Protocol for a randomised controlled feasibility trial of Piano Instruction for Adult Novices as Online Cognitive intervention (PIANO-Cog), a novel remote piano training for cognitive and motor functions in older age.

Protocol for a randomised controlled feasibility trial of Piano Instruction for Adult Novices as Online Cognitive intervention (PIANO-Cog), a novel remote piano training for cognitive and motor functions in older age. | 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 Protocol for a randomised controlled feasibility trial of Piano Instruction for Adult Novices as Online Cognitive intervention (PIANO-Cog), a novel remote piano training for cognitive and motor functions in older age. Fionnuala Rogers, Ege Erdem, Dr. Claudia Metzler-Baddeley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6023523/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: Ageing is associated with a loss of fluid intelligence and motor functions which hamper independence and quality of life. Training in a musical instrument can improve fluid intelligence and executive function (EF) in older non-musicians but the neural correlates underpinning the benefits remain elusive. The primary aims of this study are to: i) test the acceptability of Piano Instruction for Adult Novices as Online Cognitive Intervention (PIANO-Cog), a novel bespoke 8-week self-guided piano training programme for adults over the age of 50 years; and ii) to test the feasibility (in terms of recruitment, retention and adherence) of a large scale RCT comparing PIANO-Cog to a passive control. Secondary aims of this study are: i) to investigate the effects of online piano training on fluid abilities, EF and motor function; ii) to investigate training-induced microstructural brain changes using ultra-strong gradient (300mT/m) magnetic resonance imaging (MRI) and iii) to investigate how the latter may be linked to cognitive improvements post-training. Method: A two-armed unblinded RCT will be conducted on 50 healthy non-musician adults over the age of 50. Participants will be randomised to a piano training (PT) or passive control group for 8 weeks, stratified for age and sex. PT participants will receive a training manual and 20-minute video tutorials each week, and will practice 30 minutes, 5 days per week. Control participants will receive no intervention for the 8-week period. Cognitive testing and MRI of the brain will take place before and after the intervention. Discussion: The primary aim of the trial is to determine the acceptability of PIANO-Cog as an online cognitive intervention for adults over 50 who are non-musicians, and the feasibility of conducting a large-scale RCT in terms of recruitment, retention and adherence. Self-guided music training programmes could provide a cost-effective method of maintaining or improving cognitive and motor functions that individuals can implement in their own homes. Secondary aims are regarding the investigation of positive transfer of piano training to EF and fluid abilities in ageing, and to provide evidence for the relationship between training-induced cognitive enhancements and underlying white and grey matter microstructural changes. Trial Registration: ISRCTN11023869 (retrospectively registered) Protocol version: 31/10/2024 version 1.4 Cognitive Neuroscience Ageing cognitive intervention piano training neurologic music therapy therapeutic instrument music performance executive function fluid intelligence neuroplasticity microstructure MRI pilot feasibility Figures Figure 1 Figure 2 1. Background 1.1 Background and rationale Cognitive ageing is characterised by a decline in abilities important for learning and processing novel information and interacting with the environment, collectively known as fluid intelligence (1). Declines in fluid intelligence can negatively impact executive functions (EF), an umbrella term for cognitive abilities which support higher-level cognitive functioning crucial for planning, problem-solving and decision-making (2,3). EF are the first abilities to be affected in normal ageing due to age-related atrophy of the prefrontal cortex (PFC) and its connected regions (4). The negative impact of ageing on fluid abilities and EF can have a considerable impact on daily functioning, quality of life and the ability to live independently and are therefore an important target for cognitive training programmes (5) (6,7). A wealth of research has investigated the effectiveness of computerised cognitive training programmes and commercially available “brain training” applications to improve and maintain EF in ageing. Although participants show improvements on tasks which tap into the same cognitive domain (i.e. “near” transfer), meta-analyses suggest weak or no effects on daily functioning (i.e. “far” transfer) (8–11) Computerised tasks may be unsuccessful in leading to far transfer effects because they often only target one cognitive domain (e.g., working memory capacity) and therefore do not simulate the complexity of real-world tasks which involve integration and coordination between multiple sensory and motor networks (12,13). Consequently, there has been a shift in focus towards developing more ecologically valid training programmes for older adults that require multisensory-motor interactions with the environment (e.g., (14). Preserving and enhancing EF and fluid abilities through ecologically valid activities is consistent with evolutionary perspectives that EF developed as an extension of the motor control system through a necessity for movement and optimal interaction with the environment (15). The basal ganglia and cerebellum are key structures in the coordination of movement, and these structures are also important for EF (16–18) The basal ganglia are responsible for predicting stimulus-response associations, while the cerebellum controls movement through continuous predictive-error correction feedback loops and instructs the frontal lobes to simulate results of actions based on prior activities (19). In this way, EF evolved as a means of generating and anticipating the outcomes of movements (15), and therefore tasks which involve these networks, such as music making (20), could be optimal for generating far transfer to daily functioning. 1.2 Music-based behaviours stimulate neural networks important for EF: evidence from neurologic music therapy studies The role of the basal ganglia and cerebellum in music making has been demonstrated in studies of neurologic music therapy (NMT; (21). NMT is a music-based rehabilitation method for treating cognitive and motor symptoms and improving quality of life for people with neurological movement disorders, such as Parkinson’s Disease (PD) (see 22 and 23 for reviews), Huntington’s Disease (HD) (24) and stroke (25). Kasdan et al. (26) reported a dissociation between the roles of the basal ganglia and cerebellum in the interpretation of rhythm, where patients with basal ganglia lesions had greatest difficulty with rhythm complexity (i.e. syncopation), whereas cerebellar patients had most difficulty with rhythms at faster tempi. Therapeutic instrumental music performance (TIMP; 27) is a form of neurologic music therapy (NMT), which utilises the rhythms in music to create consistent internal reference intervals for initiating and regulating motor movements through incorporating exercises involving musical instruments. This is thought to occur through a process of neural entrainment (i.e. direct sensorimotor synchronization, (28) TIMP has been shown to be effective in treating motor symptoms (e.g., 29,30 but also EF. For example, Bugos et al. (31) found reductions in Stroop error rates in Parkinson’s Disease patients following 10 days’ piano training compared to a control group. Furthermore, TIMP was found to improve mental flexibility, measured using the Trail-Making Test Part B (32) in stroke patients (27). The key mechanism of these music-based trainings which has the potential to lead to cognitive enhancement is hypothesised to be the stimulation of basal ganglia and cerebellar networks and their connections to fronto-parietal brain areas associated with EF (24). For instance, in PD patients, it has been proposed that the use of a regular beat facilitates neural entrainment by bypassing the affected basal ganglia, which is normally responsible for internal cue generation, but impaired in PD (21). As discussed above, the basal ganglia and cerebellum are key structures involved in EF, and inclusion of cortico-basal-cerebellar networks through motor activity is now a recommended feature of cognitive training interventions (33). 1.3 Playing a musical instrument benefits cognition and neural structure in normal ageing Learning to play a musical instrument requires integration and coordination between different sensory and motor modalities and is regarded as a model of neuroplasticity (20,34,35). Cognitive advantages have been reported in musicians compared to non-musicians in verbal working memory(36), executive control (37), auditory and visual memory updating (38), linguistic perception abilities (39) and verbal intelligence (see 40 for review). Cross-sectional brain imaging studies have consistently reported structural differences in auditory, motor and somatosensory networks between musicians and non-musicians. Specifically, Heschel’s gyrus, the planum temporale, fusiform gyrus, cerebellum, superior parietal lobule, inferior frontal lobe, lingual gyrus hippocampus and caudate nucleus are grey matter regions consistently shown to be larger in musicians using voxel-based morphology (VBM) and cortical thickness (CT) measurements (41–47). White matter pathways connecting these brain regions have also been shown to exhibit larger white matter microstructure in musicians. For instance, musicians show greater fractional anisotropy (FA; a measure of diffusion coherence used as a proxy for white matter density; (48), in the arcuate fasciculus (49) which connects the temporal and frontal lobes and is important for learning to associate motor actions with sounds (50), as well as in the anterior corpus callosum and corticospinal tract (51), important for bimanual motor coordination and motor skills respectively. Older adults who played a musical instrument were found to have lower risk of developing dementia at 5-year follow-up (52). Additionally, in a study of 5,693 participants which included 745 musicians, musicians who played for at least an hour on most days showed the highest cognitive performance and were also 80% more likely to be in the top decile of cognitive function, measured using the Short Form Extended Mental State Exam than both musicians who played less frequently and non-musicians (53). Furthermore, twins who are musicians have been found to be 64% less likely to develop dementia than their non-musician counterpart, whilst adjusting for sex, education and physical activity (54). Collectively, the evidence suggests that playing music could have neuroprotective benefits contributing to cognitive and/or structural changes leading to lower incidence of cognitive difficulties in older age, beyond genetic factors. Lower incidence of cognitive decline in older non-musicians may be related to increased cognitive reserve (55) or structural maintenance in later life resulting from musical practice. However, the studies cited above are cross-sectional in nature and so causal relationships between cognitive advantages and structural differences cannot be drawn. We recently conducted a systematic review and meta-analysis of longitudinal behavioural studies which used short-term training in a pitched or percussion musical instrument to benefit EF and fluid abilities in adults over the age of 60 years. Random effects models on 502 participants across 13 studies revelaed a moderate effect on processing speed ( d = .47, p < .0001), a low-moderate effect on attention-switching ( d = .39, p = .0021) and inhibitory control ( d = .39, p = .0335) (56), suggesting that short-term musical training can benefit fluid abilities and EF in older non-musicians. Only one trial to date, “Train the Brain with Music ” (57) has investigated structural neural changes following piano training in healthy older adults using magnetic resonance imaging (MRI). Participants were assigned to either piano training or an active control group who attended music culture lessons for 12 months. After 6 months of training, CT increases were detected in the piano compared to the listening group in areas involved in auditory processing: left Heschel’s gyrus, left planum polare, bilateral temporal sulcus and right Heschel’s sulcus (58). A significant increase in density in the caudate nucleus, Rolandic operculum and inferior cerebellum was also detected after 6 months when data were pooled across both piano training and control groups, the latter of which was associated with an improvement in tonal working memory (59). Both groups showed an improvement in working memory capacity measured using Backward Digit Span, but this improvement was not linked to any grey matter volume increases. With regards to white matter changes, the authors reported deterioration of the fornix for the music culture group only, using fixel-based analysis (FBA) (60). To summarize, learning to play the piano as an adult novice can benefit EF, may slow or prevent normal age-related atrophy and induce structural changes in auditory and motor pathways. However, morphological differences between and across groups (using CT and VBM respectively) post-training as described above do not inform about the plastic mechanisms that drive tissue changes on the microstructural level. Moreover, the relationship between these structural effects and possible cognitive outcomes remains unclear. 1.4 Investigating microstructural neuroplasticity following piano training using ultra-strong MRI gradients and multi-shell high angular resolution diffusion imaging (ms-HARDI) The proposed study aims to address questions around biological mechanisms of music-induced neuroplasticity in later adulthood raised by 58, 59 and 60 discussed above. Macrostructural changes in grey matter density could be driven by a number of different biological mechanisms such as neurogenesis (in the hippocampus), synaptogenesis or changes in neuronal morphology. White matter neuroplasticity could be driven by changes in the number of axons, axon diameter, the packing density of fibres, axon branching, axon trajectories and myelination (61). Cell density, cell size and myelination can affect voxel intensities on a T1-weighted image, which will influence measures derived from T1 such as concentration, density or volume, and hence VBM and CT do not provide metrics that relate in a straightforward way to underlying neuronal densities (61). Intracellular diffusion properties can be estimated using the enhanced signal-to-noise ratio afforded by high diffusion-weighted b-values that can be acquired by using ultra-strong gradients (300mT/m) of the Siemens Connectom scanner (62). Multi-shell high-angular resolution imaging (ms-HARDI) will be used to acquire diffusion weighted imaging (DTI) data which allows for the separation of extracellular and intra-cellular compartments at low and high b-values respectively, and also the modelling of crossing fibres and thereby improving tractography analysing for white matter. Taken together, this will allow for a richer, more comprehensive characterization of water diffusion in different tissues. The application of novel compartment-based microstructural models to this diffusion data can provide more complete physical descriptions of diffusion processes in white and grey matter, which will be used to extract more meaningful information about the biological mechanisms involved in plasticity. For instance, the soma and neurite imaging model (SANDI; 63) is a recently-developed biophysical model which provides an estimate of grey and white matter microstructure by incorporating the size and density of grey matter soma in addition to white matter neurite density. Only one study to date has used SANDI to characterise microstructure in ageing, through the use of a cross-sectional design (64). The soma density and soma size metrics were found to be significantly negatively correlated with age ( r ≥ -.69, p < .001) in all major lobes (64). Relationships between SANDI metrics and age were stronger than those of VBM or CT with age and therefore SANDI metrics were suggested to be more sensitive measure of neurodegeneration. The proposed study will be the first to employ SANDI as a means of investigating grey matter microstructure changes over time. Changes in the soma density measure may be used as a proxy measure of synaptogenesis or neurogenesis (in the hippocampus), and soma size could reflect glial activity, swelling of astrocytes or oligodendrocytes following training. The neurite orientation and dispersion imaging (NODDI; 65) model can distinguish between signals coming from intracellular, extracellular and cerebrospinal fluid compartments. The intracellular signal