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Introduction: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore corticocortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection--source-informed reconstruction approach (SSP--SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP--SIR. Methods: We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS--EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP--SIR, and subsequent comparisons were made with the ground truth data. Results: ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP--SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP--SIR being better when difference between topographies was larger. Overall, SSP--SIR performed better than ICA across the tested conditions. Based on these simulations, SSP--SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials. Discussion: Selecting between ICA and SSP--SIR should be guided by the characteristics of the artifacts. SSP--SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP--SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse. [ABSTRACT FROM AUTHOR]

Introduction: Wide use of facemasks is one of the many consequences of the COVID-19 pandemic. Methods: We used an established working memory n-back task in functional magnetic resonance imaging (fMRI) to explore whether wearing a KN95/FFP2 facemask affects overall performance and brain activation patterns. We provide here a prospective crossover design 3 T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 24 community-dwelling male healthy control participants (mean age ± SD = 37.6 ± 12.7 years) performing a 2-back task. Data analysis was performed using the FSL toolbox, performing both task-related and functional connectivity independent component analyses. Results: Wearing an FFP2/KN95 facemask did not impact behavioral measures of the 2-back task (response time and number of errors). The 2-back task resulted in typical activations in working-memory related areas in both MASK and NOMASK conditions. There were no statistically significant differences in MASK versus NOMASK while performing the 2-back task in both task-related and functional connectivity fMRI analyses. Conclusion: The effect of wearing a tight FFP2/KN95 facemasks did not significantly affect working memory performance and brain activation patterns of functional connectivity. [ABSTRACT FROM AUTHOR]

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI. [ABSTRACT FROM AUTHOR]

IntroductionThe combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection–source-informed reconstruction approach (SSP–SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP–SIR.MethodsWe simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS–EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP–SIR, and subsequent comparisons were made with the ground truth data.ResultsICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP–SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP–SIR being better when difference between topographies was larger. Overall, SSP–SIR performed better than ICA across the tested conditions. Based on these simulations, SSP–SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials.DiscussionSelecting between ICA and SSP–SIR should be guided by the characteristics of the artifacts. SSP–SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP–SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

IntroductionWide use of facemasks is one of the many consequences of the COVID-19 pandemic.MethodsWe used an established working memory n-back task in functional magnetic resonance imaging (fMRI) to explore whether wearing a KN95/FFP2 facemask affects overall performance and brain activation patterns. We provide here a prospective crossover design 3 T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 24 community-dwelling male healthy control participants (mean age ± SD = 37.6 ± 12.7 years) performing a 2-back task. Data analysis was performed using the FSL toolbox, performing both task-related and functional connectivity independent component analyses.ResultsWearing an FFP2/KN95 facemask did not impact behavioral measures of the 2-back task (response time and number of errors). The 2-back task resulted in typical activations in working-memory related areas in both MASK and NOMASK conditions. There were no statistically significant differences in MASK versus NOMASK while performing the 2-back task in both task-related and functional connectivity fMRI analyses.ConclusionThe effect of wearing a tight FFP2/KN95 facemasks did not significantly affect working memory performance and brain activation patterns of functional connectivity.

IInvestigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visualmemory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG. [ABSTRACT FROM AUTHOR]

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain’s functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCBJ PET. Methods: Independent component analyses (ICA) were performed on restingstate fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: Insight into neural mechanisms of tremor dominant (TD) and postural instability and gait disorder (PIGD) subtypes in Parkinson's disease (PD) is vital for understanding pathophysiological hypotheses underlying this phenotype. However, network disturbances and their correlation with motor subtypes of PD remain unclear. We aimed to investigate the alterations of intra- and inter-network functional connectivity (FC) in drug-naive PD patients with different motor subtypes. Methods: Resting-state functional magnetic resonance imaging was performed on 25 drug-naive PD patients with TD (PD-TD) and 40 drug-naive PD patients with PIGD (PD-PIGD), and 37 healthy controls (HCs) underwent. The following networks were extracted using independent component analysis: sensorimotor network (SMN), left executive control network (LECN), right executive control network, anterior salience network (aSN), posterior salience network (pSN), ventral attention network (VAN), dorsal attention network (DAN), default mode network (DMN), visual network, and auditory network (AN). We measured FC values within and between these networks. Results: There were no detectable variations in intra-network FC. PD-PIGD group demonstrated lower FC between aSN and pSN, as well as between VAN and DMN, in contrast to PD-TD group. Particularly, the FC strength between VAN and DMN was positively correlated with TD and tremor scores, and the best fitting classification models of TD and PIGD subtypes were based on the FC between aSN and pSN. Compared with HCs, both PD-TD and PD-PIGD patients displayed decreased FC between two SMN subnetworks, while PD-TD patients exhibited increased FC between the SMN subnetwork and pSN, and between LECN and VAN. Furthermore, PD-PIGD patients demonstrated decreased FC between the SMN subnetwork and AN. Conclusions: The altered FC between aSN and pSN can be an imaging marker to distinguish PD-TD from PD-PIGD. We for the first time disclosed that the PD-TD patients compensated by increasing attention resources and the PD-PIGD patients displayed reduced FC between SMN and AN. Our findings provide a basis for identification and precision treatment of PD motor subtypes. [ABSTRACT FROM AUTHOR]

