning studies (90,137), allows detailed observation of shared emotional processing and the temporal dynamics of neural correlates during therapy (90,137). Such analyses might provide insights into rapid neural fluctuations and their role in emotional processing and regulation during therapy, offering a deeper understanding of how MT facilitates therapeutic outcomes on craving, depression and anxiety in SUD treatment. In summary, the qualitative insights from these interviews enrich our understanding of the interplay between craving, emotional expression, and the therapeutic relationship in MT for SUD. These findings underscore the transformative potential of music therapy in fostering emotional growth, enhancing self-awareness, and renegotiating past experiences within a therapeutic context. Limitations The pilot nature of this study presents several limitations, primarily related to sample size and study design. The small sample size, while adequate for feasibility testing and piloting (138), limits the generalizability of the findings, which should be interpreted with caution. Additionally, although comparable to another pilot study (139), the MT intervention consisted of only six sessions, which may not fully capture the potential long-term effects of MT on SUD. Participant demographics displayed some heterogeneity (see Additional file 1, A7), a common characteristic in feasibility studies where recruitment and retention rates are uncertain (138). Despite the small sample size, both groups were balanced on key variables such as age, gender, diagnosis type, and medication, which are crucial for minimizing confounding factors. This is similarly reported in previous MT research utilizing EEG (67) and psychometric tests (67,140). The diversity in substance use patterns and individual circumstances among participants is representative of the population likely to use this intervention, aiding in assessing the feasibility and acceptability of the intervention for future studies. The study utilized staggered randomization due to logistical constraints in recruiting outpatients from a community service, which was deemed the most practical approach to balancing confounding variables. In this pilot study, a blind assessment procedure could not be established. Utilizing blind assessors in data collection and analysis, particularly for subjective measures, could minimize bias in future studies. Finally, working within a community-based setting presents unique challenges, such as participants living in uncontrolled environments between sessions and the timing of post-intervention measures, which could affect the results. These limitations should be considered when interpreting the findings and planning future larger-scale studies. Conclusions In this study we were able to show for the first time that MT had a measurable influence on arousal related to craving. This is noticeable in the EEG resting-state beta band (i.e., neural level) suggesting a reduced craving-related affective state. However, participants’ conscious perception of this reduction (i.e., subjective evaluation) is evident only after MT sessions (as measured by the CT) – and not after the whole intervention (as measured with BSCS). Other results are related to introspection and emotional regulation, which appears to be increased after MT, and reflected in FAA patterns as well as in thematic analysis (theme 2: outcomes and impacts on experience and emotions). Furthermore, inhibitory control is suggested to be improved after MT and this is reflected by a task-based EEG analysis addressing sensorimotor control function and thematic analysis (subtheme 1.3 – reduced craving after MT sessions) highlighting reduced impulse towards the substance after MT. Further research might compare processes of MT SUD treatment in detoxification units and outpatients on prescription medicine to distinguish MT effects on craving, depression and anxiety related symptoms. This study is the first to provide combined neuroscientific, psychometric, and qualitative data for MT in SUD treatment and offers an opportunity to test the results and measures used in a larger, multicentred RCT. This should address limitations and would allow for the exploration of sustained benefits, optimal dosage, and session frequency. Including a mid-treatment assessment at six-weeks could help identify the potential benefits of a longer intervention and test for patterns of change in SUD participants’ symptoms more effectively. Integrating MT with other community-based interventions could provide insights into effective multidisciplinary approaches for SUD treatment. Abbreviations ARU Anglia Ruskin University BSCS Brief Substance Craving Scale EEG Electroencephalography ERP Event-related potentials FAA Frontal Alpha Asymmetry FFT Fast Fourier Transform FMT Frontal Midline Theta GAD-7 Generalized Anxiety Disorder-7 ICA Independent Component Analysis IRAS Integrated Research Application System MT Music therapy PHQ-9 Patient Health Questionnaire-9 ST Standard treatment SUD Substance Use Disorder VAS Visual Analogue Scale Declarations Authors’ contribution Pasqualitto and Maidhof share the first authorship and contributed equally to the work. Fachner developed the original design concept for the study. Fachner, Maidhof, Murtagh and De Silva contributed to the conceptualization of the study. Pasqualitto and Maidhof collected and analysed (with Fachner) EEG data and psychometric tests. Pasqualitto conducted and analysed semi-structured interviews. Pasqualitto drafted the first version of this manuscript and all authors contributed to the editing and final submission of the manuscript. All authors read and approved the final manuscript. Acknowledgements We would like to express our gratitude to Leonardo Muller-Rodriguez for his assistance with data collection and to Helen Odell-Miller for her valuable contributions during the conceptualization stages of this study. We also thank Mizan Chowdhury, from “Via” community service, for his support in organizing the recruitment process and Jufen Zhang, from Anglia Ruskin University, for providing insightful statistical advice. We would finally like to thank all the service users and “Via” staff who took part and supported with the implementation. Declaration of interests None. Funding The Faculty for Arts, Humanities and Social Science (AHSS) at Anglia Ruskin University (ARU) has provided an internal grant for the project. The funding is based on the HEFCI (Higher Education Funding Council for England) QR (Quality Research) Seed corn funding scheme for AHSS granted for ￡7600 for travel and incidental costs; QR AHSS Research and Innovation funding of ￡7300 for acoustic and electronic musical equipment and ARU funding of the Health, Performance and Wellbeing research stream towards an analysis tool (￡8300). Another grant was received from the Music Therapy Charity as a small research fund (￡1500) towards one of the PhD students (Fernie) in this project, while the other PhD student (Pasqualitto) received a Vice Chancellor’s studentship for his PhD. Ethics approval and consent to participate This mixed-methods randomized controlled feasibility study is registered with clinical trials.gov, number NCT 05180617. Furthermore, it has been approved by the NHS Integrated Research Application System (IRAS) (North East—Newcastle & North Tyneside 2 Research Ethics Committee), number WDP-AZA8C0091, and Anglia Ruskin University Ethics Boards. Additionally, the project has also been approved by “Via” community service innovation research unit which oversees research within the community service. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to sensitivity and ethical restrictions but are available from the corresponding author on reasonable request. References American Psychiatric Association. Diagnostic and statistical manual of mental disorders : DSM-5. American Psychiatric Association; 2013. 947 p. Filbey FM. 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Tracing change during music therapy for depression: Toward a markers-based understanding of communicative behaviors. Musicae Scientiae. 2023 Sep 15;27(3):637–54. Carver CS, Harmon-Jones E. Anger is an approach-related affect: Evidence and implications. Psychol Bull. 2009;135(2):183–204. Koob GF, Le Moal M. Addiction and the brain antireward system. Annu Rev Psychol. 2008;59:29–53. Markou A, Kosten TR, Koob GF. Neurobiological similarities in depression and drug dependence: A self-medication hypothesis. Vol. 18, Neuropsychopharmacology. 1998. Ghaemi SN. A Clinician’s Guide to Statistics in Mental Health. Cambridge University Press; 2023. Kouri EM, Lukas SE, Mendelson JH. P300 Assessment of Opiate and Cocaine Users: Effects of Detoxification and Buprenorphine Treatment. 1996. Burkhard A, Elmer S, Kara D, Brauchli C, Jäncke L. The Effect of Background Music on Inhibitory Functions: An ERP Study. Front Hum Neurosci. 2018 Jul 23;12. Fernández-Serrano MJ, Pérez-García M, Schmidt Río-Valle J, Verdejo-García A. Neuropsychological consequences of alcohol and drug abuse on different components of executive functions. Journal of Psychopharmacology. 2010 Sep;24(9):1317–32. Nakata H, Sakamoto K, Kakigi R. Effects of task repetition on event-related potentials in somatosensory Go/No-go paradigm. Neurosci Lett. 2015 May;594:82–6. Silverman MJ. Music Therapy and Therapeutic Alliance in Adult Mental Health: A Qualitative Investigation. J Music Ther. 2019 Feb 16;56(1):90–116. Johannessen DA, Nordfjærn T, Geirdal AØ. Substance use disorder patients’ expectations on transition from treatment to post-discharge period. Nordic Studies on Alcohol and Drugs. 2020 Jun 24;37(3):208–26. Versalovic E, Klein E, Goering S, Ngo Q, Gliske K, Boulicault M, et al. Deep Brain Stimulation for Substance Use Disorders? An Exploratory Qualitative Study of Perspectives of People Currently in Treatment. J Addict Med. 2023 Jul;17(4):e246–54. Velez CM, Nicolaidis C, Korthuis PT, Englander H. “It’s been an Experience, a Life Learning Experience”: A Qualitative Study of Hospitalized Patients with Substance Use Disorders. J Gen Intern Med. 2017 Mar 12;32(3):296–303. Bourdaghs S, Silverman M. An exploratory interpretivist study of how adults with substance use disorders experience peer social connectedness during recovery-oriented songwriting. Psychol Music. 2023 Sep 7;51(5):1440–56. Maidhof C, Müller V, Lartillot O, Agres K, Bloska J, Asano R, et al. Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study. Front Psychol. 2023 Sep 29;14. Eldridge SM, Lancaster GA, Campbell MJ, Thabane L, Hopewell S, Coleman CL, et al. Defining Feasibility and Pilot Studies in Preparation for Randomised Controlled Trials: Development of a Conceptual Framework. PLoS One. 2016;11(3). Hakvoort L, de Jong S, van de Ree M, Kok T, Macfarlane C, de Haan H. Music therapy to regulate arousal and attention in patients with substance use disorder and posttraumatic stress disorder: A feasibility study. J Music Ther. 2020;57(3):353–78. Erkkilä J, Punkanen M, Fachner J, Ala-Ruona E, Pöntiö I, Tervaniemi M, et al. Individual music therapy for depression: Randomised controlled trial. British Journal of Psychiatry. 2011 Aug;199(2):132–9. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5837441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403489644,"identity":"d8073b11-0ad1-4403-8b46-4779546c4acc","order_by":0,"name":"Filippo Pasqualitto","email":"","orcid":"","institution":"1 Cambridge Institute for Music Therapy Research, Anglia Ruskin University, Cambridge, UK 5 Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, IT 6 Center for Translational Neurophysiology of Speech and Communication, Italian Institute of Technology, Ferrara, IT","correspondingAuthor":false,"prefix":"","firstName":"Filippo","middleName":"","lastName":"Pasqualitto","suffix":""},{"id":403489645,"identity":"07688123-68b9-46e6-89b2-e1eabde57d2e","order_by":1,"name":"Clemens Maidhof","email":"","orcid":"","institution":"1 Cambridge Institute for Music Therapy Research, Anglia Ruskin University, Cambridge, UK","correspondingAuthor":false,"prefix":"","firstName":"Clemens","middleName":"","lastName":"Maidhof","suffix":""},{"id":403489646,"identity":"f62e1c1b-2bc4-4d9b-921e-199ae9b0b934","order_by":2,"name":"Daniel Murtagh","email":"","orcid":"","institution":"2 Innovation \u0026 Research Unit, “V-i-a”, London, UK","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Murtagh","suffix":""},{"id":403489647,"identity":"e16b1957-f36d-4610-8555-faa884071902","order_by":3,"name":"Devon Silva","email":"","orcid":"","institution":"2 Innovation \u0026 Research Unit, “V-i-a”, London, UK","correspondingAuthor":false,"prefix":"","firstName":"Devon","middleName":"","lastName":"Silva","suffix":""},{"id":403489648,"identity":"c7797cbc-3bfb-469e-9de6-3a00d007e87a","order_by":4,"name":"Paul Fernie","email":"","orcid":"","institution":"1 Cambridge Institute for Music Therapy Research, Anglia Ruskin University, Cambridge, UK","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Fernie","suffix":""},{"id":403489649,"identity":"02439dc4-5f03-4bc1-9c45-f81ce379a850","order_by":5,"name":"Francesca Panin","email":"","orcid":"","institution":"3 School of Life Sciences, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Panin","suffix":""},{"id":403489650,"identity":"5dddf85a-7566-4d64-ae94-47393537cf41","order_by":6,"name":"Andrew Michell","email":"","orcid":"","institution":"4 NIHR Cambridge Biomedical Research Centre, Addenbrooke’s Hospital, Cambridge, UK","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Michell","suffix":""},{"id":403489651,"identity":"324786dc-41d2-4ae8-82ec-c40dc9a82d47","order_by":7,"name":"Jörg Fachner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACxgYQacPAYACiP8DFDxDSkgbRwjiDGC0QANXCzEOMFub23scvGBLs5M2lm499tqm5l9jAfvgBM88ZPA7rOW5mwZCQbLhzzrHk2TnHihMbeNIMmHlu4NEyI43NgPEHc4LBjRxj5hy2hMQGhhygCz8Q0MKQUA/Ukv+Z2eIfUAv/G4JamB8wJBwG2cLMzNgG1CIBsgWfw3qOsTEkJBw33HAjzZixty/BuE3imcHBOXi8b9jexvzhQ0K1vMGN5McMP74lyPbzJz988OYYHi0NDGwSCcgibAwEIlIeGDV4vDoKRsEoGAWjAAgAdq1PRBtuRoYAAAAASUVORK5CYII=","orcid":"","institution":"1 Cambridge Institute for Music Therapy Research, Anglia Ruskin University, Cambridge, UK","correspondingAuthor":true,"prefix":"","firstName":"Jörg","middleName":"","lastName":"Fachner","suffix":""}],"badges":[],"createdAt":"2025-01-15 22:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5837441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5837441/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74243947,"identity":"b8ab4231-4298-4617-aa4f-c3a417e21ed2","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1022686,"visible":true,"origin":"","legend":"\u003cp\u003eTrial design (a) and outcome measures (b). Flowchart representation of trial design, randomization process, and outcome measures.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/d38d7e9905fd9dd77e126371.png"},{"id":74243946,"identity":"61417bc8-014c-4599-a7c9-13de4486ad27","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":493271,"visible":true,"origin":"","legend":"\u003cp\u003eGo/NoGo task. (A) presentation of Go stimuli, (B) presentation of NoGo stimuli, (C) button pressing box.