A systematic review of sex/gender differences in the multi-dimensional neurobiological mechanisms in addiction and their relevance to impulsivity.

Purpose Of Review: Addiction may be characterized along three functional domains: Approach Behavior, Executive Function, and Negative Emotionality. Constructs underlying impulsivity thought to be relevant in addiction map on to these three functional domains. The purpose of the present review was to evaluate the extant research regarding sex/gender differences in the multi-dimensional domains of addiction using human neuroimaging and discuss their relevance to impulsivity.

Subtypes in addiction and their neurobehavioral profiles across three functional domains.

Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes.

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments.

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD.

Recurrent neural network-based acute concussion classifier using raw resting state EEG data.

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications.

Myelin water fraction decrease in individuals with chronic mild traumatic brain injury and persistent symptoms.

The diffuse and continually evolving secondary changes after mild traumatic brain injury (mTBI) make it challenging to assess alterations in brain-behaviour relationships. In this study we used myelin water imaging to evaluate changes in myelin water fraction (MWF) in individuals with chronic mTBI and persistent symptoms and measured their cognitive status using the NIH Toolbox Cognitive Battery. Fifteen adults with mTBI with persistent symptoms and twelve age, gender and education matched healthy controls took part in this study.

Disrupted Information Flow in Resting-State in Adolescents With Sports Related Concussion.

Children and youths are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is an increase in functional connectivity, yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition.