Group Independent Component Analysis Research Articles

Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.

Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Blood pressure, executive function, and network connectivity in middle-aged adults at risk of dementia in late life

Midlife blood pressure is associated with structural brain changes, cognitive decline, and dementia in late life. However, the relationship between early adulthood blood pressure exposure, brain structure and function, and cognitive performance in midlife is not known. A better understanding of these relationships in the preclinical stage may advance our mechanistic understanding of vascular contributions to late-life cognitive decline and dementia and may provide early therapeutic targets. To identify resting-state functional connectivity of executive control networks (ECNs), a group independent components analysis was performed of functional MRI scans of 600 individuals from the Coronary Artery Risk Development in Young Adults longitudinal cohort study, with cumulative systolic blood pressure (cSBP) measured at nine visits over the preceding 30 y. Dual regression analysis investigated performance-related connectivity of ECNs in 578 individuals (mean age 55.5 ± 3.6 y, 323 female, 243 Black) with data from the Stroop color-word task of executive function. Greater connectivity of a left ECN to the bilateral anterior gyrus rectus, right posterior orbitofrontal cortex, and nucleus accumbens was associated with better executive control performance on the Stroop. Mediation analyses showed that while the relationship between cSBP and Stroop performance was mediated by white matter hyperintensities (WMH), resting-state connectivity of the ECN mediated the relationship between WMH and executive function. Increased connectivity of the left ECN to regions involved in reward processing appears to compensate for the deleterious effects of WMH on executive function in individuals across the burden of cumulative systolic blood pressure exposure in midlife.

Classifying Interictal Epileptiform Activities in Intracranial EEG Using Complex-Valued Convolutional Neural Network

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6–11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis.

An age-dependent Connectivity-based computer aided diagnosis system for Autism Spectrum Disorder using Resting-state fMRI

Autism spectrum disorder (ASD) is characterized by repetitive behaviors and social interactions. Due to the problems of diagnosing ASD using behavioral symptoms by experts, it seems necessary to propose accurate computer aided diagnosis systems (CADS) for ASD. Recent studies have reported brain connectivity as an important biomarker of ASD. Several studies have also suggested the role of age as an important factor in the brain connectivity disorders of individuals with ASD. In this study, we intend to present an age-dependent connectivity-based CADS for ASD using resting-state fMRI (rs-fMRI).First, the preprocessing was performed on the rs-fMRI data. Second, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs). This was followed by obtaining individualized components of RSNs for each subject using dual-regression. Then, full and partial correlation measures were used to extract functional connectivity features and bivariate granger causality was used to extract effective connectivity features between RSNs. To consider the role of age in the classification process, three age groups of children, adolescents and adults were taken into account, and feature selection was performed for each age group separately by using an embedded approach in which all classifiers of WEKA were used simultaneously. Finally, classification accuracy, sensitivity and specificity were obtained for each age group.The proposed CADS was able to operate with 95.23% classification accuracy in the children group using classification via clustering classifier. Furthermore, discriminative biomarkers of functional connectivity were obtained in this age group which might play an important role in diagnosing ASD.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys

ObjectiveThe age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans. MethodsWe acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis. ResultsEight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood. ConclusionWe identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.

Altered Spatial Organization of Dynamic Functional Network Associates With Deficient Sensory and Perceptual Network in Schizophrenia.

Schizophrenia is currently thought as a disorder with dysfunctional communication within and between sensory and cognitive processes. It has been hypothesized that these deficits mediate heterogeneous and comprehensive schizophrenia symptomatology. In this study, we investigated as to how the abnormal dynamic functional architecture of sensory and cognitive networks may contribute to these symptoms in schizophrenia. We calculated a sliding-window-based dynamic functional connectivity strength (FCS) and amplitude of low-frequency fluctuation (ALFF) maps. Then, using group-independent component analysis, we characterized spatial organization of dynamic functional network (sDFN) across various time windows. The spatial architectures of FCS/ALFF-sDFN were similar with traditional resting-state functional networks and cannot be accounted by length of the sliding window. Moreover, schizophrenic subjects demonstrated reduced dynamic functional connectivity (dFC) within sensory and perceptual sDFNs, as well as decreased connectivity between these sDFNs and high-order frontal sDFNs. The severity of patients' positive and total symptoms was related to these abnormal dFCs. Our findings revealed that the sDFN during rest might form the intrinsic functional architecture and functional changes associated with psychotic symptom deficit. Our results support the hypothesis that the dynamic functional network may influence the aberrant sensory and cognitive function in schizophrenia, further highlighting that targeting perceptual deficits could extend our understanding of the pathophysiology of schizophrenia.