There is an urgent need to understand the nature of awareness in people with severe Alzheimer's disease (AD) to ensure effective person-centered care. Objective biomarkers of awareness validated in other clinical groups (e.g., anesthesia, minimally conscious states) offer an opportunity to investigate awareness in people with severe AD. In this article we demonstrate the feasibility of using Transcranial magnetic stimulation (TMS) combined with EEG, event related potentials (ERPs) and fMRI to assess awareness in severe AD. TMS-EEG was performed in six healthy older controls and three people with severe AD. The perturbational complexity index (PCIST) was calculated as a measure of capacity for conscious awareness. People with severe AD demonstrated a PCIST around or below the threshold for consciousness, suggesting reduced capacity for consciousness. ERPs were recorded during a visual perception paradigm. In response to viewing faces, two patients with severe AD provisionally demonstrated similar visual awareness negativity to healthy controls. Using a validated fMRI movie-viewing task, independent component analysis in two healthy controls and one patient with severe AD revealed activation in auditory, visual and fronto-parietal networks. Activation patterns in fronto-parietal networks did not significantly correlate between the patient and controls, suggesting potential differences in conscious awareness and engagement with the movie. Although methodological issues remain, these results demonstrate the feasibility of using objective measures of awareness in severe AD. We raise a number of challenges and research questions that should be addressed using these biomarkers of awareness in future studies to improve understanding and care for people with severe AD. [ABSTRACT FROM AUTHOR]

Background: Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. Materials and methods: We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N - 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. Results: Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. Conclusion: These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP. [ABSTRACT FROM AUTHOR]

The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity ( Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimensionspecific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations. [ABSTRACT FROM AUTHOR]

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.

The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.

Pre-reading language skills develop rapidly in early childhood and are related to brain structure and functional architecture in young children prior to formal education. However, the early neurobiological development that supports these skills is not well understood. Here we acquired anatomical, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) from 35 children at 3.5 years of age. Children were assessed for prereading abilities using the NEPSY-II subtests 1 year later (4.5 years). We applied a data-driven linked independent component analysis (ICA) to explore the shared co-variation of gray and white matter measures. Two sources of structural variation at 3.5 years of age demonstrated relationships with Speeded Naming scores at 4.5 years of age. The first imaging component involved volumetric variability in reading-related cortical regions alongside microstructural features of the superior longitudinal fasciculus (SLF). The second component was dominated by cortical volumetric variations within the cerebellum and visual association area. In a subset of children with rsfMRI data, we evaluated the inter-network functional connectivity of the left-lateralized fronto-parietal language network (FPL) and its relationship with pre-reading measures. Higher functional connectivity between the FPL and the default mode and visual networks at 3.5 years significantly predicted better Phonological Processing scores at 4.5 years. Together, these results suggest that the integration of functional networks, as well as the co-development of white and gray matter brain structures in early childhood, support the emergence of pre-reading measures in preschool children. [ABSTRACT FROM AUTHOR]

AimResting state functional magnetic resonance imaging (rs-fMRI) was used to analyze changes in functional connectivity (FC) within various brain networks and functional network connectivity (FNC) among various brain regions in patients with high myopia (HM).Methodsrs-fMRI was used to scan 82 patients with HM (HM group) and 59 healthy control volunteers (HC group) matched for age, sex, and education level. Fourteen resting state networks (RSNs) were extracted, of which 11 were positive. Then, the FCs and FNCs of RSNs in HM patients were examined by independent component analysis (ICA).ResultsCompared with the HC group, FC in visual network 1 (VN1), dorsal attention network (DAN), auditory network 2 (AN2), visual network 3 (VN3), and sensorimotor network (SMN) significantly increased in the HM group. FC in default mode network 1 (DMN1) significantly decreased. Furthermore, some brain regions in default mode network 2 (DMN2), default mode network 3 (DMN3), auditory network 1 (AN1), executive control network (ECN), and significance network (SN) increased while others decreased. FNC analysis also showed that the network connection between the default mode network (DMN) and cerebellar network (CER) was enhanced in the HM group.ConclusionCompared with HCs, HM patients showed neural activity dysfunction within and between specific brain networks, particularly in the DMN and CER. Thus, HM patients may have deficits in visual, cognitive, and motor balance functions.

Previous research indicates that individuals with depressive disorders (DD) have aberrant resting state functional connectivity and may experience memory dysfunction. While resting state functional connectivity may be affected by experiences preceding the resting state scan, little is known about this relationship in individuals with DD. Our study examined this question in the context of object memory. 52 individuals with DD and 45 healthy controls (HC) completed clinical interviews, and a memory encoding task followed by a forced-choice recognition test. A 5-min resting state fMRI scan was administered immediately after the forced-choice task. Resting state networks were identified using group Independent Component Analysis across all participants. A network modeling analysis conducted on 22 networks using FSLNets examined the interaction effect of diagnostic status and memory accuracy on the between-network connectivity. We found that this interaction significantly affected the relationship between the network comprised of the medial prefrontal cortex, posterior cingulate cortex, and hippocampal formation and the network comprised of the inferior temporal, parietal, and prefrontal cortices. A stronger positive correlation between these two networks was observed in individuals with DD who showed higher memory accuracy, while a stronger negative correlation (i.e., anticorrelation) was observed in individuals with DD who showed lower memory accuracy prior to resting state. No such effect was observed for HC. The former network cross-correlated with the default mode network (DMN), and the latter cross-correlated with the dorsal attention network (DAN). Considering that the DMN and DAN typically anticorrelate, we hypothesize that our findings indicate aberrant reactivation and consolidation processes that occur after the task is completed. Such aberrant processes may lead to continuous "replay" of previously learned, but currently irrelevant, information and underlie rumination in depression. [ABSTRACT FROM AUTHOR]

