Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach.
paper
Primary
Public
- Authors
- Kinreich, Sivan; McCutcheon, Vivia V; Aliev, Fazil; Meyers, Jacquelyn L; Kamarajan, Chella; Pandey, Ashwini K; Chorlian, David B; Zhang, Jian; Kuang, Weipeng; Pandey, Gayathri; Viteri, Stacey Subbie-Saenz de; Francis, Meredith W; Chan, Grace; Bourdon, Jessica L; Dick, Danielle M; Anokhin, Andrey P; Bauer, Lance; Hesselbrock, Victor; Schuckit, Marc A; Nurnberger, John I; Foroud, Tatiana M; Salvatore, Jessica E; Bucholz, Kathleen K; Porjesz, Bernice
- Year
- 2021
- Journal
- Translational psychiatry
- PMID
- 33723218
- DOI
- 10.1038/s41398-021-01281-2
- PMCID
- PMC7960734
Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (Nβ=β1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
Model accuracy stratified by sex and ancestry.Prediction obtained by only the PRS, the combined EEG and PRS, and features from the highest accuracy scored model for every group (Table 1). Results indicate higher accuracy for the combined feature models suggesting the advantage of adding phenotypes to genetic prediction models. The error bars are standard deviations. *p < 0.05, **p < 0.01.
EEG functional connectivity AUD remission biomarkers.AUD remission prediction models reveal ancestry/sex group-specific brain connectivity biomarkers discriminating between those who recovered from AUD to those who did not. Results highlight lower connectivity in theta (blue) and gamma (red) in areas related to DMN (in bold -IT, PCC, PR, raCC, PH) and higher connectivity in theta (blue) and beta (orange) between insula and inferior and superior parietal regions respectively (in bold INS, IP, IT) specific to every sex and ancestry predicting the maintenance of AUD. blue-theta, green-alpha, orange-beta, red-gamma. Thinner linesβlower connectivity, Thicker linesβhigher connectivity. CMF Caudal middle frontal, FF fusiform, FP frontal pole, INS insula, IC Isthmus cingulate, IP inferior parietal, IT inferior temporal, LI lingual, LO Lateral Occipital, MOF medial orbito frontal, PC paracentral, PCC posterior cingulate cortex, PH parahippocampus, PO parsorbitalis, PR precuneus, raCC rostral anterior cingulate cortex, SM supramarginal, rmF Rostral middle frontal, TP temporal pole, TT Transverse temporal, SP Superior parietal, ST Superior temporal.
1β20 of 39
Next β
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| Aminoff, E. M., Kveraga, K. & Bar, M. The role of the parahippocampal cortex in cognition. Trends Cogn. Sci. 10.1016/j.tics.2013.06.009 (2013).10.1016/j.tics.2013.06.009PMC378609723850264 | β | β | β |
| Bi, J., Sun, J., Wu, Y., Tennen, H. & Armeli, S. A machine learning approach to college drinking prediction and risk factor identification. ACM Trans. Intell. Syst. Technol. 10.1145/2508037.2508053 (2013). | β | β | β |
| Bonnelle, V. et al. Salience network integrity predicts default mode network function after traumatic brain injury. Proc. Natl. Acad. Sci. USA10.1073/pnas.1113455109 (2012).10.1073/pnas.1113455109PMC331135622393019 | β | β | β |
| Brower, K. J., Aldrich, M. S., Robinson, E. A. R., Zucker, R. A. & Greden, J. F. Insomnia, self-medication, and relapse to alcoholism. Am. J. Psychiatry10.1176/appi.ajp.158.3.399 (2001).10.1176/appi.ajp.158.3.399PMC300854211229980 | β | β | β |
| Cai, S. et al. Altered functional connectivity of fusiform gyrus in subjects with amnestic mild cognitive impairment: a resting-state fMRI study. Front. Hum. Neurosci. 10.3389/fnhum.2015.00471 (2015).10.3389/fnhum.2015.00471PMC455078626379534 | β | β | β |
| Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience10.1186/s13742-015-0047-8 (2015).10.1186/s13742-015-0047-8PMC434219325722852 | β | β | β |
| Chang, L. H. et al. Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug Alcohol Depend. 10.1016/j.drugalcdep.2019.01.015 (2019).10.1016/j.drugalcdep.2019.01.015PMC1110030030875648 | β | β | β |
