Relative brain signature: a population-based feature extraction procedure to identify functional biomarkers in the brain of alcoholics

Neural correlates of visuoperceptive changes in severe alcohol use disorder: A critical review of neuroimaging and electrophysiological findings

Visuoperceptive deficits are frequently reported in severe alcohol use disorder (SAUD) and are considered as pervasive and persistent in time. While this topic of investigation has previously driven researchers' interest, far fewer studies have focused on visuoperception in SAUD since the '90s, leaving open central questions regarding the origin and implications of these deficits. To renew research in the field and provide a solid background to work upon, this paper reviews the neural correlates of visuoperception in SAUD, based on data from neuroimaging and electrophysiological studies. Results reveal structural and functional changes within the visual system but also in the connections between occipital and frontal areas. We highlight the lack of integration of these findings in the dominant models of vision which stress the dynamic nature of the visual system and consider the presence of both bottom-up and top-down cerebral mechanisms. Visuoperceptive changes are also discussed in the framework of long-lasting debates regarding the influence of demographic and alcohol-related factors, together stressing the presence of inter-individual differences. Capitalizing on this review, we provide guidelines to inform future research, and ultimately improve clinical care.

Brain functional connectivity after cognitive-bias modification and behavioral changes in abstinent alcohol-use disorder patients

The use of the cognitive-bias modification (CBM) method has emerged as a therapeutic complement in the treatment of alcoholism, producing changes at behavioral and brain level. Nevertheless, the impact of the CBM procedure could be improved by the memory retrieval-extinction process (REP). Different studies have demonstrated that the retrieval of drug memories before extinction training later reduced the reinstatement of drug-seeking behavior. The main aim of this work was to study the effect of the CBM procedure itself, as well as in combination with the activation of alcohol-related memories, on the brain oscillatory activity of abstinent patients with alcohol-use disorder. The study sample comprised 33 patients divided into three groups: A-CBM (alcohol-related memory activation + CBM), N-CBM (neutral memory activation + CBM) and N-INT (no-intervention) groups. A resting-state EEG was obtained before and after each protocol, along with the assessment of the automatic action tendencies. A-CBM group showed a general alpha synchronization increase after the protocol, while the other groups did not show any significant change. Besides, A-CBM group showed significant intra and inter-group differences in the automatic action tendencies after the protocol, reflected in higher avoidance bias toward appetitive, aversive and without context alcohol-related stimuli. The alpha phase synchronization increase could be the neural manifestation of the conditioning produced between the alcohol-related stimuli and the automatic avoidance response. Moreover, the activation of the alcohol-related memories favors this conditioning with those alcohol-related stimuli associated with the activated memories, because it increases their threat level for the abstinence maintenance.

Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness

The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.