fraction will be used as a measure of white matter, in terms of myelination, dendritic and axonal sprouting. NODDI also provides the orientation dispersion index (ODI) which quantifies the bending and fanning of axons by measuring how many voxels are classified as having crossing fibres. The ODI from NODDI can be used to map white matter connections, and provide cross-indication of dendritic and axonal sprouting. NODDI measures have been previously shown to be sensitive for characterising white matter degradation in ageing, with age being associated with lower neurite density, measured using the intra-cellular volume fraction, and lower tract complexity measured using the extracellular water diffusion in the majority of white matter tracts (66). Nazeri et al. (67) found age-related decline in ODI, mainly in frontoparietal regions, and ODI outperformed cortical thickness and white matter FA for the prediction of age. 1.5 The current study and pilot data Cognitive intervention through musical instruction is an ecologically valid training method because it taxes multiple sensory and motor networks. However, the multimodal nature of music interventions renders them more difficult to embed within experimental designs compared to traditional computerised interventions (e.g., N- back training). Music interventions also tend to be more time-consuming and expensive to run, requiring funding of instructors and equipment. Therefore, PIANO-Cog was developed to provide self-guided training which can be completed at home without the need for instructors. This training method, like computerised training programmes, can be quickly distributed at low cost and provide a more naturalistic method for how older adults may undertake activities to maintain their own cognitive functioning in real-life settings. This may also be a more realistic method of maintaining EF in older age without the need for public health funding initiatives. The intervention will be compared with a passive control to assess the effects of the training compared to life as usual for older adults. The first iteration of PIANO-Cog was piloted in 9 cognitively healthy adults over the age of 60 years (Rogers, Erdem & Metzler-Baddeley, in prep). Participants were recruited online, and cognitive testing took place remotely. The intervention was deemed feasible based on “green” rating using a traffic-light system, whereby measures for retention, acceptability and adherence were all ≥70%. Retention, measured as the percentage of participants who were tested at follow-up out of those tested at baseline was 91.3%. Adherence was measured by the number of training videos accessed (94.4%), and the average number of practice hours logged per week ( M = 3.53 hours; SD = 0.35) , 63.3 minutes longer than the requested 2.5 hours per week). The intervention was deemed acceptable by positive responses collected on an evaluation survey, indicating that the online delivery method appealed to participants, and that the content was challenging but manageable (e.g., “ I really enjoyed the course, and found it challenging but doable; difficult [exercises] got easier with practice” ). Following the pilot, the intervention was improved based on participant feedback to include more “play along” and metronome activities to improve rhythm and timing abilities hypothesized to involve EF. Positive trends for training effects on verbal memory and inhibitory control measures were observed and informed the selection of outcome measures for the current trial which aims to test feasibility of the updated intervention. Recruitment will be taking place in Cardiff and surrounding areas, rather than online, as participants will undergo MRI scanning. 1.6 Objectives Primary objective To assess the feasibility of: i) PIANO-Cog as an acceptable online cognitive training intervention for healthy adults >50 years and ii) the feasibility (recruitment, retention, adherence) of a larger RCT investigating 8 weeks of PIANO-Cog compared with usual activities in healthy adults over the age of 50 years. Secondary objectives To obtain estimates of variability and absolute change pre- and post-training compared to usual activity in the following measures: 1. Performance scores in cognitive and motor assessments for future RCT sample size calculations. 2. Grey and white matter microstructure means in basal ganglia and cerebellar networks and auditory networks to investigate possible neural mechanisms that may underpin cognitive training effects 1.6.1 Hypotheses Our primary hypotheses are: 1. PIANO-Cog will be a feasible online cognitive training intervention for healthy non-musicians over the age of 50 years old. 2. A future fully-powered RCT into the effects of 8-weeks of home-based PIANO-Cog training compared to no-training control will be feasible. Our secondary hypotheses are that, in healthy non-musicians (>50 years), 8 weeks of piano training will: i) lead to improvements in processing speed, response inhibition and attention switching as measured by digit-symbol substitution test, a Stroop test, Go/No-go test and verbal fluency category switching tasks (56) ii) lead to grey and white matter microstructural changes in auditory, motor and somatosensory networks measured using DTI and metrics from the biophysical models, NODDI and SANDI. Specifically, we expect to see an increase in soma density and soma size metrics from SANDI model and increased orientation dispersion and intracellular density metrics from the NODDI model following piano training compared to the control group. iii) underlying microstructural changes will be associated with changes in processing speed (digit-symbol task) and EF (N-back, Stroop and Go/No-go tasks). 1.7 Trial Design This study will employ a two-arm unblinded randomised controlled design. Cognitively healthy non-musicians over 50 years will be recruited and screened for cognitive impairment and < 4 years music experience. They will be assigned to either self-guided piano training or a passive control for 8 weeks. Groups will be stratified for age and sex. Cognitive assessments and diffusion MRI will take place before and after the intervention period. 2 Method 2.1 Participants, intervention, outcomes 2.1.1 Study Setting Cognitive and motor testing and MRI scanning will take place at CUBRIC before and after the 8-week intervention period, with data collection taking place between September 2024-July 2025. The intervention is self-guided, so the intervention will take place in participants’ homes. 2.1.2 Eligibility criteria Participants will be included if they are >50 years old, fluent English speakers, have normal/corrected-to-normal vision and hearing, have less than 4 years of formal musical or dance training, are not involved in any musical activities, have no neurological or psychiatric history that could affect learning (e.g., dementia, stroke or traumatic brain injury, depression requiring hospitalisation), no self-reported difficulty with hand movement and no self-reported learning disabilities. Exclusion criteria include impaired hearing or vision, neurological diagnosis, current involvement in other cognitive training or musical activities (e.g., choir singing, dance or exercise to music classes), more than 4 years of formal music or dance lessons or currently taking psycho-reactive medications which affect memory performance. Participants with MRI contra-indications (e.g., pacemakers, stents, cochlear implants, or other metal in the body such as metallic plates, screws or clips) will not be scanned, but will still be eligible for training and cognitive and motor testing. 2.1.3 Group Allocation The groups will be stratified by sex and by two age categories: 50-65 years and >65 years. Pseudo-random numbers will be used to generate randomised group allocation per stratum using R version 4.41, and implemented by the author FR. Neither participants nor the researcher conducting baseline testing will be informed of group allocation until after baseline testing is completed, as the algorithm determining group allocation will take place only at the end of testing by the lead researcher, but the researcher will not be blind at follow-up. Due to the nature of the training, participants will be unblind to condition. Participants who are randomly assigned to the piano group will receive a training manual and a 61-key portable electric keyboard (MK-2000 by Gear4music) to take home with them for practice at the end of baseline testing. 2.1.4 Piano Intervention The intervention comprises 8 training videos with an accompanying manual. The manual provides sheet-music for weekly exercises, simple explanations of new musical terms and introductory guidance on how to implement good practice routines (see Appendix A for syllabus). Practice recommendations are included because qualitative data from previous studies indicated that older music novices did not know how to effectively use practice time to maximise progress (68,69). Participants are required to practice for 30 minutes, 5 days per week for 8 weeks (2.5 hours per week, or 20 hours total), and to log the duration and content of their practice sessions in diaries provided. The content of the piano training intervention was designed by FR, who holds a piano teaching diploma from the Royal Irish Academy of Music. As EF are hypothesized to have developed as an extension of the motor control system, exercises for training bimanual coordination skills and promoting hand independence were specifically chosen. For example, exercises involve: hands playing with opposing dynamics, rhythms and articulation; learning scales in similar (both hands ascending and descending together) and contrary (both thumbs beginning on the same note and then moving in opposite directions) motions; arpeggios (training in spatial distances between the root, third, fifth and octave notes of a scale); Hanon exercises for dexterity (patterns which build strength in every finger which are repeated across 3 octaves ascending and descending); and reading music (known as “sight-reading”). Familiar melodies were specifically chosen for sight-reading exercises. Some were adapted from Alfred’s All-In-One Course, a book designed specifically for Adults Beginners, and found to be effective in previous healthy older adults (70,71). Following the initial pilot study, the intervention manual was updated based on participant feedback to include more explanations of musical terms. Exercises based on NMT principles were also added to the new version of the intervention, whereby metronome and “play along” activities were designed to develop timing mechanisms hypothesized to be linked with EF. The videos are approximately 20 minutes long each. Week 1 introduces the right hand, week 2 introduces the left hand, and then participants learn to play with hands together from Weeks 3-8. Metronome exercises are introduced in week 5 and become progressively difficult in weeks 6-8. The videos are sent via WeTransfer, an online transfer service for large files (https://wetransfer.com/), at a set time each week. This is to stagger the training content so that participants can focus on improving exercises for a given week before newer more challenging material is introduced. However, participants are encouraged to revisit exercises from previous weeks until they can be played comfortably, using the metronome as a guide. An advantage of using WeTransfer is that it notifies the researcher every time a video is downloaded by a recipient, and percentage of downloaded videos will be used as a measure of adherence. The videos consist of 3 horizontal planes: the upper plane displays the music notation of the current exercise; the middle plane displays a virtual keyboard where piano keys turn blue to indicate which note is being played; and the lower plane is a recording of the demonstration from an overhead angle ( Figure 1 ). Musical Instrument Digital Interface (MIDI) output from a Roland RD-170 (88-note) electric piano was used to record the sessions. Open Broadcasting Software (https://obsproject.com/), a High Definition (HD) webcam and tripod were used to record the demonstrations. The virtual keyboard was obtained from MIDIculous free software (Gospel Music insert URL) and the music notation was generated using MuseScore (https://musescore.org/en). Each video begins with a new finger exercise, followed by two new sight-reading exercises in the form of short familiar songs which gradually increase in difficulty in rhythm and number of notes for each hand (e.g., “O when the Saints”, “Happy Birthday”, “You are my Sunshine”). The videos first demonstrate the right hand, then the left hand, and then both hands together. Participants are signalled to pause the video to practice hands separately before attempting to play together. [Figure 1] 2.1.5 Passive Control The passive control group will be instructed to go about their lives as normal for the 8-week period, and not to engage in any musical activities, such as taking music classes, choir singing or dance classes, or in any cognitive training interventions. [Figure 2] 2.1.6 Outcome Measures Primary outcome measures are concerned with feasibility, measured in terms of recruitment, retention, acceptability and adherence (Table 1). Screening measures and secondary outcome measures concerning cognitive and motor performance are presented in Table 2. Secondary outcome measures concerning grey and white microstructure measurements are presented in Table 3. Participants will be screened for cognitive impairment, baseline musical ability and depression. Cognitive and motor testing will last for approximately 2.5 hours. Assessments were selected to measure the three core EF (inhibitory control, attention switching and working memory capacity and updating) (3), processing speed and verbal memory, because these are the domains most affected in ageing (1). Fine and gross motor ability will also be measured using the Q-motor battery. A near-transfer measure of piano performance is included to evaluate improvements in piano playing. Further details are provided in Table 2. 2.1.6.1 MRI brain morphology and microstructure A 3 Tesla MRI Siemens Connectom system with ultra-strong (300mT/m) gradients will be used to collect MRI data. Microstructural white and grey matter properties of neurite and soma density will be estimated using ms-HARDI (62) data, with maximum b-value = 6,000 s/mm 2 . Full details of MRI protocols, including their acquisition parameters are provided in Table 3. MRI protocols take 30 minutes in total to acquire, and have been previously piloted in healthy adults in the Welsh Advanced Neuroimaging Database study (WAND; McNabb et al., under review; https://git.cardiff.ac.uk/cubric/wand). Grey matter regions of interest (ROIs) include areas of the auditory system previously shown to be related to music in cross-sectional and longitudinal research: Heschel’s gyrus, Heschel’s sulcus, cerebellum, caudate nucleus, primary motor cortex, superior parietal lobule, hippocampus. White matter tracts of interest (TOI) include the arcuate fasciculus, fornix, anterior corpus callosum, corticospinal tract. Table 1: Primary outcome measures: feasibility (recruitment, retention, acceptability and adherence) Primary Outcome Measures: Feasibility Feasibility will be assessed using the following measures: recruitment, retention, acceptability and adherence. 1. Recruitment will be measured as the number of participants who are both deemed eligible for the study and who consent to taking part out of those who receive the participant information sheet. Reasons for ineligibility and declining to take part will be recorded in a screening log. 2. Retention will be measured by the number of participants who complete the study after attending the follow-up visit after 8 weeks. Reasons for withdrawal and loss to follow-up will be recorded. 3. Acceptability will be measured using a self-report questionnaire consisting of 27 Likert-scale assessing responses to the quality, difficulty level and content of the training and 4 qualitative questions on what could be improved in the training (Appendix C). 