Abnormalities and Plasticity of White Matter Functional Networks in Opioid Use Disorder: A BOLD fMRI Study

Abnormalities and Plasticity of White Matter Functional Networks in Opioid Use Disorder: A BOLD fMRI Study | 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 Article Abnormalities and Plasticity of White Matter Functional Networks in Opioid Use Disorder: A BOLD fMRI Study Jun Liu, Wenhan Yang, Xinwen Wen, Junxuan Wang, Zhe Du, Longtao Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7980274/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 This study investigated the organization and neural activity of white matter functional networks (WMFNs) in individuals with opioid use disorder (OUD) before and after detoxification, compared with healthy controls (HC). Using a data-driven k-means clustering approach, we analyzed BOLD signals in the white matter of baseline OUD (OUD1), follow-up OUD after 8 months of detoxification (OUD2), and HC groups. Results revealed that OUD1 exhibited eight distinct WMFNs, while OUD2 and HC showed six and seven WMFNs, respectively. Notably, the deep frontoparietal network in OUD1 was fragmented into the superior longitudinal fasciculus and anterior corona radiata networks but partially recovered in OUD2, resembling HC. Additionally, OUD1 displayed hyperactivation in the deep frontal network and hypoactivation in the frontotemporal parietal network, with the latter negatively correlated with years of opioid use (r = -0.306, p = 0.039). These findings suggest that OUD disrupts white matter functional connectivity, while prolonged abstinence promotes partial network restoration, highlighting the brain’s neuroplastic potential. This study provides novel insights into the neural mechanisms of OUD and recovery, supporting the development of targeted rehabilitation strategies. Health sciences/Biomarkers/Predictive markers Health sciences/Diseases/Psychiatric disorders/Addiction Health sciences/Biomarkers/Diagnostic markers White Matter Functional Networks (WMFN) opioid use disorder (OUD) abstinence MRI Neural plasticity Introduction The impact of Opioid Use Disorder (OUD) on brain white matter is a significant topic within the domain of medical research. White matter, comprising the neural fibers responsible for information transmission within the brain, is integral to cognitive function and behavioral control. The use of disorder substances such as heroin can disrupt the integrity of brain white matter, leading to cognitive impairments and neuropathological changes [ 1 ]. With the advancement of MRI technology, particularly the application of Diffusion Tensor Imaging (DTI), researchers are now able to more precisely detect and assess the microstructural changes in the white matter of OUD [ 2 ]. These techniques facilitate our understanding of the effects of OUD on brain structure and function, as well as the plasticity and potential for recovery of the brain following detoxification. Utilizing MRI technology to detect changes in the white matter of OUD before and after detoxification is of significant importance. Firstly, it aids in identifying structural brain damage caused by OUD, including the impairment of white matter fibers [ 3 ]. These injuries may be associated with cognitive impairments and emotional regulation issues in addicts [ 4 ]. Secondly, by comparing pre- and post-detoxification MRI outcomes, we can assess the plasticity and potential for recovery of the brain's white matter. Some studies indicate that after long-term detoxification, there is an improvement in the white matter integrity of certain brain regions in OUD [ 5 ], suggesting that the brain possesses a certain degree of self-repair ability. Moreover, understanding the recovery of the brain post-detoxification is crucial for developing effective treatment and rehabilitation plans. For instance, identifying white matter integrity indicators related to relapse prediction can help optimize treatment methods [ 6 ]. In recent years, functional magnetic resonance imaging (fMRI) technology has made significant strides in exploring the functional activities within brain white matter (WM) [ 7 ]. WM, comprising the neural fiber bundles that connect various brain regions, plays a crucial role in the transmission of neural information. Traditional fMRI research has primarily focused on the activity of gray matter (GM); however, accumulating evidence suggests that the Blood Oxygen Level-Dependent (BOLD) signals within WM can also be reliably detected and are intimately associated with neural activity [ 8 , 9 ]. These findings are of paramount importance for comprehending the organizational structure and functional connectivity of brain networks. The investigation into WM BOLD signals has unveiled their role in the functional networks of the brain. Studies indicate that WM BOLD signals not only reflect neural activity but are also correlated with the structural connectivity of white matter [ 10 ]. These signals are detectable not only at rest but also exhibit structurally specific temporal correlations during the performance of specific tasks [ 11 ]. Furthermore, the anisotropy of WM BOLD signals suggests that the microstructural BOLD effects within white matter tracts can be quantified through Functional Connectivity Tensor (FCT) analysis [ 12 ]. The exploration of WM BOLD signals holds potential value for understanding the pathophysiological mechanisms of neuropsychiatric disorders. For instance, research on OUD has shown that alterations in white matter networks may be associated with the recovery of brain function following detoxification. Studies indicate that the white matter networks of OUD who have undergone detoxification tend to converge towards those of healthy controls, suggesting that the restoration of white matter networks may be related to the improvement of addictive behaviors [ 13 ]. These findings afford a novel perspective for investigating white matter dysfunction in neuropsychiatric disorders such as addiction. Investigators have employed resting-state fMRI to examine the functional white matter networks in young smokers [ 13 ], providing a comparative methodology for studying white matter network alterations in OUD. In summary, research into WM BOLD signals affords us a fresh perspective for understanding the functional networks of the brain and the pathophysiological mechanisms of neuropsychiatric disorders. By integrating various MRI techniques, we can obtain a more comprehensive assessment of changes in white matter networks and explore their correlations with brain function and behavior. In this investigation, we employed a data-driven methodology to delve into the organization and the intensity of functional neural activity within white matter functional networks (WMFNs) in individuals with OUD. K-means clustering was utilized to distinctly demarcate the WMFNs of both the OUD group and the Healthy Controls (HCs), as well as to differentiate between the baseline OUD group and the follow-up OUD group [ 14 ]. The functional neural activities of the WMFNs were compared between the three groups. Correlation analyses were also performed to investigate the relationships between the intensity of functional activities within WMFNs and the clinical indicators of heroin use. We posited two hypotheses: (1) baseline OUD group would exhibit distinct WMFN patterns compared to HC/follow-up OUD group, and (2) altered WMFNs might be correlated with clinical indicators of OUD. It is our aspiration that this study will offer a more profound understanding of the neural mechanisms underlying Opioid/heroin Use Disorder from the perspective of WMFNs. Materials and methods 3.1 Participants One hundred and eight participants who had a history of opioid use were recruited from drug rehabilitation