Group Spatial Independent Component Analysis Research Articles

Detection of resting-state functional connectivity in the lumbar spinal cord with 3T MRI

Functional MRI (fMRI) of the spinal cord is an expanding area of research with potential to investigate neuronal activity in the central nervous system. We aimed to characterize the functional connectivity features of the human lumbar spinal cord using resting-state fMRI (rs-fMRI) at 3T, using region-based and data-driven analysis approaches. A 3D multi-shot gradient echo resting-state blood oxygenation level dependent-sensitive rs-fMRI protocol was implemented in 26 healthy participants. Average temporal signal-to-noise ratio in the gray matter was 16.35 ± 4.79 after denoising. Evidence of synchronous signal fluctuations in the ventral and dorsal horns with their contralateral counterparts was observed in representative participants using interactive, exploratory seed-based correlations. Group-wise average in-slice Pearson’s correlations were 0.43 ± 0.17 between ventral horns, and 0.48 ± 0.16 between dorsal horns. Group spatial independent component analysis (ICA) was used to identify areas of coherent activity¸ and revealed components within the gray matter corresponding to anatomical regions. Lower-dimensionality ICA revealed bilateral components corresponding to ventral and dorsal networks. Additional separate ICAs were run on two subsets of the participant group, yielding two sets of components that showed visual consistency and moderate spatial overlap. This work shows feasibility of rs-fMRI to probe the functional features and organization of the lumbar spinal cord.

Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders.

Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-statestaticfunctionalconnectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.

Altered dynamic functional connectivity associates with post-traumatic stress disorder.

Research has been looking into neural pathophysiology of post-traumatic stress disorder (PTSD) and dynamic functioning connectivity (dFC) applying resting state functional magnetic resonance imaging (rs-fMRI). Previous studies showed that PTSD related impairments are associated with alterations distributed across different brain regions and disorganized functional connectivity, especially in Default Mode Network and the cerebellar area. In this study, we specifically looked into dFC on a whole brain level, and we focused on critical regions such as DMN and cerebellum. To explore the characteristics of dFC among patients with PTSD, we collected rs-fMRI data from 27 PTSD patients and 30 healthy controls. The study also added a control group of 33 trauma-exposed individuals to further look into trauma impact. Utilizing group spatial independent component analysis (ICA), the dynamic properties on whole brain level were detected with sliding time window approach, and k-means clustering. Two reoccurring FC "States" were identified, with connections being more concentrated on a within-network level in one state and more strongly inter-connected in the other state. Abnormalities in dFC were found within DMN, between DMN and cerebellum, and between DMN and visual network for PTSD patients. The findings were in accordance with the study hypothesis that the dFC alterations might point to deficits in emotional modulation and dysfunctional self-referential thought. Abnormalities in dFC among PTSD patients might also be indicators of PTSD symptoms including depression and anxiety, hypervigilance, impaired cognitive functioning and self-referential information processing.

Abnormal dynamics of brain functional networks in children with Tourette syndrome

Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder characterized by the presence of multiple motor and vocal tics. Research using resting-state functional magnetic resonance imaging (rfMRI) have found aberrant static functional connectivity (FC) and its topological properties in the brain networks of TS. Our study is the first to investigate the dynamic functional connectivity (dFC) in the whole brain network of TS patients, focusing on the temporal properties of dFC states and the temporal variability of topological organization. The rfMRI data of 36 male children with TS and 27 matched healthy controls were collected and further analyzed by group spatial independent component analysis, sliding windows approach based dFC analysis, k-means clustering analysis, and graph theory analysis. The clustering analysis identified three dFC states. Of these states, state 2, characterized by increased inter-network connections in subcortical network (SCN), sensorimotor network (SMN), and default mode network (DMN), and decreased inter-network connections between salience network (SAN) and executive control network (ECN), was found to have higher fractional window and dwell time in TS, which was also positively correlated with tic severity. TS patients also exhibited higher temporal variability of whole-brain-network global efficiency and local efficiency, and higher temporal variability of nodal efficiency and local efficiency in SCN, DMN, ECN, SAN, and SMN. Additionally, temporal variability of the efficiency and local efficiency in insula was positively correlated with tic severity. Our findings revealed abnormal temporal property of dFC states and temporal variability of topological organization in TS, providing new insights into clinical diagnoses and neuropathology of TS.