4. Adherence to the training will be measured by calculating frequency and duration of practice recorded in each participant’s practice diary. Participants are reminded weekly to record practice sessions as accurately as possible. The following predefined traffic lights system will determine feasibility success: green (all feasibility rates ≥70%) will indicate that the trial was successful; amber (no rates <40%, but at least one <70%) that the project requires review and design changes; and red (at least one rating <40%) that the intervention and a future RCT are not feasible. Table 2: Screening measures and secondary outcome measures Description Screening measures Telephone Interview for Cognitive Status (TICS) The TICS is a short reliable measure of global cognition which can be administered remotely. Scores correspond to those on the Mini-Mental State Exam (72). Scores ≤ 31/41 are indicative of dementia (Elliott et al., 2020). Test of Premorbid Functioning (TOPF; 73) The TOPF is used as a measure of verbal IQ, and involves reading a list of 70 words with unusual grapheme to phoneme translations in ascending order of difficulty. The number of correctly pronounced words is recorded. Profile of Music Perception Skills (micro-PROMS; 74) The micro-PROMS is a novel 10-minute musical test battery which provides an objective measure of perceptual musical skills. Participants are asked if a short musical sequence is the same/different to a sample sequence, varying in pitch, rhythm, tempo, melody, tuning and timbre. Patient Health Questionnaire-8 (PHQ-8; 75) The PHQ-8 consists of 8 questions which is used to screen for depression in medical settings. Scores >10 are indicative of possible major depression. One question regarding thoughts about self-harm and suicide was omitted to avoid unnecessary participant distress. PHQ correctly diagnoses major depression with 92% sensitivity and excludes this condition with 80% specificity (76). Cronbach alpha values range between .86 and .89 (77). Secondary Outcome Measures Cognitive and Motor Assessments Digit-symbol test from WAIS-III (78) The digit-symbol test is a paper and pen test used as a measure of processing speed. Participants must match symbols to numbers based on a given key within a 90-second time limit. The number of correct responses will be recorded. Go/No-Go test The Go/No-Go test measures the ability to suppress dominant responses. A participant must respond as quickly as they can when one stimulus appears (e.g., yellow circle) but must suppress the automatic response when a different stimulus is presented (e.g., blue circle). Response accuracy and latencies are recorded. Stroop test The Stroop test measures the ability to ignore response interference. A computerised version of the Stroop test will be administered via PsychoPy (79). Colour words are presented in ink which is either congruous (e.g., the word “blue” printed in blue ink) or incongruous (e.g., the word “blue” printed in red ink). Depending on instructions, participants must either respond to the colour meaning of the word or the colour ink in which it is written. Two practice blocks of 24 trials will be presented before two experimental blocks of 72 trials. Digit span (forward and backward) from (78) A random sequence of numbers is read aloud to participants and they must recall the numbers either forwards or backwards, depending on the trial. Number of correctly recalled numbers is taken as a measure of working memory capacity. The test is discontinued if a participant answers two sequential trials of the same number of items incorrectly. N-Back The N- back involves the continuous presentation of a series of stimuli (e.g., letters). The participants must indicate whether the current stimulus is the same as N steps earlier in the sequence (80). Number of correct responses versus false alarms and misses is a measure of updating working memory. The N -back will be administered on the Psychology Experiment Building Language (PEBL; (79). Participants will respond to a sequence of letters, then to spatial location of squares on a grid. A dual-task block of trials will also be included where participants must respond to both letters and squares. Trail-Making Test (TMT; Reitan, 1958) The TMT consists of two parts: A and B. Part A measures visual attention. Participants are instructed to join numbered circles from 1-25 in ascending order as quickly as possible. Part B measures attention-switching, Participants must alternate between joining numbers and letters (1-A-2-B etc.) as quickly as possible. Performance is measured by completion times for both parts. The TMT is highly correlated (0.72-0.80) with other measures such as the Wechsler Adult Intelligence Scale – III (81), indicating excellent construct validity. Inter-rater reliability for TMT is also high ( r = -.96). D-KEFS Verbal fluency (82) The Delis-Kaplan Executive System Verbal Fluency subtest assesses the ability to generate words, comprising three subtests: i) letter fluency; ii) category fluency and iii) category switching. In i) letter fluency trials, participants are instructed to name as many words as possible that begin with a given letter, excluding names of people, places or numbers (standard form: F, A, S; alternate form: R, C, M). In ii) category fluency trials, participants are instructed to generate as many words as they can which belong to a given category (standard form: boy’s names and animals; alternate form: girl’s names and supermarket items). In the iii) category switching trials, generated words must alternate between two pre-established categories (standard form: fruit and furniture; alternate form: vegetables and musical instruments). All trials have a 60-second time limit. Reliability coefficients reported for D- KEFS verbal fluency subtests are .88 (letter fluency), .82 (category fluency) and .51 (category switching) (82). California Verbal Learning Test II (CVLT; (83) The CVLT-II consists of 5 trials where a 16-word list consisting of 4 categories (e.g., ways of travel, animals, vegetables, furniture) is read aloud, and the participant must try to recall as many words as they can in each trial. This is followed by immediate (after an alternate “distractor” list is read aloud) and long-delay (after 20 minutes) trials in both free (without prompts) and cued (provided four categories) recall formats. Inter-rater reliability ranges from 0.80-0.96 (83) Quantitative Motor (Q-Motor; 91) The Q-motor will be used to measure changes in fine and gross motor abilities. This is an assessment battery measuring fine motor functions of finger and foot tapping, synchronized tapping with a metronome, tapping continuation without metronome, and dual-task motor ability of tapping with one hand and pointing with the other. Piano Performance (near transfer) All participants will play two 5-finger scales starting on C and D at 60 and 80 beats per minute (bpm). Following the intervention, piano participants will also play scales of C and G major hands separately and Ode to Joy (right hand only) with metronome at 60 bpm, to measure learning acquired during the intervention. Data will be collected using a MIDI keyboard (MPK Mini MK3 25-key MIDI keyboard controller) connected to the open-source audio recording software, Reaper (www.reaper.fm/). The recordings will be exported as MIDI files, a simple and compact format that encodes the duration, pitch, and velocity (i.e., loudness) of each note played. To assess performance accuracy, a reference MIDI file containing the correct sequence of notes with ideal durations will be used to compare against the participants' performances. Discrepancies between the reference and the actual played notes will be analyzed using a custom Python script, "midi-eval," designed specifically for the PIANO-Cog experiment. Midi-eval processes each performance using well-known Python MIDI libraries, "pretty_midi" (Raffel & Ellis, 2014) and "music21" (84). Performance will be assessed by computing note accuracy (the % of correct pitches played) and time accuracy. Time accuracy will be evaluated by measuring onset time error (discrepancies in onset times between reference and participant’s performance) and note duration errors (how long the note is held compared to the reference). Table 3: MRI sequences and parameters applied to assess microstructural changes (secondary outcome measures) MRI sequences Acquisition parameters Rationale and outcome indices Magnetization-prepared rapid gradient-echo (MP-RAGE) 1 mm 3 resolution, FOV: 256 x 256, TR = 2300ms, TE = 2ms, TI = 857ms, flip angle: 9; Duration ~7min 3-dimensional T 1 -weighted anatomical image · To segment regions of interests in the basal ganglia, auditory cortex, sensorimotor cortices, hippocampus and cerebellum · to provide a reference map for all microstructural outcome maps Multi-shell High Angular Resolution Diffusion Imaging (msHARDI; 62) 2 mm 3 resolution; FOV: 220 x 200; matrix size: 110 x 110 x 66; TE/TR = 59/3000ms; δ/Δ: 7/24ms; b-values = 0 (14 volumes), 500 (30 directions), 1200 (30 directions), 2400 (60 directions), 4000 (60 directions), and 6000 (60 directions) s/mm 2 ; Duration ~20 min Multi-shell diffusion weighted imaging data · to provide multi-tissue constrained spherical deconvolution-based (Jeurissen et al., 2014) tractography to reconstruct white matter pathways of interest (corpus callosum, cortico-spinal tract, arcuate fasciculus, fornix) · to model of white matter tissue components using the Neurite Orientation Dispersion and Density Imaging (NODDI; 65) providing the isotropic signal fraction (ISOSF) as an estimate of free water, intracellular signal fraction as an estimate of axon density, and the orientation dispersion index (ODI) as an estimate of axon orientation and dispersion. · to model grey matter tissue properties using the Soma And Neurite Density Imaging (SANDI; 63) model which provides estimates of soma density (soma signal fraction) and size Assessments for all participants Screening Phone Call Baseline study visit PIANO-Cog training intervention (completed at home) or control time period Follow-up study visit Day 1 8-week intervention period Week 9 Cohort Descriptive information Telephone Interview for Cognitive Status (TICS) X Test of Premorbid Functioning (TOPF) X Micro-PROMS X Self-report questionnaire Patient Health Questionnaire-8 X X PIANO-Cog participant evaluation survey X Verbal tests California Verbal Learning Test – II X X D-KEFS Verbal fluency X X Digit-span test X X Paper and pencil tasks Trail-Making Test X X Digit-symbol test X X Computerised tasks N-back Audio (letters) X X N-back Visuospatial (square changing positions in grid) X X N-back dual (audio and visuospatial) X X Stroop test X X Go/No-go test X X Q-Motor assessments Speeded finger tapping (left/right index finger) X X Finger metronome tapping (left/right index finger) X X Pointing and tapping (dominant hand) X X Dual (pointing and tapping with dominant hand, speeded finger tapping with opposite hand) X X Piano Performance 5-finger scales beginning on C and D at 60 and 80 bpm, hands separately X X Scale of C and G major, hands separately at 60 bpm (piano group only) X Ode to Joy (piano group only) X Brain imaging T1-weighted MPRAGE X X msHARDI X X PIANO-Cog group PIANO-Cog training intervention supported by email and telephone reminders X Control group Usual activities, but without engagement in music-based activities or cognitive training X D-KEFS, Delis-Kaplan Executive Function System; Micro-PROMS; Profile of Music Perception Skills Figure 3: Schematic overview of the two-arm randomised controlled feasibility trial 2.1.7 Sample size In line with the Consolidated Standards of Reporting Trials (CONSORT) statement extension for randomized pilot and feasibility trials (85), formal power calculations have not been performed. We plan to recruit 50 cognitively healthy individuals from the local area, a target chosen pragmatically based on the required level of participant engagement and available resources. This sample size will enable estimation of recruitment, retention, and adherence rates within a 95% binomial confidence interval, with a margin of error of no more than ±15 percentage points, regardless of point estimate. 2.1.8 Recruitment Participants over the age of 50 years will be recruited from Cardiff and surrounding areas via poster advertisements in public places, community groups for over-50s, local active retirement Facebook groups, and the CUBRIC participant recruitment website (https://psychologystudies.cardiff.ac.uk/). 2.2 Data 2.2.1 Collection and management The study will be conducted in accordance with Good Clinical Practice and the Data Protection Act 2018. Cognitive and motor data will be collected on hard-copy scoring sheets and electronically via PEBL (86) and PsychoPy (79) on a computer in a quiet testing laboratory at CUBRIC. Behavioural data collected using hard-copy scoring sheets will be stored in a locked cupboard in an access-restricted office in CUBRIC. Q-MedX software will automatically capture Q-Motor data, which will be stored on a password-protected laptop. Behavioural data from PsychoPy tests will be cleaned by removing practice trials and extreme or outlier values and subsequently analysed using R statistical software. Extreme values and outliers (scores >3 standard deviations +/- the mean) will be identified using the rstatix R package (87) and will be reported and excluded where appropriate. MRI data will be collected according to CUBRIC standard operating procedures (SOP) including MRI safety and operation guidelines by trained MR operators. MRI data will be acquired on the 3 Tesla Siemens Connectom system at CUBRIC and stored on the XNAT system. 2.2.2 Statistical methods The study will be reported in line with CONSORT reporting requirements for pilot and feasibility trials (85). Since this is a feasibility trial, it is not formally powered to test for effectiveness of the intervention whilst controlling for type 1 error. The primary purpose of the trial is to assess the feasibility, by measuring recruitment, retention and adherence rates and acceptability scores of the intervention. Feasibility percentage rates will be calculated as follows: · Recruitment rate = 100 x (number of participants who provided consent / number of participants eligible) % · Retention rate = 100 x (number of participants who complete follow-up testing / number of participants who provided consent) · Adherence rate (frequency) = 100 x (number of days’ practice logged / 40 days) % · Adherence rate (duration) = 100 x (number of minutes practice logged / (40 days x 30 minutes average session duration = 1,200 minutes) % Descriptive statistics (means and standard deviations) of effect sizes and 95% confidence intervals will be calculated for all secondary outcome measures, listed in Table 2. Differences in mean and SD of participants absolute and percentage changes from baseline will be calculated to provide estimates of effect size and variability of performance changes in the two groups. Independent t- tests will be conducted on TICS, age, years of musical experience and musical aptitude to determine any significant differences between piano and control groups at baseline, which could influence the effects of the training. To investigate the effect of piano training on cognitive and motor outcome measures and neuroplasticity, linear mixed models will be conducted in R using the lme4 package (88), with group, time and baseline measures as covariates variables. Statistical methods for handling missing data will be reported if required. FreeSurfer (89) and the FAST tool in FSL (FMRIB Software Library) will be used for the segmentation of grey matter ROIs. Tractography will be carried out on TOIs using MRTrix (90). Linear mixed models will also be used to investigate relationships between cognitive ability and grey and white matter microstructure, measured using metrics of NODDI and SANDI at baseline and follow-up. 2.2.3 Monitoring Cognitive and motor scores will be entered, and constantly monitored for quality against original record forms by the author FR, and regular meetings will take place with the research supervisor (CMB). Reasons for withdrawal from the study will be recorded. Publications will report reasons for any attrition or missing data. Declarations Ethics approval and consent to participate The study received ethical approval from Cardiff University Ethics Committee on 20 th June 2024 (EC.23.05.16.6801GRA). Plans for protocol modifications will be discussed with the research supervisor and submitted as amendment to ethics committee if required. Upon expression of interest, a participant information sheet will be emailed to eligible participants, and they will receive second copy upon their arrival at CUBRIC. Written informed consent will be obtained at CUBRIC in person before any testing takes place. Participants will have the opportunity to ask questions before signing the consent form (Appendix B). Consent for publication Participants will also be asked to provide consent for their anonymised data to be made available on Open Science Framework and to other members of the research team at CUBRIC, and also to be published in academic journals and presented at conferences, with all identifying information removed. Availability of data The anonymised data will be made available to supervisors, other members of research team (other PhD and post-doctoral researchers and research assistants), ethics committees or monitors, as well as other researchers at CUBRIC who express an interest in using the data to test other hypotheses. Data will be made openly available on Open Science Framework upon publication. Competing interests The authors declare no known competing financial interests or personal relationships that could influence the proposed study. Confidentiality After providing consent, participants will be assigned a unique study code and CUBRIC ID which will be used to code all of their electronic and hard-copy data. Electronic data will be stored in a password-protected Excel file, on a computer which is also password-protected. Study and CUBRIC ID codes and any personal information will be securely stored for the duration of the study in a locked compartment in CUBRIC. Only members of the research team will have access to confidential files to allow for the matching of recorded data to participants. No paper or electronic data will leave CUBRIC without being completely coded (i.e. identifiable data will be removed). The CUBRIC partition of XNAT (www.central.xnat.org) will be used to securely store MRI data. At the end of the study, anonymised data will be archived, but will still be accessible to the research team. Personal information will not be directly linked to data, and will be destroyed after 15 years as per Cardiff University’s Research Records Retention Schedule. Ancillary and post-trial care No harm is anticipated from participation in the trial, but in case any difficult emotions arise from taking part, participants will be encouraged to speak with their general practitioner (GP) and will be made aware of mental health services that they can access for support. MRI scanning is non-invasive and once appropriate screening and safety measures are in place, there are no known significant adverse health effects. Participants will be made aware at the beginning of scanning that rare side effects of MRI could occur, which include peripheral nerve stimulation, dizziness or mild nausea. Participants will be advised to press the emergency squeeze ball to alert the operators if there is anything wrong. If a participant experiences any of these symptoms scanning, this will be closely monitored and documented. Although these side effects can be uncomfortable, they resolve themselves when the participant leaves the magnetic field. Participants will also be informed that MRI operators and cognitive test administrators are not medical doctors, and that none the tests or scans conducted will be used for any medical or diagnostic purposes. If a participant has any health concerns, they will be encouraged to consult their GP. Participants will be informed that the scans will not be routinely reviewed to detect abnormalities. In the case where the MR operator has any concern about a scan, an appropriate consultant, such as a neuroradiologist, will be asked to examine the scan. If the neuroradiologist feels it to be appropriate, a report will be sent to the participant’s GP with their consent. Dissemination policy and impact This work is undertaken as part of a PhD project and is intended to be published in academic journals and presented at conferences. The aim of this research is to demonstrate the cognitive and neural benefits of musical training in later adulthood, in the hope of contributing to a body of literature demonstrating a need for public health funding for musical training to help reduce dementia-related costs, prolong independence and quality of life. Funding This research was supported by an Open PhD studentship from the School of Psychology Cardiff University and a National Institue for Health Research (NIHR) and Health and Care Research Wales (HCRW) Advanced Fellowship [NIHR-FS(A)-2022]. 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California verbal learning test - Second edition. San Antonio, TX: The Psychological Corporation.; Cuthbert M, Ariza C. Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. International Society for Music Information Retrieval; 2010. 637–642 p. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. The BMJ. 2016;355. Mueller ST, Piper BJ. The Psychology Experiment Building Language (PEBL) and PEBL Test Battery. J Neurosci Methods. 2014 Jan 30;222:250–9. Kassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. . R package version 072. Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015 Oct 1;67(1). Fischl B. FreeSurfer. Vol. 62, NeuroImage. 2012. p. 774–81. Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Vol. 202, NeuroImage. Academic Press Inc.; 2019. Reilmann R, Schubert R. Motor outcome measures in Huntington disease clinical trials. Handb Clin Neurol 2017;144:209-25. doi: 10.1016/B978-0-12-801893-4.00018-3 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.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. We do this by developing innovative software and high quality services for the global research community. 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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-6023523","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415420049,"identity":"472a9026-ec2d-405d-9e6e-5aea60bdbc32","order_by":0,"name":"Fionnuala Rogers","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBAC+wYw9V+OH0TxGEBoBoYDuLUYQOQOGEs2gLVAaKK0JG4AMXgYiNFyvP3i44o/Bxg3Hz/87MObAgMJBvbDD5h5zuDWYt9zptjwbNsfZrMzacYz5xgAtfCkGTDz3MCtxU4iJ02yseEAm9kNBmNmHoM/dQwMOQzMPB9wazGWyEn/2fDnAI/xDPbPQC1AW/jf4NdiOCP9GGMD2wEJAwkeY4gWCZAteBxmcOYMs2Rj2wEDiTM5xYwgv7BJPDM4OAeP94Eh9vAj0GH1/e3HNzO8+WMgwc+f/PDBm2O4tYCiD5XPxoA3VkCA/QF++VEwCkbBKBgFAI5HVAsKAdEwAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1001-7653","institution":"Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Maindy Road, Cardiff University, Cardiff, United Kingdom.","correspondingAuthor":true,"prefix":"","firstName":"Fionnuala","middleName":"","lastName":"Rogers","suffix":""},{"id":415420050,"identity":"dd5fd492-258d-4a52-ae68-3bc791b1d64f","order_by":1,"name":"Ege Erdem","email":"","orcid":"","institution":"Department of Engineering, Faculty of Natural, Mathematical \u0026 Engineering Sciences, King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Ege","middleName":"","lastName":"Erdem","suffix":""},{"id":415420051,"identity":"54236e80-14d0-42a3-bdf7-d46fcc8feddd","order_by":2,"name":"Dr. Claudia Metzler-Baddeley","email":"","orcid":"","institution":"Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Maindy Road, Cardiff University, Cardiff, United Kingdom.","correspondingAuthor":false,"prefix":"Dr.","firstName":"Claudia","middleName":"","lastName":"Metzler-Baddeley","suffix":""}],"badges":[],"createdAt":"2025-02-13 13:33:32","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6023523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6023523/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79553110,"identity":"1dc2e073-c934-4d6a-8467-881c804a3981","added_by":"auto","created_at":"2025-03-31 06:58:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260498,"visible":true,"origin":"","legend":"\u003cp\u003eExample screenshot from Week 7 training video: Beethoven’s “Ode to Joy” sight-reading demonstration with hands together\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6023523/v1/4c90cf52070bf58ca5554520.png"},{"id":79551869,"identity":"5a74ad0c-a91e-4eee-8e85-1fda0ee6d918","added_by":"auto","created_at":"2025-03-31 06:42:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163219,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study design. MRI, Magnetic Resonance Imaging; TICS, Telephone Interview for Cognitive Status\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6023523/v1/5f0448ddf558055eb6d93593.png"},{"id":79554786,"identity":"84465b3c-21ff-40da-bb6e-f8cfb6ac4ffd","added_by":"auto","created_at":"2025-03-31 07:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1835439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6023523/v1/22a72bdc-ae60-4c79-b4e9-0adb5d9b1d5c.pdf"},{"id":79552477,"identity":"72415632-7ad7-4883-992b-0575d6d0149f","added_by":"auto","created_at":"2025-03-31 06:50:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":94034,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6023523/v1/6066dd8abceb7abb9c3b3bfb.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eProtocol for a randomised controlled feasibility trial of Piano Instruction for Adult Novices as Online Cognitive intervention (PIANO-Cog), a novel remote piano training for cognitive and motor functions in older age.\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003ch2\u003e1.1 Background and rationale\u003c/h2\u003e\n\u003cp\u003eCognitive ageing is characterised by a decline in abilities important for learning and processing novel information and interacting with the environment, collectively known as fluid intelligence (1). Declines in fluid intelligence can negatively impact executive functions (EF), an umbrella term for cognitive abilities which support higher-level cognitive functioning crucial for planning, problem-solving and decision-making (2,3). EF are the first abilities to be affected in normal ageing due to age-related atrophy of the prefrontal cortex (PFC) and its connected regions (4). The negative impact of ageing on fluid abilities and EF can have a considerable impact on daily functioning, quality of life and the ability to live independently and are therefore an important target for cognitive training programmes\u0026nbsp;(5) (6,7).\u003c/p\u003e\n\u003cp\u003eA wealth of research has investigated the effectiveness of computerised cognitive training programmes and commercially available \u0026ldquo;brain training\u0026rdquo; applications to improve and maintain EF in ageing. Although participants show improvements on tasks which tap into the same cognitive domain (i.e. \u0026ldquo;near\u0026rdquo; transfer), meta-analyses suggest weak or no effects on daily functioning (i.e. \u0026ldquo;far\u0026rdquo; transfer) (8\u0026ndash;11) \u0026nbsp;Computerised tasks may be unsuccessful in leading to far transfer effects because they often only target one cognitive domain (e.g., working memory capacity) and therefore do not simulate the complexity of real-world tasks which involve integration and coordination between multiple sensory and motor networks (12,13). Consequently, there has been a shift in focus towards developing more ecologically valid training programmes for older adults that require multisensory-motor interactions with the environment (e.g., (14).\u003c/p\u003e\n\u003cp\u003ePreserving and enhancing EF and fluid abilities through ecologically valid activities is consistent with evolutionary perspectives that EF developed as an extension of the motor control system through a necessity for movement and optimal interaction with the environment (15). The basal ganglia and cerebellum are key structures in the coordination of movement, and these structures are also important for EF (16\u0026ndash;18) The basal ganglia are responsible for predicting stimulus-response associations, while the cerebellum controls movement through continuous predictive-error correction feedback loops and instructs the frontal lobes to simulate results of actions based on prior activities (19). In this way, EF evolved as a means of generating and anticipating the outcomes of movements (15), and therefore tasks which involve these networks, such as music making (20), could be optimal for generating far transfer to daily functioning.\u003c/p\u003e\n\u003ch2\u003e1.2 Music-based behaviours stimulate neural networks important for EF: evidence from neurologic music therapy studies\u003c/h2\u003e\n\u003cp\u003eThe role of the basal ganglia and cerebellum in music making has been demonstrated in studies of neurologic music therapy (NMT; (21). NMT is a music-based rehabilitation method for treating cognitive and motor symptoms and improving quality of life for people with neurological movement disorders, such as Parkinson\u0026rsquo;s Disease (PD) (see 22 and 23 for reviews), Huntington\u0026rsquo;s Disease (HD) (24) and stroke (25). Kasdan et al. (26) reported a dissociation between the roles of the basal ganglia and cerebellum in the interpretation of rhythm, where patients with basal ganglia lesions had greatest difficulty with rhythm complexity (i.e. syncopation), whereas cerebellar patients had most difficulty with rhythms at faster tempi.\u003c/p\u003e\n\u003cp\u003eTherapeutic instrumental music performance (TIMP; 27) is a form of neurologic music therapy (NMT), which utilises the rhythms in music to create consistent internal reference intervals for initiating and regulating motor movements through incorporating exercises involving musical instruments. This is thought to occur through a process of neural entrainment (i.e. direct sensorimotor synchronization, (28) TIMP has been shown to be effective in treating motor symptoms (e.g., 29,30 but also EF. For example, Bugos et al. (31) found reductions in Stroop error rates in Parkinson\u0026rsquo;s Disease patients following 10 days\u0026rsquo; piano training compared to a control group. Furthermore, TIMP was found to improve mental flexibility, measured using the Trail-Making Test Part B (32) in stroke patients (27).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe key mechanism of these music-based trainings which has the potential to lead to cognitive enhancement is hypothesised to be the stimulation of basal ganglia and cerebellar networks and their connections to fronto-parietal brain areas associated with EF (24). For instance, in PD patients, it has been proposed that the use of a regular beat facilitates neural entrainment by bypassing the affected basal ganglia, which is normally responsible for internal cue generation, but impaired in PD (21). As discussed above, the basal ganglia and cerebellum are key structures involved in EF, and inclusion of cortico-basal-cerebellar networks through motor activity is now a recommended feature of cognitive training interventions (33).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e1.3 Playing a musical instrument benefits cognition and neural structure in normal ageing\u003c/h2\u003e\n\u003cp\u003eLearning to play a musical instrument requires integration and coordination between different sensory and motor modalities and is regarded as a model of neuroplasticity (20,34,35). Cognitive advantages have been reported in musicians compared to non-musicians in verbal working memory(36), executive control (37), auditory and visual memory updating (38), linguistic perception abilities (39) and verbal intelligence (see 40 for review).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCross-sectional brain imaging studies have consistently reported structural differences in auditory, motor and somatosensory networks between musicians and non-musicians. Specifically, Heschel\u0026rsquo;s gyrus, the planum temporale, fusiform gyrus, cerebellum, superior parietal lobule, inferior frontal lobe, lingual gyrus hippocampus and caudate nucleus are grey matter regions consistently shown to be larger in musicians using voxel-based morphology (VBM) and cortical thickness (CT) measurements (41\u0026ndash;47). White matter pathways connecting these brain regions have also been shown to exhibit larger white matter microstructure in musicians. For instance, musicians show greater fractional anisotropy (FA; a measure of diffusion coherence used as a proxy for white matter density; (48), in the arcuate fasciculus (49) which connects the temporal and frontal lobes and is important for learning to associate motor actions with sounds (50), as well as in the anterior corpus callosum and corticospinal tract (51), important for bimanual motor coordination and motor skills respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOlder adults who played a musical instrument were found to have lower risk of developing dementia at 5-year follow-up (52). Additionally, in a study of 5,693 participants which included 745 musicians, musicians who played for at least an hour on most days showed the highest cognitive performance and were also 80% more likely to be in the top decile of cognitive function, measured using the Short Form Extended Mental State Exam than both musicians who played less frequently and non-musicians (53). Furthermore, twins who are musicians have been found to be 64% less likely to develop dementia than their non-musician counterpart, whilst adjusting for sex, education and physical activity (54).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, the evidence suggests that playing music could have neuroprotective benefits contributing to cognitive and/or structural changes leading to lower incidence of cognitive difficulties in older age, beyond genetic factors. Lower incidence of cognitive decline in older non-musicians may be related to increased cognitive reserve (55) or structural maintenance in later life resulting from musical practice. However, the studies cited above are cross-sectional in nature and so causal relationships between cognitive advantages and structural differences cannot be drawn.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe recently conducted a systematic review and meta-analysis of longitudinal behavioural studies which used short-term training in a pitched or percussion musical instrument to benefit EF and fluid abilities in adults over the age of 60 years. Random effects models on 502 participants across 13 studies revelaed\u0026nbsp;a moderate effect on processing speed (\u003cem\u003ed =\u0026nbsp;\u003c/em\u003e.47, \u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e.0001), a low-moderate effect on attention-switching (\u003cem\u003ed =\u003c/em\u003e .39,\u003cem\u003e\u0026nbsp;p =\u0026nbsp;\u003c/em\u003e.0021) and inhibitory control (\u003cem\u003ed =\u003c/em\u003e .39,\u003cem\u003e\u0026nbsp;p =\u0026nbsp;\u003c/em\u003e.0335) (56), suggesting that short-term musical training can benefit fluid abilities and EF in older non-musicians.