| Chanraud, S., Pitel, A. L., MΓΌller-Oehring, E. M., Pfefferbaum, A. & Sullivan, E. V. Remapping the brain to compensate for impairment in recovering alcoholics. Cereb. Cortex10.1093/cercor/bhr381 (2013).10.1093/cercor/bhr381PMC351395322275479 | β | β | β |
| Chatterjee, N., Shi, J. & GarcΓa-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 10.1038/nrg.2016.27 (2016).10.1038/nrg.2016.27PMC602112927140283 | β | β | β |
| Cho, G., Yim, J., Choi, Y., Ko, J. & Lee, S. H. Review of machine learning algorithms for diagnosing mental illness. Psychiatry Investig. 10.30773/pi.2018.12.21.2 (2019).10.30773/pi.2018.12.21.2PMC650477230947496 | β | β | β |
| Clarke, T.-K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Mol. Psychiatry10.1038/mp.2017.153 (2017).10.1038/mp.2017.153PMC562212428937693 | β | β | β |
| Clewett, D. et al. Increased functional coupling between the left fronto-parietal network and anterior insula predicts steeper delay discounting in smokers. Hum. Brain Mapp. 10.1002/hbm.22436 (2014).10.1002/hbm.22436PMC686951424523255 | β | β | β |
| Cohn, T. J., Foster, J. H. & Peters, T. J. Sequential studies of sleep disturbance and quality of life in abstaining alcoholics. Addict. Biol. 10.1080/13556210310001646439 (2003).10.1080/1355621031000164643914690882 | β | β | β |
| Conway, T. L., Vickers, R. R., Ward, H. W. & Rahe, R. H. Occupational stress and variation in cigarette, coffee, and alcohol consumption. J. Health Soc. Behav. 10.2307/2136291 (1981).7240714 | β | β | β |
| DA, D. et al. Recovery from DSM-IV alcohol dependence: United States, 2001-2002. Addiction100, 281β292 (2005).10.1111/j.1360-0443.2004.00964.x15733237 | β | β | β |
| Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage10.1016/j.neuroimage.2006.01.021 (2006).10.1016/j.neuroimage.2006.01.02116530430 | β | β | β |
| Dodge, K., Krantz, B. & Kenny, P. J. How can we begin to measure recovery? Subst. Abuse Treat. Prev. Policy10.1186/1747-597X-5-31 (2010).10.1186/1747-597X-5-31PMC301636321138568 | β | β | β |
| Dvorak, RD, Am. J. Drug Alcohol Abuse, 2014, Alcohol use and alcohol-related consequences: associations with emotion regulation difficulties | 24588419 | 10.3109/00952990.2013.877920 | Cited |
| Edenberg, HJ, BMC Genet., 2005, Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14 | 16451628 | 10.1186/1471-2156-6-S1-S2 | Primary |
| Fransson, P. & Marrelec, G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. Neuroimage10.1016/j.neuroimage.2008.05.059 (2008).10.1016/j.neuroimage.2008.05.05918598773 | β | β | β |
| Gelernter, J. et al. Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol. Psychiatry10.1038/mp.2013.145 (2014).10.1038/mp.2013.145PMC416533524166409 | β | β | β |
| Ghosh, D et al., J. Biomed. Biotechnol., 2005, Classification and selection of biomarkers in genomic data using LASSO | 16046820 | 10.1155/JBB.2005.147 | Cited |
| Gonzales, R., Douglas Anglin, M., Beattie, R., Ong, C. A. & Glik, D. C. Understanding recovery barriers: youth perceptions about substance use relapse. Am. J. Health Behav. 10.5993/AJHB.36.5.3 (2012).10.5993/AJHB.36.5.3PMC402390922584088 | β | β | β |
| Gramfort, A. et al. MNE software for processing MEG and EEG data. Neuroimage10.1016/j.neuroimage.2013.10.027 (2014).10.1016/j.neuroimage.2013.10.027PMC393085124161808 | β | β | β |
| Greicius, M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 10.1097/wco.0b013e328306f2c5 (2008).10.1097/WCO.0b013e328306f2c518607202 | β | β | β |
| Halldorsdottir, T. et al. Polygenic risk: predicting depression outcomes in clinical and epidemiological cohorts of youths. Am. J. Psychiatry10.1176/appi.ajp.2019.18091014 (2019).10.1176/appi.ajp.2019.1809101430947532 | β | β | β |
| Harford, T. C., Hanna, E. Z. & Faden, V. B. The long- and short-term effects of marriage on drinking. J. Subst. Abuse10.1016/S0899-3289(94)90229-1 (1994).10.1016/s0899-3289(94)90229-17804019 | β | β | β |
| Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 10.1038/ncomms13624 (2017).10.1038/ncomms13624PMC525363228098162 | β | β | β |