clinics in Xinkaipu, PingTang, and Zhuzhou (three cities in Hunan Province, China) between March 2017 and December 2018. All the participants (n = 108) tested positive for heroin on a urine test and satisfied the diagnostic criteria for opioid use disorder according to the fifth version of the Diagnostic and Statistical Manual on Mental Disorders (DSM-V). An additional inclusion criterion for the OUD was a negative urine test for methamphetamine and ketamine. All the participants were admitted to the detoxification center and, without methadone maintenance treatment, received 8 months of education and physical training. All of them signed written consent forms and participated in the first MRI scan. Among these initial participants, 61 were invited to finish a follow-up interview and a second MRI scan approximately 8 months later. Fifteen participants were left out because of excessive head movements or low-quality MRI data. Eventually, forty-six individuals with OUD (23 males and 23 females, age: 43.10 ± 6.95 years) were included in this study. Years of opioid (Heroin) use from each participant of OUD were recorded as clinical indicators. We recruited 42 healthy control (HC) participants via WeChat, flyers, etc. All HCs had a negative urine test for heroin, methamphetamine and ketamine. All of the participants were right-handed and of Han Chinese ancestry. None of the participants had previous diagnoses of structural brain diseases, seizures, head trauma, mental or behavioral disorders, or MRI contraindications. Table 1 shows the demographic characteristics of the OUD and HC groups. This study was approved by the Institutional Review Board of the Second Xiangya Hospital, Central South University (number: 8167071216). The experimental procedures complied with the ethical guidelines of the Declaration of Helsinki. Written informed consent was obtained from participants after the experimental procedure was explained and after they read the consent form. 3.2 Image acquisition All MRI data were gathered at the Second Xiangya Hospital of Central South University in Changsha, China, by means of a 3T MRI scanner (MAGNETOM Skyra, Siemens) equipped with a 32-channel head coil. In order to generate high-quality MRI data, participants were told to lie still with their eyes open and their heads restrained by padding and a head-restraining belt during the MRI scan. T1-weighted imaging and T2-weighted imaging scans were employed to exclude obvious structural lesions in the brain. A 3D rapid gradient echo sequence was utilized to acquire high -resolution T1-weighted MRI scans with these parameters: repetition time (TR) = 1,450 ms, echo time (TE) = 2.0 ms, inversion time (TI) = 900 ms, field of view (FOV) = 256×256 mm², slice thickness = 1 mm, flip angle = 12°, voxel size = 1×1×1 mm³, and number of slices = 176. The parameters of the resting - state fMRI were as follows: TR = 2,000 ms, TE = 30 ms, flip angle = 80°, FOV = 220 × 220 mm², slice thickness = 4 mm, and number of axial slices = 36. 3.3 Image preprocessing The Data Processing Assistant for Resting - State fMRI ( http://rfmri.org/DPARSF ), Statistical Parametric Mapping toolkits (SPM12, http://www.fil.ion.ac.uk/spm ), and open MATLAB scripts ( https://www.beyropsychiatrylab.com/codes_wm ) were used to perform image preprocessing. The first 10 volumes of fMRI scans were discarded. Subsequently, slice-timing correction and realignment were carried out. Then, T1 anatomical images were coregistered to the functional images and further segmented into tissue probability maps of white matter, gray matter, and cerebrospinal fluid (CSF) by means of a diffeomorphic nonlinear registration algorithm in SPM12. Linear trends were removed to rectify signal drift. The mean CSF signals (95% threshold) and 24-parameter motion parameters (six motion parameters, their values at the previous time point, and the 12 corresponding squared values) were regressed out from the functional data. The mean white matter and global brain signals were not regressed out in order to avoid eliminating the meaningful neural signals [ 15 ]. To reduce the potential impacts of head motion, temporal scrubbing (framewise displacement > 0.2 mm) was also applied. Bandpass filtering (0.01-0.08Hz) was carried out to minimize high-frequency non-neuronal noise. To lessen the partial volume effect, spatial smoothing (with a 4-mm full-width at half-maximum Gaussian kernel) was separately applied to the white matter and gray matter images. Finally, the functional images were spatially normalized into the Montreal Neurologic Institute space through T1 segmentation and were resampled into 3×3×3 mm 3 . 3.4 Creation of group-level white matter templates We utilized the segmentation outcomes of each participant to get a mask for voxel selection in group clustering. Every voxel was recognized as white matter, grey matter, or cerebrospinal fluid (CSF) according to the segmented maximum probability graph. Subsequently, all the masks from the participants were averaged to acquire the proportion of participants classified as white or grey matter in each voxel. Voxels recognized as white matter in over 60% of the participants were employed to generate the stencil. Then, we compared the resultant masks with the functional data and eliminated the voxels identified as white matter along with functional data from less than 80% of the participants (such as parts of the medulla and spinal cord). We used the Harvard-Oxford Atlas to label the grey matter voxels within the thalamus, caudate nucleus, putamen, globus pallidus, and nucleus accumbens (excluded from the white matter mask) as grey matter voxels. [ 16 , 17 ] 3.5 Neural activity analysis of WMFNs In this study, a data-driven clustering method was used to separately create WMFNs for the baseline OUD and HC groups. According to previous researches [15 , 18] , firstly, two sets of white matter masks were created to select the voxels for clustering in individuals with OUD and healthy control individuals respectively. More precisely, we only retained voxels that were determined to be white matter and covered more than 80% of the functional data of participants. Next, a voxel-wise Pearson correlation matrix of the white matter was calculated for each participant. These individual-level correlation matrices were then averaged for each group, yielding two group-level correlation matrices. Finally, the K-means clustering method was used to cluster white matter voxels with similar functional connections for the two group-level correlation matrices [ 19 ]. The number of clusters was set between 2 and 22, and the Dice coefficient was used to evaluate cluster stability, with a value greater than 0.9 as the evaluation criterion [ 15 ]. A power spectrum analysis has been widely used to detect significant differences in signal amplitude across different neurological regions and pathological states of gray matter [ 20 , 21 ]. We used this method to analyze the neural activity of white matter networks in OUD group. Fourier transform was performed for each white matter network of each participant to extract each frequency amplitude. Frequency-power plots for each network were generated by averaging the amplitudes of all participants within each group. For the baseline and follow-up OUD group, the method is the same as above. 