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Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology. Simultaneous fMRI-EEG in 26 healthy participants indicate that the basal ganglia cortical network and the neurofeedback control network play different roles in self-regulation, providing further insight into the neural correlates for brain-machine interface control and feedback. [ABSTRACT FROM AUTHOR]

Evolution of the direct, monosynaptic connection from the primary motor cortex to the spinal cord parallels acquisition of hand dexterity and lateralization of hand preference. In non-human mammals, the indirect, multi-synaptic connections between the bilateral primary motor cortices and the spinal cord also participates in controlling dexterous hand movement. However, it remains unknown how the direct and indirect corticospinal pathways work in concert to control unilateral hand movement with lateralized preference in humans. Here we demonstrated the asymmetric functional organization of the two corticospinal networks, by combining network modelling and simultaneous functional magnetic resonance imaging techniques of the brain and the spinal cord. Moreover, we also found that the degree of the involvement of the two corticospinal networks paralleled lateralization of hand preference. The present results pointed to the functionally lateralized motor nervous system that underlies the behavioral asymmetry of handedness in humans. MRI and network modelling reveal correlation between the degree of involvement of the two corticospinal networks and the lateralization of handedness in humans. [ABSTRACT FROM AUTHOR]

Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously. [ABSTRACT FROM AUTHOR]

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions. [ABSTRACT FROM AUTHOR]

Two main neural mechanisms including loss of cortical inhibition and maladaptive plasticity have been thought to be involved in the pathophysiology of focal task‐specific dystonia. Such loss of inhibition and maladaptive plasticity likely correspond to cortical overactivity and disorganized somatotopy, respectively. However, the most plausible mechanism of focal task‐specific dystonia remains unclear. To address this question, we assessed brain activity and somatotopic representations of motor‐related brain areas using functional MRI and behavioral measurement in healthy instrumentalists and patients with embouchure dystonia as an example of focal task‐specific dystonia. Dystonic symptoms were measured as variability of fundamental frequency during long tone playing. We found no significant differences in brain activity between the embouchure dystonia and healthy wind instrumentalists in the motor‐related areas. Assessment of somatotopy, however, revealed significant differences in the somatotopic representations of the mouth area for the right somatosensory cortex between the two groups. Multiple‐regression analysis revealed brain activity in the primary motor and somatosensory cortices, cerebellum, and putamen was significantly associated with variability of fundamental frequency signals representing dystonic symptoms. Conversely, somatotopic representations in motor‐related brain areas were not associated with variability of fundamental frequency signals in embouchure dystonia. The present findings suggest that abnormal motor‐related network activity and aberrant somatotopy correlate with different aspects of mechanisms underlying focal task‐specific dystonia. [ABSTRACT FROM AUTHOR]

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called "preplay" in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain's knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself. [ABSTRACT FROM AUTHOR]

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 fromactivated brain areas and their intensities froman 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. [ABSTRACT FROM AUTHOR]

Although shoulder apprehension is an established clinical finding and is important for the prevention of shoulder dislocation, how this subjective perception is evoked remains unclear. We elucidated the functional neuroplasticity associated with apprehension in patients with recurrent anterior shoulder instability (RSI) using functional magnetic resonance imaging (fMRI). Twelve healthy volunteers and 14 patients with right-sided RSI performed a motor imagery task and a passive shoulder motion task. Brain activity was compared between healthy participants and those with RSI and was correlated with the apprehension intensity reported by participants after each task. Compared to healthy volunteers, participants with RSI exhibited decreased brain activity in the motor network, but increased activity in the hippocampus and amygdala. During the passive motion task, participants with RSI exhibited decreased activity in the left premotor and primary motor/somatosensory areas. Furthermore, brain activity was correlated with apprehension intensity in the left amygdala and left thalamus during the motor imagery task (memory-induced), while a correlation between apprehension intensity and brain activity was found in the left prefrontal cortex during the passive motion task (instability-induced). Our findings provide insight into the pathophysiology of RSI by identifying its associated neural alterations. We elucidated that shoulder apprehension was induced by two different factors, namely instability and memory. [ABSTRACT FROM AUTHOR]