Spatial Connectivity and Temporal Dynamic Functional Network Connectivity of Musical Emotions Evoked by Dynamically Changing Tempo.

Music tempo is closely connected to listeners’ musical emotion and multifunctional neural activities. Music with increasing tempo evokes higher emotional responses and music with decreasing tempo enhances relaxation. However, the neural substrate of emotion evoked by dynamically changing tempo is still unclear. To investigate the spatial connectivity and temporal dynamic functional network connectivity (dFNC) of musical emotion evoked by dynamically changing tempo, we collected dynamic emotional ratings and conducted group independent component analysis (ICA), sliding time window correlations, and k-means clustering to assess the FNC of emotion evoked by music with decreasing tempo (180–65 bpm) and increasing tempo (60–180 bpm). Music with decreasing tempo (with more stable dynamic valences) evoked higher valence than increasing tempo both with stronger independent components (ICs) in the default mode network (DMN) and sensorimotor network (SMN). The dFNC analysis showed that with time-decreasing FNC across the whole brain, emotion evoked by decreasing music was associated with strong spatial connectivity within the DMN and SMN. Meanwhile, it was associated with strong FNC between the DMN–frontoparietal network (FPN) and DMN–cingulate-opercular network (CON). The paired t-test showed that music with a decreasing tempo evokes stronger activation of ICs within DMN and SMN than that with an increasing tempo, which indicated that faster music is more likely to enhance listeners’ emotions with multifunctional brain activities even when the tempo is slowing down. With increasing FNC across the whole brain, music with an increasing tempo was associated with strong connectivity within FPN; time-decreasing connectivity was found within CON, SMN, VIS, and between CON and SMN, which explained its unstable valence during the dynamic valence rating. Overall, the FNC can help uncover the spatial and temporal neural substrates of musical emotions evoked by dynamically changing tempi.

Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder.

Fetal alcohol spectrum disorder (FASD) is a significant public health problem that is associated with a broad range of physical, neurocognitive, and behavioral effects resulting from prenatal alcohol exposure (PAE). Magnetic resonance imaging (MRI) has been an important tool for advancing our knowledge of abnormal brain structure and function in individuals with FASD. However, whereas only a small number of studies have applied graph theory-based network analysis to resting-state functional MRI (fMRI) data in individuals with FASD additional research in this area is needed. Resting-state fMRI data were collected from adolescent and young adult participants (ages 12-22) with fetal alcohol syndrome (FAS) or alcohol-related neurodevelopmental disorder (ARND) and neurotypically developing controls (CNTRL) from previous studies. Group independent components analysis (gICA) was applied to fMRI data to extract components representing functional brain networks. Functional network connectivity (FNC), measured by Pearson correlation of the average independent component (IC) time series, was analyzed under a graph theory framework to compare network modularity, the average clustering coefficient, characteristic path length, and global efficiency between groups. Cognitive intelligence, measured by the Wechsler Abbreviated Scale of Intelligence (WASI), was compared and correlated to global network measures. Group comparisons revealed significant differences in the average clustering coefficient, characteristic path length, and global efficiency. Modularity was not significantly different between groups. The FAS and ARND groups scored significantly lower than the CNTRL group on Full Scale IQ (FS-IQ) and the Vocabulary subtest, but not the Matrix Reasoning subtest. No significant associations between intelligence and graph theory measures were detected. Our results partially agree with previous studies examining global graph theory metrics in children and adolescents with FASD and suggest that the exposure to alcohol during prenatal development leads to disruptions in aspects of functional network segregation and integration.

Lateralization of Resting-State Networks in Children: Association with Age, Sex, Handedness, Intelligence Quotient, and Behavior.