3.6 Statistical analysis For each white matter network, the average amplitude of all participants was taken to represent the level of network activity. A two-sample t-test was carried out to detect the differences in neural activity between the baseline OUD group and the HC group, and a paired t-test was conducted to detect the differences in neural activity between the baseline and follow-up OUD groups. The false discovery rate (FDR) correction was employed to deal with multiple comparisons among multiple white matter networks. P < 0.05 was utilized to determine significance. Pearson correlation analysis was applied to clarify the relationships between the abnormal neural activity of white matter fiber networks (WMFNs) and the clinical indicators of years of drug use. We used the false discovery rate (FDR) correction to handle multiple correlation analysis. P < 0.05 was used to determine significance. Results 4.1 Clustering results of WMFNs Using the K-means clustering method with the white matter correlation matrix, eight WMFNs were discovered in the baseline OUD group (Fig. 1 A); six WMFNs were identified (Fig. 1 B) in the follow-up OUD group; and in the HC group, seven WMFNs were found (Fig. 1 C). Subsequently, we qualitatively defined the WMFNs based on their corresponding relationships with the known resting-state grey matter networks [ 22 ]. Eventually, the frontotemporal parietal, deep frontal, inferior corticospinal-cerebellar, visual, and somatomotor networks were observed in all three groups. It is worth noting that, compared to the HC and follow-up OUD groups, the baseline OUD group was lacking a deep frontoparietal network and seemed to be disrupted into the superior longitudinal fasciculus network and anterior corona radiate network. The WMFNs scores for each group are shown in Fig. 2 . 4.2 Different neural activity of WMFNs between baseline and follow-up OUD, HC group Eight WMFNs were obtained by the K-means clustering method in OUD1(baseline OUD group), six WMFNs were obtained by the K-means clustering method in Her2(follow-up OUD group), seven WMFNs were obtained by the K-means clustering method in HC group. Among these WMFNs, five white matter networks were found in all the three groups: frontotemporal parietal, deep frontal, visual, the somatomotor, and the inferior corticoponal-cerebellum networks. Deep frontoparietal Network appeared to be split into Superior longitudinal fasciculus network and Anterior corona radiate network in OUD1, but appeared to recover in the follow-up group. Compared with the HC/Her2 group, the higher average amplitudes of the deep frontal network were observed in OUD1 group; the lower average amplitudes of the frontotemporal parietal network were observed in OUD1 group (Fig. 3 ). Activity in the frontotemporal parietal network was negatively associated with years of opioid use (r= -0.306, p = 0.039; Fig. 4 ). Table 1 Participant demographic and clinical information HU1 N = 42 HC N = 46 HU2 N = 42 Age (years) 43.10 ± 6.95 a 37.69 ± 10.29 43.10 ± 6.95 a Gender (male/female) 23/23 16/26 23/23 Education (years) 7.71 ± 3.04 10.70/2.15 7.71 ± 3.04 Handedness 42R 46R 42R Duration of drug use (years) 16.21 ± 6.33 a -- 16.21 ± 6.33 a Abstinent time (days) 30 (5, 65) b -- 337 (104, 530) b Dosage of drug use (g/d) 0.5 (0.01, 4) b -- -- Table 1 Participant characteristics of OUD and HC group. a : average ± SD, b : median (range). OUD1: baseline opioid use disorder group, OUD2: follow-up opioid use disorder group, HC: healthy control group. Discussion In the current study, we explored the functional organization of white matter functional networks (WMFNs) in OUD1, OUD2, and Healthy Controls (HC); we also examined the alterations in neural activity and the interrelations of the WMFNs. Total of eight WMFNs were obtained in OUD1(baseline OUD group), six WMFNs were obtained by the K-means clustering method in Her2(follow-up OUD group), seven WMFNs were obtained by the K-means clustering method in HC group. Deep frontoparietal Network appeared to be split into Superior longitudinal fasciculus network and Anterior corona radiate network in OUD1, but appeared to recover in the follow-up group. Compared with the HC/Her2 group, the higher average amplitudes of the deep frontal network were observed in OUD1 group; the lower average amplitudes of the frontotemporal parietal network were observed in OUD1 group. 5.1 Abnormal WMFNs organization in OUD Previous studies have identified structural abnormalities in the white matter of individuals with OUD [ 23 ]. The current study further investigated potential alterations in white matter functional activity in individuals with OUD from the perspective of white matter functional networks (WMFNs). Deep frontoparietal Network Addiction is considered to be associated with functional changes in the frontoparietal network (FPN) [ 24 ], which is closely related to cognitive control, motivation, and goal-directed behavior. Studies have shown that individuals addicted to cocaine exhibit reduced activity in the left frontoparietal network when processing non-drug-related reward stimuli [ 25 ]. Additionally, this network shows increased activity when processing cocaine-related images, and this activity is positively correlated with the number of years the individual has been using cocaine. This suggests that abnormal activity in the frontoparietal network may be related to deficits in cognitive control in individuals with addiction. Alcohol dependence is thought to result from an overactive neural motivational system and an insufficient cognitive control system, and rebalancing these systems may reduce excessive drinking. Differences in functional connectivity between the frontoparietal cognitive control network (FPn) and the motivational network (including the striatum and orbitofrontal cortex) have been found between individuals with alcohol dependence and healthy controls [ 26 ]. This suggests that addiction may be associated with abnormalities in the functional connectivity within the frontoparietal network and between it and other brain networks. Addiction and the Role of the Frontoparietal Network in Inhibitory Control: Research on inhibitory control in individuals with addiction has shown inconsistencies in the activity of the frontoparietal network (FPN) and the ventral attention network (VAN), including resultsof both overactivity and underactivity [ 27 ]. These inconsistent results may reflect complex changes in cognitive control and motivational processing in individuals with addiction. In summary, addiction is associated with abnormal activity and functional connectivity within the frontoparietal network (FPN). These abnormalities may be related to changes in cognitive control, motivational processing, and reward responses in individuals with addiction. The findings provide important insights into the neural mechanisms underlying addiction and may facilitate the development of targeted neurointervention strategies for treating addictive disorders [ 28 ]. Frontotemporal parietal network The finding that activity in the frontotemporal parietal network is negatively associated with years of opioid use in the current study suggests that prolonged exposure to opioids may have detrimental effects on the function of this neural network. The frontotemporal parietal network [ 29 ] is involved in a variety of cognitive processes, including attention, working memory, and executive functions, which are crucial for decision-making and behavioral control [ 30 ]. The reduced activity in the frontotemporal parietal network may indicate a decline in cognitive function related to long-term opioid use. This could manifest as difficulties in attention, memory, and planning, which are important for everyday functioning and sobriety. Additionally, chronic opioid use can lead to neuroadaptive changes in the brain [ 31 ], including alterations in neurotransmitter systems and neural circuits. The decreased activity in the network could be a result of these adaptations, which may be necessary for the brain to cope with the constant presence of opioids. 