The risk of cognitive impairment in patients with type 2 diabetes mellitus (T2DM) is significantly higher than that in the general population, but the exact neurophysiological mechanism underlying this is still unclear. An abnormal change in the intrinsic anticorrelation of the dorsal attention network (DAN) and the default mode network (DMN) is thought to be the mechanism underlying cognitive deficits that occur in many psychiatric disorders, but this association has rarely been tested in T2DM. This study explored the relationship between the interaction patterns of the DAN-DMN and clinical/cognitive variables in patients with T2DM. Forty-four patients with T2DM and 47 sex-, age-, and education-matched healthy controls (HCs) underwent neuropsychological assessments, independent component analysis (ICA), and functional network connection analysis (FNC). The relationship of DAN-DMN anticorrelation with the results of a battery of neuropsychological tests was also assessed. Relative to the HC group, the DMN showed decreased functional connectivity (FC) in the right precuneus, and the DAN showed decreased FC in the left inferior parietal lobule (IPL) in patients with T2DM. Subsequent FNC analysis revealed that, compared with the HC group, the T2DM patients displayed significantly increased inter-network connectivity between the DAN and DMN. These abnormal changes were correlated with the scores of multiple neuropsychological assessments (P < 0.05). These findings indicate abnormal changes in the interaction patterns of the DAN-DMN may be involved in the neuropathology of attention and general cognitive dysfunction in T2DM patients. [ABSTRACT FROM AUTHOR]

Background: Irritable Bowel Syndrome (IBS), as a functional gastrointestinal disorder, is characterized by abdominal pain and distension. Recent studies have shown that acupuncture treatment improves symptoms of diarrhea-predominant irritable bowel syndrome (IBS-D) by altering networks in certain brain regions. However, few studies have used resting-state functional magnetic resonance imaging (fMRI) to compare altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment. Objective: To analyze altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment. Methods: A total of 74 patients with IBS-D and 31 healthy controls (HCs) were recruited for this study. fMRI examination was performed in patients with IBS-D before and after acupuncture treatment, but only at baseline in HCs. Data on the left frontoparietal network (LFPN), default mode network (DMN), salience network (SN), ventral attention network (VAN), auditory network (AN), visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and right frontoparietal network (RFPN) were subjected to independent component analysis (ICA). The functional connectivity values of inter-network were explored. Results: Acupuncture decreased irritable bowel syndrome symptom severity score (IBS-SSS) and Hamilton Anxiety Scale (HAMA). It also ameliorated symptoms related to IBS-D. Notably, functional connectivity between AN and VAN, SMN and DMN, RFPN and VAN in IBS-D patients after acupuncture treatment was different from that in HCs. Furthermore, there were differences in functional connectivity between DMN and DAN, DAN and LFPN, DMN and VAN before and after acupuncture treatment. The inter-network changes in DMN-VAN were positively correlated with changes in HAMA, life influence degree, and IBS-SSS in IBS-D. Conclusion: Altered inter-network functional connectivity is involved in several important hubs in large-scale networks. These networks are altered by acupuncture stimulation in patients with IBS-D. [ABSTRACT FROM AUTHOR]

Background: Neuronal loss in Parkinson's Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored. Objective/Hypothesis: To investigate the distributed network properties associated with STN-DBS in patients with advanced PD. Methods: Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states. Results: Dynamic analysis identified two unique brain states: a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state – STN-DBS was negatively correlated with network assortativity. Conclusion: Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS. [ABSTRACT FROM AUTHOR]

Background: Irritable Bowel Syndrome (IBS), as a functional gastrointestinal disorder, is characterized by abdominal pain and distension. Recent studies have shown that acupuncture treatment improves symptoms of diarrhea-predominant irritable bowel syndrome (IBS-D) by altering networks in certain brain regions. However, few studies have used resting-state functional magnetic resonance imaging (fMRI) to compare altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment.Objective: To analyze altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment.Methods: A total of 74 patients with IBS-D and 31 healthy controls (HCs) were recruited for this study. fMRI examination was performed in patients with IBS-D before and after acupuncture treatment, but only at baseline in HCs. Data on the left frontoparietal network (LFPN), default mode network (DMN), salience network (SN), ventral attention network (VAN), auditory network (AN), visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and right frontoparietal network (RFPN) were subjected to independent component analysis (ICA). The functional connectivity values of inter-network were explored.Results: Acupuncture decreased irritable bowel syndrome symptom severity score (IBS-SSS) and Hamilton Anxiety Scale (HAMA). It also ameliorated symptoms related to IBS-D. Notably, functional connectivity between AN and VAN, SMN and DMN, RFPN and VAN in IBS-D patients after acupuncture treatment was different from that in HCs. Furthermore, there were differences in functional connectivity between DMN and DAN, DAN and LFPN, DMN and VAN before and after acupuncture treatment. The inter-network changes in DMN-VAN were positively correlated with changes in HAMA, life influence degree, and IBS-SSS in IBS-D.Conclusion: Altered inter-network functional connectivity is involved in several important hubs in large-scale networks. These networks are altered by acupuncture stimulation in patients with IBS-D.

Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic fluctuations are static throughout the entire scan. However, it has recently been suggested that the dynamic alternations in functional connectivity (FC) may reflect the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior, and thus may act as biomarkers of disease. Methods: In this study, the resting-state functional MRI (fMRI) data were collected from 26 patients with GAD, 22 patients with PD, and 26 healthy controls (HCs). We investigated dynamic functional connectivity (DFC) by using the group spatial independent component analysis, a sliding window approach, and the k -means clustering methods. For group comparisons, the temporal properties of DFC states were analyzed statistically. Results: The dynamic analysis demonstrated two discrete connectivity "States" across the entire group, namely, a more segregated State I and a strongly integrated State II. Compared with HCs, patients with both GAD and PD spent more time in the weakly within-network State I, while performing fewer transitions and dwelling shorter in the integrated State II. Additionally, the analysis of DFC strength showed that connections associated with ADs were identified including the regions that belonged to default mode (DM), executive control (EC), and salience (SA) networks, especially the connections between SA and DM networks. However, no significant difference was found between the GAD and PD groups in temporal features and connection strength. Conclusions: More common but less specific alterations were detected in the GAD and PD groups, which implied that they might have similar state-dependent neurophysiological mechanisms and, in addition, could hopefully help us better understand their abnormal affective and cognitive performances in the clinic. [ABSTRACT FROM AUTHOR]

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding (t-SNE) neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions (whether to accept the brand extension or not). The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory (LSTM) layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis (PCA) network, a recurrent independent component correlation algorithm [independent component analysis (ICA)] network, a t-SNE support vector machine (SVM) network, a t-SNE back propagation (BP) neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency. [ABSTRACT FROM AUTHOR]

Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

Awareness of internal bodily sensations (interoceptive awareness; IA) and its connection to complex socioemotional abilities like empathy has been postulated, yet the functional neural circuitry they share remains poorly understood. The present fMRI study employs independent component analysis (ICA) to investigate which empathy facet (Cognitive or Affective) shares resting-state functional connectivity (rsFC) and/or BOLD variability (rsBOLD) with IA. Healthy participants viewed an abstract nonsocial movie demonstrated to evoke strong rsFC in brain networks resembling rest (InScapes), and resultant rsFC and rsBOLD data were correlated with self-reported empathy and IA questionnaires. We demonstrate a bidirectional behavioral and neurobiological relationship between empathy and IA, depending on the type of empathy interrogated: Affective empathy and IA share both rsFC and rsBOLD, while Cognitive empathy and IA only share rsBOLD. Specifically, increased rsFC in the right inferior frontal operculum (rIFO) of a larger attention network was associated with increased vicarious experience but decreased awareness of inner body sensations. Furthermore, increased rsBOLD between brain regions of an interoceptive network was related to increased sensitivity to internal sensations along with decreased Affective empathy. Finally, increased rsBOLD between brain regions subserving a mentalizing network related to not only an improved ability to take someone's perspective, but also a better sense of mind-body interconnectedness. Overall, these findings suggest that the awareness of one's own internal body changes (IA) is related to the socioemotional ability of feeling and understanding another's emotional state (empathy) and critically, that this relationship is reflected in the brain's resting state neuroarchitecture. Methodologically, this work highlights the importance of utilizing rsBOLD as a complementary window alongside rsFC to better understand neurological phenomena. Our results may be beneficial in aiding diagnosis in clinical populations such as autism spectrum disorder (ASD), where participants may be unable to complete tasks or questionnaires due to the severity of their symptoms. [ABSTRACT FROM AUTHOR]

Evoked potentials in the amplitude-time spectrum of the electroencephalogram are commonly used to assess the extent of brain responses to stimulation with noxious contact heat. The magnitude of the N - and P -waves are used as a semi-objective measure of the response to the painful stimulus: the higher the magnitude, the more painful the stimulus has been perceived. The strength of the N - P -wave response is also largely dependent on the chosen reference electrode site. The goal of this study was to examine which reference technique excels both in practical and theoretical terms when analyzing noxious contact heat evoked potentials (CHEPS) in the amplitude-time spectrum. We recruited 21 subjects (10 male, 11 female, mean age of 55.79 years). We applied seven noxious contact heat stimuli using two temperatures, 51°C, and 54°C, to each subject. During EEG analysis, we aimed to identify the referencing technique which produces the highest N -wave and P -wave amplitudes with as little artifactual influence as possible. For this purpose, we applied the following six referencing techniques: mathematically linked A1/A2 (earlobes), average reference, REST, AFz, Pz, and mathematically linked PO7/PO8. We evaluated how these techniques impact the N - P amplitudes of CHEPS based on our data from healthy subjects. Considering all factors, we found that mathematically linked earlobes to be the ideal referencing site to use when displaying and evaluating CHEPS in the amplitude-time spectrum. [ABSTRACT FROM AUTHOR]