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/56447ebe099f581766386793.png"},{"id":74243950,"identity":"9ec6232d-f4de-4792-a6da-82856656d1e1","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":525277,"visible":true,"origin":"","legend":"\u003cp\u003eResults from beta mean z-scored absolute power. (A) Interactional plot showing on the y-axis beta mean z-scores at a frontal ROI and on the x-axis the factor “Timepoint” (two levels: pre-intervention, post-intervention). Separate lines indicate changes according to the factor “Type of intervention” (two levels, MT, ST) with white dots for MT group and black dots for ST group. A significant difference has been found in post-intervention beta mean z-scores between the MT and ST groups (p = .00003). Asterisks legend: *** = p \u0026lt; .001. (B) Scalp topography contrast of beta mean absolute power utilizing Neuroguide function “Comparative Absolute Differences” plot with automatic contrast scaling values. The figure depicts the scalp topography contrast of Pre-MT intervention vs Post-MT intervention beta mean power. When the mean power value is larger in the first term of comparison, the numerical value is positive, and the corresponding colour shifts from green to red. Conversely, if the mean power value is larger in the second term, the numerical value is negative, with the colour shifting from green to blue.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/d600cd917bc978ab18525435.png"},{"id":74243951,"identity":"21ed1d04-8649-4d7a-9b9c-e1447db218af","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182095,"visible":true,"origin":"","legend":"\u003cp\u003eInteractional plot showing on the y-axis mean CT scores and on the x-axis the factor “Timepoint” (two levels: Pre-MT, Post-MT). Within-subjects results from the non-parametric Wilcoxon signed-rank test concerning CT administered before and after MT sessions. The plot shows a significant reduction in self-reported instantaneous craving after MT sessions (*** = p\u0026lt;.001).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/602a8c6bc89bd20d71d85345.png"},{"id":74243956,"identity":"91cce7e7-a5e6-477c-bfa2-e2e1a0469c4d","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319074,"visible":true,"origin":"","legend":"\u003cp\u003eInteractional plot showing on the y-axis FAA z-scores within the alpha band (8.0-12.0 Hz) at homologous electrode pair F7/F8 and on the x-axis the factor “Timepoint” (two levels: pre-intervention, post-intervention). Separate lines indicate changes according to the factor “Type of intervention” (two levels, MT, ST) with white dots for MT group and black dots for ST group. Significant differences have been found in the following comparisons: ST, pre-intervention – ST, post-intervention (p = .04076) and MT, post-intervention – ST, post-intervention (p = .00013). Asterisks legend: * = p \u0026lt; .05, or ** = p \u0026lt; .01, or *** = p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/fd6dfbd93160c94ee545dd5e.png"},{"id":74246857,"identity":"3043f1cb-19cd-4296-a704-1c8200ee5bc2","added_by":"auto","created_at":"2025-01-20 09:56:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":241896,"visible":true,"origin":"","legend":"\u003cp\u003eInteractional plots showing on the y-axis P3d mean amplitude (μV) at Fz, FCz, and Cz fronto-central midline electrodes’ sites measured only in participants receiving MT. On the x-axis, the factor “Timepoint” (two levels: pre-intervention, post-intervention) is displayed. From the parametric repeated measures ANOVA, a significant difference has been found in P3d mean amplitude between pre-intervention and post-intervention of MT (\u003cem\u003ep \u003c/em\u003e= .041). Asterisks legend: * = p \u0026lt; .05.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/5c1ac283ab11740bb90692d2.png"},{"id":74243976,"identity":"e895dbaf-75c1-4120-b5d1-24e1c0262dd5","added_by":"auto","created_at":"2025-01-20 09:48:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":866282,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution and scalp topography of evoked P3d pre-MT intervention (red lines) and post-MT intervention (blue lines). The figure displays the temporal distribution (A) and scalp topography (B) of P3d (NoGo-minus-Go) at a fronto-central ROI (i.e., Fz, FCz, Cz).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/b7ff8a85da173193a8fb8a4e.png"},{"id":74243982,"identity":"b9c4096f-bcf5-45d1-8166-3d260c67a28b","added_by":"auto","created_at":"2025-01-20 09:48:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1044671,"visible":true,"origin":"","legend":"\u003cp\u003eThematic analysis map from qualitative analysis. The semi-structured interview conducted after individual-MT sessions suggested different aspects of the participants’ experiences regarding the music-therapeutic process.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/b683bf5b6619436c2f1a5e2b.png"},{"id":74693123,"identity":"eceb5dff-fc31-45d5-926d-fa68aeda74ee","added_by":"auto","created_at":"2025-01-24 19:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6990725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/f9689013-fed7-405c-a9aa-6146806a915c.pdf"},{"id":74243963,"identity":"eaf76f41-436d-4742-93bb-080c4be5f3a2","added_by":"auto","created_at":"2025-01-20 09:48:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1479387,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5837441/v1/a130763682492f200120316f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMusic Therapy modulates Craving, Inhibitory Control, and Emotional Regulation: EEG, Psychometric, and Qualitative Findings from a Pilot RCT in a Community Outpatient Service\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSubstance Use Disorder (SUD) has been defined as a chronic, and often relapsing disorder (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) targeting multiple interacting neural circuits in the brain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Craving is the key symptom of SUD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), often accompanied by cognitive, physiological, and behavioural indicators that the individual continues to use the substance despite adverse consequences such as difficulties in emotional regulation and inhibitory cognitive control, as well as symptoms related to depression and anxiety (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, interventions aimed at improving mental health symptoms, quality of life and reducing relapses in individuals with SUD show variable outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) with limited and inconsistent reports for cognitive-behavioural therapy (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), pharmacotherapy, and psychotherapy/psychosocial interventions (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Despite inconsistent results that have also been noted in the field of music therapy (MT) for SUD (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), a recent Cochrane systematic review has identified MT as a valuable type of intervention for SUD in addition to standard treatment (ST) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). A similar conclusion was suggested by a World Health Organization report, which examined over 3,000 studies on the impact of the arts and art-based interventions (including MT) on health and well-being (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Those reviews analysed evidence from a broad spectrum of research designs, including uncontrolled pilot studies, case reports, small-scale cross-sectional surveys, nationally representative longitudinal cohort studies, community-wide ethnographies, and RCTs (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While no previous research has specifically used neuroscientific methods, these studies have provided insights into the potential of MT for addressing SUD. Incorporating neuroscientific methods is recommended to enhance our understanding of the brain-based mechanisms underlying the effects of MT and art-based interventions on health and well-being (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This pilot intended to lay the foundations for larger clinical trials, presenting, for the first time in a community setting, findings on craving and psychological dimensions examined through multiple lenses. Indeed, in the clinical trial protocol (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) we proposed a multi-domain approach within a mixed-methods design integrating neurophysiological data from electroencephalography (EEG) recordings with subjective assessments obtained via psychometric tests and semi-structured interviews. These assessments covered various aspects, including craving symptoms, depressive and anxiety symptoms, inhibitory control, emotional expression, and therapeutic alliance (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMT is defined as a clinical intervention delivered by an accredited music therapist who adopts music as a therapeutic tool to accomplish individualized goals within a therapeutic relationship (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It has also been defined as a psychological therapy that aims to create an interpersonal relationship between the client and the therapist to relieve symptoms and determine positive changes related to emotional regulation, motivation, and social engagement (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Recognizing the distinction between therapeutic music interventions and the general use of music without therapeutic intent is crucial (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Key elements of a therapeutic intervention include the involvement of a certified professional, a therapeutic environment, and an underlying framework guiding the intervention (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is extensive literature showing the emotional impact of music, even outside therapeutic settings, suggesting its potential for therapeutic applications (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Emotional responses to music involve complex psycho-physiological mechanisms, including evocation of autobiographical memories, automatic physiological reactions, and visual imagery (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Neuroimaging studies revealed that music appreciation is processed in analogous brain regions (i.e., the mesocorticolimbic system) as other intensely pleasurable and rewarding stimuli, such as psychoactive drugs (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). These findings led researchers and clinicians to consider that engaging with music might alleviate SUD-related symptoms opening various possibilities for clinical application (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, music can also represent a cue inducing the feeling of craving (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), underscoring the importance of investigating MT mechanisms of change (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Although a definite neural mechanism of therapeutic change is still based on empirical speculations, it has been proposed that within a MT setting, emotional experiences can be retrained and reframed (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Such a process would gradually mitigate and recalibrate the emotional impact related to music-induced memories (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) by targeting neurobiological elements that function as a common denominator in the neural underpinning of reward processing, addiction, and craving (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree systematic reviews (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) have explored the impact of MT on SUD, revealing positive impact on depressive (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and anxiety symptoms (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), negative emotions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and craving (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). However, there is a need for rigorous evaluations of MT effectiveness within community services, particularly combining neuroscientific methods, such as EEG, with subjective evaluation instruments (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). As detailed in our clinical trial protocol paper (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), the current pilot study addresses this gap by combining EEG-based neurophysiological data with psychometric assessments to examine MT\u0026rsquo;s impact on craving, depression, and anxiety. Specifically, we aim: (a) to investigate whether MT is associated with a reduction of craving, depressive, and anxiety symptoms; (b) to investigate whether MT is associated with an improvement in inhibitory cognitive control; (c) to explore the subjective experiences of SUD outpatients regarding the music-therapeutic process, focusing on the influence of MT sessions on their craving sensations, emotional expression and the dynamics of the therapeutic relationship.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eDesign\u003c/h2\u003e\n\u003cp\u003eThis pilot study is a part of a parent feasibility project for MT in community settings for SUD - ClinicalTrial.gov number: NCT05180617 (https://clinicaltrials.gov/ct2/show/NCT05180617). The study was designed as a three-arm non-blinded mixed-methods randomized trial. Participants were systematically randomized into three groups: individual-MT, group-MT, and ST. Outcome measures were taken at pre-, post-intervention stages and at in-session timepoints. The study commenced and concluded with pre- and post-intervention assessments, respectively in the first and eighth week. Measures included in these two stages were named \u0026quot;Assessment Module 1\u0026quot; (Figure 1). The intervention phase, from weeks 2 to 7, involved distinct protocols for group-MT and individual-MT groups, with the former undergoing \u0026quot;Assessment Module 2\u0026quot; and the latter completing both \u0026quot;Assessment Module 2\u0026quot; and an additional \u0026quot;Assessment Module 3\u0026quot; during specified sessions.\u003c/p\u003e\n\u003cp\u003e[Figure 1] \u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eSettings\u003c/h2\u003e\n\u003cp\u003eAn initial cohort of 18 service users from \u0026ldquo;V-i-a\u0026rdquo; community service (based in London, UK) were assessed for eligibility based on the inclusion/exclusion criteria defined beforehand in the protocol (16). The sample\u0026apos;s heterogeneity is addressed in the limitations section, while participants\u0026apos; demographic information and medication details are provided in the supplementary materials (Additional file 1, A7).\u003c/p\u003e\n\u003ch2\u003eRecruitment and allocation\u003c/h2\u003e\n\u003cp\u003eAfter two participants declined to participate, a cohort of 16 SUD participants was formed in two recruitment phases (Figure 1). In an initial two-week recruitment phase, 6 participants voluntarily consented to join the study and subsequently underwent pre-intervention measurements (i.e., Assessment Module 1). Due to logistical considerations related to the scheduling of group-MT, a staggered random allocation approach was employed: (I) 4 participants out of the first 6 recruited were randomly assigned to group-MT arm utilizing the GraphPad software (www.graphpad.com); (II) the allocation of the remaining 2 participants to either the ST or the individual-MT group was determined taking into consideration the order of returned consent forms and a stochastic method: a random number between 0 and 1 was generated, where a result smaller than 0.5 led to the earlier respondent being placed in the ST, while the subsequent participant was assigned to individual-MT.