| Jilka, S. R. et al. Damage to the salience network and interactions with the default mode network. J. Neurosci. 10.1523/JNEUROSCI.0518-14.2014 (2014).10.1523/JNEUROSCI.0518-14.2014PMC413100625122883 | β | β | β |
| Johnson, E. O., Roehrs, T., Roth, T. & Breslau, N. Epidemiology of alcohol and medication as aids to sleep in early adulthood. Sleep10.1093/sleep/21.2.178 (1998).10.1093/sleep/21.2.1789542801 | β | β | β |
| Jonas, K. G. et al. Schizophrenia polygenic risk score and 20-year course of illness in psychotic disorders. Transl. Psychiatry10.1038/s41398-019-0612-5 (2019).10.1038/s41398-019-0612-5PMC685616831727878 | β | β | β |
| Keers, R. et al. A genome-wide test of the differential susceptibility hypothesis reveals a genetic predictor of differential response to psychological treatments for child anxiety disorders. Psychother. Psychosom. 10.1159/000444023 (2016).10.1159/000444023PMC507910327043157 | β | β | β |
| Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 10.1038/s41588-018-0183-z (2018).10.1038/s41588-018-0183-zPMC612840830104762 | β | β | β |
| Kinreich, S. et al. Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol. Psychiatry10.1038/s41380-019-0534-x (2019).10.1038/s41380-019-0534-xPMC713869231595034 | β | β | β |
| Kinreich, S., Podlipsky, I., Jamshy, S., Intrator, N. & Hendler, T. Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG neurofeedback. Neuroimage97, 19β28 (2014).10.1016/j.neuroimage.2014.04.04424768931 | β | β | β |
| Knight, K et al., Ann. Stat., 2000, Asymptotics for lasso-type estimators | β | 10.1214/aos/1015957397 | Cited |
| Kranzler, H. R. et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat. Commun. 10.1038/s41467-019-09480-8 (2019).10.1038/s41467-019-09480-8PMC644507230940813 | β | β | β |
| Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 10.1038/s41588-018-0147-3 (2018).10.1038/s41588-018-0147-3PMC639376830038396 | β | β | β |
| Li, J. J. et al. Polygenic risk, personality dimensions, and adolescent alcohol use problems: a longitudinal study. J. Stud. Alcohol Drugs10.15288/jsad.2017.78.442 (2017).10.15288/jsad.2017.78.442PMC544036828499112 | β | β | β |
| Loeber, S. et al. Impairment of cognitive abilities and decision making after chronic use of alcohol: the impact of multiple detoxifications. Alcohol Alcohol. 10.1093/alcalc/agp030 (2009).10.1093/alcalc/agp03019487491 | β | β | β |
| Mahedy, L. et al. Parental alcohol use and risk of behavioral and emotional problems in offspring. PLoS ONE10.1371/journal.pone.0178862 (2017).10.1371/journal.pone.0178862PMC546084828586358 | β | β | β |
| Manuel, J. K. et al. Community reinforcement and family training: a pilot comparison of group and self-directed delivery. J. Subst. Abuse Treat. 10.1016/j.jsat.2011.10.020 (2012).10.1016/j.jsat.2011.10.020PMC333196922154038 | β | β | β |
| Mies, G. W. et al. Polygenic risk for alcohol consumption and its association with alcohol-related phenotypes: Do stress and life satisfaction moderate these relationships? Drug Alcohol Depend. 10.1016/j.drugalcdep.2017.10.018 (2018).10.1016/j.drugalcdep.2017.10.01829220643 | β | β | β |
| Moos, R. H. & Moos, B. S. Participation in treatment and Alcoholics Anonymous: a 16-year follow-up of initially untreated individuals. J. Clin. Psychol. 10.1002/jclp.20259 (2006).10.1002/jclp.20259PMC222001216538654 | β | β | β |
| Moos, RH et al., Addiction, 2006, Rates and predictors of relapse after natural and treated remission from alcohol use disorders | 16445550 | 10.1111/j.1360-0443.2006.01310.x | Cited |
| Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature10.1038/nature17671 (2016).10.1038/nature17671PMC488359527225129 | β | β | β |
| Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry10.1038/mp.2015.197 (2016).10.1038/mp.2016.1126857599 | β | β | β |
| OβFarrell, T. J. & Clements, K. Review of outcome research on marital and family therapy in treatment for alcoholism. J. Marital Fam. Ther. 10.1111/j.1752-0606.2011.00242.x (2012).10.1111/j.1752-0606.2011.00242.xPMC327089022283384 | β | β | β |