BackgroundHigh-tension glaucoma (HTG) is the most common type of primary open angle glaucoma and elevated intraocular pressure (IOP) is the major risk factor of the disease. The aim of this study was to assess alterations in resting-state visual networks in patients with HTG and investigate the effect of elevated IOP on the visual networks.MethodsT1-weighted and resting-state functional MRI images were acquired from 36 HTG patients (aged 49.22 ± 15.26 years) and 20 healthy controls (aged 49.90 ± 5.62 years). Group independent component analysis (ICA) was utilized to evaluate altered functional connectivity (FC) in resting-state visual networks between HTG patients and healthy controls. Pearson correlation analysis between mean IOP and altered FCs in the visual networks was performed.ResultsICA demonstrated decreased FCs in HTG group in the left calcarine cortex of the lateral visual network, in the bilateral lingual gyrus of the medial visual network and in the bilateral lingual gyrus of the occipital visual network compared with healthy controls. Furthermore, correlation analysis revealed negative correlation between mean IOP and altered FC within the lateral visual network.ConclusionThe results suggested reduced FCs between primary and higher visual cortices in HTG brain. The IOP elevation might be responsible for the functional alterations in the visual networks.

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities. Artifacts generated from body activities (e.g., eyeblinks and teeth clenches) often contaminate EEGs and make EEG-based classification/identification hard. Although independent component analysis (ICA) is the gold-standard technique for attenuating the effects of such contamination, the estimated independent components are still mixed with artifactual and neuronal information because ICA relies only on the independence assumption. The same problem occurs when using independent vector analysis (IVA), an extended ICA method. To solve this problem, we designed an independent low-rank matrix analysis (ILRMA)-based automatic artifact reduction technique that clearly models sources from observations under the independence assumption and a low-rank nature in the frequency domain. For automatic artifact reduction, we combined the signal separation technique with an independent component classifier for EEGs named ICLabel. To assess the comparative efficiency of the proposed method, the discriminabilities of artifact-reduced EEGs using ICA, IVA, and ILRMA were determined using an open-access EEG dataset named OpenBMI, which contains EEG data obtained through three BCI paradigms [motor-imagery (MI), event-related potential (ERP), and steady-state visual evoked potential (SSVEP)]. BCI performances were obtained using these three paradigms after applying artifact reduction techniques, and the results suggested that our proposed method has the potential to achieve higher discriminability than ICA and IVA for BCIs. In addition, artifact reduction using the ILRMA approach clearly improved (by over 70%) the averaged BCI performances using artifact-reduced data sufficiently for most needs of the BCI community. The extension of ICA families to supervised separation that leaves the discriminative ability would further improve the usability of BCIs for real-life environments in which artifacts frequently contaminate EEGs.

Although anatomical brain hemispheric asymmetries have been clearly documented in the infant brain, findings concerning functional hemispheric specialization have been inconsistent. The present report aims to assess whether bilaterally symmetric synchronous activity between the two hemispheres is a characteristic of the infant brain. To asses cortical bilateral synchronicity, we used decomposition by independent component analysis (ICA) of high-density electroencephalographic (EEG) data collected in an auditory passive oddball paradigm. Decompositions of concatenated 64-channel EEG data epochs from each of 34 typically developing 6-month-old infants and from 18 healthy young adults participating in the same passive auditory oddball protocol were compared to characterize differences in functional brain organization between early life and adulthood. Our results show that infant EEG decompositions comprised a larger number of independent component (IC) effective source processes compatible with a cortical origin and having bilaterally near-symmetric scalp projections (13.8% of the infant data ICs presented a bilateral pattern vs. 4.3% of the adult data ICs). These IC projections could be modeled as the sum of potentials volume-conducted to the scalp from synchronous locally coherent field activities in corresponding left and right cortical source areas. To conclude, in this paradigm, source-resolved infant brain EEG exhibited more bilateral synchronicity than EEG produced by the adult brain, supporting the hypothesis that more strongly unilateral and likely more functionally specialized unihemispheric cortical field activities are concomitants of brain maturation.

