One may have experienced his or her footsteps unconsciously synchronize with the footsteps of a friend while walking together, or heard an audience's clapping hands naturally synchronize into a steady rhythm. However, the mechanisms of body movement synchrony and the role of this phenomenon in implicit interpersonal interactions remain unclear. We aimed to evaluate unconscious body movement synchrony changes as an index of implicit interpersonal interaction between the participants, and also to assess the underlying neural correlates and functional connectivity among and within the brain regions. We found that synchrony of both fingertip movement and neural activity between the two participants increased after cooperative interaction. These results suggest that the increase of interpersonal body movement synchrony via interpersonal interaction can be a measurable basis of implicit social interaction. The paradigm provides a tool for identifying the behavioral and the neural correlates of implicit social interaction.

Perception, cognition and consciousness may be modulated as a function of oscillating neural activity. Ongoing neuronal activity is susceptible to the modulation of synaptic activity and membrane potentials. Because of this susceptibility, transcranial alternating current stimulation (tACS) may be used for neuroplastic interventions. The advantageous features of tACS include the biphasic and sinusoidal tACS current, the ability to entrain large neuronal populations, and subtle control over somatic effects. Through neuromodulation of phasic, neural activity, tACS presents a powerful tool for investigations of the neural correlates of cognition alongside other forms of transcranial electric stimulation (tES) and noninvasive brain stimulation (NIBS). The rapid pace of development in this area requires clarification of best practices. Here, we briefly introduce tACS and review the most compelling findings from the literature to provide a starting point for the use of tACS under research conditions.

Objective: Although distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm. Method: Five public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network. Results: The deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm’s classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images. Conclusions: The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.

The midbrain lies deep within the brain and has an important role in reward, motivation, movement and the pathophysiology of various neuropsychiatric disorders such as Parkinson’s disease, schizophrenia, depression and addiction. To date, the primary means of acting on this region has been with pharmacological interventions or implanted electrodes. Here we introduce a new noninvasive brain stimulation technique that exploits the highly interconnected nature of the midbrain and prefrontal cortex to stimulate deep brain regions. Using transcranial direct current stimulation (tDCS) of the prefrontal cortex, we were able to remotely activate the interconnected midbrain and cause increases in participants’ appraisals of facial attractiveness. Participants with more enhanced prefrontal/midbrain connectivity following stimulation exhibited greater increases in attractiveness ratings. These results illustrate that noninvasive direct stimulation of prefrontal cortex can induce neural activity in the distally connected midbrain, which directly effects behavior. Furthermore, these results suggest that this tDCS protocol could provide a promising approach to modulate midbrain functions that are disrupted in neuropsychiatric disorders.

Background: Mild cognitive impairment (MCI) is a syndrome that disrupts an individual’s cognitive function but preserves activities of daily living. MCI is thought to be a prodromal stage of dementia, which disrupts patients’ daily lives and causes severe cognitive dysfunction. Although extensive clinical trials have attempted to slow or stop the MCI to dementia conversion, the results have been largely unsuccessful. The purpose of this study was to determine whether noninvasive electrical stimulation of MCI changes glucose metabolism. Methods: Sixteen MCI patients participated in this study. We used transcranial direct current stimulation (tDCS) (2 mA/day, three times per week for 3 weeks) and assessed positron emission tomography (18 F-FDG) before and after 3 weeks of stimulation. Results: We showed that regular and relatively long-term use of tDCS significantly increased regional cerebral metabolism in MCI patients. Furthermore, subjective memory satisfaction and improvement of the memory strategies of participants were observed only in the real tDCS group after 3 weeks of stimulation. Conclusion: Our findings suggest that neurophysiological intervention of MCI could improve glucose metabolism and transient memory function in MCI patients.