Dark Chocolate Intake May Reduce Fatigue and Mediate Cognitive Function and Gray Matter Volume in Healthy Middle-Aged Adults.

Background: Dark chocolate has attracted attention for its potential for cognitive improvement. Though some reports indicate that dark chocolate is good for cognitive function, others raise doubts. This inconsistency in past results reflecting the relationship between dark chocolate and cognitive function indicates the potential existence of factors that mediate between dark chocolate intake and cognitive function. Methods: With the hypothesis that fatigue may be one such mediating factor, we performed a four-week randomized control study to seek a link between dark chocolate consumption, cognitive function, fatigue, and the brain in middle-aged adults. Results: We found that dark chocolate reduced mental and physical fatigue, and a path analysis revealed that it enhanced vitality, executive function, memory, and gray matter volume both directly and indirectly. Fatigue reduction was also associated with an improvement in physical function, which had a positive impact on emotional functioning, relief of bodily pain, and social functioning. Conclusions: Our results suggest that dark chocolate may help reduce fatigue in individuals, leading to improvements in brain health and various cognitive functions as well as in quality of life.

An fMRI-study of leading and following using rhythmic tapping.

Leading and following is about synchronizing and joining actions in accordance with the differences that the leader and follower roles provide. The neural reactivity representing these roles was measured in an explorative fMRI-study, where two persons lead and followed each other in finger tapping using simple, individual, pre-learnt rhythms. All participants acted both as leader and follower. Neural reactivity for both lead and follow related to social awareness and adaptation distributed over the lateral STG, STS and TPJ. Reactivity for follow contrasted with lead mostly reflected sensorimotor and rhythmic processing in cerebellum IV, V, somatosensory cortex and SMA. During leading, as opposed to following, neural reactivity was observed in the insula and bilaterally in the superior temporal gyrus, pointing toward empathy, sharing of feelings, temporal coding and social engagement. Areas for continuous adaptation, in the posterior cerebellum and Rolandic operculum, were activated during both leading and following. This study indicated mutual adaptation of leader and follower during tapping and that the roles gave rise to largely similar neuronal reactivity. The differences between the roles indicated that leading was more socially focused and following had more motoric- and temporally related neural reactivity.

Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis.

In this study, we propose a deep-learning technique for functional MRI analysis. We introduced a novel self-supervised learning scheme that is particularly useful for functional MRI wherein the subject identity is used as the teacher signal of a neural network. The neural network is trained solely based on functional MRI-scans, and the training does not require any explicit labels. The proposed method demonstrated that each temporal volume of resting state functional MRI contains enough information to identify the subject. The network learned a feature space in which the features were clustered per subject for the test data as well as for the training data; this is unlike the features extracted by conventional methods including region of interests (ROIs) pooling signals and principal component analysis. In addition, applying a simple linear classifier to the per-subject mean of the features (namely "identity feature"), we demonstrated that the extracted features could contribute to schizophrenia diagnosis. The classification accuracy of our identity features was comparable to that of the conventional functional connectivity. Our results suggested that our proposed training scheme of the neural network captured brain functioning related to the diagnosis of psychiatric disorders as well as the identity of the subject. Our results together highlight the validity of our proposed technique as a design for self-supervised learning.

Age-Related Decline of Sensorimotor Integration Influences Resting-State Functional Brain Connectivity.

Age-related decline in sensorimotor integration involves both peripheral and central components related to proprioception and kinesthesia. To explore the role of cortical motor networks, we investigated the association between resting-state functional connectivity and a gap-detection angle measured during an arm-reaching task. Four region pairs, namely the left primary sensory area with the left primary motor area (S1left-M1left), the left supplementary motor area with M1left (SMAleft-M1left), the left pre-supplementary motor area with SMAleft (preSMAleft-SMAleft), and the right pre-supplementary motor area with the right premotor area (preSMAright-PMdright), showed significant age-by-gap detection ability interactions in connectivity in the form of opposite-sign correlations with gap detection ability between younger and older participants. Morphometry and tractography analyses did not reveal corresponding structural effects. These results suggest that the impact of aging on sensorimotor integration at the cortical level may be tracked by resting-state brain activity and is primarily functional, rather than structural. From the observation of opposite-sign correlations, we hypothesize that in aging, a "low-level" motor system may hyper-engage unsuccessfully, its dysfunction possibly being compensated by a "high-level" motor system, wherein stronger connectivity predicts higher gap-detection performance. This hypothesis should be tested in future neuroimaging and clinical studies.