Environmental hyperthermia is a common risk factor for occupational safety in many situations due to decreased vigilance performances. Previously, we have reported that decreased resting-state functional connectivity within the default mode network (DMN) and decreased activations in dorsal attention network (DAN) such as dorsolateral prefrontal cortex (DLPFC) were correlated with selective attention deficits during hyperthermia. However, whether the inherent functionally organized anti-correlation between the DMN and DAN would contribute to the behavioral deficits remains unclear. In this study, we collected the resting-state fMRI data of 25 participants during two simulated thermal conditions: normothermic condition (25°C for 1 h) and hyperthermic condition (50°C for 1 h). Using group independent component analysis (ICA), we investigated the functional connectivity within the DMN and DAN, as well as the anti-correlations between both networks. Paired comparisons revealed that decreased intranetwork functional connectivity in the medial prefrontal cortex (mPFC)/anterior cingulate cortex (ACC) in the DMN contributed to executive control performance during hyperthermia using multivariate linear regression analysis. Paired comparison on the DAN showed that increased one in the posterior part of the middle and inferior temporal gyrus nearby the temporal–parietal junction area contributed to preserved alerting performance. Lastly but most importantly, we found that decreased correlation between mPFC in the DMN and intraparietal sulcus (IPS) area in the DAN contributed to the executive control deficit, suggesting a weaker intrinsic anti-correlation between DMN and DAN during hyperthermia. These findings indicated that a functional reorganized architecture of DMN and DAN might provide a potential neural basis of the selective deficits for different cognitive-demand attention tasks in high-temperature environments. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities. Artifacts generated from body activities (e.g., eyeblinks and teeth clenches) often contaminate EEGs and make EEG-based classification/identification hard. Although independent component analysis (ICA) is the gold-standard technique for attenuating the effects of such contamination, the estimated independent components are still mixed with artifactual and neuronal information because ICA relies only on the independence assumption. The same problem occurs when using independent vector analysis (IVA), an extended ICA method. To solve this problem, we designed an independent low-rank matrix analysis (ILRMA)-based automatic artifact reduction technique that clearly models sources from observations under the independence assumption and a low-rank nature in the frequency domain. For automatic artifact reduction, we combined the signal separation technique with an independent component classifier for EEGs named ICLabel. To assess the comparative efficiency of the proposed method, the discriminabilities of artifact-reduced EEGs using ICA, IVA, and ILRMA were determined using an open-access EEG dataset named OpenBMI, which contains EEG data obtained through three BCI paradigms [motor-imagery (MI), event-related potential (ERP), and steady-state visual evoked potential (SSVEP)]. BCI performances were obtained using these three paradigms after applying artifact reduction techniques, and the results suggested that our proposed method has the potential to achieve higher discriminability than ICA and IVA for BCIs. In addition, artifact reduction using the ILRMA approach clearly improved (by over 70%) the averaged BCI performances using artifact-reduced data sufficiently for most needs of the BCI community. The extension of ICA families to supervised separation that leaves the discriminative ability would further improve the usability of BCIs for real-life environments in which artifacts frequently contaminate EEGs. [ABSTRACT FROM AUTHOR]

Although anatomical brain hemispheric asymmetries have been clearly documented in the infant brain, findings concerning functional hemispheric specialization have been inconsistent. The present report aims to assess whether bilaterally symmetric synchronous activity between the two hemispheres is a characteristic of the infant brain. To asses cortical bilateral synchronicity, we used decomposition by independent component analysis (ICA) of high-density electroencephalographic (EEG) data collected in an auditory passive oddball paradigm. Decompositions of concatenated 64-channel EEG data epochs from each of 34 typically developing 6-month-old infants and from 18 healthy young adults participating in the same passive auditory oddball protocol were compared to characterize differences in functional brain organization between early life and adulthood. Our results show that infant EEG decompositions comprised a larger number of independent component (IC) effective source processes compatible with a cortical origin and having bilaterally near-symmetric scalp projections (13.8% of the infant data ICs presented a bilateral pattern vs. 4.3% of the adult data ICs). These IC projections could be modeled as the sum of potentials volume-conducted to the scalp from synchronous locally coherent field activities in corresponding left and right cortical source areas. To conclude, in this paradigm, source-resolved infant brain EEG exhibited more bilateral synchronicity than EEG produced by the adult brain, supporting the hypothesis that more strongly unilateral and likely more functionally specialized unihemispheric cortical field activities are concomitants of brain maturation. [ABSTRACT FROM AUTHOR]

Stuttering is a disorder that impacts the smooth flow of speech production and is associated with a deficit in sensorimotor integration. In a previous experiment, individuals who stutter were able to vocally compensate for pitch shifts in their auditory feedback, but they exhibited more variability in the timing of their corrective responses. In the current study, we focused on the neural correlates of the task using functional MRI. Participants produced a vowel sound in the scanner while hearing their own voice in real time through headphones. On some trials, the audio was shifted up or down in pitch, eliciting a corrective vocal response. Contrasting pitch-shifted vs. unshifted trials revealed bilateral superior temporal activation over all the participants. However, the groups differed in the activation of middle temporal gyrus and superior frontal gyrus [Brodmann area 10 (BA 10)], with individuals who stutter displaying deactivation while controls displayed activation. In addition to the standard univariate general linear modeling approach, we employed a data-driven technique (independent component analysis, or ICA) to separate task activity into functional networks. Among the networks most correlated with the experimental time course, there was a combined auditory-motor network in controls, but the two networks remained separable for individuals who stuttered. The decoupling of these networks may account for temporal variability in pitch compensation reported in our previous work, and supports the idea that neural network coherence is disturbed in the stuttering brain. [ABSTRACT FROM AUTHOR]

Stimulating the primary motor cortex (M1) using transcranial magnetic stimulation (TMS) causes unique multisensory experience such as the targeted muscle activity, afferent/reafferent sensory feedback, tactile sensation over the scalp and "click" sound. Although the human M1 has been intensively investigated using TMS, the experience of the M1 stimulation has not been elucidated at the whole brain. Here, using concurrent TMS/fMRI, we investigated the acute effect of the M1 stimulation of functional brain networks during task and at rest. A short train of 1 Hz TMS pulses applied to individuals' hand area in the M1 during motor execution or at rest. Employing the independent component analysis (ICA), we showed the M1 stimulation decreased the motor networks activity when the networks were engaged in the task and increased the deactivation of networks when the networks were not involved in the ongoing task. The M1 stimulation induced the activation in the key networks involved in bodily self-consciousness (BSC) including the insular and rolandic operculum systems regardless of states. The degree of activation in these networks was prominent at rest compared to task conditions, showing the state-dependent TMS effect. Furthermore, we demonstrated that the M1 stimulation modulated other domain-general networks such as the default mode network (DMN) and attention network and the inter-network connectivity between these networks. Our results showed that the M1 stimulation induced the widespread changes in the brain at the targeted system as well as non-motor, remote brain networks, specifically related to the BSC. Our findings shed light on understanding the neural mechanism of the complex and multisensory experience of the M1 stimulation. [ABSTRACT FROM AUTHOR]