Introduction: Lateralization in brain function has been associated with age and sex in previous work; however, there has been less focus on lateralization of functional networks during development. Aim: We aim to examine laterality in typical development; a clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. Material and Methods: In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years. This is an extension of our previous work on normal aging in adults, where we now assess whether there are similar patterns in children. Results: Unlike the results from our study of healthy aging in adults, which showed a decrease in laterality with increasing age, in this study we found both decreases and increases in lateralization in multiple networks with development. For example, auditory and sensorimotor regions had greater bilateral connectivity with development, whereas regions including the dorsolateral frontal cortex (Brodmann area left 9 and left 46) showed an increase in left lateralization with development. Conclusion: Our findings support a complex, nonlinear association between laterality and age in school-age children, a time when brain function and structure are developing rapidly. We also found brain networks in which laterality was significantly associated with sex, handedness, and intelligence quotient, but we did not find any significant association with behavioral scores. Impact statement Lateralization in brain function has been associated with age and sex in several previous studies; however, there has been less focus on lateralization of functional networks during development. A clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years.

Altered static and dynamic functional network connectivity in temporal lobe epilepsy with different disease duration and their relationships with attention.

The brain network alterations associated with temporal lobe epilepsy (TLE) progression are still unclear. The purpose of this study was to investigate altered patterns of static and dynamic functional network connectivity (sFNC and dFNC) in TLE with different durations of disease. In this study, 19 TLE patients with a disease duration of ≤5years (TLE-SD), 24 TLE patients with a disease duration of >5years (TLE-LD), and 21 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and attention network test. We used group independent component analysis to determine the target resting-state networks. Sliding window correlation and k-means clustering analysis methods were used to obtain different dFNC states, temporal properties, and temporal variability. We then compared sFNC and dFNC between groups and found that compared with HCs, TLE-SD patients had increased sFNC between the dorsal attention network and sensorimotor network/visual network (VN), but decreased sFNC between the inferior-posterior default mode network and VN. In the strongly connected dFNC state, TLE-SD patients spent more time, had greater mean dwell time, and showed greater inconsistent abnormal network connectivity. There was a significant negative correlation between the temporal variability of auditory network- left fronto-parietal network connectivity and orienting effect. No significant differences in sFNC and dFNC were detected between TLE-LD and HC groups. These findings suggest that the damage and functional brain network abnormalities gradually occur in TLE patients after the onset of epilepsy, which might lead to functional network reorganization and compensatory remodeling as the disease progresses.

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder.

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

Altered Functional Network Architecture of the Brain in Prader-Willi Syndrome.

Background: Prader-Willi syndrome (PWS) is a genetic syndrome with clinical behavioral phenotypes, including autistic characteristics. However, brain functional connectivity (Fc) remains underreported. This study aimed at investigating alterations in functional network architecture in the cortical and subcortical structures of brains in individuals with PWS. Methods: Twelve individuals with PWS (age range: 15-42 years; female 4, male 8), and 14 age- and sex-matched controls with typical development (TD), participated in a 3 Tesla resting-state functional magnetic resonance imaging study. Fc was analyzed: (1) voxel-based group independent component analysis and correlations with Autism-Spectrum Quotient (AQ) scores, (2) seed-based neuroanatomical region of interest (ROI) analysis. Results: In individuals with PWS, AQ showed a significant positive correlation with Fc in the right frontal area, and the ROI analysis exhibited enhanced dorsolateral prefrontal Fcs compared with those in TD controls; the frontopolar-parietotemporal Fcs were attenuated. Discussion: The observed Fc indicated altered Fc in specific brain regions, which is consistent with the behavioral features in individuals with PWS. The enhanced versus attenuated connectivity in distinct frontal regions may contribute to not only autistic features but also other behavioral characteristics, and it provides a clue for better understanding of the brain-behavior relationship in PWS.