5.2 Brain plasticity after OUD withdrawal In our study, we have observed a disintegration of the deep fronto temporoparietal network in OUD, a network that typically functions as a unified entity in healthy individuals. Specifically, this network in individuals with OUD has been split into the superior longitudinal fasciculus network and the anterior corona radiata network. This disintegration may be due to the damage that OUD inflicts on the white matter fibers of the brain, leading to interruptions in neural connections and functional dysregulation [ 32 ]. The superior longitudinal fasciculus (SLF) [ 33 ]is a major white matter pathway that connects the frontal, parietal, and temporal lobes, playing a crucial role in language, attention, and the integration of information across these regions. The anterior corona radiata [ 34 ] is a bundle of fibers that projects from the cortex to other parts of the brain, involved in motor and sensory functions. The observed splitting of these networks could have significant implications for the cognitive and behavioral deficits seen in OUD, potentially affecting the ability of individuals to perform tasks that require complex cognitive processing and executive functions. This finding suggests that the integrity of white matter pathways is crucial for maintaining normal cognitive function, and that disruption of these pathways may contribute to the pathophysiology of addiction. Understanding these changes could lead to the development of new interventions aimed at restoring or bypassing these disrupted connections, potentially improving treatment outcomes for heroin addiction. In conclusion, the abnormal organization of white matter functional networks (WMFNs) in heroin addicts reveals the impact of addiction on brain structure and function, while the recovery phenomena observed after withdrawal provide crucial insights into the brain's plasticity and potential for rehabilitation. These findings not only enhance our understanding of the neural mechanisms underlying heroin addiction but also offer a scientific basis for developing more effective rehabilitation strategies. The abnormalities in WMFNs highlight the profound changes that addiction can induce in the brain's connective pathways, affecting cognitive, emotional, and behavioral functions. The observation of recovery after withdrawal underscores the brain's capacity to reorganize and heal, suggesting that targeted interventions [ 35 ] can harness this plasticity to facilitate recovery. Conclusion By leveraging these insights, researchers and clinicians can design interventions that specifically address the neural disruptions caused by heroin addiction, potentially leading to more personalized and effective treatment approaches. These strategies may include cognitive rehabilitation, pharmacological treatments, and other therapeutic modalities aimed at restoring normal brain function and supporting long-term recovery. Declarations Authors ’ contributions WY, JuL and KY conceived and designed the study. LY, XW, JW and ZD conducted the behavioral and imaging analyses. JZ and XW conducted the assessments. JuL and KY revised the manuscript and supervised the study. WY wrote the first draft, and all of the authors provided input on the final version of the manuscript. Compliance with ethical standards All of the human studies were approved by the local Institutional Review Board (IRB) of the Second Xiang-Ya Hospital of Central South University. All of the subjects provided signed written consent forms before participating in any of the experiments. Conflict of interest The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This study was supported by grants from the National Natural Science Foundation of China (Grant numbers: 82501862, U22A20303, 61971451), Natural Science Foundation of Hunan Province (Grant number: 2024JJ6574), Clinical Research Center for Medical Imaging in Hunan Province (Grant number: 2020SK4001), Leading Talents of Scientific and Technological Innovation in Hunan Province in 2021 (Grant number: 2021RC4016). References Li W, Li Q, Zhu J, Qin Y, Zheng Y, Chang H, et al. White matter impairment in chronic heroin dependence: a quantitative DTI study. Brain Res 2013;1531:58-64. Bora E, Yucel M, Fornito A, Pantelis C, Harrison BJ, Cocchi L, et al. White matter microstructure in opiate addiction. Addict Biol 2012;17(1):141-8. 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBAC9gYGxgMJDBJy8kAWCDA2ENICUgHUYmFs2HOAFC0MDBWJDTcSiNUyI/fAgYdtEoyNMx8//szDYCO74QDzswf4teQlHEhsk2Bml04zk+ZhSDPecIDN3AC/lhwDkBY2xtk5bMw8DIcTNxzgYZMgRgsPw80zzECH/SesRRCqRYLhBg8D0GEHCGuR5nljcCDhnISBYU+ameQcg2TjmYfZzPBq4WPPMXz4o6yufj774ccf3lTYyfYdb36GVwsaAAUVMwnqR8EoGAWjYBRgBwCYNUe1mMeVugAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""},{"id":537658253,"identity":"196b0827-a2a0-480e-907d-a1242131f85a","order_by":1,"name":"Wenhan Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenhan","middleName":"","lastName":"Yang","suffix":""},{"id":537658254,"identity":"33f76060-d5ce-425f-916b-05ff8426d6ca","order_by":2,"name":"Xinwen Wen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinwen","middleName":"","lastName":"Wen","suffix":""},{"id":537658255,"identity":"d307fcdf-51b0-4244-84c6-d6485e338578","order_by":3,"name":"Junxuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junxuan","middleName":"","lastName":"Wang","suffix":""},{"id":537658256,"identity":"59382805-11e4-4e3f-aaab-cc5cdfcd4b17","order_by":4,"name":"Zhe Du","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Du","suffix":""},{"id":537658257,"identity":"2d64f02e-f90c-41c1-9547-526c5d820aa4","order_by":5,"name":"Longtao Yang","email":"","orcid":"","institution":"Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China","correspondingAuthor":false,"prefix":"","firstName":"Longtao","middleName":"","lastName":"Yang","suffix":""},{"id":537658258,"identity":"e4c25c59-6ef5-4997-82c1-aefa8670113a","order_by":6,"name":"Jun Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":537658259,"identity":"fd6d654d-3f2b-4105-996c-2e950de391dc","order_by":7,"name":"Kai Yuan","email":"","orcid":"https://orcid.org/0000-0002-3098-1124","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-10-29 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15:42:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7980274/v1/d3b71357-6f10-4c93-b4b9-b65a7b91fcfd.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Abnormalities and Plasticity of White Matter Functional Networks in Opioid Use Disorder: A BOLD fMRI Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe impact of Opioid Use Disorder (OUD) on brain white matter is a significant topic within the domain of medical research. White matter, comprising the neural fibers responsible for information transmission within the brain, is integral to cognitive function and behavioral control. The use of disorder substances such as heroin can disrupt the integrity of brain white matter, leading to cognitive impairments and neuropathological changes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the advancement of MRI technology, particularly the application of Diffusion Tensor Imaging (DTI), researchers are now able to more precisely detect and assess the microstructural changes in the white matter of OUD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These techniques facilitate our understanding of the effects of OUD on brain structure and function, as well as the plasticity and potential for recovery of the brain following detoxification. Utilizing MRI technology to detect changes in the white matter of OUD before and after detoxification is of significant importance. Firstly, it aids in identifying structural brain damage caused by OUD, including the impairment of white matter fibers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These injuries may be associated with cognitive impairments and emotional regulation issues in