Independent Components of EEG Activity Correlating with Emotional State.

Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell's valence-arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

Brain condition may mediate the association between training and work engagement.

Over the past two decades, the number of studies on work engagement has increased rapidly. Work engagement refers to a positive, affective-motivational state of high energy combined with high levels of dedication and a strong focus on work, leading to various work-related outcomes, including higher work performance. Several studies have indicated that training or coaching may increase work engagement, but other studies have shown contradicting results. These inconsistencies may be due to the indirectness between training/coaching and work engagement. Therefore, we investigated the relationship between training and brain structure as well as between brain structure and work engagement in cognitively normal participants. Brain structure was assessed using neuroimaging-derived measures, including the gray-matter brain healthcare quotient (GM-BHQ) and the fractional-anisotropy brain healthcare quotient (FA-BHQ), which are approved as the international standard (H.861.1) by ITU-T. Work engagement was assessed using the Utrecht Work Engagement Scale. To validate and enrich the analysis, we employed another two representative questionnaires, which are known to be close to but different from work engagement: The Social interaction Anxiety Scale and the Maslach Burnout Inventory-General Survey to gauge the levels of human relation ineffectiveness and burnout. The latter scale is subdivided into three variables including "Exhaustion," "Cynicism," and "Professional Efficacy." The results of the present study indicate that training is associated with an increase of FA-BHQ scores, and that an increase of the FA-BHQ scores is associated with an increase in Work Engagement and a decrease in Cynicism. On the other hand, the training with coaching was associated with a decrease in Interaction Anxiety. However, no correlation was observed for training with Work Engagement or the subscales of Burnout. Likewise, no correlation was observed for FA-BHQ with Exhaustion, Professional Efficacy, and Interaction Anxiety. The results of the current research provide the possibility to use brain information to evaluate training effectiveness from the viewpoint of neuroscience.

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory.

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles' activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of the method our group introduced in to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method's validity showing its potential in diagnostic and rehabilitation fields.

Impaired prefrontal activity to regulate the intrinsic motivation-action link in schizophrenia.

A core feature of schizophrenia (SCZ) is impairment in intrinsic motivation. Although intrinsic motivation plays an important role in enhancing improvement of the social functioning, its neural mechanisms of impairment have yet to be clarified. We hypothesized that abnormal function of the frontostriatal loop consisting of the striatum and lateral prefrontal cortex (LPFC) may be related to impaired intrinsic motivation in SCZ. We tested this by comparing the brain activity measured by functional magnetic resonance imaging and behavioral parameters associated with movement, motivation, and cognitive control between 18 stable SCZ patients and 17 healthy control (HC) participants during a task that elicits intrinsic motivation. We also compared the functional connectivity during resting-state and the fractional anisotropy using diffusion tensor imaging analysis between the two groups. We adopted an enjoyable timing task to stop a stopwatch at an exact time, which in our previous study has demonstrated to elicit intrinsic motivation. Although the performance level in general was not different between groups, the SCZ group performed worse than the HC group in trials following "overshoot" errors (i.e., the response was too late). SCZ participants showed lower intrinsic motivation to the task than the HC group in an inventory report. The striatal activity during the prediction at the task cue period was consistently lower in SCZ participants than in HC. The LPFC activity at the task cue period positively correlated with intrinsic motivation and also with the rate of success following overshoot errors in the HC group, but not in the SCZ group. The LPFC activity at the task cue period was also positively correlated with the striatal activity in both groups. The striatal activity during the feedback period was not significantly different between groups. These results suggest that, unlike HC, the neural activity in the LPFC fails to mediate between prediction of hedonic events and cognitive control of action plans in SCZ, whereas the hedonic response is retained.

