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.

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.

Perception, cognition and consciousness can be modulated as a function of oscillating neural activity, while ongoing neuronal dynamics are influenced by synaptic activity and membrane potential. Consequently, transcranial alternating current stimulation (tACS) may be used for neurological intervention. The advantageous features of tACS include the biphasic and sinusoidal tACS currents, the ability to entrain large neuronal populations, and subtle control over somatic effects. Through neuromodulation of phasic, neural activity, tACS is a powerful tool to investigate the neural correlates of cognition. The rapid development in this area requires clarity about best practices. Here we briefly introduce tACS and review the most compelling findings in the literature to provide a starting point for using tACS. We suggest that tACS protocols be based on functional brain mechanisms and appropriate control experiments, including active sham and condition blinding.

BackgroundMild 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.MethodsSixteen 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.ResultsWe 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.ConclusionOur findings suggest that neurophysiological intervention of MCI could improve glucose metabolism and transient memory function in MCI patients.

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.

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

Nutrition Examination Survey (NHANES) datasets using deep learning and machine learning algorithms.  Deep-learning achieved a high performance for identifying depression on the NHANES datasets of both the United States and South Korea.  Trained deep-learning and machine learning algorithms are useful for estimating the prevalence of depression.

What if you could affect both neuroplasticity and human cognitive performance by parametrically modulating neural oscillations? Ongoing neuronal activity is susceptible to the modulation of synaptic activity and membrane potentials. This susceptibility leverages transcranial alternating current stimulation (tACS) for neuroplastic interventions. 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 dogma and review the most compelling findings from the tACS literature to provide a starting point for the use of tACS under research conditions.