Alzheimer’s pathology disrupts cortical processing of repeated stimuli in cognitively intact older adults

AbstractBackgroundWe used functional imaging (fMRI) and generative modelling to investigate effects of Alzheimer’s disease (AD) pathology (β‐amyloid/Aβ, pathological tau) on cortical mechanisms that support differentiation of novel and repeated stimuli.Method66 subjects (45 Older Adults (OA), 21 Younger Adults) underwent an fMRI task involving stimuli of novel and repeated scenes and objects (stimulus conditions; scenes and objects, context condition; repetition); 42 OA also had PIB‐PET measurement of Aβ and flortaucipir (FTP)‐PET of pathological tau. fMRI data were decomposed into functional networks using group spatial Independent Component Analysis (ICA). We used Dynamic Causal Modelling (DCM) to predict local cortical activity from the time‐course associated with stimulus‐related activity and stimulus repetition. AD pathology was defined as Entorhinal cortex FTP‐SUVR (EC‐tau) and continuous global Aβ. A hierarchical approach uncovered the optimal model that best described our data while considering subject variability due to Aβ and EC‐tau.ResultFive ICA networks were selected for DCM analysis (Figure 1), two input; related to stimulus category (LOC Objects>Scenes: t(65) = 21.64 p<0.001; PPA Scenes>Objects: t(65) = 22.4 p<0.001) and three higher order showing significant effects of repetition (DMN (t(65) = 14.04 p<0.001, MTL t(65) = 11 p<0.001, SAL ‐13.14, p<0.001). Time‐courses modelled using DCM included all possible connections (except between LOC and PPA), allowing repetition to modulate any of these connections with stimuli modelled as inputs to LOC and PPA. From this analysis we observed very strong evidence that increased Aβ removes the inhibition of the DMN by MTL leading to over stimulation when stimuli are repeated (Commonality(repeated) ‐0.38 Pp = 1; Ab(repeated) 1.99, Pp = 1). Conversely, we observed very strong evidence that increased EC‐tau removes the inhibition of the MTL by DMN when stimuli are repeated leading to over stimulation when stimuli are repeated (Commonality(repeated) ‐0.29, Pp = 1, EC‐tau(repeated) 1.7, Pp = 1).ConclusionAD pathology disrupts the normal cortical processes that subserve the ability to recognise repeated stimuli in cognitively intact OA. Critically, global Aβ disinhibits the DMN and EC‐tau disinhibits the MTL, two previously described functional phenomena linked to AD pathology. These models provide a systems‐level account of how pathology affects interactions between functional brain networks that may underlie cognitive decline.

Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease.

The aim of this study was to clarify the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group). Group spatial independent component analysis and sliding-window approach were employed. Moreover, we applied a k-means clustering algorithm on windowed functional connectivity (FC) matrices to identify reoccurring FC patterns (i.e., states). The optimal number of states was determined to be five, the so-called State 1, 2, 3, 4, and 5. In ON phase, compared with No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong positive connections between visual network (VIS) and sensorimotor network (SMN). When switching from OFF to ON phase, LID group occurred less frequently in State 3 and State 4. Meanwhile, LID group dwelled longer in State 2 and shorter in State 3. No-LID group occurred more frequently in State 5 and less frequently in State 3. Additionally, correlation analysis demonstrated that dyskinesia's severity was associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN). Using dFNC analysis, we found that dyskinesia may be related to the dysfunctional inhibition of CEN on motor loops and excessive excitation of VIS and SMN, which provided evidence of the changes in brain dynamics associated with the occurrence of dyskinesia.

Altered Temporal Variations of Functional Connectivity Associated With Surgical Outcomes in Drug-Resistant Temporal Lobe Epilepsy.