Interhemispheric Resting-State Functional Connectivity Predicts Severity of Idiopathic Normal Pressure Hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is characterized by a clinical triad (gait disturbance, dementia, and urinary incontinence), and by radiological findings of enlarged ventricles reflecting disturbance of central spinal fluid circulation. A diagnosis of iNPH is sometimes challenging, and the pathophysiological mechanisms underlying the clinical symptoms of iNPH remain largely unknown. Here, we used an emerging MRI technique, resting-state functional connectivity MRI (rsfcMRI), to develop a subsidiary diagnostic technique and to explore the underlying pathophysiological mechanisms of iNPH. rsfcMRI data were obtained from 11 patients with iNPH and 11 age-matched healthy volunteers, yielding rsfcMRI-derived functional connectivity (FC) from both groups. A linear support vector machine classifier was trained to distinguish the patterns of FCs of the patients with iNPH from those of the healthy volunteers. After dimensional reduction, the support vector machine successfully classified the two groups with an accuracy of 80%. Moreover, we found that rsfcMRI-derived FC carried information to predict the severity of the triad in iNPH. FCs relevant to the classification of severity were mainly based on interhemispheric connectivity, suggesting that disruption of the corpus callosum fibers due to ventricular enlargement may explain the triad of iNPH. The present results support the usefulness of rsfcMRI as a tool to understand pathophysiology of iNPH, and also to help with its clinical diagnosis.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

A single nucleotide polymorphism of the neuropeptide B/W receptor-1 gene influences the evaluation of facial expressions.

Neuropeptide B/W receptor-1 (NPBWR1) is expressed in discrete brain regions in rodents and humans, with particularly strong expression in the limbic system, including the central nucleus of the amygdala. Recently, Nagata-Kuroiwa et al. reported that Npbwr1(-/-) mice showed changes in social behavior, suggesting that NPBWR1 plays important roles in the emotional responses of social interactions.The human NPBWR1 gene has a single nucleotide polymorphism at nucleotide 404 (404A>T; SNP rs33977775). This polymorphism results in an amino acid change, Y135F. The results of an in vitro experiment demonstrated that this change alters receptor function. We investigated the effect of this variation on emotional responses to stimuli of showing human faces with four categories of emotional expressions (anger, fear, happiness, and neutral). Subjects' emotional levels on seeing these faces were rated on scales of hedonic valence, emotional arousal, and dominance (V-A-D). A significant genotype difference was observed in valence evaluation; the 404AT group perceived facial expressions more pleasantly than did the 404AA group, regardless of the category of facial expression. Statistical analysis of each combination of [V-A-D and facial expression] also showed that the 404AT group tended to feel less submissive to an angry face than did the 404AA group. Thus, a single nucleotide polymorphism of NPBWR1 seems to affect human behavior in a social context.

Comparison of the choice effect and the distance effect in a number-comparison task by FMRI.

Behavioral and neurophysiological studies of numerical comparisons have shown a "distance effect," whereby smaller numerical distances between two digits are associated with longer response times and higher activity in the parietal region. In this experiment, we introduced a two-choice condition (between either the smaller/lower or the larger/higher of two digits) and examined its effect on brain activity by fMRI. We observed longer response times and greater activity with the choice of smaller numbers ("choice effect") in several brain regions including the right temporo-parietal region, (pre)cuneus, superior temporal sulcus, precentral gyrus, superior frontal gyrus, bilateral insula, and anterior cingulate cortex. These regions correspond to areas that have been suggested to play a role in attentional shift and response conflict. However, brain activity associated with the distance effect disappeared even though the behavioral distance effect remained. Despite the absence of the distance effect on brain activity, several areas changed activity in relation to response time, including regions that were reported to change activity in both a distance effect and a reaction-time-related manner. The result suggested that the level of task load may change the activity of regions that are responsible for magnitude detection.

Behavioral and near-infrared spectroscopy study of the effects of distance and choice in a number comparison task.

Extensive behavioral and neurophysiological numerical comparison studies have shown that response times are longer and parietal activities are stronger when the numerical distance between two digits is smaller (the distance effect). However, only a few behavioral studies have considered the effect of the choice of larger or smaller numerals in numerical comparisons. Using near-infrared spectroscopy (NIRS), we investigated the neural basis of choosing larger/smaller numerals in number comparison tasks in which subjects were required to choose a larger or smaller digit. Our results showed that choosing a smaller digit induced significantly longer response times (the choice effect) and stronger parietal activities. We also obtained significantly longer response times as the distance effect in accordance with previous works. However, NIRS data did not show any significant difference corresponding to distance effect. Our results and previous studies suggest that the parietal cortex is involved not only in measuring numerical quantities, but also in choosing a numerically larger/smaller quantity among the categories of choice. Potentials and limitations of NIRS were discussed.