| Pappa, I. et al. A genome-wide approach to childrenβs aggressive behavior: The EAGLE consortium. Am. J. Med. Genet. B Neuropsychiatr. Genet. 10.1002/ajmg.b.32333 (2016).10.1002/ajmg.b.3233326087016 | β | β | β |
| Peterson, R. E. et al. The utility of empirically assigning ancestry groups in cross-population genetic studies of addiction. Am. J. Addict. 10.1111/ajad.12586 (2017).10.1111/ajad.12586PMC564681928714599 | β | β | β |
| Price, A. L. et al. Discerning the ancestry of European Americans in genetic association studies. PLoS Genet. 10.1371/journal.pgen.0030236 (2008).10.1371/journal.pgen.0030236PMC221154218208327 | β | β | β |
| PrivΓ©, F., VilhjΓ‘lmsson, B. J., Aschard, H. & Blum, M. G. B. Making the most of Clumping and Thresholding for polygenic scores. Am. J. Hum. Genet. 10.1016/j.ajhg.2019.11.001 (2019).10.1016/j.ajhg.2019.11.001PMC690479931761295 | β | β | β |
| Quello, S. B., Brady, K. T. & Sonne, S. C. Mood disorders and substance use disorder: a complex comorbidity. Sci. Pract. perspect. 10.1151/spp053113 (2005).10.1151/spp053113PMC285102718552741 | β | β | β |
| Rohrbaugh, M., Shoham, V., Spungen, C. & Steinglass, P. in Comprehensive Textbook of Psychotherapy: Theory and Practice 228β253 (Oxford University Press, 1995). | β | β | β |
| Royal, C. D. et al. Inferring genetic ancestry: opportunities, challenges, and implications. Am. J. Hum. Genet. 10.1016/j.ajhg.2010.03.011 (2010).10.1016/j.ajhg.2010.03.011PMC286901320466090 | β | β | β |
| Schumann, G. et al. KLB is associated with alcohol drinking, and its gene product Ξ²-Klotho is necessary for FGF21 regulation of alcohol preference. Proc. Natl. Acad. Sci. USA10.1073/pnas.1611243113 (2016).10.1073/pnas.1611243113PMC516719827911795 | β | β | β |
| Srinivasan, R., Winter, W. R., Ding, J. & Nunez, P. L. EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods10.1016/j.jneumeth.2007.06.026 (2007).10.1016/j.jneumeth.2007.06.026PMC215196217698205 | β | β | β |
| Stavro, K., Pelletier, J. & Potvin, S. Widespread and sustained cognitive deficits in alcoholism: a meta-analysis. Addict. Biol. 10.1111/j.1369-1600.2011.00418.x (2013).10.1111/j.1369-1600.2011.00418.x22264351 | β | β | β |
| Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B10.1111/j.2517-6161.1996.tb02080.x (2018). | β | β | β |
| Trim, R. S., Schuckit, M. A. & Smith, T. L. Predictors of initial and sustained remission from alcohol use disorders: findings from the 30-year follow-up of the San Diego Prospective Study. Alcohol. Clin. Exp. Res. 10.1111/acer.12107 (2013).10.1111/acer.12107PMC367518823458300 | β | β | β |
| Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 10.1038/s41593-018-0275-1 (2018).10.1038/s41593-018-0275-1PMC643020730482948 | β | β | β |
| Weissman, M. M., Greenwald, S., NiΓ±o-Murcia, G. & Dement, W. C. The morbidity of insomnia uncomplicated by psychiatric disorders. Gen. Hosp. Psychiatry10.1016/S0163-8343(97)00056-X (1997).10.1016/s0163-8343(97)00056-x9327253 | β | β | β |
| Werb, D. et al. The effectiveness of compulsory drug treatment: a systematic review. Int. J. Drug Policy10.1016/j.drugpo.2015.12.005 (2016).10.1016/j.drugpo.2015.12.005PMC475287926790691 | β | β | β |
| Whelan, R. et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature10.1038/nature13402 (2014).10.1038/nature13402PMC448620725043041 | β | β | β |
| Woodward, N. D. & Cascio, C. J. Resting-state functional connectivity in psychiatric disorders. JAMA Psychiatry10.1001/jamapsychiatry.2015.0484 (2015).10.1001/jamapsychiatry.2015.0484PMC469359926061674 | β | β | β |
| Yaeger, R. et al. Comparing genetic ancestry and self-described race in African Americans born in the United States and in Africa. Cancer Epidemiol. Biomarkers Prev. 10.1158/1055-9965.EPI-07-2505 (2008).10.1158/1055-9965.EPI-07-2505PMC250787018559547 | β | β | β |
