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      Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

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          Abstract

          Background

          Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders (SUD).

          Aims

          This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling (CPM).

          Methods

          One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging (fMRI) scans at baseline. The Heroin Craving Questionnaire (HCQ) was used to assess craving levels at baseline and at the 8-month follow-up of abstinence. CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ (HCQ follow-up−HCQ baseline). Then, the predictive ability of identified networks was tested in a separate, heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.

          Results

          CPM could predict craving changes induced by long-term abstinence, as shown by a significant correlation between predicted and actual HCQ follow-up (r=0.417, p<0.001) and changes in HCQ (negative: r=0.334, p=0.002;positive: r=0.233, p=0.038). Identified craving-related prediction networks included the somato-motor network (SMN), salience network (SALN), default mode network (DMN), medial frontal network, visual network and auditory network. In addition, decreased connectivity of frontal-parietal network (FPN)-SMN, FPN-DMN and FPN-SALN and increased connectivity of subcortical network (SCN)-DMN, SCN-SALN and SCN-SMN were positively correlated with craving levels.

          Conclusions

          These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence, as well as the generalisation ability; the identified brain networks might be the focus of innovative therapies in the future.

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          Most cited references30

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          Neurobiology of addiction: a neurocircuitry analysis.

          Drug addiction represents a dramatic dysregulation of motivational circuits that is caused by a combination of exaggerated incentive salience and habit formation, reward deficits and stress surfeits, and compromised executive function in three stages. The rewarding effects of drugs of abuse, development of incentive salience, and development of drug-seeking habits in the binge/intoxication stage involve changes in dopamine and opioid peptides in the basal ganglia. The increases in negative emotional states and dysphoric and stress-like responses in the withdrawal/negative affect stage involve decreases in the function of the dopamine component of the reward system and recruitment of brain stress neurotransmitters, such as corticotropin-releasing factor and dynorphin, in the neurocircuitry of the extended amygdala. The craving and deficits in executive function in the so-called preoccupation/anticipation stage involve the dysregulation of key afferent projections from the prefrontal cortex and insula, including glutamate, to the basal ganglia and extended amygdala. Molecular genetic studies have identified transduction and transcription factors that act in neurocircuitry associated with the development and maintenance of addiction that might mediate initial vulnerability, maintenance, and relapse associated with addiction.
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            Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity

            While fMRI studies typically collapse data from many subjects, brain functional organization varies between individuals. Here, we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a “fingerprint” that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual’s connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but notably, the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence; the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects based on functional connectivity fMRI.
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              Predicting human resting-state functional connectivity from structural connectivity.

              In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks--including their spatial statistics and their persistence across time--can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
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                Author and article information

                Journal
                Gen Psychiatr
                Gen Psychiatr
                gpsych
                gpsych
                General Psychiatry
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2517-729X
                2023
                29 December 2023
                : 36
                : 6
                : e101304
                Affiliations
                [1 ] Ringgold_70566The Second Xiangya Hospital of Central South University , Changsha, Hunan, China
                [2 ] departmentDepartment of Computer Science , Ringgold_1026Aberystwyth University , Aberystwyth, UK
                [3 ] departmentDepartment of Radiology , Ringgold_70566The Second Xiangya Hospital of Central South University , Changsha, Hunan, China
                [4 ] Hunan Judicial Police Academy , Changsha, Hunan, China
                [5 ] Siemens Healthineers Ltd , Wuhan, Hubei, China
                [6 ] Clinical Research Center for Medical Imaging in Hunan Province , Changsha, Hunan, China
                [7 ] Department of Radiology Quality Control Center in Hunan Province , Changsha, Hunan, China
                Author notes
                [Correspondence to ] Dr Jun Liu; junliu123@ 123456csu.edu.cn
                Author information
                http://orcid.org/0000-0002-7851-6782
                Article
                gpsych-2023-101304
                10.1136/gpsych-2023-101304
                10759048
                7448ae11-0db1-4283-9713-58f9f33d2754
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 24 August 2023
                : 01 December 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 61971451
                Award ID: 81671671
                Funded by: Clinical Research Center for Medical Imaging In Hunan Province;
                Award ID: 2020SK4001
                Funded by: Leading talents of scientific and technological innovation in Hunan Province in 2021;
                Award ID: 2021RC4016
                Funded by: FundRef http://dx.doi.org/10.13039/100016104, Key Project of Research and Development Plan of Hunan Province;
                Award ID: 2019SK2131
                Categories
                Original Research
                1506
                2618
                Custom metadata
                unlocked

                behaviour,brain,behavior mechanisms,biological psychiatry,psychiatry,addictive

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