MARGM: A multi-subjects adaptive region growing method for group fMRI data analysis

Region growing has been utilized in the analysis of functional magnetic resonance imaging (fMRI) data for many years, while some influential factors, such as the definition of growing criterion and the selection of threshold have a great impact on the application and development of region growing. In this paper, a multi-subjects adaptive region growing method (MARGM) is proposed for the group fMRI analysis, where initial seed-region of multi-subjects is automatically determined by combining the split-merge based seed-region selection method with a prior template. Next, an improved homogeneity criterion is defined to control region growing, and an adaptive threshold is introduced to accelerate the convergence process of region growing. Finally, reproducible coefficients are used to obtain spatial information at the group level by considering temporal information simultaneously. The experimental results of hybrid-data and real fMRI data showed that MARGM could generate effective and reliable results compared to the classical fMRI data analysis methods including group independent component analysis and statistical parametric mapping. It could be used to obtain more representative information at the group level, which reflected the commonalities of the subjects in the group. Therefore, it is expected to have wide applicability for the fMRI data analysis.

Longitudinal stability of resting-state networks in normal aging, mild cognitive impairment, and Alzheimer's disease

Test–retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test–retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test–retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from −0.06 to 0.44 in patients with AD. The overall test–retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test–retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test–retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test–retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.

Detecting functional connectivity disruptions in a translational pediatric traumatic brain injury porcine model using resting-state and task-based fMRI

Functional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.

Altered resting-state functional network connectivity in profound sensorineural hearing loss infants within an early sensitive period: A group ICA study.

Data from both animal models and deaf children provide evidence for that the maturation of auditory cortex has a sensitive period during the first 2–4 years of life. During this period, the auditory stimulation can affect the development of cortical function to the greatest extent. Thus far, little is known about the brain development trajectory after early auditory deprivation within this period. In this study, independent component analysis (ICA) technique was used to detect the characteristics of brain network development in children with bilateral profound sensorineural hearing loss (SNHL) before 3 years old. Seven resting‐state networks (RSN) were identified in 50 SNHL and 36 healthy controls using ICA method, and further their intra‐and inter‐network functional connectivity (FC) were compared between two groups. Compared with the control group, SNHL group showed decreased FC within default mode network, while enhanced FC within auditory network (AUN) and salience network. No significant changes in FC were found in the visual network (VN) and sensorimotor network (SMN). Furthermore, the inter‐network FC between SMN and AUN, frontal network and AUN, SMN and VN, frontal network and VN were significantly increased in SNHL group. The results implicate that the loss and the compensatory reorganization of brain network FC coexist in SNHL infants. It provides a network basis for understanding the brain development trajectory after hearing loss within early sensitive period.

Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10–80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2–20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

An Approach to Automatically Label and Order Brain Activity/Component Maps.

Background: Functional magnetic resonance imaging (fMRI) is a brain imaging technique that provides detailed insights into brain function and its disruption in various brain disorders. The data-driven analysis of fMRI brain activity maps involves several postprocessing steps, the first of which is identifying whether the estimated brain network maps capture signals of interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This is followed by linking the ICNs to standardized anatomical and functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary to facilitate interpretation. Methods: Here we develop a novel and efficient method (Autolabeler) for implementing and integrating all of these processes in a fully automated manner. The Autolabeler method is pretrained on a cross-validated elastic-net regularized general linear model from the noisecloud toolbox to separate neuroscientifically meaningful ICNs from artifacts. It is capable of automatically labeling activity maps with labels from several well-known anatomical and functional parcellations. Subsequently, this method also maximizes the modularity within functional domains to generate a more systematically structured FNC matrix for post hoc network analyses. Results: Results show that our pretrained model achieves 86% accuracy at classifying ICNs from artifacts in an independent validation data set. The automatic anatomical and functional labels also have a high degree of similarity with manual labels selected by human raters. Discussion: At a time of ever-increasing rates of generating brain imaging data and analyzing brain activity, the proposed Autolabeler method is intended to automate such analyses for faster and more reproducible research. Impact statement Our proposed method is capable of implementing and integrating some of the crucial tasks in functional magnetic resonance imaging (fMRI) studies. It is the first to incorporate such tasks without the need for expert intervention. We develop an open-source toolbox for the proposed method that can function as stand-alone software and additionally provides seamless integration with the widely used group independent component analysis for fMRI toolbox (GIFT). This integration can aid investigators to conduct fMRI studies in an end-to-end automated manner.

Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study

Objective. Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data. Approach. A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100–900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales. Main results. Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus. Significance. Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson’s disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.