\u003c/p\u003e\n\u003cp\u003eTo address recruitment challenges within the community service, a second two-week recruitment phase was added. Because the group-MT sessions could begin after the first recruitment phase, all incoming participants were randomly assigned either to individual-MT group or to the ST. Thus, a sample of 16 participants with SUD was formed: 4 participants allocated to the group-MT, 6 to the individual-MT and 6 to the ST. It\u0026apos;s worth noting that the original, published recruitment strategy (16), required a modification due to complexities inherent to the clinical setting.\u003c/p\u003e\n\u003ch2\u003eOutcome measures\u003c/h2\u003e\n\u003ch3\u003eEEG and self-reported assessment of craving\u003c/h3\u003e\n\u003cp\u003eTo measure participants\u0026rsquo; feeling of craving in the preceding 24 hours, the Brief Substance Craving Scale (BSCS) questionnaire was completed. The neurophysiological signature associated with craving, resting-state EEG beta frequency band, was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages.\u003c/p\u003e\n\u003cp\u003eThe BSCS is a validated questionnaire to assess craving in the SUD population over a period of 24 hours (43,44). It is a 4-item questionnaire with three major factors (i.e., duration, frequency, and intensity of craving) structured at 5-point Likert scale. Although the BSCS has initially been used and validated in cocaine studies, this questionnaire has been shown to be easy to use and reliable across different substances (43). The frontally-distributed EEG power in the beta frequency band is a neural EEG rhythm underlying both abstinence-related and cue-induced craving (45,46). Key studies found an increase in beta frequency band mean absolute power (\u0026mu;V\u003csup\u003e2\u003c/sup\u003e) in a frontal region of interest (i.e., Fz, FP1, FP2, F3, F4, F7, F8 electrodes\u0026rsquo; sites) during exposure to drug-related cues (45). These increased values in frontal beta mean absolute power were found both in ERPs (45) and resting-state EEG activity (46\u0026ndash;49). Modulations in the frontally-distributed beta frequency bands have been proposed as a marker for craving-related arousal (46,50). For this outcome measure, population-normalized z-scores over a frontal region-of-interest (ROI), derived from the beta absolute power values, have been assessed.\u003c/p\u003e\n\u003cp\u003eTo measure instantaneous levels of craving intensity, an additional short self-report has been included. A \u0026ldquo;craving thermometer\u0026rdquo; (CT) - in the form of a visual analogue scale (VAS) - was completed before and after each MT session (group-MT and individual-MT sessions) by each participant. This measure has been adopted in SUD populations, both as a standalone subjective outcome (51) and combined with a neurophysiological assessment (52). The CT offers a quick-scan of craving intensity with one item: \u0026ldquo;please, rate how strong your drug craving is right now by putting a mark on the line going from 0 - \u003cem\u003enot craving at all\u0026nbsp;\u003c/em\u003e- to 10 - \u003cem\u003ethe most ever\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eEEG and self-reported assessment of depressive symptoms\u003c/h3\u003e\n\u003cp\u003eTo assess frequency and severity of depressive symptoms in the preceding 2 weeks, the Patient Health Questionnaire \u0026ndash; 9 (PHQ-9) was completed. The neural signature associated with depressive symptoms, resting-state EEG frontal alpha asymmetry (FAA), was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages.\u003c/p\u003e\n\u003cp\u003eThe PHQ-9, a widely recognized self-report tool for assessing depressive symptoms (53\u0026ndash;55), has demonstrated a good internal consistency when administered to participants with SUD (Cronbach\u0026rsquo;s \u0026alpha;=.90; (56)). It consists of one central question (\u0026ldquo;over the last two weeks, how often have you been bothered by any of the following problems\u0026rdquo;) to assess nine symptoms in nine items of depression on a 4-point Likert scale. The PHQ-9 total score, given by the sum of the nine items is ranked in one of the following severity categories: 1-4 \u0026ndash; minimal depression; 5-9 \u0026ndash; mild depression; 10-14 \u0026ndash; moderate depression; 15-19 \u0026ndash; moderately severe depression; 20-27 \u0026ndash; severe depression) (53). The FAA is an EEG hemispheric asymmetry measure representing the difference between the left and right alpha activity over the frontal regions of the brain (57\u0026ndash;62). Modulations of power imbalances between the left and right hemispheres in the alpha frequency band have been associated with the approach/withdrawal theory of emotions (58,60) and proposed as a marker of affective processing underlying symptoms of depression (58\u0026ndash;60,63,64). FAA has also been analysed in cue-exposure paradigm as an indicator craving-related approach towards the substance (65,66). This signature is generally examined over homologous pairs of frontal electrodes, namely F3/F4, F7/F8 and FP1/FP2 (67\u0026ndash;70). For this outcome measure, population-normalized z-scores on the above-cited electrode pairs, derived from the FAA absolute power values, have been assessed.\u003c/p\u003e\n\u003ch3\u003eEEG and self-reported assessment of anxiety symptoms\u003c/h3\u003e\n\u003cp\u003eTo measure participants\u0026rsquo; anxiety symptoms in the preceding 2 weeks, the Generalised Anxiety Disorder Assessment \u0026ndash; 7 (GAD-7) questionnaire was completed. The neural signature associated with anxiety symptoms, resting-state frontal midline theta (FMT), was recorded for 5 minutes with eyes closed. Both measures were collected at pre- and post-intervention stages. The GAD-7 is a validated (71) and standardized (72) self-report to screen for anxiety symptoms showing a good internal consistency when completed by SUD participants (Cronbach\u0026rsquo;s \u0026alpha; = .91; (72)). The 7-item GAD-7 has been identified a reliable screening tool for anxiety (71,72). It consists of one central question (\u0026ldquo;over the last two weeks, how often have you been bothered by any of the following problems\u0026rdquo;) to assess seven symptoms in seven items of anxiety on a 4-point Likert scale (or forced Likert scale). The GAD-7 total score, given by the sum of the seven Items, is ranked in one of the following categories: 0-4 \u0026ndash; minimal anxiety; 5-9 \u0026ndash; mild anxiety; 10-14 \u0026ndash; moderate anxiety; 15-21 \u0026ndash; severe anxiety.\u003c/p\u003e\n\u003cp\u003eThe FMT is an EEG measure that has been associated with anxiety (73). It has been suggested that modulations in resting-state FMT power (maximal at Fz electrode\u0026rsquo;s site) might represent a neural marker of cognitive and emotional demands underlying anxiety symptoms, where lower FMT values are associated with more severe anxiety symptoms, and higher values with less severe symptoms (67,68,73). For this outcome measure, population-normalized mean z-scores over Fz electrode\u0026rsquo;s site, derived from the FMT absolute power values, are the main outcome measure.\u003c/p\u003e\n\u003ch3\u003eGo/NoGo ERP task to assess inhibitory control\u003c/h3\u003e\n\u003cp\u003eAfter the 5-minute eyes-closed resting-state EEG recordings, participants completed a behavioural Go/No-Go task while event-related potentials (ERPs) were recorded to assess inhibitory cognitive control at pre- and post-intervention stages. This task required participants to respond with a button press when a set of stimuli was presented in a monitor (e.g., circles appearing at the top right and bottom left corners of the screen) and to withhold the response when another set of stimuli, with the same probability of occurrence, was presented (e.g., circles appearing at the top left and bottom right corners of the screen) (74,75). The Go/NoGo task serves as a tool for assessing the ability to execute or inhibit motor responses based on the nature of the presented stimuli and it is instrumental in examining sensorimotor response inhibition (76\u0026ndash;80), a key aspect of cognitive control that is often compromised in individuals with SUD (81,82).\u003c/p\u003e\n\u003cp\u003eThe P3 is a positive ERP component, emerging approximately 300\u0026ndash;800 ms after stimulus onset, and analysed primarily in the \u0026quot;oddball\u0026quot; and the \u0026quot;Go-NoGo\u0026quot; tasks (83\u0026ndash;85). The P3d, or the difference wave obtained by subtracting Go-P3 amplitudes from NoGo-P3 amplitudes, offers a refined measure of the neural activity specifically associated with inhibitory control, by isolating the neural responses to inhibition from those related to general task engagement and response execution (78,80). The task was constituted of two types of visual stimuli: (I) a fixation cross and (II) a circle that, depending on the position on the screen, could be either a Go or a NoGo stimulus (Figure 2). Participants were instructed to press a button on a response box (The Black Box Toolkit Ltd., www.blackboxtoolkit.com) as quickly as possible, when a circle appeared at the top right or at the bottom left-hand corner of the screen (Figure 2.A) and to withhold the response any time a circle appeared at the top left and bottom right corners (Figure 2.B). Both Go and NoGo conditions had a fixed inter-trial interval (ITI) of 2000 ms and an inter-stimulus interval (ISI) of 1000 ms. The Go and NoGo stimuli occurred with an equal probability (100 trials evenly distributed in 50 Go and 50 NoGo) and their presentation order was randomized for each participant adhering to two restrictions: a stimulus type (i.e., Go or NoGo) (I) could not appear for more than 10 times in a row and (II) could not appear for more than 50 times (i.e., to guarantee equiprobability). The stimuli were presented for 100 ms and between the presentation of Go and NoGo stimuli, a fixation cross was presented (500 ms before a stimulus and 400 ms after the presentation of the stimulus). Any response that occurred outside a 400 ms window post-stimulus presentation was considered incorrect. The experimental design comprised two distinct phases: a practice session (40 trials) and a recording session (100 trials). The total duration of the task was approximately 5 minutes, ensuring it remains manageable for patient compliance. The task was designed with a custom-made script, developed, and displayed for participants using Presentation\u0026reg; software (Version 23.0, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com).\u003c/p\u003e\n\u003ch3\u003eDescription of semi-structured interviews\u003c/h3\u003e\n\u003cp\u003eThe semi-structured interview used in this study served as a qualitative outcome measure to explore participants\u0026rsquo; perspectives on the music-therapeutic process by focusing on craving, emotional expression, and the therapeutic relationship. Three main topics were addressed with three open questions: \u0026ldquo;Was there any moment or moments in today\u0026rsquo;s session where you felt the urge for something?\u0026rdquo;, \u0026ldquo;Was there something particularly unpleasant or enjoyable that stood out?\u0026rdquo;, \u0026ldquo;Thinking about the relationship with your therapist, is there anything that you\u0026rsquo;d like to share about today\u0026rsquo;s session?\u0026rdquo;. These three questions represented the structured part and were followed (or not, depending on participants\u0026rsquo; availability to share more information) by a non-structured part. In the non-structured part, participants could share more detailed responses regarding the three main topics or explore other elements that were relevant to them in their therapeutic journey.\u003c/p\u003e\n\u003ch2\u003eMeasurement tools\u003c/h2\u003e\n\u003cp\u003eThe instruments used for data collection encompassed a range of psychometric, neurophysiological, and qualitative tools to ensure a comprehensive assessment. Psychometric measures, including the BSCS, PHQ-9, GAD-7, and the CT, were administered in a traditional pencil-and-paper format, alongside a pre-EEG screening questionnaire completed by participants. The screening questionnaire and all the other measures were detailed in the clinical trial protocol paper (16) beforehand. Neurophysiological data were collected using EEG recordings, which included both 5-minutes resting-state and 5-minutes ERP measurements. The EEG system employed R-Net passive electrodes with Ag/AgCl pellets and sponges soaked in an electrolyte/saltwater solution. The data acquisition was managed through BrainVision Recorder software, and signals were amplified using the LiveAmp amplifier (Brain Products, GmbH, Gilching, Germany). A total of 32 channels were used (Fp1, Fp2, Fz, F3, F4, F7, F8, F9, F10, FC5, FC6, FC1, FC2, Cz, C3, C4, T7, T8, CP5, CP6, CP1, CP2, Pz, P3, P4, P7, P8, TP9, TP10, Oz, O1, O2), positioned according to the international 10/20 system. The ground electrode was placed at FPz, while the reference electrode at FCz. The amplifier\u0026rsquo;s sampling rate was set at 250 Hz, and electrode impedances were carefully adjusted between 0-100 k\u0026Omega; to align with the amplifier\u0026rsquo;s input impedance, following recommendations from the literature (86) and the manufacturer\u0026rsquo;s guidelines for optimal data quality. EEG signals were inspected for quality and artifacts, with noisy segments excluded. Pre-intervention EEG resting-state signals were inspected (in terms of electrographic seizures, interictal epileptiform discharges, asymmetry, and slowing) by a clinical neurophysiologist (A.M.) who confirmed no clinically relevant incidental findings.\u003c/p\u003e\n\u003cp\u003eFor the ERP-Go/NoGo task, the LiveAmp amplifier received stimulus onset and response triggers via the Presentation software, interfaced through a Trigger Box (Brain Products, GmbH, Gilching, Germany). Stimulus and response markers were processed with a custom MATLAB (2022b) script, which re-coded markers in the EEG file to match the corresponding Presentation software log entries. Unique labels were assigned for Go stimuli (S_Go), NoGo stimuli (S_NoGo), and participant responses (R). The script also calculated behavioural performance metrics and cross-verified latencies between EEG markers and logfiles (details in Appendix A.3). Semi-structured interviews were conducted as part of the qualitative data collection, using a smartphone for recording. These recordings were promptly transferred to a password-protected computer at Anglia Ruskin University (ARU) and deleted from the smartphone to ensure data security. This comprehensive approach integrated psychometric assessments, advanced EEG recordings, and qualitative interviews to provide a robust dataset for analysis.