| Yan, C. G. et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc. Natl. Acad. Sci. USA10.1073/pnas.1900390116 (2019).10.1073/pnas.1900390116PMC650016830979801 | β | β | β |
| Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics10.1093/bioinformatics/bts606 (2012).10.1093/bioinformatics/bts606PMC351945423060615 | β | β | β |
| Zola-Morgan, S., Squire, L. R., Amarel, D. G. & Suzuki, W. A. Lesions of perirhinal and parahippocampal cortex that spare the amygdala and hippocampal formation produce severe memory impairment. J. Neurosci. 10.1523/jneurosci.09-12-04355.1989 (1989).10.1523/JNEUROSCI.09-12-04355.1989PMC65696352593004 | β | β | β |
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state. | Richard CD et al. | β | 2026 | β |
| A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals. | Yasin S et al. | β | 2025 | β |
| Alzheimerβs subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study | Kinreich S et al. | β | 2025 | β |
| Integrating multiple data sources to predict all-cause readmission or mortality in patients with substance misuse. | Gruenloh T et al. | β | 2025 | β |
| Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: AΒ critical review. | Clark SL et al. | β | 2025 | β |
| Parental Remission From Alcohol Use Disorder Shows Limited Protective Effects on Offspring Alcohol Outcomes. | McCutcheon VV et al. | β | 2025 | β |
| Reviewing the Effectiveness of Machine Learning Algorithm for Detecting Risk Behaviours Through Speech Patterns. | Nagasubramanian H et al. | β | 2025 | β |
| The effects of polygenic risks for alcohol misuse on negative emotion processing in young adult binge drinkers. | Chen Y et al. | β | 2025 | β |
| Associations between alcohol use disorder polygenic score andΒ remission in participants from high-risk families and the Indiana Biobank. | Lai D et al. | β | 2024 | β |
| Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders. | Kember RL et al. | β | 2024 | β |
| Disentangling heterogeneity in substance use disorder: Insights from genome-wide polygenic scores. | Vilar-RibΓ³ L et al. | β | 2024 | β |
| Drinking behavior patterns may be associated with persistent depressive symptoms after alcohol abstinence in alcohol use disorder. | Kurihara K et al. | β | 2024 | β |
| Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder. | Wen X et al. | β | 2024 | β |
| Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. | Chen Z et al. | β | 2023 | β |
| Implications of neuroimaging findings in addiction. | Wen X et al. | β | 2023 | β |
| Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. | Kamarajan C et al. | β | 2023 | β |
| Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. | Chen Z et al. | β | 2023 | β |
| The collaborative study on the genetics of alcoholism: Brain function. | Meyers JL et al. | β | 2023 | β |
| The Collaborative Study on the Genetics of Alcoholism: Overview. | Agrawal A et al. | β | 2023 | β |
| The collaborative study on the genetics of alcoholism: Sample and clinical data. | Dick DM et al. | β | 2023 | β |
| The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder. | Zhang X et al. | β | 2023 | β |
| A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. | Bel-Bahar TS et al. | β | 2022 | β |
| Insights on rehabilitation programs, women, families, and COVID19. | Kinreich S | β | 2022 | β |
| Multimodal neuroimaging of metabotropic glutamate 5 receptors and functional connectivity in alcohol use disorder. | Smart K et al. | β | 2022 | β |
| Occupational factors associated with long-term abstinence among persons treated for alcohol dependence: A follow-up study. | Ezhumalai S et al. | β | 2022 | β |
| Using machine learning to predict heavy drinking during outpatient alcohol treatment. | Roberts W et al. | β | 2022 | β |
| Drinking and smoking polygenic risk is associated with childhood and early-adulthood psychiatric and behavioral traits independently of substance use and psychiatric genetic risk. | De Angelis F et al. | β | 2021 | β |