Background Alcohol dependence is characterized by impulsiveness toward consumption despite negative consequences. Although neuroimaging studies have implicated some regions underlying this disorder, there is little information regarding its large-scale connectivity pattern. This study investigated the within- and between-network functional connectivity (FC) in alcohol dependence and examined its relationship with clinical impulsivity measures. Methods Using Probabilistic Independent Component Analysis (PICA) on resting-state fMRI (rs-fMRI) data from 25 alcohol dependent (AD) and 26 healthy control (HC) participants, we compared the within- and between-network FC between AD and HC. Then, the relationship between FC and impulsiveness as measured by the Barratt Impulsiveness Scale (BIS-11), the UPPS-P Impulsive Scale and the delay-discounting task (DDT) was explored. Results Compared to HC, AD exhibited increased within-network FC in salience (SN), default-mode (DMN), orbitofrontal cortex (OFCN), left executive control (LECN) and amygdala-striatum (ASN) networks. Increased between-network FC was found among LECN, ASN and SN. Between-network FC correlations were significantly negative between Negative-Urgency and OFCN pairs with RECN, anterior DMN (aDMN), and posterior DMN (pDMN) in AD. DDT was significantly correlated with the between-network FC among the LECN, aDMN and SN in AD. Conclusions These findings add evidence to the concept of altered within-network FC and also highlight the role of between-network FC in the pathophysiology of AD. Additionally, this study suggests differential neurobiological bases for different clinical measures of impulsivity that may be used as a systems-level biomarker for alcohol dependence severity and treatment efficacy.

R. A. (2021). Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium. Molecular Psychiatry,26,[4315][4316][4317][4318][4319][4320][4321][4322][4323][4324][4325][4326][4327][4328][4329][4330]

Currently, classification of alcohol use disorder (AUD) is made on clinical grounds; however, robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry adaptations. Identifications of the neuronal networks that are affected by alcohol would provide a more systematic way of diagnosis and provide novel insights into the pathophysiology of AUD. In this study, we identified network-level brain features of AUD, and further quantified resting-state within-network, and between-network connectivity features in a multivariate fashion that are classifying AUD, thus providing additional information about how each network contributes to alcoholism. Resting-state fMRI were collected from 92 individuals (46 controls and 46 AUDs). Probabilistic Independent Component Analysis (PICA) was used to extract brain functional networks and their corresponding time-course for AUD and controls. Both within-network connectivity for each network and between-network connectivity for each pair of networks were used as features. Random forest was applied for pattern classification. The results showed that within-networks features were able to identify AUD and control with 87.0% accuracy and 90.5% precision, respectively. Networks that were most informative included Executive Control Networks (ECN), and Reward Network (RN). The between-network features achieved 67.4% accuracy and 70.0% precision. The between-network connectivity between RN-Default Mode Network (DMN) and RN-ECN contribute the most to the prediction. In conclusion, within-network functional connectivity offered maximal information for AUD classification, when compared with between-network connectivity. Further, our results suggest that connectivity within the ECN and RN are informative in classifying AUD. Our findings suggest that machine-learning algorithms provide an alternative technique to quantify large-scale network differences and offer new insights into the identification of potential biomarkers for the clinical diagnosis of AUD.