addicts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Secondly, by comparing pre- and post-detoxification MRI outcomes, we can assess the plasticity and potential for recovery of the brain's white matter. Some studies indicate that after long-term detoxification, there is an improvement in the white matter integrity of certain brain regions in OUD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], suggesting that the brain possesses a certain degree of self-repair ability. Moreover, understanding the recovery of the brain post-detoxification is crucial for developing effective treatment and rehabilitation plans. For instance, identifying white matter integrity indicators related to relapse prediction can help optimize treatment methods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, functional magnetic resonance imaging (fMRI) technology has made significant strides in exploring the functional activities within brain white matter (WM) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. WM, comprising the neural fiber bundles that connect various brain regions, plays a crucial role in the transmission of neural information. Traditional fMRI research has primarily focused on the activity of gray matter (GM); however, accumulating evidence suggests that the Blood Oxygen Level-Dependent (BOLD) signals within WM can also be reliably detected and are intimately associated with neural activity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings are of paramount importance for comprehending the organizational structure and functional connectivity of brain networks. The investigation into WM BOLD signals has unveiled their role in the functional networks of the brain. Studies indicate that WM BOLD signals not only reflect neural activity but are also correlated with the structural connectivity of white matter [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These signals are detectable not only at rest but also exhibit structurally specific temporal correlations during the performance of specific tasks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, the anisotropy of WM BOLD signals suggests that the microstructural BOLD effects within white matter tracts can be quantified through Functional Connectivity Tensor (FCT) analysis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe exploration of WM BOLD signals holds potential value for understanding the pathophysiological mechanisms of neuropsychiatric disorders. For instance, research on OUD has shown that alterations in white matter networks may be associated with the recovery of brain function following detoxification. Studies indicate that the white matter networks of OUD who have undergone detoxification tend to converge towards those of healthy controls, suggesting that the restoration of white matter networks may be related to the improvement of addictive behaviors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings afford a novel perspective for investigating white matter dysfunction in neuropsychiatric disorders such as addiction. Investigators have employed resting-state fMRI to examine the functional white matter networks in young smokers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], providing a comparative methodology for studying white matter network alterations in OUD. In summary, research into WM BOLD signals affords us a fresh perspective for understanding the functional networks of the brain and the pathophysiological mechanisms of neuropsychiatric disorders. By integrating various MRI techniques, we can obtain a more comprehensive assessment of changes in white matter networks and explore their correlations with brain function and behavior.\u003c/p\u003e\u003cp\u003eIn this investigation, we employed a data-driven methodology to delve into the organization and the intensity of functional neural activity within white matter functional networks (WMFNs) in individuals with OUD. K-means clustering was utilized to distinctly demarcate the WMFNs of both the OUD group and the Healthy Controls (HCs), as well as to differentiate between the baseline OUD group and the follow-up OUD group [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The functional neural activities of the WMFNs were compared between the three groups. Correlation analyses were also performed to investigate the relationships between the intensity of functional activities within WMFNs and the clinical indicators of heroin use. We posited two hypotheses: (1) baseline OUD group would exhibit distinct WMFN patterns compared to HC/follow-up OUD group, and (2) altered WMFNs might be correlated with clinical indicators of OUD. It is our aspiration that this study will offer a more profound understanding of the neural mechanisms underlying Opioid/heroin Use Disorder from the perspective of WMFNs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Participants\u003c/h2\u003e\u003cp\u003e One hundred and eight participants who had a history of opioid use were recruited from drug rehabilitation clinics in Xinkaipu, PingTang, and Zhuzhou (three cities in Hunan Province, China) between March 2017 and December 2018. All the participants (n\u0026thinsp;=\u0026thinsp;108) tested positive for heroin on a urine test and satisfied the diagnostic criteria for opioid use disorder according to the fifth version of the Diagnostic and Statistical Manual on Mental Disorders (DSM-V). An additional inclusion criterion for the OUD was a negative urine test for methamphetamine and ketamine. All the participants were admitted to the detoxification center and, without methadone maintenance treatment, received 8 months of education and physical training. All of them signed written consent forms and participated in the first MRI scan. Among these initial participants, 61 were invited to finish a follow-up interview and a second MRI scan approximately 8 months later. Fifteen participants were left out because of excessive head movements or low-quality MRI data. Eventually, forty-six individuals with OUD (23 males and 23 females, age: 43.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95 years) were included in this study. Years of opioid (Heroin) use from each participant of OUD were recorded as clinical indicators. We recruited 42 healthy control (HC) participants via WeChat, flyers, etc. All HCs had a negative urine test for heroin, methamphetamine and ketamine. All of the participants were right-handed and of Han Chinese ancestry. None of the participants had previous diagnoses of structural brain diseases, seizures, head trauma, mental or behavioral disorders, or MRI contraindications. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of the OUD and HC groups.\u003c/p\u003e\u003cp\u003e This study was approved by the Institutional Review Board of the Second Xiangya Hospital, Central South University (number: 8167071216). The experimental procedures complied with the ethical guidelines of the Declaration of Helsinki. Written informed consent was obtained from participants after the experimental procedure was explained and after they read the consent form.