The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.

The study of addiction and impulsion control disorders has shown that behaviors of seeking and consumption of addictive substances are subserved by neurobiological alterations specifically related to brain networks for reward, stress, and executive control, representing the brain's adaptation to the continued use of an addictive substance. In parallel, studies using neuromodulation techniques such as transcranial direct current stimulation (tDCS) have demonstrated promising effects in modulating cognitive and motor functions. This review aims to describe the neurobiology of addiction and some of the most relevant cognitive models of addictive behavior and to clarify how tDCS application modulates the intake and craving for several addictive substances, such as food, alcohol, nicotine, cocaine, crack, methamphetamine, and cannabis. We also discuss the positive and null outcomes of the use of this neuromodulatory technique in the treatment of addiction disorders resulting from the use of these substances. The reviewed findings lead us to conclude that tDCS interventions hold several promising clinical avenues in addiction and impulsive control. However, methodological investigations are necessary for undercover optimal parameters before implementing its clinical application.

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Face recognition is characterized by the interaction between discrete brain regions and consecutive stages of detection and individuation, that is, categorizing an object as a face and categorizing a face at the individual level. We address recent divergences in the literature and integrate theoretical and empirical evidence into a novel neurofunctional model. The main features of the model are 2 separate, but interdependent, neural networks for the tasks of face detection and face individuation, distinguished by distinct patterns of neural activation and temporal signatures. Specifically, we take into account differences in the degree to which view-dependent or -independent processing is integrated, the differential contributions of coarse-template and fine-detail processing to each task, the suggestion that accurate face detection may depend on both face-specific and category-general processing and the role of the lateral occipital complex in the central network for face detection.

The superior temporal gyrus (STG) has been found to play a crucial role in the recognition of actions and facial expressions and may, therefore, be critical for the processing of humorous information. Here we investigated whether tDCS application to the STG would modulate the ability to recognize and appreciate the comic element in serious and comedic situations of misfortune. To this aim, the effects of different types of tDCS stimulation on the STG were analyzed during a task in which the participants were instructed to categorize various misfortunate situations as "comic" or "not comic". Participants underwent three different tDCS conditions: Anodal-right/Cathodal-left; Cathodal-right/Anodal-left; Sham. Images depicting people involved in accidents were grouped into three categories based on the facial expression of the victim: angry or painful (Affective); bewildered and funny (Comic); and images that did not contain the victim's face (No Face). An improvement in mean reaction times in response to both the Comic and No Face stimuli was observed following Anodal-left/Cathodal-right stimulation when compared to sham stimulation. This suggests that this stimulation type reduced the reaction times to socio-emotional complex scenes, regardless of facial expression. The Anodal-right/Cathodal-left stimulation reduced the mean reaction times for Comic stimuli only, suggesting that specifically the right STG may be involved in facial expression recognition and in the appreciation of the comic element in misfortunate situations. These results suggest a functional hemispheric asymmetry in STG response to social stimuli: the left STG might have a role in a general comprehension of social complex situations, while the right STG may be involved in the ability to recognize and integrate specific emotional aspects in a complex scene.