Background Eye-tracking-based attentional research implicates sustained attention to threat in posttraumatic stress disorder (PTSD). However, most of this research employed small stimuli set-sizes, small samples that did not include both trauma-exposed healthy participants and non-trauma-exposed participants, and generally failed to report the reliability of used tasks and attention indices. Here, using an established eye-tracking paradigm, we explore attention processes to different negatively-valenced cues in PTSD while addressing these limitations. Methods PTSD patients (n = 37), trauma-exposed healthy controls (TEHC; n = 34), and healthy controls (HC; n = 30) freely viewed three blocks of 30 different matrices of faces, each presented for 6 s. Each block consisted of matrices depicting eight negatively-valenced faces (anger, fear, or sadness) and eight neutral faces. Gaze patterns on negative and neural areas of interest were compared. Internal consistency and test-retest reliability were evaluated for the entire sample and within groups. Results The two trauma-exposed groups dwelled longer on negatively-valenced faces over neutral faces, while HC participants showed the opposite pattern. This attentional bias was more prominent in the PTSD than the TEHC group. Similar results emerged for first-fixation dwell time, but with no differences between the two trauma-exposed groups. No group differences emerged for first-fixation latency or location. Internal consistency and 1-week test-retest reliability were adequate, across and within groups. Conclusions Sustained attention on negatively-valenced stimuli emerges as a potential target for therapeutic intervention in PTSD designed to divert attention away from negatively-valenced stimuli and toward neutral ones.

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Purpose of Review Sex differences in the epidemiology and clinical presentation of trauma-related psychopathology have long been documented. Multiple underlying mechanisms have been examined, both psychosocial and biological. Among the most promising biological mechanisms are neural substrates of trauma-related psychopathology that have been uncovered in recent years. Recent Findings Neuroimaging studies of sex-related heterogeneity published over the past 3 years (2014–2017) demonstrate an interaction between sex and type, timing, and load of trauma exposure. These studies suggest that, for males, early trauma exposure may involve a loss of gray matter in the limbic system, including the prefrontal cortex (PFC), amygdala, and hippocampus, and an over-activity and increased connectivity of salience hubs, and particularly dorsal anterior cingulate cortex (dACC). For females, however, early trauma exposure may involve overactive and possibly an enlarged amygdala, as well as decreased connectivity of salience hubs such as the dACC. Underlying mechanisms may include interaction with several endocrine systems and result in differential neural response to naturally occurring and added endocrine ligands, as well as sex-specific genetic and epigenetic risk and resilience factors. This complex interaction between multiple biological systems may be associated with sex-specific behavioral patterns, in turn associated with trauma-related psychopathology. Summary While substantial number of published studies present preliminary evidence for neural mechanisms of sex-specific posttraumatic responses, there is a paucity of research directly designed to examine sex as a biological factor in trauma-related psychopathology. Specific foci for future studies aiming to bridge current gaps in the literature are discussed.

I. (2021). Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup megaanalysis. Molecular Psychiatry,26,[4331][4332][4333][4334][4335][4336][4337][4338][4339][4340][4341][4342][4343]

Alcohol Dependence (AD) is a chronic relapsing disorder with high degrees of morbidity and mortality. While multiple neurotransmitter systems are involved in the complex symptomatology of AD, monoamine dysregulation and subsequent neuroadaptations have been long postulated to play an important role. Presynaptic monoamine transporters, such as the vesicular monoamine transporter 1 (VMAT1), are likely critical as they represent a key common entry point for monoamine regulation and may represent a shared pathway for susceptibility to AD. Excessive monoaminergic signaling as mediated by genetic variation in VMAT1 might affect functional brain connectivity in particular in alcoholics compared to controls. We conducted resting state fMRI functional connectivity analysis using the independent component analysis (ICA) approach in 68 AD subjects and 72 controls. All subjects were genotyped for the Thr136Ile (rs1390938) variant in VMAT1. Functional connectivity analyses showed a significant increase of resting state FC in 4 networks in alcoholics compared to controls (p<0.05, corrected). The FC was significantly positively correlated with Alcohol Dependence Scale (ADS). The hyperfunction allele 136Ile was associated with a significantly decreased FC in the Default Mode Network, Prefrontal Cortex Network, and Executive Control Network in alcohol dependent participants (p<0.05, corrected), but not controls. Our data suggest that increased FC might represent a neuroadaptive mechanism relevant to AD that is furthermore mediated by genetic variation in VMAT1. The hyperfunction allele Thr136Ile might have a protective effect that is, in particular, relevant in AD by mechanism of increased monoamine transport into presynaptic storage vesicles.