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Image acquisition\u003c/h2\u003e\u003cp\u003eAll MRI data were gathered at the Second Xiangya Hospital of Central South University in Changsha, China, by means of a 3T MRI scanner (MAGNETOM Skyra, Siemens) equipped with a 32-channel head coil. In order to generate high-quality MRI data, participants were told to lie still with their eyes open and their heads restrained by padding and a head-restraining belt during the MRI scan. T1-weighted imaging and T2-weighted imaging scans were employed to exclude obvious structural lesions in the brain. A 3D rapid gradient echo sequence was utilized to acquire high -resolution T1-weighted MRI scans with these parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;1,450 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.0 ms, inversion time (TI)\u0026thinsp;=\u0026thinsp;900 ms, field of view (FOV)\u0026thinsp;=\u0026thinsp;256\u0026times;256 mm\u0026sup2;, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, voxel size\u0026thinsp;=\u0026thinsp;1\u0026times;1\u0026times;1 mm\u0026sup3;, and number of slices\u0026thinsp;=\u0026thinsp;176. The parameters of the resting - state fMRI were as follows: TR\u0026thinsp;=\u0026thinsp;2,000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;80\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;220 \u0026times; 220 mm\u0026sup2;, slice thickness\u0026thinsp;=\u0026thinsp;4 mm, and number of axial slices\u0026thinsp;=\u0026thinsp;36.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Image preprocessing\u003c/h2\u003e\u003cp\u003eThe Data Processing Assistant for Resting - State fMRI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/DPARSF\u003c/span\u003e\u003cspan address=\"http://rfmri.org/DPARSF\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Statistical Parametric Mapping toolkits (SPM12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ac.uk/spm\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ac.uk/spm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and open MATLAB scripts (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.beyropsychiatrylab.com/codes_wm\u003c/span\u003e\u003cspan address=\"https://www.beyropsychiatrylab.com/codes_wm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to perform image preprocessing. The first 10 volumes of fMRI scans were discarded. Subsequently, slice-timing correction and realignment were carried out. Then, T1 anatomical images were coregistered to the functional images and further segmented into tissue probability maps of white matter, gray matter, and cerebrospinal fluid (CSF) by means of a diffeomorphic nonlinear registration algorithm in SPM12. Linear trends were removed to rectify signal drift. The mean CSF signals (95% threshold) and 24-parameter motion parameters (six motion parameters, their values at the previous time point, and the 12 corresponding squared values) were regressed out from the functional data. The mean white matter and global brain signals were not regressed out in order to avoid eliminating the meaningful neural signals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To reduce the potential impacts of head motion, temporal scrubbing (framewise displacement\u0026thinsp;\u0026gt;\u0026thinsp;0.2 mm) was also applied. Bandpass filtering (0.01-0.08Hz) was carried out to minimize high-frequency non-neuronal noise. To lessen the partial volume effect, spatial smoothing (with a 4-mm full-width at half-maximum Gaussian kernel) was separately applied to the white matter and gray matter images. Finally, the functional images were spatially normalized into the Montreal Neurologic Institute space through T1 segmentation and were resampled into 3\u0026times;3\u0026times;3 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Creation of group-level white matter templates\u003c/h2\u003e\u003cp\u003e We utilized the segmentation outcomes of each participant to get a mask for voxel selection in group clustering. Every voxel was recognized as white matter, grey matter, or cerebrospinal fluid (CSF) according to the segmented maximum probability graph. Subsequently, all the masks from the participants were averaged to acquire the proportion of participants classified as white or grey matter in each voxel.\u003c/p\u003e\u003cp\u003e Voxels recognized as white matter in over 60% of the participants were employed to generate the stencil. Then, we compared the resultant masks with the functional data and eliminated the voxels identified as white matter along with functional data from less than 80% of the participants (such as parts of the medulla and spinal cord). We used the Harvard-Oxford Atlas to label the grey matter voxels within the thalamus, caudate nucleus, putamen, globus pallidus, and nucleus accumbens (excluded from the white matter mask) as grey matter voxels. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Neural activity analysis of WMFNs\u003c/h2\u003e\u003cp\u003eIn this study, a data-driven clustering method was used to separately create WMFNs for the baseline OUD and HC groups. According to previous researches\u003csup\u003e[15\u003c/sup\u003e, \u003csup\u003e18]\u003c/sup\u003e, firstly, two sets of white matter masks were created to select the voxels for clustering in individuals with OUD and healthy control individuals respectively. More precisely, we only retained voxels that were determined to be white matter and covered more than 80% of the functional data of participants. Next, a voxel-wise Pearson correlation matrix of the white matter was calculated for each participant. These individual-level correlation matrices were then averaged for each group, yielding two group-level correlation matrices. Finally, the K-means clustering method was used to cluster white matter voxels with similar functional connections for the two group-level correlation matrices [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The number of clusters was set between 2 and 22, and the Dice coefficient was used to evaluate cluster stability, with a value greater than 0.9 as the evaluation criterion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A power spectrum analysis has been widely used to detect significant differences in signal amplitude across different neurological regions and pathological states of gray matter [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We used this method to analyze the neural activity of white matter networks in OUD group. Fourier transform was performed for each white matter network of each participant to extract each frequency amplitude. Frequency-power plots for each network were generated by averaging the amplitudes of all participants within each group. For the baseline and follow-up OUD group, the method is the same as above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eFor each white matter network, the average amplitude of all participants was taken to represent the level of network activity. A two-sample t-test was carried out to detect the differences in neural activity between the baseline OUD group and the HC group, and a paired t-test was conducted to detect the differences in neural activity between the baseline and follow-up OUD groups. The false discovery rate (FDR) correction was employed to deal with multiple comparisons among multiple white matter networks. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was utilized to determine significance.\u003c/p\u003e\u003cp\u003ePearson correlation analysis was applied to clarify the relationships between the abnormal neural activity of white matter fiber networks (WMFNs) and the clinical indicators of years of drug use. We used the false discovery rate (FDR) correction to handle multiple correlation analysis. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to determine significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Clustering results of WMFNs\u003c/h2\u003e\u003cp\u003eUsing the K-means clustering method with the white matter correlation matrix, eight WMFNs were discovered in the baseline OUD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA); six WMFNs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) in the follow-up OUD group; and in the HC group, seven WMFNs were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Subsequently, we qualitatively defined the WMFNs based on their corresponding relationships with the known resting-state grey matter networks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Eventually, the frontotemporal parietal, deep frontal, inferior corticospinal-cerebellar, visual, and somatomotor networks were observed in all three groups. It is worth noting that, compared to the HC and follow-up OUD groups, the baseline OUD group was lacking a deep frontoparietal network and seemed to be disrupted into the superior longitudinal fasciculus network and anterior corona radiate network. The WMFNs scores for each group are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Different neural activity of WMFNs between baseline and follow-up OUD, HC group\u003c/h2\u003e\u003cp\u003eEight WMFNs were obtained by the K-means clustering method in OUD1(baseline OUD group), six WMFNs were obtained by the K-means clustering method in Her2(follow-up OUD group), seven WMFNs were obtained by the K-means clustering method in HC group. Among these WMFNs, five white matter networks were found in all the three groups: frontotemporal parietal, deep frontal, visual, the somatomotor, and the inferior corticoponal-cerebellum networks. Deep frontoparietal\u003c/p\u003e\u003cp\u003eNetwork appeared to be split into Superior longitudinal fasciculus network and Anterior corona radiate network in OUD1, but appeared to recover in the follow-up group. Compared with the HC/Her2 group, the higher average amplitudes of the deep frontal network were observed in OUD1 group; the lower average amplitudes of the frontotemporal parietal network were observed in OUD1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Activity in the frontotemporal parietal network was negatively associated with years of opioid use (r= -0.306, p\u0026thinsp;=\u0026thinsp;0.039; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant demographic and clinical information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHU1\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHU2\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23/23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.70/2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of drug use (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33 \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33 \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbstinent time (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (5, 65) \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e337 (104, 530)\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage of drug use (g/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5 (0.01, 4)\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Participant characteristics of OUD and HC group. \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e: average\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e: median (range). OUD1: baseline opioid use disorder group, OUD2: follow-up opioid use disorder group, HC: healthy control group.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current study, we explored the functional organization of white matter functional networks (WMFNs) in OUD1, OUD2, and Healthy Controls (HC); we also examined the alterations in neural activity and the interrelations of the WMFNs. Total of eight WMFNs were obtained in OUD1(baseline OUD group), six WMFNs were obtained by the K-means clustering method in Her2(follow-up OUD group), seven WMFNs were obtained by the K-means clustering method in HC group. Deep frontoparietal Network appeared to be split into Superior longitudinal fasciculus network and Anterior corona radiate network in OUD1, but appeared to recover in the follow-up group. Compared with the HC/Her2 group, the higher average amplitudes of the deep frontal network were observed in OUD1 group; the lower average amplitudes of the frontotemporal parietal network were observed in OUD1 group.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Abnormal WMFNs organization in OUD\u003c/h2\u003e\u003cp\u003ePrevious studies have identified structural abnormalities in the white matter of individuals with OUD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The current study further investigated potential alterations in white matter functional activity in individuals with OUD from the perspective of white matter functional networks (WMFNs).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDeep frontoparietal Network\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAddiction is considered to be associated with functional changes in the frontoparietal network (FPN) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which is closely related to cognitive control, motivation, and goal-directed behavior. Studies have shown that individuals addicted to cocaine exhibit reduced activity in the left frontoparietal network when processing non-drug-related reward stimuli [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, this network shows increased activity when processing cocaine-related images, and this activity is positively correlated with the number of years the individual has been using cocaine. This suggests that abnormal activity in the frontoparietal network may be related to deficits in cognitive control in individuals with addiction.\u003c/p\u003e\u003cp\u003eAlcohol dependence is thought to result from an overactive neural motivational system and an insufficient cognitive control system, and rebalancing these systems may reduce excessive drinking. Differences in functional connectivity between the frontoparietal cognitive control network (FPn) and the motivational network (including the striatum and orbitofrontal cortex) have been found between individuals with alcohol dependence and healthy controls [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This suggests that addiction may be associated with abnormalities in the functional connectivity within the frontoparietal network and between it and other brain networks. Addiction and the Role of the Frontoparietal Network in Inhibitory Control: Research on inhibitory control in individuals with addiction has shown inconsistencies in the activity of the frontoparietal network (FPN) and the ventral attention network (VAN), including resultsof both overactivity and underactivity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These inconsistent results may reflect complex changes in cognitive control and motivational processing in individuals with addiction.\u003c/p\u003e\u003cp\u003eIn summary, addiction is associated with abnormal activity and functional connectivity within the frontoparietal network (FPN). These abnormalities may be related to changes in cognitive control, motivational processing, and reward responses in individuals with addiction. The findings provide important insights into the neural mechanisms underlying addiction and may facilitate the development of targeted neurointervention strategies for treating addictive disorders [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eFrontotemporal parietal network\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe finding that activity in the frontotemporal parietal network is negatively associated with years of opioid use in the current study suggests that prolonged exposure to opioids may have detrimental effects on the function of this neural network. The frontotemporal parietal network [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] is involved in a variety of cognitive processes, including attention, working memory, and executive functions, which are crucial for decision-making and behavioral control [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The reduced activity in the frontotemporal parietal network may indicate a decline in cognitive function related to long-term opioid use. This could manifest as difficulties in attention, memory, and planning, which are important for everyday functioning and sobriety. Additionally, chronic opioid use can lead to neuroadaptive changes in the brain [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], including alterations in neurotransmitter systems and neural circuits. The decreased activity in the network could be a result of these adaptations, which may be necessary for the brain to cope with the constant presence of opioids.