Deficits in basal ganglia-based inhibitory and timing circuits along with sensorimotor internal modeling mechanisms are thought to underlie stuttering. However, much remains to be learned regarding the precise manner how these deficits contribute to disrupting both speech and cognitive functions in those who stutter. Herein, we examine the suitability of electroencephalographic (EEG) mu rhythms for addressing these deficits. We review some previous findings of mu rhythm activity differentiating stuttering from non-stuttering individuals and present some new preliminary findings capturing stuttering-related deficits in working memory. Mu rhythms are characterized by spectral peaks in alpha (8–13 Hz) and beta (14–25 Hz) frequency bands (mu-alpha and mu-beta). They emanate from premotor/motor regions and are influenced by basal ganglia and sensorimotor function. More specifically, alpha peaks (mu-alpha) are sensitive to basal ganglia-based inhibitory signals and sensory-to-motor feedback. Beta peaks (mu-beta) are sensitive to changes in timing and capture motor-to-sensory (i.e., forward model) projections. Observing simultaneous changes in mu-alpha and mu-beta across the time-course of specific events provides a rich window for observing neurophysiological deficits associated with stuttering in both speech and cognitive tasks and can provide a better understanding of the functional relationship between these stuttering symptoms. We review how independent component analysis (ICA) can extract mu rhythms from raw EEG signals in speech production tasks, such that changes in alpha and beta power are mapped to myogenic activity from articulators. We review findings from speech production and auditory discrimination tasks demonstrating that mu-alpha and mu-beta are highly sensitive to capturing sensorimotor and basal ganglia deficits associated with stuttering with high temporal precision. Novel findings from a non-word repetition (working memory) task are also included. They show reduced mu-alpha suppression in a stuttering group compared to a typically fluent group. Finally, we review current limitations and directions for future research. [ABSTRACT FROM AUTHOR]

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity. [ABSTRACT FROM AUTHOR]

In the rubber hand illusion (RHI), the subject recognizes a fake hand as his or her own. We recently found that the observation of embodied fake hand movement elicited mu-rhythm (8–13 Hz) desynchronization on electroencephalography (EEG), suggesting brain activation in the sensorimotor regions. However, it is known that mu-rhythm desynchronization during action observation is confounded with occipital alpha-rhythm desynchronization, which is modulated by attention. This study examined the independence of brain activities in the sensorimotor and occipital regions relating to the movement observation under the RHI. The invisible participant’s left and fake right hands were stroked simultaneously, which was interrupted by unexpected fake hand movements. A mirror-reversed image of the fake hand was shown on a monitor in front of the participant with a delay of 80, 280, or 480 ms. Illusion strength decreased as a function of the delay. EEG independent component analysis (ICA) and ICA clustering revealed six clusters with observation-induced desynchronization of 8–13 Hz frequency band. In the right sensorimotor cluster, mu-rhythm desynchronization was the greatest under the 80-ms delay, while alpha-rhythm desynchronization of the occipital clusters did not show delay-dependence. These results suggest that brain activation in the sensorimotor areas (i.e., mu-rhythm desynchronization) induced by embodied fake hand movement is independent of that in the occipital areas (alpha-rhythm desynchronization).

Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.

Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.

Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) simulates real-world scenarios by creating a sense of presence in its users. Such immersive scenarios lead to behavior that is more similar to that displayed in real world settings, which may facilitate the transfer of knowledge and skills acquired in VR to similar real world situations. VR has already been used in education, psychotherapy, rehabilitation and it comes as an appealing choice for training intervention purposes. The aim of the present study was to investigate to what extent VR technology for games presented via goggles can be used in a magnetic resonance imaging scanner (MRI), addressing the question of whether brain connectivity differs between VR stimulation via goggles and a presentation from a screen via mirror projection. Moreover, we wanted to investigate whether stereoscopic goggle stimulation, where both eyes receive different visual input, would elicit stronger brain connectivity than a stimulation in which both eyes receive the same visual input (monoscopic). To our knowledge, there is no previous research using games and functional connectivity (FC) in MRI to address this question. Multiple analyses approaches were taken so that different aspects of brain connectivity could be covered: fractional low-frequency fluctuation, independent component analysis (ICA), seed-based FC (SeedFC) and graph analysis. In goggle presentation (mono and stereoscopic) as contrasted to screen, we found differences in brain activation in left cerebellum and postcentral gyrus as well as differences in connectivity in the visual cortex and frontal inferior cortex [when focusing on the visual and default mode network (DMN)]. When considering connectivity in specific areas of interest, we found higher connectivity between bilateral superior frontal cortex and the temporal lobe, as well as bilateral inferior parietal cortex with right calcarine and right lingual cortex. Furthermore, we found superior frontal cortex and insula/putamen to be more strongly connected in goggle stereoscopic vs. goggle monoscopic, in line with our hypothesis. We assume that the condition that elicits higher brain connectivity values should be most suited for long-term brain training interventions given that, extended training under these conditions could permanently improve brain connectivity on a functional as well as on a structural level. [ABSTRACT FROM AUTHOR]

