Neuroimaging in alcohol-use disorders: clinical implications and future directions

An EEG-based machine learning method to screen alcohol use disorder

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.

Psychological and Neurobiological Correlates of Food Addiction

Food addiction (FA) is loosely defined as hedonic eating behavior involving the consumption of highly palatable foods (ie, foods high in salt, fat, and sugar) in quantities beyond homeostatic energy requirements. FA shares some common symptomology with other pathological eating disorders, such as binge eating. Current theories suggest that FA shares both behavioral similarities and overlapping neural correlates to other substance addictions. Although preliminary, neuroimaging studies in response to food cues and the consumption of highly palatable food in individuals with FA compared to healthy controls have shown differing activation patterns and connectivity in brain reward circuits including regions such as the striatum, amygdala, orbitofrontal cortex, insula, and nucleus accumbens. Additional effects have been noted in the hypothalamus, a brain area responsible for regulating eating behaviors and peripheral satiety networks. FA is highly impacted by impulsivity and mood. Chronic stress can negatively affect hypothalamic-pituitary-adrenal axis functioning, thus influencing eating behavior and increasing desirability of highly palatable foods. Future work will require clearly defining FA as a distinct diagnosis from other eating disorders.