\u003c/p\u003e\n\u003cp\u003e[Figure 2]\u003c/p\u003e\n\u003ch2\u003eTypes of intervention\u003c/h2\u003e\n\u003cp\u003eThe MT intervention proposed in this study aligned with the integrative improvisational MT model, characterized by its integration of musical improvisation and verbal therapeutic techniques (19\u0026ndash;21). This approach leverages the dynamic interplay between spontaneous musical creation and verbal reflection to facilitate therapeutic outcomes. In the present study, the MT sessions had the aim to support and encourage physical, mental, social, emotional, and spiritual wellbeing (16) and were delivered by a qualified music therapist registered with the UK Regulator for health and care professions (Health and Care Professions Council). The music therapist trained through an accredited training course, offered in the UK in the form of a master\u0026rsquo;s level training. The MT sessions were multifaceted, encompassing a range of musical activities such as improvisation, composition, music production, song writing, lyric analysis of preferred songs, singing, and dialogue.\u003c/p\u003e\n\u003cp\u003eThese sessions were delivered by a music therapist (who also was a music technology researcher), utilising music production technologies and mobile/smart technologies (e.g., iPads, MIDI [Musical Instrument Digital Interface] keyboards, Ableton Digital Audio Workstation) playing a supportive role in enhancing the therapeutic environment. A distinctive feature of these sessions, in line with the principles of improvisational MT, was that participants were not required to have any prior musical experience or skill level (16,20). During the sessions, a hybrid approach was employed, integrating digital and acoustic instruments to create a multifaceted therapeutic environment. Participants in the community service could be engaged in different treatment statuses which involved different types of ST received (see Additional file 1, A7). Participants engaged in structured treatment (tier 3) were offered 1:1 individual session with a keyworker, group-based activities, prescribing, detoxification, blood-borne virus testing and vaccination. Structured treatment could also include referrals to or help in accessing support from other services within the community, e.g., mental health services, social services, employment support. All service users in structured treatment are also provided a \u0026ldquo;Capital Card\u0026rdquo;, which acts as a form of contingency management (87). The \u0026ldquo;Capital Card\u0026rdquo; allows service users to accrue points when they engaged in structured treatment activities. Service users are then able to spend these points within Via\u0026apos;s \u0026ldquo;Capital Card\u0026rdquo; shops and at local partner agencies such as cinemas and gyms. Service users who successfully completed the structured treatment (tier 3) stage could enter \u0026ldquo;tier 2\u0026rdquo; - recovery support. Recovery support included supporting service users with sustaining their recovery, determining what their future life will look like and reintegrating into the local community. The tier 2 stage included recovery check-ups and activities related to educational opportunities or to support in the search of vocational training or support in finding employment. This type of support was also associated with peer mentoring opportunities.\u003c/p\u003e\n\u003ch2\u003eProcessing and analysis\u003c/h2\u003e\n\u003ch3\u003eResting-state EEG preprocessing\u003c/h3\u003e\n\u003cp\u003eEEG resting-state data were preprocessed and processed using Neuroguide software (88,89) (Applied Neuroscience; St. Petersburg, Florida, United States), which is an FDA-approved tool and incorporates a normative database from 625 healthy individuals aged 2 months to 82 years. The processing of resting-state EEG involved several steps. (I) Data Recording and Segmentation. Manual notes were taken during EEG recording to identify artifacts and disturbances. The raw data were visually inspected and segmented using BrainVision Analyzer (BrainVision Analyzer, Version 2.2.2, Brain Products GmbH, Gilching, Germany) to include only task-relevant resting-state sections (e.g., excluding moments at the beginning or at the end of the resting period). (II) Data Import and Re-referencing. Segmented resting-state recordings were exported in EDF+ format and imported into Neuroguide. Participant information (sex, age, recording date, etc.) was entered for normative comparison. The EEG data were re-referenced to a standard linked-ears montage. Neuroguide applied preprocessing steps: down-sampling to 128 Hz, and bandpass filtering (1-55 Hz). (III) Artifact Rejection. Neuroguide\u0026apos;s semi-automatic artifact rejection procedure was employed, using a 10-second artifact-free data selection and a z-score pattern recognition routine to identify and exclude artifacts (drowsiness, eye movements, muscle activity). Sensitivity was set to \u0026quot;high\u0026quot; with a 2.0 SD z-score threshold. (IV) Reliability Estimation. Selected artifact-free data were assessed for split-half reliability (\u0026gt; .95) and test-retest reliability (\u0026gt; .90) for data segments longer than 60 seconds. Specifically, artifact-free recordings in the MT group ranged from 01:16 and 04:07 minutes (mean at pre-intervention stage of 2.1 minutes; mean at post-intervention stage of 2.2 minutes). Similarly, in the ST group, artifact-free lengths ranged from 01:14 and 03:27 minutes (mean at pre-intervention stage of 1:30 minutes; mean at post-intervention stage of 1:34 minutes).\u003c/p\u003e\n\u003ch3\u003eResting-state EEG processing\u003c/h3\u003e\n\u003cp\u003eNeuroguide calculates the power spectrum for artifact-free EEG selections using Fast Fourier Transform (FFT). EEG data were divided into 2-second epochs at 128 samples/s, covering a frequency range of 0.5-55 Hz. Frequency ranges of interest were theta (4-8 Hz), alpha (8-12 Hz), and beta (12-25 Hz). For beta and theta power, the linked-ears montage was used, while a Laplacian re-referencing was applied for FAA power calculation as in previous MT studies exploring resting-state FAA power (90). The software log-transforms amplitudes within the frequency range for 19 channels to compute population-normalized z-scores.\u003c/p\u003e\n\u003ch3\u003eERP \u0026ndash; Go/NoGo task preprocessing and processing\u003c/h3\u003e\n\u003cp\u003eEEG datasets were imported into BrainVision Analyzer and re-referenced to linked-mastoids. A high-pass filter (0.1 Hz) was applied before Independent Component Analysis (ICA) to identify and remove ocular artifacts. ICA separates the EEG into independent components, aiding artifact detection(91) Components reflecting blinks and eye movements were identified through their topography, time course, and spectral distribution. On average, 1.6 components per participant were corrected to zero. Post-ICA, a low-pass filter (30 Hz) and a Notch filter (50 Hz; Butterworth 4\u003csup\u003eth\u003c/sup\u003e order) were applied to correct for electromyogram-related artifacts and electrical noise. Data were segmented relative to Go and NoGo stimuli onsets (from -200 to +800 ms), and segments were baseline corrected (from -200 to 0 ms). Artifact rejection was performed based on predefined parameters (gradient: 50 \u0026mu;V; max/min 200 \u0026mu;V min allowed amplitude: -100 \u0026mu;V; max allowed amplitude: 100 \u0026mu;V) marking the data violating those parameters with a window going from -200 to 200 ms from the artifact. After that, flagged segments were visually inspected and only artifact-free epochs retained for analysis. Artifact-free EEG epochs were averaged to generate ERP waveforms for each subject, including Go and NoGo trials, and difference waves (NoGo-minus-Go). For statistical analyses, data were averaged separating conditions and timepoints (pre- and post-intervention) to examine effects within the 350-500 ms time window for Go-P3, NoGo-P3, and NoGo-minus-Go P3 (i.e., P3d) waveforms. A multi-layered approach has been used to determine the optimal time-window for P3 component analysis. Combining information from (a) previous literature employing similar designs (74), (b) visual inspection, and (c) latency analysis with Brain Vision Analyzer\u0026rsquo;s peak detection tool, a consistent 350-500 ms time window has been identified. The region-of-interest (ROI) for Go-P3, NoGo-P3, and P3d was based on existing literature (74,92,93), focusing on midline and frontocentral electrodes.\u003c/p\u003e\n\u003ch3\u003ePsychometric reports processing\u003c/h3\u003e\n\u003cp\u003eData from pencil-and-paper psychometric reports were entered into Excel files for subsequent statistical analysis using JASP software (Version 0.19.0, https://jasp-stats.org/).\u003c/p\u003e\n\u003ch3\u003eSemi-structured interview processing\u003c/h3\u003e\n\u003cp\u003eA modified version of Reflexive Thematic Analysis was employed to analyse qualitative data from semi-structured interviews in six steps (94,95). The six-phase process included familiarization with data, systematic coding, initial theme generation, theme development and review, theme refinement and naming, and report writing. The adaptation lies in the fact that a Defense Mechanism Rating Scales framework was used to guide and enhance the coding procedure of the latent meanings (96,97).\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe independent variable introduced in the current study is the type of intervention: MT or ST. MT included group-MT and individual-MT, which were treated as a unique group for quantitative data analysis. Constructs of interest were short-term and present-moment craving symptoms (as expressed with dependent variables related to BSCS, beta frequency band, and CT), depressive symptoms (as expressed with dependent variables related to PHQ-9 and FAA) and anxiety symptoms (as expressed with dependent variables related to GAD-7 and FMT), sensorimotor response inhibition (as expressed with dependent variables related to P3d ERP and behavioural indicators), and qualitative perceptions of the music-therapeutic process (as expressed with dependent variables related to the content of participants\u0026rsquo; interviews).\u003c/p\u003e\n\u003ch3\u003eStatistics Approach\u003c/h3\u003e\n\u003cp\u003eThe analysis strategy for pre-/post-intervention outcome measures involved the use of a mixed-design ANCOVA to examine within-subjects (Timepoint: pre-intervention, post-intervention) and between-subjects (Type of intervention: MT, ST) changes, adjusting for baseline differences (i.e., using baseline scores as a covariate) (98,99). Parametric assumptions (i.e., normality of residuals, linearity of relationships, sphericity, homogeneity of variance, and independence of observations) were checked, and because of the small sample size, non-parametric alternatives were considered when necessary. This involved the use of mean ranks as a substitute for parametric tests to yield comparable outcomes (100,101).\u003c/p\u003e\n\u003cp\u003eConcerning relational analyses, Spearman\u0026rsquo;s rho correlations explored relationships between EEG z-scores and questionnaire scores. Concerning the CT, administered pre-/post-MT sessions, the Wilcoxon signed-rank test was adopted.\u003c/p\u003e\n\u003cp\u003eOverall, the study utilized a comprehensive data analysis strategy incorporating both quantitative and qualitative methods.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003ePre-/post-intervention craving symptoms\u003c/h2\u003e\n\u003ch3\u003eResults from beta mean z-scored absolute power\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBeta power decreased after MT from 11.7 \u0026mu;V\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(SD = 4.8, SE = .6) to 10.7 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 5.0, SE=.7) and this is reflected in beta mean z-scores trajectory, i.e., from .63 to .44. Similarly, beta power decreased after ST from 25.5 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 18.2, SE = 3.3) to 22.2 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 14.3, SE = 2.6) and this is reflected in beta mean z-scores trajectory, i.e., from 1.66 to 1.58. Beta mean z-scores, both at pre-intervention and post-intervention stages, from MT and ST, stayed within the \u0026plusmn;1.96 standard deviation range, indicating no significant deviations from the normative population benchmarks (88,89).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Within-subjects effects.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe main effect of \u0026ldquo;Timepoint\u0026rdquo; did not reach significance levels (\u003cem\u003eF(\u003c/em\u003e1,13) = 1.45, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .232). However, a statistically significant interaction with a small effect size has been observed between \u0026ldquo;Timepoint\u0026rdquo; and \u0026ldquo;Type of intervention\u0026rdquo; (\u003cem\u003eF(\u003c/em\u003e1,13) = 9.80, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .002, \u003cem\u003e\u0026omega;\u0026sup2;\u0026nbsp;\u003c/em\u003e= .049).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Between-subjects effects.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe model indicated a significant main effect of \u0026ldquo;Type of intervention\u0026rdquo; with a medium effect size (\u003cem\u003eF(\u003c/em\u003e1,13) = 12.27, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .001, \u003cem\u003e\u0026omega;\u0026sup2;\u0026nbsp;\u003c/em\u003e= .059).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost-hoc comparisons.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBonferroni\u0026rsquo;s pairwise post-hoc comparisons were conducted. At post-intervention stage, participants in the MT group had significantly lower beta mean z-scores than participants in the ST group (\u003cem\u003et\u0026nbsp;\u003c/em\u003e= -4.68; \u003cem\u003eCohen\u0026rsquo;s d\u0026nbsp;\u003c/em\u003e= -1.06; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .00003). This significant difference with a large effect size explained the main effect of \u0026ldquo;Type of intervention\u0026rdquo; suggesting a different neurophysiological state, in the beta frequency band, at post-intervention stage (Figure 3.A). This effect can be discerned in Figure 3.B, which depicts a comparative beta power scalp topography within the frontal ROI for the MT group versus the ST group at post-intervention stage. The topographical plot illustrates the differences in power values, which are significantly smaller in the MT group at post-intervention stage. \u0026nbsp;Importantly, this effect is not driven by baseline differences: in fact, post-hoc tests revealed no significant difference between the MT and ST groups at the pre-intervention stage (\u003cem\u003et\u0026nbsp;\u003c/em\u003e= .14; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 1.0).\u003c/p\u003e\n\u003ch3\u003eResults from the Brief Substance Craving Scale\u003c/h3\u003e\n\u003cp\u003eAt pre-intervention stage, the MT group exhibited a mean total score of 2.6 (SD = 3.1, SE = 1.0), in contrast to the ST group displaying a higher mean score of 5.8 (SD = 3.4, SE = 1.4). At post-intervention stage, the mean total score in the MT group increased to 4.2 (SD = 4.1, SE = 1.4), whereas in the ST group, it decreased to 2.5 (SD = 2.3, SE = .9).