Abstract: Interest has continued in the neural substrates of language switch, which allows multilingual people to select an appropriate language. Using functional magnetic resonance imaging, we investigated the neural substrates for switching between different languages (cross-language switch) in comparison with those for switching between different tasks or between different action sequences. Subjects were 20 native Japanese (L1) speakers with moderate to high proficiency in English (L2). They were asked to judge pronunciation of visually presented Arabic numerals in either L1 or L2 (phonological judgment task) or the numerical meaning of the same stimuli (numerical judgment task). The switching of the tasks was semi-randomly cued by a background color change. Several brain regions showed significantly greater activity for the forward cross-language switching (L1 to L2) than the backward cross-language switching (L2 to L1). Such cross-language switch regions included the right prefrontal cortex (PFC), left superior temporal/supramarginal gyrus (STG/SMG), anterior cingulate cortex (ACC), left inferior frontal gyrus (IFG), and caudate nucleus. Among these cross-language switch regions, the left IFG and caudate nucleus were also involved in the task switching (switching between the phonological and numerical tasks). These findings were supported by an action-sequence switch experiment examining brain activity during switching among different tapping sequences. In the right PFC, moreover, activity during the forward cross-language switching was positively correlated with the subjects’ proficiency in L2. The present study suggests that the right PFC, left IFG, left STG/SMG, ACC, and caudate nucleus might subserve differential aspects of cross-language switch in late bilinguals. [Copyright &y& Elsevier]

Prompted by our neuroimaging findings in 60 normal people, we examined whether focal damage to the hand section of precentral motor regions impairs hand kinesthesia in a patient, and investigated brain regions related to recovery of kinesthetic function. The damage impaired contralateral kinesthesia. The peri-lesional cerebral motor region, together with the ipsilateral intermediate cerebellum, participated in the recovered kinesthetic processing. The study confirmed the importance of precentral motor regions in human kinesthesia, and indicated a contribution of the peri-lesional cerebral region in recovered kinesthesia after precentral damage, which conceptually fits with cases of recovery of motor function. [ABSTRACT FROM AUTHOR]

Abstract: Computation of object orientation could be an independent process from those of other object features, but currently neither the location of human brain areas selectively coding orientation information nor an optimum experimental paradigm have yet been established. In this study, functional magnetic resonance imaging was used to investigate brain activation in the parietal cortices related to object orientation. Using an Arabic digit whose spatial attributes were carefully manipulated, we found parietal areas exclusively sensitive to object orientation, but not to general spatial attention. It seems that, by excluding confounds such as mental manipulation or working memory as well as inherent spatial information within the stimuli, functional segregation within the parietal lobe can be effectively probed. [Copyright &y& Elsevier]

Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD. • We used resting-state functional connectivity to classify between isolated REM behavior disorder and healthy controls. • We used random forest for selecting connectivity and logistic regression/support vector machine for classification. • Classification performance was modest but significant against the classification of data with a random disease label. • Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms. • Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD. [ABSTRACT FROM AUTHOR]

Highlights: [•] The effects of tDCS on cognitive tasks vary among individuals. [•] Bilateral tDCS can improve the performance of mental calculations. [•] Improvement is only seen in subjects with left-hemispheric parietal lateralization. [•] Lateralization of brain activity may predict the effects of bilateral tDCS. [ABSTRACT FROM AUTHOR]

Introduction: Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) of rapid eye movement (REM) sleep behavior disorder (RBD) may provide an early biomarker of α-synucleinopathy. However, few rsfcMRI studies have examined cognitive networks. To elucidate brain network changes in RBD, we performed rsfcMRI in patients with polysomnography-confirmed RBD and healthy controls (HCs), with a sufficiently large sample size in each group.Methods: We analyzed rsfcMRI data from 50 RBD patients and 70 age-matched HCs. Although RBD patients showed no motor signs, some exhibited autonomic and cognitive problems. Several resting-state functional networks were extracted by group independent component analysis from HCs, including the executive-control (ECN), default-mode (DMN), basal ganglia (BGN), and sensory-motor (SMN) networks. Functional connectivity (FC) was compared between groups using dual regression analysis. In the RBD group, correlation analysis was performed between FC and clinical/cognitive scales.Results: Patients with RBD showed reduced striatal-prefrontal FC in ECN, consistent with executive dysfunctions. No abnormalities were found in DMN. In the motor networks, we identified reduced midbrain-pallidum FC in BGN and reduced motor and somatosensory cortex FC in SMN.Conclusion: We found abnormal ECN and normal DMN as a possible hallmark of cognitive dysfunctions in early α-synucleinopathies. We replicated abnormalities in BGN and SMN corresponding to subclinical movement disorder of RBD. RsfcMRI may provide an early biomarker of both cognitive and motor network dysfunctions of α-synucleinopathies. [ABSTRACT FROM AUTHOR]