\u003c/p\u003e\n\u003cp\u003eOnly one trial to date, \u003cem\u003e\u0026ldquo;Train the Brain with Music\u003c/em\u003e\u0026rdquo; (57) has investigated structural neural changes following piano training in healthy older adults using magnetic resonance imaging (MRI). Participants were assigned to either piano training or an active control group who attended music culture lessons for 12 months. After 6 months of training, CT increases were detected in the piano compared to the listening group in areas involved in auditory processing: left Heschel\u0026rsquo;s gyrus, left planum polare, bilateral temporal sulcus and right Heschel\u0026rsquo;s sulcus (58). A significant increase in density in the caudate nucleus, Rolandic operculum and inferior cerebellum was also detected after 6 months when data were pooled across \u003cem\u003eboth\u003c/em\u003e piano training and control groups, the latter of which was associated with an improvement in tonal working memory (59). Both groups showed an improvement in working memory capacity measured using Backward Digit Span, but this improvement was not linked to any grey matter volume increases. With regards to white matter changes, the authors reported deterioration of the fornix for the music culture group only, using fixel-based analysis (FBA) (60).\u003c/p\u003e\n\u003cp\u003eTo summarize, learning to play the piano as an adult novice can benefit EF, may slow or prevent normal age-related atrophy and induce structural changes in auditory and motor pathways. However, morphological differences between and across groups (using CT and VBM respectively) post-training as described above do not inform about the plastic mechanisms that drive tissue changes on the microstructural level. Moreover, the relationship between these structural effects and possible cognitive outcomes remains unclear.\u003c/p\u003e\n\u003ch2\u003e1.4 Investigating microstructural neuroplasticity following piano training using ultra-strong MRI gradients and multi-shell high angular resolution diffusion imaging (ms-HARDI)\u003c/h2\u003e\n\u003cp\u003eThe proposed study aims to address questions around biological mechanisms of music-induced neuroplasticity in later adulthood raised by 58, 59 and 60 discussed above. Macrostructural changes in grey matter density could be driven by a number of different biological mechanisms such as neurogenesis (in the hippocampus), synaptogenesis or changes in neuronal morphology. White matter neuroplasticity could be driven by changes in the number of axons, axon diameter, the packing density of fibres, axon branching, axon trajectories and myelination (61). Cell density, cell size and myelination can affect voxel intensities on a T1-weighted image, which will influence measures derived from T1 such as concentration, density or volume, and hence VBM and CT do not provide metrics that relate in a straightforward way to underlying neuronal densities (61).\u003c/p\u003e\n\u003cp\u003eIntracellular diffusion properties can be estimated using the enhanced signal-to-noise ratio afforded by high diffusion-weighted b-values that can be acquired by using ultra-strong gradients (300mT/m) of the Siemens Connectom scanner (62). Multi-shell high-angular resolution imaging (ms-HARDI) will be used to acquire diffusion weighted imaging (DTI) data which allows for the separation of extracellular and intra-cellular compartments at low and high b-values respectively, and also the modelling of crossing fibres and thereby improving tractography analysing for white matter. Taken together, this will allow for a richer, more comprehensive characterization of water diffusion in different tissues.\u003c/p\u003e\n\u003cp\u003eThe application of novel compartment-based microstructural models to this diffusion data can provide more complete physical descriptions of diffusion processes in white and grey matter, which will be used to extract more meaningful information about the biological mechanisms involved in plasticity. For instance, the soma and neurite imaging model (SANDI; 63) is a recently-developed biophysical model which provides an estimate of grey and white matter microstructure by incorporating the size and density of grey matter soma in addition to white matter neurite density. Only one study to date has used SANDI to characterise microstructure in ageing, through the use of a cross-sectional design (64). The soma density and soma size metrics were found to be significantly negatively correlated with age (\u003cem\u003er\u003c/em\u003e \u0026ge; -.69, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) in all major lobes (64). Relationships between SANDI metrics and age were stronger than those of VBM or CT with age and therefore SANDI metrics were suggested to be more sensitive measure of neurodegeneration. The proposed study will be the first to employ SANDI as a means of investigating grey matter microstructure changes over time. Changes in the soma density measure may be used as a proxy measure of synaptogenesis or neurogenesis (in the hippocampus), and soma size could reflect glial activity, swelling of astrocytes or oligodendrocytes following training.\u003c/p\u003e\n\u003cp\u003eThe neurite orientation and dispersion imaging (NODDI; 65) model can distinguish between signals coming from intracellular, extracellular and cerebrospinal fluid compartments. The intracellular signal fraction will be used as a measure of white matter, in terms of myelination, dendritic and axonal sprouting. NODDI also provides the orientation dispersion index (ODI) which quantifies the bending and fanning of axons by measuring how many voxels are classified as having crossing fibres. The ODI from NODDI can be used to map white matter connections, and provide cross-indication of dendritic and axonal sprouting. NODDI measures have been previously shown to be sensitive for characterising white matter degradation in ageing, with age being associated with lower neurite density, measured using the intra-cellular volume fraction, and lower tract complexity measured using the extracellular water diffusion in the majority of white matter tracts (66). Nazeri et al. (67) found age-related decline in ODI, mainly in frontoparietal regions, and ODI outperformed cortical thickness and white matter FA for the prediction of age.\u003c/p\u003e\n\u003ch2\u003e1.5 The current study and pilot data\u003c/h2\u003e\n\u003cp\u003eCognitive intervention through musical instruction is an ecologically valid training method because it taxes multiple sensory and motor networks. However, the multimodal nature of music interventions renders them more difficult to embed within experimental designs compared to traditional computerised interventions (e.g., \u003cem\u003eN-\u003c/em\u003eback training). Music interventions also tend to be more time-consuming and expensive to run, requiring funding of instructors and equipment. Therefore, PIANO-Cog was developed to provide self-guided training which can be completed at home without the need for instructors. This training method, like computerised training programmes, can be quickly distributed at low cost and provide a more naturalistic method for how older adults may undertake activities to maintain their own cognitive functioning in real-life settings. This may also be a more realistic method of maintaining EF in older age without the need for public health funding initiatives. The intervention will be compared with a passive control to assess the effects of the training compared to life as usual for older adults.\u003c/p\u003e\n\u003cp\u003eThe first iteration of PIANO-Cog was piloted in 9 cognitively healthy adults over the age of 60 years (Rogers, Erdem \u0026amp; Metzler-Baddeley, in prep). Participants were recruited online, and cognitive testing took place remotely. The intervention was deemed feasible based on \u0026ldquo;green\u0026rdquo; rating using a traffic-light system, whereby measures for retention, acceptability and adherence were all \u0026ge;70%. Retention, measured as the percentage of participants who were tested at follow-up out of those tested at baseline was 91.3%. Adherence was measured by the number of training videos accessed (94.4%), and the average number of practice hours logged per week (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 3.53 hours;\u003cem\u003e\u0026nbsp;SD =\u0026nbsp;\u003c/em\u003e0.35)\u003cem\u003e,\u0026nbsp;\u003c/em\u003e63.3 minutes longer than the requested 2.5 hours per week). The intervention was deemed acceptable by positive responses collected on an evaluation survey, indicating that the online delivery method appealed to participants, and that the content was challenging but manageable (e.g., \u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003cem\u003eI really enjoyed the course, and found it challenging but doable; difficult\u0026nbsp;\u003c/em\u003e[exercises]\u003cem\u003e\u0026nbsp;got easier with practice\u0026rdquo;\u003c/em\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the pilot, the intervention was improved based on participant feedback to include more \u0026ldquo;play along\u0026rdquo; and metronome activities to improve rhythm and timing abilities hypothesized to involve EF. Positive trends for training effects on verbal memory and inhibitory control measures were observed and informed the selection of outcome measures for the current trial which aims to test feasibility of the updated intervention. Recruitment will be taking place in Cardiff and surrounding areas, rather than online, as participants will undergo MRI scanning.\u003c/p\u003e\n\u003ch2\u003e1.6 Objectives\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003ePrimary objective\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the feasibility of: i) PIANO-Cog as an acceptable online cognitive training intervention for healthy adults \u0026gt;50 years and ii) the feasibility (recruitment, retention, adherence) of a larger RCT investigating 8 weeks of PIANO-Cog compared with usual activities in healthy adults over the age of 50 years.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSecondary objectives\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain estimates of variability and absolute change pre- and post-training compared to usual activity in the following measures:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Performance scores in cognitive and motor assessments for future RCT sample size calculations.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Grey and white matter microstructure means in basal ganglia and cerebellar networks and auditory networks to investigate possible neural mechanisms that may underpin cognitive training effects\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e1.6.1 \u0026nbsp;Hypotheses\u003c/h3\u003e\n\u003cp\u003eOur primary hypotheses are:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; PIANO-Cog will be a feasible online cognitive training intervention for healthy non-musicians over the age of 50 years old.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u0026nbsp;A future fully-powered RCT into the effects of 8-weeks of home-based PIANO-Cog training compared to no-training control will be feasible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur secondary hypotheses are that, in healthy non-musicians (\u0026gt;50 years), 8 weeks of piano training will:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ei) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lead to improvements in processing speed, response inhibition and attention switching as measured by digit-symbol substitution test, a Stroop test, Go/No-go test and verbal fluency category switching tasks (56)\u003c/p\u003e\n\u003cp\u003eii) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; lead to grey and white matter microstructural changes in auditory, motor and somatosensory networks measured using DTI and metrics from the biophysical models, NODDI and SANDI. Specifically, we expect to see an increase in soma density and soma size metrics from SANDI model and increased orientation dispersion and intracellular density metrics from the NODDI model following piano training compared to the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiii) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;underlying microstructural changes will be associated with changes in processing speed (digit-symbol task) and EF (N-back, Stroop and Go/No-go tasks).\u003c/p\u003e\n\u003ch2\u003e1.7 Trial Design\u003c/h2\u003e\n\u003cp\u003eThis study will employ a two-arm unblinded randomised controlled design. Cognitively healthy non-musicians over 50 years will be recruited and screened for cognitive impairment and \u0026lt; 4 years music experience. They will be assigned to either self-guided piano training or a passive control for 8 weeks. Groups will be stratified for age and sex. Cognitive assessments and diffusion MRI will take place before and after the intervention period.\u0026nbsp;\u003c/p\u003e"},{"header":"2 Method","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Participants, intervention, outcomes\u003c/h2\u003e\n\u003ch3\u003e2.1.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Study Setting\u003c/h3\u003e\n\u003cp\u003eCognitive and motor testing and MRI scanning will take place at CUBRIC before and after the 8-week intervention period, with data collection taking place between September 2024-July 2025. The intervention is self-guided, so the intervention will take place in participants\u0026rsquo; homes.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1.2\u0026nbsp; \u0026nbsp;\u0026nbsp;Eligibility criteria\u003c/h3\u003e\n\u003cp\u003eParticipants will be included if they are \u0026gt;50 years old, fluent English speakers, have normal/corrected-to-normal vision and hearing, have less than 4 years of formal musical or dance training, are not involved in any musical activities, have no neurological or psychiatric history that could affect learning (e.g., dementia, stroke or traumatic brain injury, depression requiring hospitalisation), no self-reported difficulty with hand movement and no self-reported learning disabilities.\u003c/p\u003e\n\u003cp\u003eExclusion criteria include impaired hearing or vision, neurological diagnosis, current involvement in other cognitive training or musical activities (e.g., choir singing, dance or exercise to music classes), more than 4 years of formal music or dance lessons or currently taking psycho-reactive medications which affect memory performance. Participants with MRI contra-indications (e.g., pacemakers, stents, cochlear implants, or other metal in the body such as metallic plates, screws or clips) will not be scanned, but will still be eligible for training and cognitive and motor testing.\u003c/p\u003e\n\u003ch3\u003e2.1.3 \u0026nbsp; \u0026nbsp;Group Allocation\u003c/h3\u003e\n\u003cp\u003eThe groups will be stratified by sex and by two age categories: 50-65 years and \u0026gt;65 years. Pseudo-random numbers will be used to generate randomised group allocation per stratum using R version 4.41, and implemented by the author FR. Neither participants nor the researcher conducting baseline testing will be informed of group allocation until after baseline testing is completed, as the algorithm determining group allocation will take place only at the end of testing by the lead researcher, but the researcher will not be blind at follow-up. Due to the nature of the training, participants will be unblind to condition. Participants who are randomly assigned to the piano group will receive a training manual and a 61-key portable electric keyboard (MK-2000 by Gear4music) to take home with them for practice at the end of baseline testing.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1.4 \u0026nbsp; \u0026nbsp;Piano Intervention\u003c/h3\u003e\n\u003cp\u003eThe intervention comprises 8 training videos with an accompanying manual. The manual provides sheet-music for weekly exercises, simple explanations of new musical terms and introductory guidance on how to implement good practice routines (see Appendix A for syllabus). Practice recommendations are included because qualitative data from previous studies indicated that older music novices did not know how to effectively use practice time to maximise progress (68,69). Participants are required to practice for 30 minutes, 5 days per week for 8 weeks (2.5 hours per week, or 20 hours total), and to log the duration and content of their practice sessions in diaries provided.