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Within-subjects effects.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe analysis revealed that the main effect of \u0026ldquo;Timepoint\u0026rdquo; on BSCS total scores did not reach statistical significance (\u003cem\u003eF(\u003c/em\u003e1, 13) = .11, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .743). The interaction between \u0026ldquo;Timepoint\u0026rdquo; and \u0026ldquo;Type of intervention\u0026rdquo; was also not statistically significant (\u003cem\u003eF(\u003c/em\u003e1, 13) = 2.81, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Between-subjects effects.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe effect of the \u0026ldquo;Type of intervention\u0026rdquo; while approaching significance levels was not significant (\u003cem\u003eF(\u003c/em\u003e1, 13) = 3.18, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .097, \u003cem\u003e\u0026omega;\u0026sup2;\u0026nbsp;\u003c/em\u003e= .072).\u003c/p\u003e\n\u003ch3\u003eRelational results: beta mean z-scored and Brief Substance Craving Scale\u003c/h3\u003e\n\u003cp\u003eNo significant correlations have been found between beta z-scores and BSCS total scores.\u003c/p\u003e\n\u003ch3\u003eResults from the Craving Thermometer\u003c/h3\u003e\n\u003cp\u003eAll the participants assigned to individual-MT and group-MT (i.e., 10) compiled the CT, however not all participants compiled it for all the 6 sessions. Indeed, we collected 31 CTs pre-MT and post-MT sessions out of 60 (i.e., 29 pre-MT and post-MT sessions missing) with a mean of 3.1 CTs completed per participant. As shown in Figure 4, the CT mean score prior to MT sessions was 1.8 (SD = 1.8, SE = .3), with a median score of 1.3. Following the MT sessions, the CT mean score decreased to .8 (SD = 1.2, SE = .2), with a median score of .0.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric Wilcoxon test: Within-subjects effects.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe results of the Wilcoxon signed-rank test (Figure 4) indicated a significant difference between Pre-MT (Median = 1.3) and Post-MT (Median = .00) scores on the subjective evaluation of instantaneous levels of craving (\u003cem\u003ez\u0026nbsp;\u003c/em\u003e= 3.83, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .0001).\u003c/p\u003e\n\u003cp\u003e[Figure 3]\u003c/p\u003e\n\u003cp\u003eOn average, participants showed a significantly lower evaluation of instantaneous craving following the MT session (M=.8, SD=1.2, SE=.2) than before the MT session (M=1.8, SD=1.8, SE=.33) (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Figure 4]\u003c/p\u003e\n\u003ch2\u003ePre-/post-intervention depressive symptoms/emotional regulation\u003c/h2\u003e\n\u003ch3\u003eResults from FAA mean z-scored absolute power\u003c/h3\u003e\n\u003cp\u003eWhile the analysis of FAA was conducted on the F7/F8, F3/F4, and FP1/FP2 electrode pairs as typically investigated in the literature (67\u0026ndash;70), we have chosen to report only the results for F7/F8 in the main text. The findings at F3/F4 showed the same picture but no significant changes, significant and consistent changes were observed at FP1/FP2 (Additional file 1, A3.1. and A3.2.). FAA mean z-scores have been measured at F7/F8 electrode pair within the alpha band (8.0-12.0 Hz). At pre-intervention stage, the MT group showed an FAA mean of -.3 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 52.9; SE=17.7) and a mean z-score of -.02 while post-intervention the mean FAA power increased to 10.7 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 42.7; SE=14.4) and a mean z-score increased to .27 suggesting a change in the hemispheric asymmetry activity. Specifically, this increase indicated higher alpha power in the left frontal hemisphere relative to the right frontal hemisphere after MT, signifying less brain activity in the left hemisphere as alpha power is inversely related to brain activity (102,103). A different pattern has been displayed by the ST group which showed a mean FAA of -19.5 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 35.6; SE=17.8) with a mean z-score of -.28 at pre-intervention stage, while a mean FAA of -45.7 \u0026mu;V\u003csup\u003e2\u003c/sup\u003e (SD = 10.8; SE=5.4) and a mean z-score of -.93 after the intervention. This suggests an opposite trajectory compared to the MT group. Indeed, the ST group showed a decreasing alpha power (more negative values) in the left hemisphere relative to the right hemisphere after the intervention, signifying more brain activity in the left compared to the right hemisphere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Within-subjects effects.\u003c/em\u003e The main effect of \u0026ldquo;Timepoint\u0026rdquo; did not reach significance levels (\u003cem\u003eF\u003c/em\u003e(1,11) = 2.36; \u003cem\u003ep\u003c/em\u003e = .153). However, a statistically significant interaction with a medium effect size has been observed between \u0026ldquo;Timepoint\u0026rdquo; and \u0026ldquo;Type of Intervention\u0026rdquo; on the FAA mean z-scores (\u003cem\u003eF\u003c/em\u003e(1,11) = 15.95, \u003cem\u003ep\u003c/em\u003e = .002, \u003cem\u003e\u0026omega;\u0026sup2;\u003c/em\u003e = .381).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNon-parametric mixed-design ANCOVA: Between-subjects effects.\u003c/em\u003e Concerning between-subject effects, the model indicated a significant difference, with a medium effect size, between groups receiving MT and ST showing a main effect of \u0026ldquo;Type of intervention\u0026rdquo; (\u003cem\u003eF\u003c/em\u003e(1,11) = 13.09, \u003cem\u003ep\u003c/em\u003e = .004, \u003cem\u003e\u0026omega;\u0026sup2;\u003c/em\u003e = .335).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost-hoc comparisons.\u003c/em\u003e To understand which comparison was responsible for within- and between-subjects effects, Bonferroni\u0026rsquo;s pairwise post-hoc comparisons were run. Two significant results emerged (Figure 5). Firstly, within the ST group, there was a significant, with a large effect size, change in FAA mean z-scores from pre- to post-intervention (t = 3.32; Cohen\u0026rsquo;s d = 2.34; p = .041). This result explained the significant interaction between \u0026ldquo;Timepoint\u0026rdquo; and \u0026ldquo;Type of Intervention\u0026rdquo; and suggested a decrease in FAA mean z-scores after ST (from pre-ST (-.28) to post-ST (-.93)). In other words, there was a significant neurophysiological modulation of the ST intervention on the FAA at F7/F8. Secondly, a comparison between the two groups (MT and ST) at the post-intervention stage revealed a significant difference, with a large effect size, in FAA mean z-scores (\u003cem\u003et\u003c/em\u003e = 5.38; \u003cem\u003eCohen\u0026rsquo;s d\u003c/em\u003e = 3.23; \u003cem\u003ep\u003c/em\u003e = .0001). The FAA mean z-score increased in MT (from -.02 to .27) and decreased in ST (from -.28 to -.93). Post-hoc analysis suggested that the main between-subjects effect comes from the mean z-score difference at post-intervention stage MT (.27) and ST (-.93). The decreased FAA mean z-scores after ST suggested a lower left-sided alpha power (i.e., higher left-sided brain activity) while the increase after MT suggested a higher left-sided alpha power (i.e., lower left-sided brain activity). In other words, the main effect of \u0026ldquo;Type of intervention\u0026rdquo; suggested a differential neurophysiological state, in FAA, at post-intervention stage.\u003c/p\u003e\n\u003cp\u003e[Figure 5]\u003c/p\u003e\n\u003ch3\u003eResults from the Patient Health Questionnaire-9\u003c/h3\u003e\n\u003cp\u003eThe evaluation of PHQ-9 mean total scores indicated no significant within- (\u003cem\u003eF(\u003c/em\u003e1,14) = .03, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .862) nor between-subjects (\u003cem\u003eF(\u003c/em\u003e1,14) = .63, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .441) differences in the effects of MT and ST on self-reported depressive symptoms. Descriptive statistics showed a slight increase in mean total scores for both groups.\u003c/p\u003e\n\u003ch3\u003eRelational results: FAA mean z-scored and Patient Health Questionnaire-9\u003c/h3\u003e\n\u003cp\u003eNo significant correlations have been found between FAA z-scores and PHQ-9 total scores.\u003c/p\u003e\n\u003ch2\u003ePre-/post-intervention anxiety symptoms\u003c/h2\u003e\n\u003ch3\u003eResults from FMT mean z-scores and GAD-7\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eAnxiety symptoms before and after the intervention were assessed using z-score values from resting-state FMT frequency bands, derived from absolute power values, and the GAD-7 questionnaire. No significant changes were observed within or between groups at pre- and post-intervention stages in either measure (see Additional file 1, A1.1. and A1.5)\u003c/p\u003e\n\u003ch2\u003ePre-/post-intervention inhibitory cognitive control\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eDue to the findings of a noise quantification analysis (see Additional file 1, A4), we excluded data from the ST group from further analysis. This decision was made following previous studies (104) to ensure the reliability of the results, as the ST group exhibited significantly higher noise levels in their ERP waveforms, rendering them unsuitable for accurate comparison with the MT group.\u003c/p\u003e\n\u003ch3\u003eEvoked P3d (NoGo-minus-Go P3)\u003c/h3\u003e\n\u003cp\u003eAt pre-intervention stage, the mean P3d amplitude was .27 \u0026mu;V (SD = 4.39, SE = 1.03). At post-intervention stage, there was an increase in mean amplitude to 1.07 \u0026mu;V (SD = 3.49, SE = .82).\u003c/p\u003e\n\u003cp\u003eA repeated measures ANOVA was utilized to analyse the P3d in the MT group with the factor \u0026ldquo;Timepoint\u0026rdquo; (pre- and post-intervention) as within-subjects factor. The results (Figure 6) indicated a main effect of \u0026ldquo;Timepoint\u0026rdquo; indicating a significant increase in P3d mean amplitude following MT with a small effect size (\u003cem\u003eF\u003c/em\u003e(1, 17) = 4.87, \u003cem\u003ep\u003c/em\u003e = .041, \u003cem\u003e\u0026omega;\u0026sup2;\u003c/em\u003e = .00798). Figure 7 provides a comparative analysis of the P3d component in SUD participants before and after MT showing increased P3d component in the 350-500 ms time range following MT in the time-domain (Figure 7.A) and scalp topography (Figure 7.B). This effect has been observed over fronto-central electrodes (Fz, FCz, Cz) and reflects a stronger neural response post-intervention, indicating the potential impact of MT on neural activity.\u003c/p\u003e\n\u003cp\u003e[Figure 6]\u003c/p\u003e\n\u003ch3\u003eBehavioural indicators\u003c/h3\u003e\n\u003cp\u003eBehavioural results from the Go/NoGo task showed stable patterns at pre- and post-intervention stages (see Additional file 1, A2). Hit rates numerically increased marginally in both the MT (from .79 to .82) and ST groups (from .85 to .88), while miss rates decreased (MT: .21 to .17; ST: .15 to .11). False alarm rates numerically increased post-MT (.05 to .07) and post-ST (.03 to .12), with corresponding decreases in correct rejection rates (MT: .96 to .94; ST: .97 to .89). Reaction times remained stable (MT: from 299.74 ms to 301.70 ms; ST: from 292.12 ms to 286.91 ms). Lastly, d\u0026apos;prime decreased slightly post-MT (2.84 to 2.64) and ST (3.42 to 3.07), but none of these changes reached statistical significance.\u003c/p\u003e\n\u003cp\u003e[Figure 7]\u003c/p\u003e\n\u003ch2\u003eResults from semi-structured interviews: participants perceptions on the music-therapeutic process.\u003c/h2\u003e\n\u003cp\u003e[Figure 8]\u003c/p\u003e\n\u003cp\u003eData from the semi-structured interviews were collected following the second or third and fifth sessions of individual-MT. This section presents the results of the qualitative analysis, which utilized a modified reflexive thematic analysis to identify themes, subthemes, and sub-subthemes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTheme 1. Outcomes and impacts on craving.\u003c/em\u003e This theme explores participants\u0026apos; perceptions and insights on the outcomes and the impacts that MT sessions had on the feeling of craving.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 1.1. Absence of craving in music therapy sessions\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e This subtheme reflects interview data indicating that participants typically do not experience craving while they were in their individual-MT sessions. When asked about sensations potentially associated with cravings during the semi-structured interviews, participants uniformly reported a lack of such feelings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 1.2. Resistance to craving conversations.\u0026nbsp;\u003c/em\u003eThis subtheme emerged from an examination of the participants\u0026apos; apparent resistance to discussing cravings. It delves into the observed reluctance and opposition among participants when addressing the topic of craving. Typically, participants would answer the questions but limit their responses to one or two words, often simply saying, \u0026ldquo;No, No,\u0026rdquo; before either remaining silent or steering the conversation away from the topic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 1.3. Reduced craving after music therapy sessions.\u003c/em\u003e This subtheme examines the reports from participants on reduced feeling of craving following their MT sessions. Common expressions from participants after the sessions included statements such as, \u0026ldquo;I don\u0026rsquo;t feel the urge to consume a substance\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTheme 2. Outcomes and impacts on experience and emotions.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis theme examines the impact of individual-MT on participants\u0026apos; emotional expression and their perception of past life experiences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 2.1. Echoes from the past.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis subtheme describes the interplay between past experiences and current behaviour in the context of emotional expression felt by participants that have a history of SUD with craving symptoms.\u0026nbsp;For instance, when one participant was asked about positive emotions S/He was experiencing during the MT, S/He spontaneously began sharing their past struggles with substance use: \u0026quot;Yes, I am always happy... I was sent into rehab 7 times... I stopped in 2012... this year I said no, enough is enough, so I just stopped and started coming here regularly.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 2.2. Emotional transformation.