Introduction: Adults with Parkinson's disease (PD) experience gait disturbances that can sometimes be improved with rhythmic auditory stimulation (RAS); however, the underlying physiological mechanism for this improvement is not well understood. We investigated brain activation patterns in adults with PD and healthy controls (HC) using functional magnetic resonance imaging (fMRI) while participants imagined gait with or without RAS.Methods: Twenty-seven adults with PD who could walk independently and walked more smoothly with rhythmic auditory cueing than without it, and 25 age-matched HC participated in this study. Participants imagined gait in the presence of RAS or white noise (WN) during fMRI.Results: In the PD group, gait imagery with RAS activated cortical motor areas, including supplementary motor areas and the cerebellum, while gait imagery with WN additionally recruited the left parietal operculum. In HC, the induced activation was limited to cortical motor areas and the cerebellum for both the RAS and WN conditions. Within- and between-group analyses demonstrated that RAS reduced the activity of the left parietal operculum in the PD group but not in the HC group (condition-by-group interaction by repeated measures analysis of variance, p < 0.05).Conclusion: During gait imagery in adults with PD, the left parietal operculum was less activated by RAS than by WN, while no change was observed in HC, suggesting that rhythmic auditory stimulation may support the sensory-motor networks involved in gait, thus alleviating the overload of the parietal operculum and compensating for its dysfunction in these patients. [ABSTRACT FROM AUTHOR]

Impaired finger motor function in post-stroke hemiplegia is a debilitating condition with no evidence-based or accessible treatments. Here, we evaluated the neurophysiological effectiveness of direct brain control of robotic exoskeleton that provides movement support contingent with brain activity. To elucidate the mechanisms underlying the neurofeedback intervention, we assessed resting-state functional connectivity with functional magnetic resonance imaging (rsfcMRI) between the ipsilesional sensory and motor cortices before and after a single 1-h intervention. Eighteen stroke patients were randomly assigned to crossover interventions in a double-blind and sham-controlled design. One patient dropped out midway through the study, and 17 patients were included in this analysis. Interventions involved motor imagery, robotic assistance, and neuromuscular electrical stimulation administered to a paretic finger. The neurofeedback intervention delivered stimulations contingent on desynchronized ipsilesional electroencephalographic (EEG) oscillations during imagined movement, and the control intervention delivered sensorimotor stimulations that were independent of EEG oscillations. There was a significant time × intervention interaction in rsfcMRI in the ipsilesional sensorimotor cortex. Post-hoc analysis showed a larger gain in increased functional connectivity during the neurofeedback intervention. Although the neurofeedback intervention delivered fewer total sensorimotor stimulations compared to the sham-control, rsfcMRI in the ipsilesional sensorimotor cortices was increased during the neurofeedback intervention compared to the sham-control. Higher coactivation of the sensory and motor cortices during neurofeedback intervention enhanced rsfcMRI in the ipsilesional sensorimotor cortices. This study showed neurophysiological evidence that EEG-contingent neurofeedback is a promising strategy to induce intrinsic ipsilesional sensorimotor reorganization, supporting the importance of integrating closed-loop sensorimotor processing at a neurophysiological level. • We assessed the neurophysiological effectiveness of direct brain control of neurofeedback intervention. • The experiment was conducted with a crossover design with double-blinded outcome evaluation. • Seventeen stroke patients with hemiplegia participated in this study. • Neurofeedback intervention enhances resting-state functional connectivity on the ipsilesional sensorimotor cortices. • Higher coactivation of the sensorimotor cortices during neurofeedback intervention enhanced the functional connectivity. [ABSTRACT FROM AUTHOR]

Introduction: Attentional bias modification (ABM) alleviates anxiety by moderating biased attentional processing toward threat; however, its neural mechanisms remain unclear. We examined how ABM changes functional connectivity (FC) and functional network measures, leading to anxiety reduction.Methods: Fifty-four healthy anxious individuals received either ABM or sham training for 1 month in a double-blind randomized controlled trial. Anxious traits, attentional control, and attentional bias were assessed. Thirty-five participants completed resting-state functional magnetic resonance imaging (MRI) scans before and after training.Results: ABM significantly mitigated an anxious traits regarding physical stress vulnerability (η2 = 0.12, p = 0.009). As compared to sham training, ABM significantly strengthened FC between the pulvinar and transverse temporal gyrus along the temporoparietal junction (T = 3.90, FDR-correctedp = 0.010), whereas it decreased FC between the postCG and ventral fronto-parietal network (vFPN) regions such as the anterior insula and ventrolateral prefrontal cortex (all T ≤ - 3.19, FDR-correctedp ≤ 0.034). Although ABM diminished network measures of the postcentral gyrus (postCG) (all T ≤ - 4.30, FDR-correctedp ≤ 0.006), only the pulvinar-related FC increase was specifically correlated with anxiety reduction (r = - 0.46, p = 0.007).Limitations: Per-protocol analysis and reduced sample size in MRI analysis.Conclusions: ABM might augment the pulvinar's control over vFPN to maintain endogenous attention to a behavioral goal, while diminishing the information exchanges of the postCG with vFPN to inhibit the capture of exogenous attention by potential threats. The pulvinar might play a critical role in ABM anxiolytic efficacy. [ABSTRACT FROM AUTHOR]