BackgroundCurrently, more than one-third of patients with drug-resistant temporal lobe epilepsy (TLE) continue to develop seizures after resection surgery. Dynamic functional network connectivity (DFNC) analyses, capturing temporal properties of functional connectivity during MRI acquisition, may help us identify unfavorable surgical outcomes. The purpose of this work was to explore the association of DFNC variations of preoperative resting-state MRI and surgical outcomes in patients with drug-resistant TLE.MethodsWe evaluated 61 patients with TLE matched for age and gender with 51 healthy controls (HC). Patients with TLE were classified as seizure-free (n = 39) and not seizure-free (n = 16) based on the Engel surgical outcome scale. Six patients were unable to confirm the postoperative status and were not included in the subgroup analysis. The DFNC was calculated using group spatial independent component analysis and the sliding window approach.ResultsDynamic functional network connectivity analyses suggested two distinct connectivity “States.” The dynamic connectivity state of patients with TLE was different from HC. TLE subgroup analyses showed not seizure-free (NSF) patients spent significantly more time in State II compared to seizure-free (SF) patients and HC. Further, the number of transitions from State II to State I was significantly lower in NSF patients. SF patients had compensatory enhancement of DFNC strengths between default and dorsal attention network, as well as within the default network. While reduced DFNC strengths of within-network and inter-network were both observed in NSF patients, patients with abnormally temporal properties and more extension DFNC strength alterations were less likely to receive seizure freedom.ConclusionsOur study indicates that DFNC could offer a better understanding of dynamic neural impairment mechanisms of drug-resistant TLE functional network, epileptic brain network reorganization, and provide an additional preoperative evaluation support for surgical treatment of drug-resistant TLE.

Intrinsic neural network dynamics in catatonia.

Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.

Aberrant functional brain network dynamics in patients with functional constipation.

The aberrant static functional connectivity of brain network has been widely investigated in patients with functional constipation (FCon). However, the dynamics of brain functional connectivity in FCon patients remained unknown. This study aimed to detect the brain dynamics of functional connectivity states and network topological organizations of FCon patients and investigate the correlations of the aberrant brain dynamics with symptom severity. Eighty‐three FCon patients and 80 healthy subjects (HS) were included in data analysis. The spatial group independent component analysis, sliding‐window approach, k‐means clustering, and graph‐theoretic analysis were applied to investigate the dynamic temporal properties and coupling patterns of functional connectivity states, as well as the time‐variation of network topological organizations in FCon patients. Four reoccurring functional connectivity states were identified in k‐means clustering analysis. Compared to HS, FCon patients manifested the lower occurrence rate and mean dwell time in the state with a complex connection between default mode network and cognitive control network, as well as the aberrant anterior insula–cortical coupling patterns in this state, which were significantly correlated with the symptom severity. The graph‐theoretic analysis demonstrated that FCon patients had higher sample entropy at the nodal efficiency of anterior insula than HS. The current findings provided dynamic perspectives for understanding the brain connectome of FCon and laid the foundation for the potential treatment of FCon based on brain connectomics.

Dynamic functional connectivity changes in the triple networks and its association with cognitive impairment in hemodialysis patients.

IntroductionCognitive impairment is common in hemodialysis (HD) patients; however, the underlying mechanisms have not been fully understood. The "triple‐network model" that consists of the salience network (SN), central executive network (CEN), and default mode network (DMN) has been suggested to play an important role in various cognitive functions. However, dynamic functional connectivity (FC) alterations within the triple networks have not been investigated in HD patients.MethodsSixty‐six HD patients and 66 healthy controls (HCs) were included in this study. The triple networks were identified using a group spatial independent component analysis, and dynamic FC was analyzed using a sliding window approach and k‐means clustering algorithm. Furthermore, we analyzed the relationships between altered dynamic FC parameters and clinical variables in HD patients.ResultsThe intrinsic brain FC within the triple networks was clustered into four configuration states. Compared with HCs, HD patients spent more time in State 1, which was characterized by weak connections between the DMN and CEN and SN. HD patients showed lower number of transitions across different states than HCs. Moreover, the number of transitions and mean dwell time in State 1 were associated with cognitive performance in HD patients.ConclusionOur findings suggest abnormal dynamic FC properties within the triple networks in HD patients, which may provide new insights into the pathophysiological mechanisms of their cognitive deficits from the perspective of dynamic FC.

Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder.

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.