\u003c/em\u003e focuses on the quality and nature of emotional expression displayed by participants. Within this subtheme, two additional layers are present: \u0026quot;Active engagement and hope in therapeutic journey\u0026quot; (Subsubtheme 2.2.1) and \u0026quot;Perception of therapeutic change\u0026quot; (Subsubtheme 2.2.2). These explore, respectively, the level of active participation in therapy and how participants perceive their own emotional and therapeutic changes over time. Overall, this theme serves to explore the broader emotional dimensions that shaped participants\u0026apos; experiences throughout their therapeutic journey.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTheme 3. Process dynamics in music therapy.\u003c/em\u003e The theme captures participants\u0026rsquo; perception on the multi-dimensional aspects of music therapy, ranging from the individual\u0026apos;s interaction with the music itself to the interpersonal therapeutic relationship that contributed to therapeutic success.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 3.1. Positive influence from music.\u003c/em\u003e The subtheme \u0026quot;Positive influence from music\u0026quot; explores participants\u0026apos; experiences with music as an effective therapeutic resource for emotional exploration and expression. For instance, one participant commented \u0026quot;I can get out of my comfort zone; that\u0026rsquo;s why I was more comfortable playing in the central part \u003cem\u003e[of the piano keyboard]\u003c/em\u003e, but now and then I did go on the black buttons, and I did go on the sides.\u0026quot; This remark not only suggested the participant\u0026apos;s growing comfort level and willingness to explore different emotional terrains through music but also highlighted how music facilitated a mature articulation of wishes and feelings, serving as a bridge to deeper emotional expression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubtheme 3.2. Therapeutic connection.\u003c/em\u003e The subtheme \u0026quot;Therapeutic connection\u0026quot; emphasizes the crucial role that the relationship between the therapist and the participant played in the therapeutic process. For example, one participant stated, \u0026quot;I was looking at Paul [The music therapist] and trying to mimic him\u0026hellip;\u0026quot; and added, \u0026quot;I feel connected; he understands where I come from\u0026quot;. These remarks effectively illustrate the importance of a strong therapeutic alliance in enhancing the outcomes of therapy. Another participant reported a transition in the musical interaction with the therapist from playing \u0026quot;aggressive music\u0026quot; to adopting a \u0026quot;calm and calm and just going through it\u0026quot; approach. This further underlined the impact of therapeutic relationship on facilitating change and fostering personal growth within a music therapeutic setting.\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003eImpact of music therapy on craving symptoms\u003c/h2\u003e\n\u003cp\u003eThere is an extensive body of literature exploring the link between the beta frequency band in neurophysiological findings and the experience of craving. Increased power in the beta frequency band has been associated with the feeling of craving in resting-state EEG studies with SUD participants undergoing periods of abstinence (46\u0026ndash;49,105\u0026ndash;107)\u0026nbsp; and in task-based cue-reactivity paradigms with SUD participants exposed to drug-related stimuli (45,52). The increased beta power has been interpreted as indicating heightened craving arousal (46,50,106,107).\u003c/p\u003e\n\u003cp\u003eTherefore, it has been suggested that increased power in the frontally-distributed beta frequency band could serve as a biomarker for hyperarousal related to craving intensity (46,50,106). In light of existing literature, the neuromodulation that MT might facilitate in the beta frequency band could potentially be associated with lower post-intervention levels of arousal related to craving compared to the ST group. The lower level of frontally-distributed beta power in the MT group at the post-intervention stage suggests that MT and ST may be more effective than ST alone in mitigating the arousal linked to the craving\u0026apos;s motivational drive or in enhancing the mechanisms that suppress this drive.\u003c/p\u003e\n\u003cp\u003eThe neurophysiological findings presented here indicate that MT may facilitate neuromodulation within the beta frequency band, which is a biomarker of craving sensations, i.e., indicating less craving after MT. This is suggested by the lower beta mean z-score values in the MT group compared to the ST group. However, the conscious perception (i.e., subjective evaluation) of this change is only noticeable in the CT administered before and after MT sessions and not in the BSCS administered before and after the interventions.\u003c/p\u003e\n\u003ch3\u003eDifferences in Neurophysiological and Self-Reported Craving Measures\u003c/h3\u003e\n\u003cp\u003eThe divergence between the intrinsic neurophysiological activity related to craving and the psychometric assessment underscores the multifaceted nature of craving: while the EEG measurements suggested a non-significant pre-post intervention decrease in arousal related to craving symptoms following MT, self-reported data from the BSCS depicted a different picture, indicating a non-significant pre-post intervention increase in self-reported craving post-MT.\u003c/p\u003e\n\u003cp\u003eWhile measures showed a consistent stable pattern (i.e., non-significant differences), the direction of those patterns is opposite highlighting the challenge of capturing the multi-dimensional construct of craving (51,108) and emphasizing the intricate interplay between the brain\u0026apos;s intrinsic activity \u0026ndash; also called \u0026quot;interoceptive signals\u0026quot; related to craving (108) \u0026ndash; and an individual\u0026apos;s subjective experience over time (51,108). Importantly, the translation of interoceptive signals of craving into a conscious craving experience does not always occur (109,110). In other words, craving processes are not consistently and invariably subject to conscious awareness (109,110).\u0026nbsp;An important aspect to consider is that, although BSCS and EEG measurements were conducted at the same points in time, they provide information on different timescales of craving: EEG offers an instantaneous measure of intrinsic brain activity (without any instructed mental activity), while BSCS requires participants to reflect on their state over the previous 24 hours. Thus, psychometric tests, compared to resting-state EEG recordings, rely on higher-order cognitive functions such as introspection and retrospective thinking, encompassing an assessment of one\u0026apos;s mental state that may be influenced by subjective interpretation, social desirability, and limited self-awareness \u0026ndash; especially during a vulnerable period (111).\u003c/p\u003e\n\u003ch3\u003eThe Role of Interventions in Modulating Resting-State Beta Frequency Band\u003c/h3\u003e\n\u003cp\u003eThe neurophysiological results from this pilot investigation contribute to the existing body of literature in a new setting: a clinical trial involving MT for SUD outpatients engaged in a CSMTS. These findings suggest that MT may significantly facilitate neuromodulation in the beta frequency band, which could potentially reflect a reduction in craving arousal. However, the conscious perception of this neuromodulation may differ depending on the measures used to assess craving.\u003c/p\u003e\n\u003ch3\u003ePresent-moment craving symptoms \u0026ndash; craving thermometer\u003c/h3\u003e\n\u003cp\u003eThe CT, a single-item psychometric tool presented in the form of a VAS, was utilized to capture instantaneous or present-moment self-evaluations of craving intensity exclusively among MT participants before and after each session.\u003c/p\u003e\n\u003cp\u003eUnlike the subjective evaluation through the BSCS (showing a non-significant increase), the analysis of the CT revealed a significant reduction in self-reported craving levels post-MT session. The consistency across individual responses (Additional file 1, A5), underscores the uniform effectiveness of MT sessions in reducing immediate craving conscious feelings.\u003c/p\u003e\n\u003cp\u003eThese findings are consistent with recent studies conducted on a larger sample size in a detoxification unit showing present-moment self-reported reduction in craving symptoms following a single MT session (112,113). The reduction in craving levels as measured by the CT was in line with the pre- to post-intervention neurophysiological changes observed in the beta EEG biomarker analysis, indicative of a neurophysiological response to MT associated with reduced craving symptoms. When comparing the CT findings with the pre- to post- BSCS changes, a contrasting picture emerged. While the CT indicated a significant reduction in craving levels immediately following MT sessions, the BSCS results, which measured self-reported craving before and after a 6-week MT intervention, showed an increase in craving scores post-MT intervention. This instability aligns with the literature (52,114) on the fluctuations of craving symptoms, suggesting that as a multi-dimensional construct (51,108), craving may manifest different patterns at various timepoints and with different measures (109,111), especially if an interpretation of the concept of craving is required (109,110). The findings from the CT, have completed neurophysiological and self-reported findings on short-term craving symptoms highlighting the importance of considering the temporal dynamics of craving. Furthermore, they suggested the potential for MT to have varying impacts on immediate (when administered before and after single MT sessions) and short-term (when administered before and after the 6-week intervention) craving experiences, especially regarding a conscious evaluation of the feelings of craving.\u003c/p\u003e\n\u003ch2\u003eImpact of music therapy on depressive symptoms\u003c/h2\u003e\n\u003cp\u003eThe analysis of FAA at the F7/F8 homologous pairs revealed differences in the neurophysiological correlates of depressive symptoms related to emotional and affective processing. The ST group showed a significant increase in left-sided frontal brain activity from pre- to post-intervention. In contrast, the MT group exhibited a slight, non-significant decrease in left-sided frontal activity, indicating stable frontal asymmetry. Notably, a significant post-intervention difference between groups was observed: the MT group had higher FAA mean z-scores, suggesting lower left frontal activity compared to the right, while the ST group showed lower z-scores, indicating higher left frontal activity. These results imply that the two interventions may differentially affect a neurophysiological marker associated with affective processing underlying depressive symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcerning the PHQ-9 questionnaire, no significant differences have been found.\u003c/p\u003e\n\u003ch3\u003eThe role of interventions in modulating frontal alpha asymmetry\u003c/h3\u003e\n\u003cp\u003eNumerous studies have demonstrated a link between functional hemispheric asymmetry and positive/negative emotions (valence-based distinction) as well as approach/withdrawal emotional dichotomy. Initially, it has been noted that a damage to the left hemisphere affected the perception of positive emotions, while an injury to the right hemisphere impaired the recognition of negative emotions (101,102).\u003c/p\u003e\n\u003cp\u003eContextually, efforts have increased to clarify the role of frontal hemisphere asymmetry through EEG alpha wave pattern analysis.\u003c/p\u003e\n\u003cp\u003eThe aim in those studies was to understand how individual differences in emotional processing relate\u0026nbsp;to approach and withdrawal behaviours (57\u0026ndash;62). An approach system has been proposed to facilitate appetitive behaviour and generate positive emotions, such as enjoyment, in the context of a goal-oriented motivational processing. Contrarily, a withdrawal system has been associated with aversive or defensive behaviours and negative emotions like fear, sadness, or disgust. The concept of affective style, as proposed by Davidson and colleagues (58,59), is linked to the lateralization of the approach and withdrawal systems, as measured by EEG alpha asymmetry. Research indicates that the approach system is primarily associated with increased left frontal activity, while the withdrawal system is linked to increased right frontal activity (57\u0026ndash;59,61,62). FAA serves as a measure of the balance between these systems, with variations in FAA correlating with changes in affective style and depressive symptoms (67,70,117). This positions FAA as a potential biomarker, though not a diagnostic marker, for depression (118). The significant post-intervention difference between the MT and ST groups indicates lower left-sided frontal activity in the MT group and higher left-sided activity in the ST group. Notably, the MT group\u0026apos;s z-scores were closer to normative values, suggesting a more balanced, though right-lateralized, frontal activity. This result may imply that MT enhances the withdrawal system, potentially reducing approach-related positive affect and impacting emotional and motivational regulation. Conversely, the ST group\u0026rsquo;s increased left-sided frontal activity suggests an improvement in approach-related processing, potentially enhancing positive emotional engagement and goal-directed behaviours.\u003c/p\u003e\n\u003cp\u003eAddressing and experiencing negative emotions in therapy can strengthen the therapist-patient relationship, increasing the therapy\u0026rsquo;s effectiveness (119,120). Accepting and being aware of negative and mixed emotions, rather than focusing solely on positive emotions can lead to psychological health benefits (121,122). Comparing ST and MT intervention in an RCT with 79 depressed patients indicated comparable F7/F8 asymmetry patterns, i.e. less left hemispheric activation after music therapy (67). The authors discussed the involvement of the right inferior frontal gyrus (homologous to Broca\u0026rsquo;s area in the left hemisphere) in processing and expressing negative emotions and related prosodic aspects of verbal expression in MT (67). \u0026nbsp;Supporting this, a recent review highlighted prosodic expression as a critical indicator of depression severity, suggesting an additional avenue for exploring the therapeutic potential of music therapy in addressing the emotional and communicative dimensions of depression\u0026nbsp;(123).\u003c/p\u003e\n\u003ch3\u003eDepressive symptoms and craving in approach/withdrawal theory\u003c/h3\u003e\n\u003cp\u003eThe approach/withdrawal theory is particularly interesting because it does not exclude or overlap with the valence theory, which divides positive and negative emotions (61).\u0026nbsp;Rather, it complements the valence theory by emphasizing the motivational and behavioural aspects of emotional states, for example claiming that negative but approach-related emotions, such as anger, would be lateralized over the left hemisphere like positive approach-related emotions (61,124). This element of the theoretical framework is important because it allows the distinction between a valence-based dissociation and an action-tendency dissociation in the understanding of affective states.