\u003c/p\u003e\n\u003cp\u003eThe content of the piano training intervention was designed by FR, who holds a piano teaching diploma from the Royal Irish Academy of Music. As EF are hypothesized to have developed as an extension of the motor control system, exercises for training bimanual coordination skills and promoting hand independence were specifically chosen. For example, exercises involve: hands playing with opposing dynamics, rhythms and articulation; learning scales in similar (both hands ascending and descending together) and contrary (both thumbs beginning on the same note and then moving in opposite directions) motions; arpeggios (training in spatial distances between the root, third, fifth and octave notes of a scale); Hanon exercises for dexterity (patterns which build strength in every finger which are repeated across 3 octaves ascending and descending); and reading music (known as \u0026ldquo;sight-reading\u0026rdquo;). Familiar melodies were specifically chosen for sight-reading exercises. Some were adapted from Alfred\u0026rsquo;s All-In-One Course, a book designed specifically for Adults Beginners, and found to be effective in previous healthy older adults (70,71). Following the initial pilot study, the intervention manual was updated based on participant feedback to include more explanations of musical terms. Exercises based on NMT principles were also added to the new version of the intervention, whereby metronome and \u0026ldquo;play along\u0026rdquo; activities were designed to develop timing mechanisms hypothesized to be linked with EF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe videos are approximately 20 minutes long each. Week 1 introduces the right hand, week 2 introduces the left hand, and then participants learn to play with hands together from Weeks 3-8. Metronome exercises are introduced in week 5 and become progressively difficult in weeks 6-8. The videos are sent via WeTransfer, an online transfer service for large files (https://wetransfer.com/), at a set time each week. This is to stagger the training content so that participants can focus on improving exercises for a given week before newer more challenging material is introduced. However, participants are encouraged to revisit exercises from previous weeks until they can be played comfortably, using the metronome as a guide. An advantage of using WeTransfer is that it notifies the researcher every time a video is downloaded by a recipient, and percentage of downloaded videos will be used as a measure of adherence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe videos consist of 3 horizontal planes: the upper plane displays the music notation of the current exercise; the middle plane displays a virtual keyboard where piano keys turn blue to indicate which note is being played; and the lower plane is a recording of the demonstration from an overhead angle (\u003cem\u003eFigure 1\u003c/em\u003e). Musical Instrument Digital Interface (MIDI) output from a Roland RD-170 (88-note) electric piano was used to record the sessions. Open Broadcasting Software (https://obsproject.com/), a High Definition (HD) webcam and tripod were used to record the demonstrations. The virtual keyboard was obtained from MIDIculous free software (Gospel Music insert URL) and the music notation was generated using MuseScore (https://musescore.org/en).\u003c/p\u003e\n\u003cp\u003eEach video begins with a new finger exercise, followed by two new sight-reading exercises in the form of short familiar songs which gradually increase in difficulty in rhythm and number of notes for each hand (e.g., \u0026ldquo;O when the Saints\u0026rdquo;, \u0026ldquo;Happy Birthday\u0026rdquo;, \u0026ldquo;You are my Sunshine\u0026rdquo;). The videos first demonstrate the right hand, then the left hand, and then both hands together. Participants are signalled to pause the video to practice hands separately before attempting to play together.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Figure 1]\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e2.1.5 \u0026nbsp; \u0026nbsp;Passive Control\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe passive control group will be instructed to go about their lives as normal for the 8-week period, and not to engage in any musical activities, such as taking music classes, choir singing or dance classes, or in any cognitive training interventions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Figure 2]\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e2.1.6 \u0026nbsp; \u0026nbsp;Outcome Measures\u003c/h3\u003e\n\u003cp\u003ePrimary outcome measures are concerned with feasibility, measured in terms of recruitment, retention, acceptability and adherence (Table 1). Screening measures and secondary outcome measures concerning cognitive and motor performance are presented in Table 2. Secondary outcome measures concerning grey and white microstructure measurements are presented in Table 3.\u003c/p\u003e\n\u003cp\u003eParticipants will be screened for cognitive impairment, baseline musical ability and depression. Cognitive and motor testing will last for approximately 2.5 hours. Assessments were selected to measure the three core EF (inhibitory control, attention switching and working memory capacity and updating) (3), processing speed and verbal memory, because these are the domains most affected in ageing (1). Fine and gross motor ability will also be measured using the Q-motor battery. A near-transfer measure of piano performance is included to evaluate improvements in piano playing. Further details are provided in Table 2.\u003c/p\u003e\n\u003ch4\u003e2.1.6.1 \u0026nbsp; MRI brain morphology and microstructure\u003c/h4\u003e\n\u003cp\u003eA 3 Tesla MRI Siemens Connectom system with ultra-strong (300mT/m) gradients will be used to collect MRI data. Microstructural white and grey matter properties of neurite and soma density will be estimated using ms-HARDI (62) data, with maximum b-value = 6,000 s/mm\u003csup\u003e2\u003c/sup\u003e. Full details of MRI protocols, including their acquisition parameters are provided in Table 3. MRI protocols take 30 minutes in total to acquire, and have been previously piloted in healthy adults in the Welsh Advanced Neuroimaging Database study (WAND; McNabb et al., under review; https://git.cardiff.ac.uk/cubric/wand).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrey matter regions of interest (ROIs) include areas of the auditory system previously shown to be related to music in cross-sectional and longitudinal research: Heschel\u0026rsquo;s gyrus, Heschel\u0026rsquo;s sulcus, cerebellum, caudate nucleus, primary motor cortex, superior parietal lobule, hippocampus.\u0026nbsp;White matter tracts of interest (TOI) include the arcuate fasciculus, fornix, anterior corpus callosum, corticospinal tract.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Primary outcome measures: feasibility (recruitment, retention, acceptability and adherence)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Outcome Measures: Feasibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eFeasibility will be assessed using the following measures: recruitment, retention, acceptability and adherence.\u003c/p\u003e\n \u003cp\u003e1. \u003cem\u003eRecruitment\u003c/em\u003e will be measured as the number of participants who are both deemed eligible for the study and who consent to taking part out of those who receive the participant information sheet. Reasons for ineligibility and declining to take part will be recorded in a screening log.\u003c/p\u003e\n \u003cp\u003e2. \u003cem\u003eRetention\u003c/em\u003e will be measured by the number of participants who complete the study after attending the follow-up visit after 8 weeks. Reasons for withdrawal and loss to follow-up will be recorded.\u003c/p\u003e\n \u003cp\u003e3. \u003cem\u003eAcceptability\u0026nbsp;\u003c/em\u003ewill be measured using a self-report questionnaire consisting of\u0026nbsp;27 Likert-scale assessing responses to the quality, difficulty level and content of the training and 4 qualitative questions on what could be improved in the training (Appendix C).\u003c/p\u003e\n \u003cp\u003e4. \u003cem\u003eAdherence\u003c/em\u003e to the training will be measured by calculating frequency and duration of practice recorded in each participant\u0026rsquo;s practice diary. Participants are reminded weekly to record practice sessions as accurately as possible.\u003c/p\u003e\n \u003cp\u003eThe following predefined traffic lights system will determine feasibility success: green (all feasibility rates\u0026nbsp;\u0026ge;70%) will indicate that the trial was successful; amber (no rates \u0026lt;40%, but at least one \u0026lt;70%) that the project requires review and design changes; and red (at least one rating \u0026lt;40%) that the intervention and a future RCT are not feasible.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Screening measures and secondary outcome measures\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreening measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eTelephone Interview for Cognitive Status (TICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe TICS is a short reliable measure of global cognition which can be administered remotely. Scores correspond to those on the Mini-Mental State Exam (72). Scores \u0026le; 31/41 are indicative of dementia (Elliott et al., 2020).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eTest of Premorbid Functioning (TOPF; 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe TOPF is used as a measure of verbal IQ, and involves reading a list of 70 words with unusual grapheme to phoneme translations in ascending order of difficulty. The number of correctly pronounced words is recorded.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eProfile of Music Perception Skills (micro-PROMS; 74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe micro-PROMS is a novel 10-minute musical test battery which provides an objective measure of perceptual musical skills. Participants are asked if a short musical sequence is the same/different to a sample sequence, varying in pitch, rhythm, tempo, melody, tuning and timbre.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003ePatient Health Questionnaire-8 (PHQ-8; 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe PHQ-8 consists of 8 questions which is used to screen for depression in medical settings. Scores \u0026gt;10 are indicative of possible major depression. One question regarding thoughts about self-harm and suicide was omitted to avoid unnecessary participant distress. PHQ correctly diagnoses major depression with 92% sensitivity and excludes this condition with 80% specificity (76). Cronbach alpha values range between .86 and .89 (77).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary Outcome Measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCognitive and Motor Assessments\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eDigit-symbol test from WAIS-III (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe digit-symbol test is a paper and pen test used as a measure of processing speed. Participants must match symbols to numbers based on a given key within a 90-second time limit. The number of correct responses will be recorded.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eGo/No-Go test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe Go/No-Go test measures the ability to suppress dominant responses. A participant must respond as quickly as they can when one stimulus appears (e.g., yellow circle) but must suppress the automatic response when a different stimulus is presented (e.g., blue circle). Response accuracy and latencies are recorded.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eStroop test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe Stroop test measures the ability to ignore response interference. A computerised version of the Stroop test will be administered via PsychoPy (79). Colour words are presented in ink which is either congruous (e.g., the word \u0026ldquo;blue\u0026rdquo; printed in blue ink) or incongruous (e.g., the word \u0026ldquo;blue\u0026rdquo; printed in red ink). Depending on instructions, participants must either respond to the colour meaning of the word or the colour ink in which it is written. Two practice blocks of 24 trials will be presented before two experimental blocks of 72 trials.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eDigit span (forward and backward) from (78)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eA random sequence of numbers is read aloud to participants and they must recall the numbers either forwards or backwards, depending on the trial. Number of correctly recalled numbers is taken as a measure of working memory capacity. The test is discontinued if a participant answers two sequential trials of the same number of items incorrectly.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eN-Back\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe \u003cem\u003eN-\u003c/em\u003eback involves the continuous presentation of a series of stimuli (e.g., letters). The participants must indicate whether the current stimulus is the same as \u003cem\u003eN\u0026nbsp;\u003c/em\u003esteps earlier in the sequence (80). Number of correct responses versus false alarms and misses is a measure of updating working memory. The \u003cem\u003eN\u003c/em\u003e-back will be administered on the Psychology Experiment Building Language (PEBL; (79). Participants will respond to a sequence of letters, then to spatial location of squares on a grid. A dual-task block of trials will also be included where participants must respond to both letters and squares.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eTrail-Making Test (TMT; Reitan, 1958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe TMT consists of two parts: A and B. Part A measures visual attention. Participants are instructed to join numbered circles from 1-25 in ascending order as quickly as possible. Part B measures attention-switching, Participants must alternate between joining numbers and letters (1-A-2-B etc.) as quickly as possible. Performance is measured by completion times for both parts. The TMT is highly correlated (0.72-0.80) with other measures such as the Wechsler Adult Intelligence Scale \u0026ndash; III (81),\u0026nbsp;indicating excellent construct validity. Inter-rater reliability for TMT is also high (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= -.96).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eD-KEFS Verbal fluency (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe Delis-Kaplan Executive System Verbal Fluency subtest assesses the ability to generate words, comprising three subtests: i) letter fluency; ii) category fluency and iii) category switching. In i) letter fluency trials, participants are instructed to name as many words as possible that begin with a given letter, excluding names of people, places or numbers (standard form: F, A, S; alternate form: R, C, M). In ii) category fluency trials, participants are instructed to generate as many words as they can which belong to a given category (standard form: boy\u0026rsquo;s names and animals; alternate form: girl\u0026rsquo;s names and supermarket items). In the iii) category switching trials, generated words must alternate between two pre-established categories (standard form: fruit and furniture; alternate form: vegetables and musical instruments). All trials have a 60-second time limit. Reliability coefficients reported for D-\u003c/p\u003e\n \u003cp\u003eKEFS verbal fluency subtests are .88 (letter fluency), .82 (category fluency) and .51 (category switching) (82).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eCalifornia Verbal Learning Test II (CVLT; (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe CVLT-II consists of 5 trials where a 16-word list consisting of 4 categories (e.g., ways of travel, animals, vegetables, furniture) is read aloud, and the participant must try to recall as many words as they can in each trial. This is followed by immediate (after an alternate \u0026ldquo;distractor\u0026rdquo; list is read aloud) and long-delay (after 20 minutes) trials in both free (without prompts) and cued (provided four categories) recall formats. Inter-rater reliability ranges from 0.80-0.96 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003eQuantitative Motor (Q-Motor; 91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eThe Q-motor will be used to measure changes in fine and gross motor abilities. This is an assessment battery measuring fine motor functions of finger and foot tapping, synchronized tapping with a metronome, tapping continuation without metronome, and dual-task motor ability of tapping with one hand and pointing with the other.