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n=48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n=24; 12 PAE, 12 SAC), and a females-only sample (n=24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Functional Decoupling of Language and Self-Reference Networks in Patients with Persistent Auditory Verbal Hallucinations

Background: Accumulating neuroimaging evidence suggests that abnormal intrinsic neural activity could underlie auditory verbal hallucinations (AVH) in patients with schizophrenia. However, little is known about the functional interplay between distinct intrinsic neural networks and their association with AVH. Methods: We investigated functional network connectivity (FNC) of distinct resting-state networks as well as the relationship between FNC strength and AVH symptom severity. Resting-state functional MRI data at 3 T were obtained for 14 healthy controls and 10 patients with schizophrenia presenting with persistent AVH. The data were analyzed using a spatial group independent component analysis, followed by constrained maximal lag correlations to determine FNC within and between groups. Results: Four components of interest, comprising language, attention, executive control networks, as well as the default-mode network (DMN), were selected for subsequent FNC analyses. Patients with persistent AVH showed lower FNC between the language network and the DMN (p < 0.05, corrected for false discovery rate). FNC strength, however, was not significantly related to symptom severity, as measured by the Psychotic Symptom Rating Scale. Conclusion: These findings suggest that disrupted FNC between a speech-related system and a network subserving self-referential processing is associated with AVH. The data are consistent with a model of disrupted self-attribution of speech generation and perception.

Disrupted salience network dynamics in Parkinson's disease patients with impulse control disorders

BackgroundDynamic functional network analysis may add relevant information about the temporal nature of the neurocognitive alterations in PD patients with impulse control disorders (PD-ICD). Our aim was to investigate changes in dynamic functional network connectivity (dFNC) in PD-ICD patients, and topological properties of such networks. MethodsResting state fMRI was performed on 16 PD PD-ICD patients, 20 PD patients without ICD and 17 healthy controls, whose demographic, clinical and behavioral scores were assessed. We conducted a group spatial independent component analysis, sliding window and graph-theory analyses. ResultsPD-ICD patients, in contrast to PD-noICD and HC subjects, were engaged across time in a brain configuration pattern characterized by a lack of between-network connections at the expense of strong within-network connections (State III) in temporal, frontoinsular and cingulate cortices, all key nodes of the salience network. Moreover, this increased maintenance of State III in PD-ICD patients was positively correlated with the severity of impulsivity and novelty seeking as measured by specific scales. While in State III, these patients also exhibited increased local efficiency in all the aforementioned areas. ConclusionsOur findings show for the first time that PD-ICD patients have a dynamic functional engagement of local connectivity involving the limbic circuit, leading to the inefficient modulation in emotional processing and reward-related decision-making. These results provide new insights into the pathophysiology of ICD in PD patients and indicate that the dFC study of fMRI could be a useful biomarker to identify patients at risk to develop ICD.

Transdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depression

Major depressive disorder (MDD) and schizophrenia (SCZ) share neurobiological and clinical commonalities. Altered functional connectivity of large-scale brain networks has been associated with both disorders. Electroconvulsive therapy (ECT) has proven to be an effective treatment in severe forms of MDD and SCZ. However, the role of ECT on the modulation of the dynamics of brain networks is still unknown. In this study, we used resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity in 16 pharmacoresistant patients with SCZ or MDD and a matched group of normal controls. Patients were scanned before and after right-sided unilateral ECT. Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate the effects of ECT treatment on intrinsic components (INs). Functional network connectivity (FNC) was calculated between pairs of INs. Patients had reduced connectivity within a striato-thalamic network in the thalamus as well as increased low frequency oscillations in a striatal network. ECT reduced low frequency oscillations (LFOs) on a striatal network along with increasing functional connectivity in the medial prefrontal cortex within the DMN. Following ECT treatment, the FNC of the executive network was reduced with the DMN and increased with the salience network, respectively.Our findings suggest transnosological effects of ECT on the connectivity of large-scale networks as well as at the level of their interplay. Furthermore, they support a transnosological approach for the investigation not only of the neural correlates of the disease but also of the brain mechanism of treatment of mental disorders.

State-Dependent Functional Dysconnectivity in Youth With Psychosis Spectrum Symptoms.

Psychosis spectrum disorders are conceptualized as neurodevelopmental disorders accompanied by disruption of large-scale functional brain networks. Dynamic functional dysconnectivity has been described in patients with schizophrenia and in help-seeking individuals at clinical high risk for psychosis. Less is known, about developmental aspects of dynamic functional network connectivity (dFNC) associated with psychotic symptoms (PS) in the general population. Here, we investigate resting state functional magnetic resonance imaging data using established dFNC methods in the Philadelphia Neurodevelopmental Cohort (ages 8–22 years), including 129 participants experiencing PS and 452 participants without PS (non-PS). Functional networks were identified using group spatial independent component analysis. A sliding window approach and k-means clustering were applied to covariance matrices of all functional networks to identify recurring whole-brain connectivity states. PS-associated dysconnectivity of default mode, salience, and executive networks occurred only in a few states, whereas dysconnectivity in the sensorimotor and visual systems in PS youth was more pervasive, observed across multiple states. This study provides new evidence that disruptions of dFNC are present even at the less severe end of the psychosis continuum in youth, complementing previous work on help-seeking and clinically diagnosed cohorts that represent the more severe end of this spectrum.