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCraving has been defined as a combination of negative emotions and an approach bias towards substances and related stimuli (51,125,126). Studies on individuals with SUD have examined FAA in response to cannabis-related cues, revealing greater left frontal activation to both drug-related and neutral cues compared to healthy controls, suggesting a generalized approach motivation (66). Furthermore, it has been noted how addiction may reshape brain functionality and behaviour, leading to an imbalance in the left hemisphere associated with reward and approach behaviours (65). This imbalance, particularly the increased left frontal activation in the alpha band, may serve as a marker for compulsive behaviours, impaired decision-making, and approach motivation towards addictive substances (65,66).\u003c/p\u003e\n\u003cp\u003eIn this study, changes in FAA are interpreted as markers of affective processing within the framework of approach/withdrawal theory, underlying depressive symptoms (58\u0026ndash;60,63,64). Additionally, FAA changes may also indicate the neural basis of craving-related approach tendencies (65,66).\u003c/p\u003e\n\u003ch3\u003eIntrospection and emotional regulation in MT and ST\u003c/h3\u003e\n\u003cp\u003eHere, we suggest a complex impact of MT and ST interventions on depressive symptoms and affective processing. Although psychometric values did not significantly correlate with FAA values, the PHQ-9 results showed a non-significant increase in self-reported depressive symptoms in MT and ST, that potentially involves different affective states represented by FAA lateralization patterns. Post-intervention, the MT group\u0026rsquo;s decrease in left-sided frontal activity may indicate deeper introspection and more stable emotional regulation, contrasting with the ST group\u0026rsquo;s higher left-sided activity. This introspective state in the MT group might also explain the mismatch between reduced brain-related (i.e., beta activity) craving arousal and the non-significant conscious (i.e., subjective evaluation from questionnaire) increases in craving symptoms. According to a systematic (Cochrane) review, MT is associated with higher motivation to change in participants with SUD (12). The introspective state combined with potentially more frequent thoughts about craving (as suggested by BSCS results and qualitative findings in theme 2: Outcomes and impacts on experience and emotions), could represent a predisposition achieved after MT. In contrast, the approach-related affective state observed in the ST group appears to align with heightened post-intervention craving arousal, reflected in increased beta activity.\u003c/p\u003e\n\u003ch2\u003eImpact of music therapy on inhibitory cognitive control\u003c/h2\u003e\n\u003ch3\u003eDiscussion on the noise quantification analysis\u003c/h3\u003e\n\u003cp\u003eThe exclusion of ERP data from the ST group was due to significantly higher noise levels in their ERP waveforms, making comparison with the MT group unfeasible. It is essential to clarify that this decision was not made to systematically exclude ST group data to artificially enhance the significance of the results, thereby contributing to publication bias (127). In fact, the P3d waveforms observed in the ST group were characterized by a negative polarity, which became more pronounced (i.e., more negative) following the ST intervention (see Additional file 1, A6). Because higher P3d amplitudes have been associated with improved inhibitory control (74,75,78,80), this pattern suggests that including the ST group data would likely have amplified, rather than diminished, the observed beneficial effects of the MT intervention.\u003c/p\u003e\n\u003ch3\u003eNo differences in behavioural responses to Go and NoGo stimuli\u003c/h3\u003e\n\u003cp\u003eRegarding behavioural performance, neither MT nor ST seemed to modulate the participants\u0026apos; abilities to successfully discern between Go and NoGo stimuli, nor did they seem to impact the correct response to Go or NoGo stimuli, suggesting that the fundamental behaviour in responding to such stimuli remains unaffected by interventions. This result aligns with the understanding that SUD patients often show neurophysiological changes that may change beyond overt behavioural indicators (92,128). This could potentially indicate the presence of compensatory brain processes that mask the behavioural response (92,128). Here, it is possible that SUD participants maintained their level of behavioural performance even after expending significantly higher brain energy before MT and reduced effort afterward as shown by P3d changes.\u003c/p\u003e\n\u003ch3\u003eThe role of music therapy in modulating P3d\u003c/h3\u003e\n\u003cp\u003eModulations of P3d amplitudes have been associated with sensorimotor response inhibition capabilities in SUD and healthy participants (75,78,80). Relative to the higher amplitude values found in healthy controls, diminished P3d and NoGo-P3 amplitudes in\u0026nbsp;individuals with SUD suggest a compromised sensorimotor response inhibition, potentially reflecting a neurophysiological underpinning of the unsuccessful effort to control an impulsive behaviour (74).\u003c/p\u003e\n\u003cp\u003eIn the current pilot study, there was a pre- to post-MT significant increase in P3d amplitude. This increase suggests that after undergoing MT, SUD participants demonstrated improved sensorimotor response inhibition capabilities, aligning with preliminary indications that music has the potential to modulate brain networks related to inhibitory cognitive control (14,129,130). In the current analysis, the exclusion of the ST group due to noise-related issues prevents us from fully ruling out potential practice effects on P3d amplitudes. However, previous research (131) on Go/NoGo P3 amplitudes in task repetition paradigms demonstrated a reduction in both Go-P3 and NoGo-P3 amplitudes with repeated task performance. This reduction reflects the automation of the stimulus discrimination process and a decreased reliance on attentional resources: two elements that contribute to the parent waveform of NoGo- and Go-P3 (whose difference computes the P3d). While acknowledging the limitation of not controlling for practice effect, this line of reasoning supports the notion that MT might play a role in improving neural mechanisms (i.e., increased P3d) linked to sensorimotor response inhibition in outpatients with SUD. In other words, MT might serve as a potential intervention to reverse the usual compromised inhibitory cognitive control (1,81,82) and support a recovery journey.\u003c/p\u003e\n\u003ch2\u003eParticipants\u0026rsquo; perceptions on music therapeutic process: discussion on themes from the semi-structured interview\u003c/h2\u003e\n\u003cp\u003eThe qualitative analysis of semi-structured interviews conducted post individual-MT sessions provides a nuanced understanding of participants\u0026apos; experiences, focusing on craving, emotional expression, and the therapeutic relationship. This analysis offers insights that complement the quantitative data previously discussed.\u003c/p\u003e\n\u003ch3\u003eAbsence of Craving During and After Individual-MT Sessions\u003c/h3\u003e\n\u003cp\u003eParticipants consistently reported an absence of craving during individual-MT sessions, indicating that the immersive nature of MT may temporarily disrupt habitual craving patterns associated with SUD. This observation aligns with previous quantitative findings showing reduced craving post-MT sessions (132). Qualitative themes in MT for SUD include the emphasis on music and non-music related elements, such as using music intentionally to target therapeutic goals or fostering genuine therapeutic relationships (132). The use of music and the establishment of therapeutic relationships appear to contribute to this effect, providing a distraction from cravings and a coping mechanism during and after sessions.\u003c/p\u003e\n\u003ch3\u003eResistance to craving conversations\u003c/h3\u003e\n\u003cp\u003eSome participants displayed instances of resistance, such as abrupt interruptions, when discussing cravings, suggesting discomfort or denial in acknowledging these feelings within a therapeutic setting. This resistance may explain the differences between neurophysiological craving biomarkers and self-reported craving scores, highlighting the challenges of introspection on sensitive issues. This theme resonates with prior research indicating a tendency among individuals with SUD to conceal aspects of themselves to protect their self-image during interventions (133,134).\u003c/p\u003e\n\u003ch3\u003eEchoes from the Past and Perception of Therapeutic Change\u003c/h3\u003e\n\u003cp\u003eThe emergence of past experiences during MT sessions points to the reflective and integrative nature of the therapy. Recounting past struggles appears to facilitate a therapeutic re-contextualization of experiences, fostering resilience and a proactive approach to recovery.\u003c/p\u003e\n\u003cp\u003eThe increasing focus on past experiences in later sessions suggests a deepening therapeutic process that enhances engagement and hope. This dynamic aligns with existing literature that links the exploration of past experiences in therapy to a sense of empowerment and motivation for change (135).\u003c/p\u003e\n\u003ch3\u003eProcess Dynamics in Music Therapy\u003c/h3\u003e\n\u003cp\u003eConcerning this main theme, two main sub-themes emerged: positive influence from music and therapeutic connection. Participants\u0026apos; experiences of music as a therapeutic tool for emotional exploration and expression, reinforce the core principles of music therapy. The reported shift towards more adventurous musical engagement and the positive impact on mood and self-awareness highlight music\u0026apos;s capacity to facilitate emotional growth and transformation. This is in accordance with previous research showing how pivotal emotional experiences facilitated by music can lead to profound insights and emotional change (90,136). The emphasis on the therapeutic relationship as a foundational element of the therapeutic process underscores the relational dynamics integral to music therapy. Participants\u0026apos; reflections on their interactions with the therapist and the evolving nature of their musical collaboration reflect the therapeutic alliance\u0026apos;s centrality in facilitating therapeutic change (136).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe were also keen to investigate the mechanisms of therapeutic change and processes in MT using advanced neuroimaging techniques, such as EEG hyperscanning, and comprehensive behavioural assessments, including music technology. In this regard, EEG hyperscanning data during MT sessions for SUD have been collected (see study protocol (16)) to consider moment-to-moment analysis of brain oscillations during MT sessions that will contribute to the SUD literature as individual case studies and out of the scope of this pilot RCT. In this way, we capture the dynamic interplay of neural and emotional processes. This approach, exemplified by recent dual-EEG hyperscanning studies (90,137), allows detailed observation of shared emotional processing and the temporal dynamics of neural correlates during therapy (90,137). Such analyses might provide insights into rapid neural fluctuations and their role in emotional processing and regulation during therapy, offering a deeper understanding of how MT facilitates therapeutic outcomes on craving, depression and anxiety in SUD treatment.\u003c/p\u003e\n\u003cp\u003eIn summary, the qualitative insights from these interviews enrich our understanding of the interplay between craving, emotional expression, and the therapeutic relationship in MT for SUD. These findings underscore the transformative potential of music therapy in fostering emotional growth, enhancing self-awareness, and renegotiating past experiences within a therapeutic context.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eThe pilot nature of this study presents several limitations, primarily related to sample size and study design. The small sample size, while adequate for feasibility testing and piloting (138), limits the generalizability of the findings, which should be interpreted with caution. Additionally, although comparable to another pilot study (139), the MT intervention consisted of only six sessions, which may not fully capture the potential long-term effects of MT on SUD.\u003c/p\u003e\n\u003cp\u003eParticipant demographics displayed some heterogeneity (see Additional file 1, A7), a common characteristic in feasibility studies where recruitment and retention rates are uncertain (138). Despite the small sample size, both groups were balanced on key variables such as age, gender, diagnosis type, and medication, which are crucial for minimizing confounding factors. This is similarly reported in previous MT research utilizing EEG (67) and psychometric tests (67,140). The diversity in substance use patterns and individual circumstances among participants is representative of the population likely to use this intervention, aiding in assessing the feasibility and acceptability of the intervention for future studies. The study utilized staggered randomization due to logistical constraints in recruiting outpatients from a community service, which was deemed the most practical approach to balancing confounding variables. In this pilot study, a blind assessment procedure could not be established. Utilizing blind assessors in data collection and analysis, particularly for subjective measures, could minimize bias in future studies. Finally, working within a community-based setting presents unique challenges, such as participants living in uncontrolled environments between sessions and the timing of post-intervention measures, which could affect the results. These limitations should be considered when interpreting the findings and planning future larger-scale studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study we were able to show for the first time that MT had a measurable influence on arousal related to craving. This is noticeable in the EEG resting-state beta band (i.e., neural level) suggesting a reduced craving-related affective state. However, participants\u0026rsquo; conscious perception of this reduction (i.e., subjective evaluation) is evident only after MT sessions (as measured by the CT) \u0026ndash; and not after the whole intervention (as measured with BSCS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther results are related to introspection and emotional regulation, which appears to be increased after MT, and reflected in FAA patterns as well as in thematic analysis (theme 2: outcomes and impacts on experience and emotions). Furthermore, inhibitory control is suggested to be improved after MT and this is reflected by a task-based EEG analysis addressing sensorimotor control function and thematic analysis (subtheme 1.3 \u0026ndash; reduced craving after MT sessions) highlighting reduced impulse towards the substance after MT.