Neuroimaging evidence for structural correlates in adolescents resilient to polysubstance use: A five-year follow-up study

Filippi, Irina; Galinowski, A.; Lemaître, Hervé; Massot, Christian; Zillé, Pascal; Frère, Pauline; Miranda, Rubén; Trichard, Christian; Guldner, Stella; Vulser, Hélène; Paillère-Martinot, Marie-Laure; Quinlan, Erin Burke; Desrivières, Sylvane; Gowland, Penny; Bokde, Arun L.W.; Garavan, Hugh; Heinz, Andreas; Walter, Henrik; Daedelow, Laura S.; Büchel, Christian; Bromberg, Uli; Conrod, Patricia J.; Flor, Herta; Banaschewski, Tobias; Nees, Frauke; Heintz, Stefan; Smolka, Michael N.; Vetter, Nora C.; Papadopoulos‐Orfanos, Dimitri; Whelan, Robert; Poustka, Louise; Paus, Tomáš; Schumann, Günter; Artiges, Éric; Martinot, Jean‐Luc

doi:10.1016/j.euroneuro.2021.03.001

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Multivariate exploratory analysis: Over the last years, data collection drives such as IMAGEN ( Mascarell Maričić et al, 2020 ), NCANDA ( Brown et al, 2015 ), and UK Biobank ( Sudlow et al, 2015 ) made available large-sample multi-site data with

that are representative of the general population. This enabled researchers to use multivariate, data-driven, and exploratory analysis tools such as machine learning (ML) to detect effects of alcohol misuse on multiple brain regions ( Whelan et al, 2014 ; Squeglia et al, 2017 ; Seo et al, 2019 ; Filippi et al, 2021 ; Jia et al, 2021 ; Yip et al, 2022 ). Such whole-brain multivariate methods are preferable over the previous mass-univariate methods as they have a higher sensitivity to detect true positives ( Hebart and Baker, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Structural differences in adolescent brains can predict alcohol misuse

Rane

Man

Kim

et al. 2022

eLife

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Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 - 78% in the IMAGEN dataset (n ~1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted ten phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.

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Section: Introductionmentioning

confidence: 99%

Structural differences in adolescent brains can predict alcohol misuse

Rane

Man

Kim

et al. 2022

eLife

Self Cite

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“…In one large cohort study, transition from no use to frequent drinking in early to mid‐adolescence was predicted by blunted activity of the medial orbitofrontal cortex during reward outcome 372 . Also in adolescents, structural characteristics – such as a larger cingulate gyrus – were predictive of resilience to problematic use after 3 years 373 . In a review of 44 longitudinal neurobehavioral studies predicting substance use in youth 374 , functional vulnerability markers of substance use – i.e., markers that predict onset of subsequent substance use – included increased fMRI activation during reward feedback and risk evaluation in prefrontal and ventral striatal regions, and fronto‐parietal hypoactivation during working memory.…”

Section: Biomarkers In Substance Use Disordersmentioning

confidence: 99%

Candidate biomarkers in psychiatric disorders: state of the field

et al. 2023

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The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post‐traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event‐related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting‐state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error‐related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting‐state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well‐defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

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“…The application of novel computational models [32] and greater understanding of the unique influence of PSU on brain structure and function [33 ▪ ] —particularly in the developing brain—requires more focussed attention to advance our understanding of PSU.…”

Section: Future Researchmentioning

confidence: 99%

Seeking order in patterns of polysubstance use

Connor

Leung

Chan

et al. 2023

Current Opinion in Psychiatry

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Purpose of reviewThis review provides an overview of recent developments in understanding polysubstance use patterns across the lifespan, and advances made in the prevention and treatment of harm arising from polysubstance use.Recent findingsA comprehensive understanding of the patterns of polysubstance use is hampered by heterogeneity across study methods and types of drugs measured. Use of statistical techniques such as latent class analysis has aided in overcoming this limitation, identifying common patterns or classes of polysubstance use. These typically include, with decreasing prevalence, (1) Alcohol use only; (2) Alcohol and Tobacco; (3) Alcohol, Tobacco, and Cannabis; and finally (4) a low prevalence, Extended Range cluster that includes other illicit drugs, New Psychoactive Substances (NPS), and nonmedical prescription medications.SummaryAcross studies, there are commonalities present in clusters of substances used. Future work that integrates novel measures of polysubstance use and leverages advances in drug monitoring, statistical analysis and neuroimaging will improve our understanding of how and why drugs are combined, and more rapidly identify emerging trends in multiple substance use. Polysubstance use is prevalent but there is a paucity of research exploring effective treatments and interventions.

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Neuroimaging evidence for structural correlates in adolescents resilient to polysubstance use: A five-year follow-up study

Cited by 7 publications

References 48 publications

Structural differences in adolescent brains can predict alcohol misuse

Structural differences in adolescent brains can predict alcohol misuse

Candidate biomarkers in psychiatric disorders: state of the field

Seeking order in patterns of polysubstance use

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