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia

An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brain meta-states (repeating patterns of network correlations which are allowed to overlap in time) significantly more often and to states which are more different, relative to HC. Here, we extend our previous work to investigate the relationship between symptomology in SP and four meta-state metrics. We found a significant correlation between positive symptoms and the two meta-state metrics which showed significant differences between HC and SP. These two statistics quantified 1) how often individuals change state and 2) the total distance traveled within the state-space. We additionally found that a clustering of the meta-state metrics divides SP into groups which vary in symptomology. These results indicate specific relationships between symptomology and brain function for SP.

Altered functional connectivity in binge eating disorder and bulimia nervosa: A resting-state fMRI study.

IntroductionThe etiology of bulimic‐type eating (BTE) disorders such as binge eating disorder (BED) and bulimia nervosa (BN) is still largely unknown. Brain networks subserving the processing of rewards, emotions, and cognitive control seem to play a crucial role in the development and maintenance of eating disorders. Therefore, further investigations into the neurobiological underpinnings are needed to discern abnormal connectivity patterns in BTE disorders.MethodsThe present study aimed to investigate functional as well as seed‐based connectivity within well‐defined brain networks. Twenty‐seven individuals with BED, 29 individuals with BN, 28 overweight, and 30 normal‐weight control participants matched by age, gender, and education underwent resting‐state functional magnetic resonance imaging. Functional connectivity was assessed by spatial group independent component analysis and a seed‐based correlation approach by examining the default mode network (DMN), salience network (SN), and executive network (EN).ResultsGroup comparisons revealed that BTE disorder patients exhibit aberrant functional connectivity in the dorsal anterior cingulate cortex (dACC) within the SN, as well as in the medial prefrontal cortex within the DMN. Furthermore, BED and BN groups differed from each other in functional connectivity within each network. Seed‐based correlational analysis revealed stronger synchronous dACC‐retrosplenial cortex activity in the BN group.ConclusionOur findings demonstrate abnormalities in brain networks involved in salience attribution, self‐referential processing, and cognitive control in bulimic‐type eating disorders. Together with our observation of functional connectivity differences between BED and BN, this study offers a differentiated account of both similarities and differences regarding brain connectivity in BED and BN.

Examining resting-state functional connectivity in first-episode schizophrenia with 7T fMRI and MEG.

Schizophrenia is often characterized by dysconnections in the brain, which can be estimated via functional connectivity analyses. Commonly measured using resting-state functional magnetic resonance imaging (fMRI) in order to characterize the intrinsic or baseline function of the brain, fMRI functional connectivity has significantly contributed to the understanding of schizophrenia. However, these measures may not capture the full extent of functional connectivity abnormalities in schizophrenia as fMRI is temporally limited by the hemodynamic response. In order to extend fMRI functional connectivity findings, the complementary modality of magnetoencephalography (MEG) can be utilized to capture electrophysiological functional connectivity abnormalities in schizophrenia that are not obtainable with fMRI. Therefore, we implemented a multimodal functional connectivity analysis using resting-state 7 Tesla fMRI and MEG data in a sample of first-episode patients with schizophrenia (n = 19) and healthy controls (n = 24). fMRI and MEG data were decomposed into components reflecting resting state networks using a group spatial independent component analysis. Functional connectivity between resting-state networks was computed and group differences were observed. In fMRI, patients demonstrated hyperconnectivity between subcortical and auditory networks, as well as hypoconnectivity between interhemispheric homotopic sensorimotor network components. In MEG, patients demonstrated hypoconnectivity between sensorimotor and task positive networks in the delta frequency band. Results not only support the dysconnectivity hypothesis of schizophrenia, but also suggest the importance of jointly examining multimodal neuroimaging data as critical disorder-related information may not be detectable in a single modality alone.

Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data.

Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.