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.2775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePiano Performance (near transfer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68.7225%;\"\u003e\n \u003cp\u003eAll participants will play two 5-finger scales starting on C and D at 60 and 80 beats per minute (bpm). Following the intervention, piano participants will also play scales of C and G major hands separately and Ode to Joy (right hand only) with metronome at 60 bpm, to measure learning acquired during the intervention. Data will be collected using a MIDI keyboard (MPK Mini MK3 25-key MIDI keyboard controller) connected to the open-source audio recording software, Reaper (www.reaper.fm/). The recordings will be exported as MIDI files, a simple and compact format that encodes the duration, pitch, and velocity (i.e., loudness) of each note played.\u003c/p\u003e\n \u003cp\u003eTo assess performance accuracy, a reference MIDI file containing the correct sequence of notes with ideal durations will be used to compare against the participants\u0026apos; performances. Discrepancies between the reference and the actual played notes will be analyzed using a custom Python script, \u0026quot;midi-eval,\u0026quot; designed specifically for the PIANO-Cog experiment. Midi-eval processes each performance using well-known Python MIDI libraries, \u0026quot;pretty_midi\u0026quot; (Raffel \u0026amp; Ellis, 2014) and \u0026quot;music21\u0026quot; (84).\u003c/p\u003e\n \u003cp\u003ePerformance will be assessed by computing note accuracy (the % of correct pitches played) and time accuracy. Time accuracy will be evaluated by measuring onset time error (discrepancies in onset times between reference and participant\u0026rsquo;s performance) and note duration errors (how long the note is held compared to the reference). \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3: MRI sequences and parameters applied to assess microstructural changes (secondary outcome measures)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquisition parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51.2903%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRationale and outcome indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMagnetization-prepared rapid gradient-echo (MP-RAGE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4624%;\"\u003e\n \u003cp\u003e1 mm\u003csup\u003e3\u003c/sup\u003e resolution, FOV: 256 x 256, TR = 2300ms, TE = 2ms, TI = 857ms, flip angle: 9; Duration ~7min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51.2903%;\"\u003e\n \u003cp\u003e3-dimensional T\u003csub\u003e1\u003c/sub\u003e-weighted anatomical image\u003c/p\u003e\n \u003cp\u003e\u0026middot; To segment regions of interests in the basal ganglia, auditory cortex, sensorimotor cortices, hippocampus and cerebellum\u003c/p\u003e\n \u003cp\u003e\u0026middot; to provide a reference map for all microstructural outcome maps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulti-shell High Angular Resolution Diffusion Imaging (msHARDI;\u0026nbsp;\u003c/strong\u003e62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4624%;\"\u003e\n \u003cp\u003e2 mm\u003csup\u003e3\u003c/sup\u003e resolution; FOV: 220 x 200; matrix size: 110 x 110 x 66; TE/TR = 59/3000ms; \u0026delta;/\u0026Delta;: 7/24ms; b-values = 0 (14 volumes), 500 (30 directions), 1200 (30 directions), 2400 (60 directions), 4000 (60 directions), and 6000 (60 directions) s/mm\u003csup\u003e2\u003c/sup\u003e; Duration ~20 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51.2903%;\"\u003e\n \u003cp\u003eMulti-shell diffusion weighted imaging data\u003c/p\u003e\n \u003cp\u003e\u0026middot; to provide multi-tissue constrained spherical deconvolution-based (Jeurissen et al., 2014)\u0026nbsp;tractography to reconstruct white matter pathways of interest (corpus callosum, cortico-spinal tract, arcuate fasciculus, fornix)\u003c/p\u003e\n \u003cp\u003e\u0026middot; to model of white matter tissue components using the Neurite Orientation Dispersion and Density Imaging (NODDI;\u0026nbsp;65)\u0026nbsp;providing the isotropic signal fraction (ISOSF) as an estimate of free water, intracellular signal fraction as an estimate of axon density, and the orientation dispersion index (ODI) as an estimate of axon orientation and dispersion.\u003c/p\u003e\n \u003cp\u003e\u0026middot; to model grey matter tissue properties using the Soma And Neurite Density Imaging (SANDI;\u0026nbsp;63)\u0026nbsp;model which provides estimates of soma density (soma\u0026nbsp;signal fraction) and size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eAssessments for all participants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eScreening Phone Call\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eBaseline study visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003ePIANO-Cog training intervention (completed at home) or control time period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eFollow-up study visit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eDay 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e8-week intervention period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eWeek 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eCohort Descriptive information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eTelephone Interview for Cognitive Status (TICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eTest of Premorbid Functioning (TOPF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eMicro-PROMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eSelf-report questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePatient Health Questionnaire-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePIANO-Cog participant evaluation survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eVerbal tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eCalifornia Verbal Learning Test \u0026ndash; II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eD-KEFS Verbal fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eDigit-span test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePaper and pencil tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eTrail-Making Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eDigit-symbol test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eComputerised tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eN-back Audio (letters)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eN-back Visuospatial (square changing positions in grid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eN-back dual (audio and visuospatial)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eStroop test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eGo/No-go test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eQ-Motor assessments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eSpeeded finger tapping (left/right index finger)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eFinger metronome tapping (left/right index finger)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePointing and tapping (dominant hand)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eDual (pointing and tapping with dominant hand, speeded finger tapping with opposite hand)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePiano Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003e5-finger scales beginning on C and D at 60 and 80 bpm, hands separately\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eScale of C and G major, hands separately at 60 bpm (piano group only)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eOde to Joy (piano group only)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eBrain imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eT1-weighted MPRAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003emsHARDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003ePIANO-Cog group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003ePIANO-Cog training intervention supported by email and telephone reminders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.5455%;\"\u003e\n \u003cp\u003eUsual activities, but without engagement in music-based activities or cognitive training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.6364%;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eD-KEFS, Delis-Kaplan Executive Function System; Micro-PROMS; Profile of Music Perception Skills\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3: Schematic overview of the two-arm randomised controlled feasibility trial\u003c/p\u003e\n\u003ch3\u003e2.1.7 \u0026nbsp; \u0026nbsp;Sample size\u003c/h3\u003e\n\u003cp\u003eIn line with the Consolidated Standards of Reporting Trials (CONSORT) statement extension for randomized pilot and feasibility trials (85), formal power calculations have not been performed. We plan to recruit 50 cognitively healthy individuals from the local area, a target chosen pragmatically based on the required level of participant engagement and available resources. This sample size will enable estimation of recruitment, retention, and adherence rates within a 95% binomial confidence interval, with a margin of error of no more than \u0026plusmn;15 percentage points, regardless of point estimate.\u003c/p\u003e\n\u003ch3\u003e2.1.8\u0026nbsp; \u0026nbsp;\u0026nbsp;Recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants over the age of 50 years will be recruited from Cardiff and surrounding areas via poster advertisements in public places, community groups for over-50s, local active retirement Facebook groups, and the CUBRIC participant recruitment website (https://psychologystudies.cardiff.ac.uk/).\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Data\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003e2.2.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Collection and management\u003c/h3\u003e\n\u003cp\u003eThe study will be conducted in accordance with Good Clinical Practice and the Data Protection Act 2018. Cognitive and motor data will be collected on hard-copy scoring sheets and electronically via PEBL (86) and PsychoPy (79) on a computer in a quiet testing laboratory at CUBRIC. Behavioural data collected using hard-copy scoring sheets will be stored in a locked cupboard in an access-restricted office in CUBRIC. Q-MedX software will automatically capture Q-Motor data, which will be stored on a password-protected laptop.\u003c/p\u003e\n\u003cp\u003eBehavioural data from PsychoPy tests will be cleaned by removing practice trials and extreme or outlier values and subsequently analysed using R statistical software. Extreme values and outliers (scores \u0026gt;3 standard deviations +/- the mean) will be identified using the rstatix R package (87) and will be reported and excluded where appropriate.\u003c/p\u003e\n\u003cp\u003eMRI data will be collected according to CUBRIC standard operating procedures (SOP) including MRI safety and operation guidelines by trained MR operators. MRI data will be acquired on the 3 Tesla Siemens Connectom system at CUBRIC and stored on the XNAT system.\u003c/p\u003e\n\u003ch3\u003e2.2.2 \u0026nbsp; \u0026nbsp;Statistical methods\u003c/h3\u003e\n\u003cp\u003eThe study will be reported in line with CONSORT reporting requirements for pilot and feasibility trials (85). Since this is a feasibility trial, it is not formally powered to test for effectiveness of the intervention whilst controlling for type 1 error. The primary purpose of the trial is to assess the feasibility, by measuring recruitment, retention and adherence rates and acceptability scores of the intervention. Feasibility percentage rates will be calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Recruitment rate = 100 x (number of participants who provided consent / number of participants eligible) %\u003c/p\u003e\n\u003cp\u003e\u0026middot; Retention rate = 100 x (number of participants who complete follow-up testing / number of participants who provided consent)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Adherence rate (frequency) = 100 x (number of days\u0026rsquo; practice logged / 40 days) %\u003c/p\u003e\n\u003cp\u003e\u0026middot; Adherence rate (duration) = 100 x (number of minutes practice logged / (40 days x 30 minutes average session duration = 1,200 minutes) %\u003c/p\u003e\n\u003cp\u003eDescriptive statistics (means and standard deviations) of effect sizes and 95% confidence intervals will be calculated for all secondary outcome measures, listed in Table 2. \u0026nbsp;Differences in mean and SD of participants absolute and percentage changes from baseline will be calculated to provide estimates of effect size and variability of performance changes in the two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndependent \u003cem\u003et-\u003c/em\u003etests will be conducted on TICS, age, years of musical experience and musical aptitude to determine any significant differences between piano and control groups at baseline, which could influence the effects of the training. To investigate the effect of piano training on cognitive and motor outcome measures and neuroplasticity, linear mixed models will be conducted in R using the lme4 package (88), with group, time and baseline measures as covariates variables. Statistical methods for handling missing data will be reported if required.\u003c/p\u003e\n\u003cp\u003eFreeSurfer (89) and the \u003cem\u003eFAST\u0026nbsp;\u003c/em\u003etool in FSL (FMRIB Software Library) will be used for the segmentation of grey matter ROIs. Tractography will be carried out on TOIs using MRTrix (90). Linear mixed models will also be used to investigate relationships between cognitive ability and grey and white matter microstructure, measured using metrics of NODDI and SANDI at baseline and follow-up.\u003c/p\u003e\n\u003ch3\u003e2.2.3 \u0026nbsp; \u0026nbsp;Monitoring\u003c/h3\u003e\n\u003cp\u003eCognitive and motor scores will be entered, and constantly monitored for quality against original record forms by the author FR, and regular meetings will take place with the research supervisor (CMB). Reasons for withdrawal from the study will be recorded. Publications will report reasons for any attrition or missing data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe study received ethical approval from Cardiff University Ethics Committee on 20\u003csup\u003eth\u003c/sup\u003e June 2024 (EC.23.05.16.6801GRA). Plans for protocol modifications will be discussed with the research supervisor and submitted as amendment to ethics committee if required. Upon expression of interest, a participant information sheet will be emailed to eligible participants, and they will receive second copy upon their arrival at CUBRIC. Written informed consent will be obtained at CUBRIC in person before any testing takes place. Participants will have the opportunity to ask questions before signing the consent form (Appendix B).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eParticipants will also be asked to provide consent for their anonymised data to be made available on Open Science Framework and to other members of the research team at CUBRIC, and also to be published in academic journals and presented at conferences, with all identifying information removed.\u003c/p\u003e\n\u003ch3\u003eAvailability of data\u003c/h3\u003e\n\u003cp\u003eThe anonymised data will be made available to supervisors, other members of research team (other PhD and post-doctoral researchers and research assistants), ethics committees or monitors, as well as other researchers at CUBRIC who express an interest in using the data to test other hypotheses. Data will be made openly available on Open Science Framework upon publication.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no known competing financial interests or personal relationships that could influence the proposed study.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConfidentiality\u003c/h3\u003e\n\u003cp\u003eAfter providing consent, participants will be assigned a unique study code and CUBRIC ID which will be used to code all of their electronic and hard-copy data. Electronic data will be stored in a password-protected Excel file, on a computer which is also password-protected. Study and CUBRIC ID codes and any personal information will be securely stored for the duration of the study in a locked compartment in CUBRIC. Only members of the research team will have access to confidential files to allow for the matching of recorded data to participants. No paper or electronic data will leave CUBRIC without being completely coded (i.e. identifiable data will be removed). The CUBRIC partition of XNAT (www.central.xnat.org) will be used to securely store MRI data. At the end of the study, anonymised data will be archived, but will still be accessible to the research team. Personal information will not be directly linked to data, and will be destroyed after 15 years as per Cardiff University\u0026rsquo;s Research Records Retention Schedule.\u003c/p\u003e\n\u003ch3\u003eAncillary and post-trial care\u003c/h3\u003e\n\u003cp\u003eNo harm is anticipated from participation in the trial, but in case any difficult emotions arise from taking part, participants will be encouraged to speak with their general practitioner (GP) and will be made aware of mental health services that they can access for support.