In the rubber hand illusion (RHI), the subject recognizes a fake hand as his or her own. We recently found that the observation of embodied fake hand movement elicited mu-rhythm (8–13 Hz) desynchronization on electroencephalography (EEG), suggesting brain activation in the sensorimotor regions. However, it is known that mu-rhythm desynchronization during action observation is confounded with occipital alpha-rhythm desynchronization, which is modulated by attention. This study examined the independence of brain activities in the sensorimotor and occipital regions relating to the movement observation under the RHI. The invisible participant's left and fake right hands were stroked simultaneously, which was interrupted by unexpected fake hand movements. A mirror-reversed image of the fake hand was shown on a monitor in front of the participant with a delay of 80, 280, or 480 ms. Illusion strength decreased as a function of the delay. EEG independent component analysis (ICA) and ICA clustering revealed six clusters with observation-induced desynchronization of 8–13 Hz frequency band. In the right sensorimotor cluster, mu-rhythm desynchronization was the greatest under the 80-ms delay, while alpha-rhythm desynchronization of the occipital clusters did not show delay-dependence. These results suggest that brain activation in the sensorimotor areas (i.e., mu-rhythm desynchronization) induced by embodied fake hand movement is independent of that in the occipital areas (alpha-rhythm desynchronization). [ABSTRACT FROM AUTHOR]

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a useful tool to inspect the brain activity in resting state and allows to characterize spontaneous brain activity that is not detected when a subject is cognitively engaged. Moreover, taking advantage of the high time resolution in EEG, it is possible to perform fast topographical reference-free analysis, since different scalp potential fields correspond to changes in the underlying sources within the brain. In this study, the spontaneous EEG resting state (eyes closed) was compared between 10 young adults ages 18–30 years with autism spectrum disorder (ASD) and 13 neurotypical controls. A microstate analysis was applied, focusing on four temporal parameters: mean duration, the frequency of occurrence, the ratio of time coverage, and the global explained variance (GEV). Using data that were acquired from a 65-channel EEG system, six resting-state topographies that best describe the dataset across all subjects were identified by running a two-step cluster analysis labeled as microstate classes A–F. The results indicated that microstates B and E displayed statistically significant differences between both groups among the temporal parameters evaluated. Classes B, D, E, and F were consistently more present in ASD, and class C in controls. The combination of these findings with the putative functional significance of the different classes suggests that during resting state, the ASD group was more focused on visual scene reconstruction, while the control group was more engaged with self-memory retrieval. Furthermore, from a connectivity perspective, the resting-state networks that have been previously associated with each microstate class overlap the brain regions implicated in impaired social communication and repetitive behaviors that characterize ASD. [ABSTRACT FROM AUTHOR]

Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI. [ABSTRACT FROM AUTHOR]

Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state.Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later.Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < |r| < 0.346, p < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time.Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.

It has been shown that reward motivation can facilitate proactive control, a cognitive control mode that is characterized of prior preparation and sustained holding of the goal-relevant information in working memory. However, it remains to be established the neural networks that may be involved in this promotion effect. In this study, participants underwent the AX-Continuous Performance Task (AX-CPT) that measures relative proactive control during functional magnetic resonance imaging (fMRI) scanning. We employed independent component analysis to decompose multiple brain networks and identified the task related network. Results showed that the salience network (SN) was engaged in the AX-CPT protocol. Importantly, our data demonstrated that reward modulated the association between task engagement of SN and proactive control, whereby the positive correlation was particularly observed in the reward condition. Moreover, reward modulated task engagement of the SN in a proactive manner, which may contribute to the behavioral proactive performance. Overall, our data suggest the involvement of SN in the reward facilitation effect of proactive control.

Objective: To determine whether changes in sensorimotor control resulting from speaking conditions that induce fluency in people who stutter (PWS) can be measured using electroencephalographic (EEG) mu rhythms in neurotypical speakers.Methods: Non-stuttering (NS) adults spoke in one control condition (solo speaking) and four experimental conditions (choral speech, delayed auditory feedback (DAF), prolonged speech and pseudostuttering). Independent component analysis (ICA) was used to identify sensorimotor μ components from EEG recordings. Time-frequency analyses measured μ-alpha (8–13 Hz) and μ-beta (15–25 Hz) event-related synchronization (ERS) and desynchronization (ERD) during each speech condition.Results: 19/24 participants contributed μ components. Relative to the control condition, the choral and DAF conditions elicited increases in μ-alpha ERD in the right hemisphere. In the pseudostuttering condition, increases in μ-beta ERD were observed in the left hemisphere. No differences were present between the prolonged speech and control conditions.Conclusions: Differences observed in the experimental conditions are thought to reflect sensorimotor control changes. Increases in right hemisphere μ-alpha ERD likely reflect increased reliance on auditory information, including auditory feedback, during the choral and DAF conditions. In the left hemisphere, increases in μ-beta ERD during pseudostuttering may have resulted from the different movement characteristics of this task compared with the solo speaking task. Relationships to findings in stuttering are discussed.Significance: Changes in sensorimotor control related feedforward and feedback control in fluency-enhancing speech manipulations can be measured using time-frequency decompositions of EEG μ rhythms in neurotypical speakers. This quiet, non-invasive, and temporally sensitive technique may be applied to learn more about normal sensorimotor control and fluency enhancement in PWS.