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Brain plasticity after OUD withdrawal\u003c/h2\u003e\u003cp\u003eIn our study, we have observed a disintegration of the deep fronto temporoparietal network in OUD, a network that typically functions as a unified entity in healthy individuals. Specifically, this network in individuals with OUD has been split into the superior longitudinal fasciculus network and the anterior corona radiata network. This disintegration may be due to the damage that OUD inflicts on the white matter fibers of the brain, leading to interruptions in neural connections and functional dysregulation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The superior longitudinal fasciculus (SLF) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]is a major white matter pathway that connects the frontal, parietal, and temporal lobes, playing a crucial role in language, attention, and the integration of information across these regions. The anterior corona radiata [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] is a bundle of fibers that projects from the cortex to other parts of the brain, involved in motor and sensory functions. The observed splitting of these networks could have significant implications for the cognitive and behavioral deficits seen in OUD, potentially affecting the ability of individuals to perform tasks that require complex cognitive processing and executive functions. This finding suggests that the integrity of white matter pathways is crucial for maintaining normal cognitive function, and that disruption of these pathways may contribute to the pathophysiology of addiction. Understanding these changes could lead to the development of new interventions aimed at restoring or bypassing these disrupted connections, potentially improving treatment outcomes for heroin addiction.\u003c/p\u003e\u003cp\u003eIn conclusion, the abnormal organization of white matter functional networks (WMFNs) in heroin addicts reveals the impact of addiction on brain structure and function, while the recovery phenomena observed after withdrawal provide crucial insights into the brain's plasticity and potential for rehabilitation. These findings not only enhance our understanding of the neural mechanisms underlying heroin addiction but also offer a scientific basis for developing more effective rehabilitation strategies. The abnormalities in WMFNs highlight the profound changes that addiction can induce in the brain's connective pathways, affecting cognitive, emotional, and behavioral functions. The observation of recovery after withdrawal underscores the brain's capacity to reorganize and heal, suggesting that targeted interventions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] can harness this plasticity to facilitate recovery.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy leveraging these insights, researchers and clinicians can design interventions that specifically address the neural disruptions caused by heroin addiction, potentially leading to more personalized and effective treatment approaches. These strategies may include cognitive rehabilitation, pharmacological treatments, and other therapeutic modalities aimed at restoring normal brain function and supporting long-term recovery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003econtributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWY, JuL and KY conceived and designed the study. LY, XW, JW and ZD conducted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe behavioral and imaging analyses. JZ and XW conducted the assessments. JuL and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKY revised the manuscript and supervised the study. WY wrote the first draft, and all\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eof the authors provided input on the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the human studies were approved by the local Institutional Review Board (IRB)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eof the Second Xiang-Ya Hospital of Central South University. All of the subjects\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eprovided signed written consent forms before participating in any of the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest with respect to the research,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eauthorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChina (Grant numbers: 82501862, U22A20303, 61971451), Natural Science Foundation of Hunan Province (Grant number: 2024JJ6574), Clinical Research Center for Medical Imaging in Hunan Province (Grant number: 2020SK4001), Leading Talents of Scientific and Technological Innovation in Hunan Province in 2021 (Grant number: 2021RC4016).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi W, Li Q, Zhu J, Qin Y, Zheng Y, Chang H, et al. 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Predictive value of acute neuroplastic response to taVNS in treatment outcome in persistent abdominal pain: a concurrent taVNS-EEG trial. \u003cem\u003eBrain Stimul\u003c/em\u003e 2025;18(5):1511-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"White Matter Functional Networks (WMFN), opioid use disorder (OUD), abstinence, MRI, Neural plasticity","lastPublishedDoi":"10.21203/rs.3.rs-7980274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7980274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the organization and neural activity of white matter functional networks (WMFNs) in individuals with opioid use disorder (OUD) before and after detoxification, compared with healthy controls (HC). Using a data-driven k-means clustering approach, we analyzed BOLD signals in the white matter of baseline OUD (OUD1), follow-up OUD after 8 months of detoxification (OUD2), and HC groups. Results revealed that OUD1 exhibited eight distinct WMFNs, while OUD2 and HC showed six and seven WMFNs, respectively. Notably, the deep frontoparietal network in OUD1 was fragmented into the superior longitudinal fasciculus and anterior corona radiata networks but partially recovered in OUD2, resembling HC. Additionally, OUD1 displayed hyperactivation in the deep frontal network and hypoactivation in the frontotemporal parietal network, with the latter negatively correlated with years of opioid use (r = -0.306, p = 0.039). These findings suggest that OUD disrupts white matter functional connectivity, while prolonged abstinence promotes partial network restoration, highlighting the brain’s neuroplastic potential. This study provides novel insights into the neural mechanisms of OUD and recovery, supporting the development of targeted rehabilitation strategies.\u003c/p\u003e","manuscriptTitle":"Abnormalities and Plasticity of White Matter Functional Networks in Opioid Use Disorder: A BOLD fMRI Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 10:11:46","doi":"10.21203/rs.3.rs-7980274/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":"6f7252da-0479-42d3-a03c-9907008a7641","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57181012,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":57181013,"name":"Health sciences/Diseases/Psychiatric disorders/Addiction"},{"id":57181014,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2026-01-29T15:36:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 10:11:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7980274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7980274","identity":"rs-7980274","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}