\u003c/p\u003e\n\u003cp\u003eMRI scanning is non-invasive and once appropriate screening and safety measures are in place, there are no known significant adverse health effects. Participants will be made aware at the beginning of scanning that rare side effects of MRI could occur, which include peripheral nerve stimulation, dizziness or mild nausea. Participants will be advised to press the emergency squeeze ball to alert the operators if there is anything wrong. If a participant experiences any of these symptoms scanning, this will be closely monitored and documented. Although these side effects can be uncomfortable, they resolve themselves when the participant leaves the magnetic field.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants will also be informed that MRI operators and cognitive test administrators are not medical doctors, and that none the tests or scans conducted will be used for any medical or diagnostic purposes. If a participant has any health concerns, they will be encouraged to consult their GP. Participants will be informed that the scans will not be routinely reviewed to detect abnormalities. In the case where the MR operator has any concern about a scan, an appropriate consultant, such as a neuroradiologist, will be asked to examine the scan. If the neuroradiologist feels it to be appropriate, a report will be sent to the participant\u0026rsquo;s GP with their consent.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDissemination policy and impact\u003c/h3\u003e\n\u003cp\u003eThis work is undertaken as part of a PhD project and is intended to be published in academic journals and presented at conferences. The aim of this research is to demonstrate the cognitive and neural benefits of musical training in later adulthood, in the hope of contributing to a body of literature demonstrating a need for public health funding for musical training to help reduce dementia-related costs, prolong independence and quality of life.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research was supported by an Open PhD studentship from the School of Psychology Cardiff University and a National Institue for Health Research (NIHR) and Health and Care Research Wales (HCRW) Advanced Fellowship [NIHR-FS(A)-2022].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAuthors Contributions\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eFR: study conception and design, draft manuscript preparation; CMB: study conception and design, supervision, writing \u0026ndash; review and editing; EE: development of a Python script for analysing and evaluating audio data, writing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHarada CN, Natelson Love MC, Triebel KL. 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Annu Rev Psychol. 2012;63:201\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eVaughan L, Giovanello K. Executive function in daily life: Age-related influences of executive processes on instrumental activities of daily living. Psychol Aging. 2010;25(2):343\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eMelby-Lerv\u0026aring;g M, Redick TS, Hulme C. Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of \u0026ldquo;Far Transfer\u0026rdquo;: Evidence From a Meta-Analytic Review. Perspectives on Psychological Science. 2016 Jul 1;11(4):512\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eKelly ME, Loughrey D, Lawlor BA, Robertson IH, Walsh C, Brennan S. The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Vol. 15, Ageing Research Reviews. 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Vol. 226, Behavioural Brain Research. 2012. p. 579\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eHerholz SC, Zatorre RJ. Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure. Vol. 76, Neuron. 2012. p. 486\u0026ndash;502. \u003c/li\u003e\n\u003cli\u003eThaut M, Hoemberg V. Handbook of neurologic music therapy. Oxford University Press, USA; 2014. \u003c/li\u003e\n\u003cli\u003eBurrai F, Apuzzo L, Zanotti R. Effectiveness of Rhythmic Auditory Stimulation on Gait in Parkinson Disease: A Systematic Review and Meta-analysis. Holistic Nursing Practice. Lippincott Williams and Wilkins; 2021. \u003c/li\u003e\n\u003cli\u003eLee H, Ko B. Effects of Music-Based Interventions on Motor and Non-Motor Symptoms in Patients with Parkinson\u0026rsquo;s Disease: A Systematic Review and Meta-Analysis. Vol. 20, International Journal of Environmental Research and Public Health. MDPI; 2023. \u003c/li\u003e\n\u003cli\u003eCasella C, Bourbon-Teles J, Bells S, Coulthard E, Parker GD, Rosser A, et al. Drumming motor sequence training induces apparent myelin remodelling in huntington\u0026rsquo;s disease: A longitudinal diffusion mri and quantitative magnetization transfer study. J Huntingtons Dis. 2020;9(3):303\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eStreet A, Zhang J, Pethers S, Wiffen L, Bond K, Palmer H. Neurologic music therapy in multidisciplinary acute stroke rehabilitation: Could it be feasible and helpful? Top Stroke Rehabil. 2020 Oct 2;27(7):541\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eKasdan A V., Burgess AN, Pizzagalli F, Scartozzi A, Chern A, Kotz SA, et al. Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review. Vol. 136, Neuroscience and Biobehavioral Reviews. Elsevier Ltd; 2022. \u003c/li\u003e\n\u003cli\u003eHaire CM, Vuong V, Tremblay L, Patterson KK, Chen JL, Thaut MH. Effects of therapeutic instrumental music performance and motor imagery on chronic post-stroke cognition and affect: A randomized controlled trial. NeuroRehabilitation. 2021;48(2):195\u0026ndash;208. \u003c/li\u003e\n\u003cli\u003eThaut MH, Kenyon GP, Hurt CP, Mcintosh GC, Hoemberg V. Kinematic optimization of spatiotemporal patterns in paretic arm training with stroke patients. Vol. 40, Neuropsychologia. 2002. \u003c/li\u003e\n\u003cli\u003eSchneider S, Sch\u0026ouml;nle PW, Altenm\u0026uuml;ller E, M\u0026uuml;nte TF. Using musical instruments to improve motor skill recovery following a stroke. J Neurol. 2007 Oct;254(10):1339\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eAltenm\u0026uuml;ller E, Marco-Pallares J, M\u0026uuml;nte TF, Schneider S. Neural reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy. In: Annals of the New York Academy of Sciences. Blackwell Publishing Inc.; 2009. p. 395\u0026ndash;405. \u003c/li\u003e\n\u003cli\u003eBugos JA, Lesiuk T, Nathani S. Piano training enhances Stroop performance and musical self-efficacy in older adults with Parkinson\u0026rsquo;s disease. Psychol Music. 2021 May 1;49(3):615\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eReitan RM. Validity of the Trail Making Test as an indicator of organic brain damage. . Percept Mot Skills. 1958;8(3):271\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eBernard JA, Seidler RD. Moving forward: Age effects on the cerebellum underlie cognitive and motor declines. Vol. 42, Neuroscience and Biobehavioral Reviews. Elsevier Ltd; 2014. p. 193\u0026ndash;207. \u003c/li\u003e\n\u003cli\u003eMunte 2002 The musician\u0026rsquo;s brain as a model of neuroplasticity. \u003c/li\u003e\n\u003cli\u003eJ\u0026auml;ncke L. The plastic human brain. Vol. 27, Restorative Neurology and Neuroscience. 2009. p. 521\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eChan AS, Ho YC. 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Journal of Neuroscience. 2008 Jul 9;28(28):7031\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eBermudez P, Zatorre RJ. Differences in gray matter between musicians and nonmusicians. Ann N Y Acad Sci. 2005;1060:395\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eElmer S, H\u0026auml;nggi J, Meyer M, J\u0026auml;ncke L. Increased cortical surface area of the left planum temporale in musicians facilitates the categorization of phonetic and temporal speech sounds. Cortex. 2013;49(10):2812\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eJames CE, Oechslin MS, Van De Ville D, Hauert CA, Descloux C, Lazeyras F. Musical training intensity yields opposite effects on grey matter density in cognitive versus sensorimotor networks. Brain Struct Funct. 2014 Jan;219(1):353\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eKarpati FJ, Giacosa C, Foster NEV, Penhune VB, Hyde KL. Dance and music share gray matter structural correlates. 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Brain Cogn. 2024 Mar 1;175. \u003c/li\u003e\n\u003cli\u003eJames CE, Altenm\u0026uuml;ller E, Kliegel M, Kr\u0026uuml;ger THC, Van De Ville D, Worschech F, et al. Train the brain with music (TBM): brain plasticity and cognitive benefits induced by musical training in elderly people in Germany and Switzerland, a study protocol for an RCT comparing musical instrumental practice to sensitization to music. BMC Geriatr. 2020;20(1):1\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003eWorschech F, Altenm\u0026uuml;ller E, J\u0026uuml;nemann K, Sinke C, Kr\u0026uuml;ger THC, Scholz DS, et al. Evidence of cortical thickness increases in bilateral auditory brain structures following piano learning in older adults. Ann N Y Acad Sci. 2022 Jul 1;1513(1):21\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eMarie D, M\u0026uuml;ller CAH, Altenm\u0026uuml;ller E, Van De Ville D, J\u0026uuml;nemann K, Scholz DS, et al. 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Magn Reson Med. 2002 Oct 1;48(4):577\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003ePalombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, et al. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage. 2020 Jul 15;215. \u003c/li\u003e\n\u003cli\u003eLee H, Lee HH, Ma Y, Eskandarian L, Gaudet K, Tian Q, et al. Age-related alterations in human cortical microstructure across the lifespan: Insights from high-gradient diffusion MRI. Aging Cell. 2024; \u003c/li\u003e\n\u003cli\u003eZhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012 Jul 16;61(4):1000\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eCox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, Davies G, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016 Dec 15;7. \u003c/li\u003e\n\u003cli\u003eNazeri A, Chakravart M, Rotenberg DJ, Rajji TK, Rathi X, Michailovich O V., et al. Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan. Journal of Neuroscience. 2015 Jan 28;35(4):1753\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eMacRitchie J, Breaden M, Milne AJ, McIntyre S. Cognitive, Motor and Social Factors of Music Instrument Training Programs for Older Adults\u0026rsquo; Improved Wellbeing. Front Psychol. 2020 Jan 10;10. \u003c/li\u003e\n\u003cli\u003eBugos JA, Wang Y. Piano Training Enhances Executive Functions and Psychosocial Outcomes in Aging: Results of a Randomized Controlled Trial. J Gerontol B Psychol Sci Soc Sci. 2022 Sep 1;77(9):1625\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eBugos JA, Perlstein WM, McCrae CS, Brophy TS, Bedenbaugh PH. Individualized Piano Instruction enhances executive functioning and working memory in older adults. 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The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009 Apr;114(1\u0026ndash;3):163\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eGilbody S, Richards D, Brealey S, Hewitt C. Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): A diagnostic meta-analysis. J Gen Intern Med. 2007 Nov;22(11):1596\u0026ndash;602. \u003c/li\u003e\n\u003cli\u003eSun Y, Fu Z, Bo Q, Mao Z, Ma X, Wang C. The reliability and validity of PHQ-9 in patients with major depressive disorder in psychiatric hospital. BMC Psychiatry. 2020 Sep 29;20(1). \u003c/li\u003e\n\u003cli\u003eWechsler D. WAIS-III administration and scoring manual. San Antomia, TX: Psychological Corporation; 1977. \u003c/li\u003e\n\u003cli\u003ePeirce J, Gray JR, Simpson S, MacAskill M, H\u0026ouml;chenberger R, Sogo H, et al. PsychoPy2: Experiments in behavior made easy. Behav Res Methods. 2019 Feb 15;51(1):195\u0026ndash;203. \u003c/li\u003e\n\u003cli\u003eKirchner WK. 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San Antonio, TX: The Psychological Corporation.; \u003c/li\u003e\n\u003cli\u003eCuthbert M, Ariza C. Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. International Society for Music Information Retrieval; 2010. 637\u0026ndash;642 p. \u003c/li\u003e\n\u003cli\u003eEldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. The BMJ. 2016;355. \u003c/li\u003e\n\u003cli\u003eMueller ST, Piper BJ. The Psychology Experiment Building Language (PEBL) and PEBL Test Battery. J Neurosci Methods. 2014 Jan 30;222:250\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eKassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. . R package version 072. \u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015 Oct 1;67(1). \u003c/li\u003e\n\u003cli\u003eFischl B. FreeSurfer. Vol. 62, NeuroImage. 2012. p. 774\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eTournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Vol. 202, NeuroImage. Academic Press Inc.; 2019. \u003c/li\u003e\n\u003cli\u003eReilmann R, Schubert R. Motor outcome measures in Huntington disease clinical trials. \u003cem\u003eHandb Clin Neurol\u003c/em\u003e 2017;144:209-25. doi: 10.1016/B978-0-12-801893-4.00018-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Cardiff University","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":"Ageing, cognitive intervention, piano training, neurologic music therapy, therapeutic instrument music performance, executive function, fluid intelligence, neuroplasticity, microstructure, MRI, pilot, feasibility","lastPublishedDoi":"10.21203/rs.3.rs-6023523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6023523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAgeing is associated with a loss of fluid intelligence and motor functions which hamper independence and quality of life.\u003cstrong\u003e \u003c/strong\u003eTraining in a musical instrument can improve fluid intelligence and executive function (EF) in older non-musicians but the neural correlates underpinning the benefits remain elusive. The primary aims of this study are to: i) test the acceptability of Piano Instruction for Adult Novices as Online Cognitive Intervention (PIANO-Cog), a novel bespoke 8-week self-guided piano training programme for adults over the age of 50 years; and ii) to test the feasibility (in terms of recruitment, retention and adherence) of a large scale RCT comparing PIANO-Cog to a passive control. Secondary aims of this study are: i) to investigate the effects of online piano training on fluid abilities, EF and motor function; ii) to investigate training-induced microstructural brain changes using ultra-strong gradient (300mT/m) magnetic resonance imaging (MRI) and iii) to investigate how the latter may be linked to cognitive improvements post-training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eA two-armed unblinded RCT will be conducted on 50 healthy non-musician adults over the age of 50. Participants will be randomised to a piano training (PT) or passive control group for 8 weeks, stratified for age and sex. PT participants will receive a training manual and 20-minute video tutorials each week, and will practice 30 minutes, 5 days per week. Control participants will receive no intervention for the 8-week period. Cognitive testing and MRI of the brain will take place before and after the intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eThe primary aim of the trial is to determine the acceptability of PIANO-Cog as an online cognitive intervention for adults over 50 who are non-musicians, and the feasibility of conducting a large-scale RCT in terms of recruitment, retention and adherence. Self-guided music training programmes could provide a cost-effective method of maintaining or improving cognitive and motor functions that individuals can implement in their own homes. Secondary aims are regarding the investigation of positive transfer of piano training to EF and fluid abilities in ageing, and to provide evidence for the relationship between training-induced cognitive enhancements and underlying white and grey matter microstructural changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration: \u003c/strong\u003eISRCTN11023869 (retrospectively registered)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtocol version: \u003c/strong\u003e31/10/2024 version 1.4\u003c/p\u003e","manuscriptTitle":"Protocol for a randomised controlled feasibility trial of Piano Instruction for Adult Novices as Online Cognitive intervention (PIANO-Cog), a novel remote piano training for cognitive and motor functions in older age.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 06:42:31","doi":"10.21203/rs.3.rs-6023523/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":"1ea181f4-cbe7-4a26-b74a-bdc31f9fd430","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44299373,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2025-03-31T06:42:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 06:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6023523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6023523","identity":"rs-6023523","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}