\u003c/p\u003e\n\u003cp\u003eFurther research might compare processes of MT SUD treatment in detoxification units and outpatients on prescription medicine to distinguish MT effects on craving, depression and anxiety related symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is the first to provide combined neuroscientific, psychometric, and qualitative data for MT in SUD treatment and offers an opportunity to test the results and measures used in a larger, multicentred RCT. This should address limitations and would allow for the exploration of sustained benefits, optimal dosage, and session frequency. Including a mid-treatment assessment at six-weeks could help identify the potential benefits of a longer intervention and test for patterns of change in SUD participants\u0026rsquo; symptoms more effectively. Integrating MT with other community-based interventions could provide insights into effective multidisciplinary approaches for SUD treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARU Anglia Ruskin University\u003c/p\u003e\n\u003cp\u003eBSCS Brief Substance Craving Scale\u003c/p\u003e\n\u003cp\u003eEEG Electroencephalography\u003c/p\u003e\n\u003cp\u003eERP Event-related potentials\u003c/p\u003e\n\u003cp\u003eFAA Frontal Alpha Asymmetry\u003c/p\u003e\n\u003cp\u003eFFT Fast Fourier Transform\u003c/p\u003e\n\u003cp\u003eFMT Frontal Midline Theta\u003c/p\u003e\n\u003cp\u003eGAD-7 Generalized Anxiety Disorder-7\u003c/p\u003e\n\u003cp\u003eICA Independent Component Analysis\u003c/p\u003e\n\u003cp\u003eIRAS Integrated Research Application System\u003c/p\u003e\n\u003cp\u003eMT Music therapy\u003c/p\u003e\n\u003cp\u003ePHQ-9 Patient Health Questionnaire-9\u003c/p\u003e\n\u003cp\u003eST Standard treatment\u003c/p\u003e\n\u003cp\u003eSUD Substance Use Disorder\u003c/p\u003e\n\u003cp\u003eVAS Visual Analogue Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthors\u0026rsquo; contribution\u003c/h2\u003e\n\u003cp\u003ePasqualitto and Maidhof share the first authorship and contributed equally to the work. Fachner developed the original design concept for the study. Fachner, Maidhof, Murtagh and De Silva contributed to the conceptualization of the study. Pasqualitto and Maidhof collected and analysed (with Fachner) EEG data and psychometric tests. Pasqualitto conducted and analysed semi-structured interviews. Pasqualitto drafted the first version of this manuscript and all authors contributed to the editing and final submission of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to express our gratitude to Leonardo Muller-Rodriguez for his assistance with data collection and to Helen Odell-Miller for her valuable contributions during the conceptualization stages of this study. We also thank Mizan Chowdhury, from \u0026ldquo;Via\u0026rdquo; community service, for his support in organizing the recruitment process and Jufen Zhang, from Anglia Ruskin University, for providing insightful statistical advice. We would finally like to thank all the service users and \u0026ldquo;Via\u0026rdquo; staff who took part and supported with the implementation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe Faculty for Arts, Humanities and Social Science (AHSS) at Anglia Ruskin University (ARU) has provided an internal grant for the project. The funding is based on the HEFCI (Higher Education Funding Council for England) QR (Quality Research) Seed corn funding scheme for AHSS granted for\u0026nbsp;￡7600 for travel and incidental costs; QR AHSS Research and Innovation funding of\u0026nbsp;￡7300 for acoustic and electronic musical equipment and ARU funding of the Health, Performance and Wellbeing research stream towards an analysis tool (￡8300). Another grant was received from the Music Therapy Charity as a small research fund (￡1500) towards one of the PhD students (Fernie) in this project, while the other PhD student (Pasqualitto) received a Vice Chancellor\u0026rsquo;s studentship for his PhD.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis mixed-methods randomized controlled feasibility study is registered\u003c/p\u003e\n\u003cp\u003ewith clinical trials.gov, number NCT 05180617. Furthermore, it has been\u003c/p\u003e\n\u003cp\u003eapproved by the NHS Integrated Research Application System (IRAS) (North\u003c/p\u003e\n\u003cp\u003eEast\u0026mdash;Newcastle \u0026amp; North Tyneside 2 Research Ethics Committee), number\u003c/p\u003e\n\u003cp\u003eWDP-AZA8C0091, and Anglia Ruskin University Ethics Boards. Additionally, the\u003c/p\u003e\n\u003cp\u003eproject has also\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ebeen approved by \u0026ldquo;Via\u0026rdquo; community service innovation research unit which oversees research within the community service.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot\u0026nbsp;applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to sensitivity and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. Diagnostic and statistical manual of mental disorders : DSM-5. American Psychiatric Association; 2013. 947 p. \u003c/li\u003e\n\u003cli\u003eFilbey FM. The Neuroscience of Addiction. The Neuroscience of Addiction. 2019. \u003c/li\u003e\n\u003cli\u003eKoob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Vol. 3, The Lancet Psychiatry. Elsevier Ltd; 2016. p. 760\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eKoob GF, Volkow ND. Neurocircuitry of addiction. Vol. 35, Neuropsychopharmacology. Nature Publishing Group; 2010. p. 217\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eGeorge O, Koob GF. Individual differences in the neuropsychopathology of addiction. Dialogues Clin Neurosci. 2017 Sep 30;19(3):217\u0026ndash;29. \u003c/li\u003e\n\u003cli\u003eNo\u0026euml;l X, Van der Linden M, Brevers D, Campanella S, Verbanck P, Hanak C, et al. Separating intentional inhibition of prepotent responses and resistance to proactive interference in alcohol-dependent individuals. Drug Alcohol Depend. 2013;128(3):200\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eGauld C, Baillet E, Micoulaud-Franchi JA, Kervran C, Serre F, Auriacombe M. 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Issues and assumptions on the road from raw signals to metrics of frontal EEG asymmetry in emotion. Biol Psychol. 2004;67(1\u0026ndash;2):183\u0026ndash;218. \u003c/li\u003e\n\u003cli\u003evan der Vinne N, Vollebregt MA, van Putten MJAM, Arns M. Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis. Neuroimage Clin. 2017;16:79\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eBarnes M, Sherlock S, Thomas L, Kessler D, Kuyken W, Owen‐Smith A, et al. No pain, no gain: Depressed clients\u0026rsquo; experiences of cognitive behavioural therapy. British Journal of Clinical Psychology. 2013 Nov 17;52(4):347\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eMcWilliams N. Psychoanalytic Diagnosis: Understanding Personality Structure in Clinical Process. 2nd ed. New York, NY, USA: Guilford Press; 2011. \u003c/li\u003e\n\u003cli\u003eFord BQ, Lam P, John OP, Mauss IB. The psychological health benefits of accepting negative emotions and thoughts: Laboratory, diary, and longitudinal evidence. J Pers Soc Psychol. 2018 Dec;115(6):1075\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eHershfield HE, Scheibe S, Sims TL, Carstensen LL. When Feeling Bad Can Be Good. Soc Psychol Personal Sci. 2013 Jan 30;4(1):54\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eKnight S, Spiro N. Tracing change during music therapy for depression: Toward a markers-based understanding of communicative behaviors. Musicae Scientiae. 2023 Sep 15;27(3):637\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eCarver CS, Harmon-Jones E. Anger is an approach-related affect: Evidence and implications. Psychol Bull. 2009;135(2):183\u0026ndash;204. \u003c/li\u003e\n\u003cli\u003eKoob GF, Le Moal M. Addiction and the brain antireward system. Annu Rev Psychol. 2008;59:29\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eMarkou A, Kosten TR, Koob GF. Neurobiological similarities in depression and drug dependence: A self-medication hypothesis. Vol. 18, Neuropsychopharmacology. 1998. \u003c/li\u003e\n\u003cli\u003eGhaemi SN. A Clinician\u0026rsquo;s Guide to Statistics in Mental Health. Cambridge University Press; 2023. \u003c/li\u003e\n\u003cli\u003eKouri EM, Lukas SE, Mendelson JH. P300 Assessment of Opiate and Cocaine Users: Effects of Detoxification and Buprenorphine Treatment. 1996. \u003c/li\u003e\n\u003cli\u003eBurkhard A, Elmer S, Kara D, Brauchli C, J\u0026auml;ncke L. The Effect of Background Music on Inhibitory Functions: An ERP Study. Front Hum Neurosci. 2018 Jul 23;12. \u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Serrano MJ, P\u0026eacute;rez-Garc\u0026iacute;a M, Schmidt R\u0026iacute;o-Valle J, Verdejo-Garc\u0026iacute;a A. Neuropsychological consequences of alcohol and drug abuse on different components of executive functions. Journal of Psychopharmacology. 2010 Sep;24(9):1317\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eNakata H, Sakamoto K, Kakigi R. Effects of task repetition on event-related potentials in somatosensory Go/No-go paradigm. Neurosci Lett. 2015 May;594:82\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eSilverman MJ. Music Therapy and Therapeutic Alliance in Adult Mental Health: A Qualitative Investigation. J Music Ther. 2019 Feb 16;56(1):90\u0026ndash;116. \u003c/li\u003e\n\u003cli\u003eJohannessen DA, Nordfj\u0026aelig;rn T, Geirdal A\u0026Oslash;. Substance use disorder patients\u0026rsquo; expectations on transition from treatment to post-discharge period. Nordic Studies on Alcohol and Drugs. 2020 Jun 24;37(3):208\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eVersalovic E, Klein E, Goering S, Ngo Q, Gliske K, Boulicault M, et al. Deep Brain Stimulation for Substance Use Disorders? An Exploratory Qualitative Study of Perspectives of People Currently in Treatment. J Addict Med. 2023 Jul;17(4):e246\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eVelez CM, Nicolaidis C, Korthuis PT, Englander H. \u0026ldquo;It\u0026rsquo;s been an Experience, a Life Learning Experience\u0026rdquo;: A Qualitative Study of Hospitalized Patients with Substance Use Disorders. J Gen Intern Med. 2017 Mar 12;32(3):296\u0026ndash;303. \u003c/li\u003e\n\u003cli\u003eBourdaghs S, Silverman M. An exploratory interpretivist study of how adults with substance use disorders experience peer social connectedness during recovery-oriented songwriting. Psychol Music. 2023 Sep 7;51(5):1440\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eMaidhof C, M\u0026uuml;ller V, Lartillot O, Agres K, Bloska J, Asano R, et al. Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study. Front Psychol. 2023 Sep 29;14. \u003c/li\u003e\n\u003cli\u003eEldridge SM, Lancaster GA, Campbell MJ, Thabane L, Hopewell S, Coleman CL, et al. Defining Feasibility and Pilot Studies in Preparation for Randomised Controlled Trials: Development of a Conceptual Framework. PLoS One. 2016;11(3). \u003c/li\u003e\n\u003cli\u003eHakvoort L, de Jong S, van de Ree M, Kok T, Macfarlane C, de Haan H. Music therapy to regulate arousal and attention in patients with substance use disorder and posttraumatic stress disorder: A feasibility study. J Music Ther. 2020;57(3):353\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eErkkil\u0026auml; J, Punkanen M, Fachner J, Ala-Ruona E, P\u0026ouml;nti\u0026ouml; I, Tervaniemi M, et al. Individual music therapy for depression: Randomised controlled trial. British Journal of Psychiatry. 2011 Aug;199(2):132\u0026ndash;9. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Music therapy, Substance Use Disorder, community service, electroencephalography, psychometric tests, semi-structured interviews","lastPublishedDoi":"10.21203/rs.3.rs-5837441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5837441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e Music therapy (MT) has been shown to be effective for multiple clinical endpoints in clients with Substance Use Disorder (SUD). However, a gap remains in understanding the impact of MT interventions in community services, primarily due to the lack of studies that combine neural measures (e.g., EEG), psychometric tests, and semi-structured interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eThis pilot study is a three-arm, non-blinded, mixed-methods randomized trial. Sixteen participants with Substance Use Disorder (SUD) were recruited from a community service in London. Ten of these participants received six weekly group or individual music therapy (MT) sessions in addition to the standard treatment (ST) provided by the community outpatient service. The remaining six participants received only the ST.\u003c/p\u003e\n\u003cp\u003ePre-/post-intervention as well as in-session measures have been collected utilizing EEG in addition to psychometric tests and semi-structured interviews addressing craving, depressive, and anxiety symptoms, inhibitory cognitive control, and participants’ perceptions on the music-therapeutic process. An intention-to-treat approach was employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Fourteen participants completed the study. Results showed (1) lower beta frequency band related to craving arousal post-MT intervention as compared to ST; (2) lower subjective evaluation of craving intensity after MT sessions; (3) different impact of MT and ST on frontal alpha asymmetry related to affective processing; (4) enhanced neural mechanisms (i.e., P3d in a Go/NoGo task) related to sensorimotor response inhibition following MT; (5) qualitative themes reflecting absence of craving, reluctance towards craving discussions, narratives on experiences, emotions, and the therapeutic process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e MT might facilitate lower post-intervention arousal related to craving as compared to ST. While this effect is evident at the neural level, the conscious perception of the decrease emerges only after MT sessions and not after the entire intervention. The differential brain asymmetry may represent higher emotional regulation and introspection associated with MT compared to ST. MT may facilitate neuromodulation that boosts inhibitory cognitive control functions. Themes emerging from semi-structured interviews highlight the transformative potential of MT in alleviating craving and stimulating reflection. Findings from this pilot study are promising but further research through a larger clinical trial is necessary to confirm and expand upon this pilot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration.\u003c/strong\u003eNCT05180617.\u003c/p\u003e","manuscriptTitle":"Music Therapy modulates Craving, Inhibitory Control, and Emotional Regulation: EEG, Psychometric, and Qualitative Findings from a Pilot RCT in a Community Outpatient Service","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 09:48:36","doi":"10.21203/rs.3.rs-5837441/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df45419e-a4a1-4f6a-8b7e-9fdb9da9348a","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-01T06:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 09:48:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5837441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5837441","identity":"rs-5837441","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}