Impulsive trait mediates the relationship between white matter integrity of prefrontal-striatal circuits and the severity of dependence in alcoholism

Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking

Background

Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between the severity of alcohol consumption and white matter FA changes, and its sex differences, in young adults, using data from the Human Connectome Project.

Methods

We analyzed DTI data from 949 participants (491 females) and used principal component analysis (PCA) of 15 drinking metrics to quantify drinking severity. Connectome-based predictive modeling (CPM) was employed to predict the principal component of drinking severity from network FA values in a matrix of 116×116 regions. Mediation analyses were conducted to explore the interrelationships among networks identified by CPM, drinking severity, and rule-breaking behavior.

Results

Significant correlations were found between drinking severity and network FA values. Both men and women showed significant correlations between negative network connectivity and drinking severity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences were observed in the brain regions contributing to drinking severity predictions. Mediation analyses revealed significant inter-relationships between network features, drinking severity, and rule-breaking behavior.

Conclusions

The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.

The Role of Unawareness, Volition, and Neural Hyperconnectivity in Alcohol Use Disorder: A Functional Magnetic Resonance Imaging Study

BACKGROUND

Automated alcohol craving and habitual alcohol consumption characterize the later stages of alcohol use disorder (AUD). This study reanalyzed previously collected functional neuroimaging data in combination with the Craving Automated Scale for Alcohol (CAS-A) questionnaire to investigate the neural correlates and brain networks underlying automated drinking characterized by unawareness and nonvolition.

METHODS

We assessed 49 abstinent male patients with AUD and 36 male healthy control participants during a functional magnetic resonance imaging-based alcohol cue-reactivity task. We performed whole-brain analyses examining the associations between CAS-A scores and other clinical instruments and neural activation patterns in the alcohol versus neutral contrast. Furthermore, we performed psychophysiological interaction analyses to assess the functional connectivity between predefined seed regions and other brain areas.

RESULTS

In patients with AUD, higher CAS-A scores correlated with greater activation in dorsal striatal, pallidal, and prefrontal regions, including frontal white matter, and with lower activation in visual and motor processing regions. Between-group psychophysiological interaction analyses showed extensive connectivity between the seed regions inferior frontal gyrus and angular gyrus and several frontal, parietal, and temporal brain regions in AUD versus healthy control participants.

CONCLUSIONS

The present study applied a new lens to previously acquired alcohol cue-reactivity functional magnetic resonance imaging data by correlating neural activation patterns with clinical CAS-A scores to elucidate potential neural correlates of automated alcohol craving and habitual alcohol consumption. Our results support previous findings showing that alcohol addiction is associated with hyperactivation in habit-processing regions, with hypoactivation in areas mediating motor and attention processing, and with general hyperconnectivity.