Changes In Brain Functional Networks Research Articles

Occipital connectivity networks mediate the neural effects of childhood maltreatment on depressive symptoms in major depressive disorder

BackgroundChildhood maltreatment (CM) is a well-established risk factor for major depressive disorder (MDD). The neural mechanisms linking childhood maltreatment experiences to changes in brain functional networks and the onset of depression are not fully understood. MethodsIn this study, we enrolled 66 patients with MDD and 31 healthy controls who underwent resting-state fMRI scans and neuropsychological assessments. We employed multivariate linear regression to examine the neural associations of CM and depression, specifically focusing on the bilateral occipital functional connectivity (OFC) networks relevant to MDD. Subsequently, a two-step mediation analysis was conducted to assess whether the OFC network mediated the relationship between CM experiences and the severity of depression. ResultsOur study showed that patients with MDD exhibited reduced OFC strength, particularly in the occipito-temporal, parietal, and premotor regions. These reductions were negatively correlated with CM scores and the severity of depression. Notably, the overlapping regions in the bilateral OFC networks, affected by both CM experiences and depressive severity, were primarily observed in the bilateral cuneus, left angular and calcarine, as well as the right middle frontal cortex and superior parietal cortex. Furthermore, the altered strengths of the OFC networks were identified as positive mediators of the impact of CM history on depression symptoms in patients with MDD. ConclusionWe have demonstrated that early exposure to CM may increase vulnerability to depression by influencing the brain's network. These findings provide new insights into understanding the pathological mechanism underlying depressive symptoms induced by CM.

Aberrant Brain Networks and Relative Band Power in Patients with Acute Anti-NMDA Receptor Encephalitis: A Study of Resting-State EEG.

The alterations of the functional network (FN) in anti-N-methyl-Daspartate receptor (NMDAR) encephalitis have been recognized by functional magnetic resonance imaging studies. However, few studies using the electroencephalogram (EEG) have been performed to explore the possible FN changes in anti-NMDAR encephalitis. In this study, the aim was to explore any FN changes in patients with anti-NMDAR encephalitis. Twenty-nine anti-NMDAR encephalitis patients and 29 age- and gender-matched healthy controls (HC) were assessed using 19-channel EEG examination. For each participant, five 10-second epochs of resting state EEG with eyes closed were extracted. The cortical source signals of 84 Brodmann areas were calculated using the exact low resolution brain electromagnetic tomography (eLORETA) inverse solution by LORETA-KEY. Phase Lag Index (PLI) matrices were then obtained and graph and relative band power (RBP) analyses were performed. Compared with healthy controls, functional connectivity (FC) in the delta, theta, beta 1 and beta 2 bands significantly increased within the 84 cortical source signals of anti-NMDAR encephalitis patients (p < 0.05) and scalp FC in the alpha band decreased within the 19 electrodes. Additionally, the anti-NMDAR encephalitis group exhibited higher local efficiency and clustering coefficient compared to the healthy control group in the four bands. The slowing band RBP increased while the fast band RBP decreased in multiple-lobes and some of these changes in RBP were correlated with the modified Rankin Scale (mRS) and Mini-mental State Examination (MMSE) in anti-NMDAR encephalitis patients. This study further deepens the understanding of related changes in the abnormal brain network and power spectrum of anti-NMDA receptor encephalitis. The decreased scalp alpha FC may indicate brain dysfunction, while the increased source beta FC may indicate a compensatory mechanism for brain function in anti-NMDAR encephalitis patients. These findings extend understanding of how the brain FN changes from a cortical source perspective. Further studies are needed to detect correlations between altered FNs and clinical features and characterize their potential value for the management of anti-NMDAR encephalitis.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Changes in brain functional networks in remitted major depressive disorder: a six-month follow-up study

BackgroundPatients with remitted major depressive disorder (rMDD) show abnormal functional connectivity of the central executive network (CEN), salience networks (SN) and default mode network (DMN). It is unclear how these change during remission, or whether changes are related to function.MethodsThree spatial networks in 17 patients with rMDD were compared between baseline and the six-month follow-up, and to 22 healthy controls. Correlations between these changes and psychosocial functioning were also assessed.ResultsIn the CEN, patients at baseline had abnormal functional connectivity in the right anterior cingulate, right dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) compare with HCs. There were functional connection differences in the right DLPFC and left IPL at baseline during follow-up. Abnormal connectivity in the right DLPFC and medial prefrontal cortex (mPFC) were found at follow-up. In the SN, patients at baseline had abnormal functional connectivity in the insula, left anterior cingulate, left IPL, and right precuneus; compared with baseline, patients had higher connectivity in the right DLPFC at follow-up. In the DMN, patients at baseline had abnormal functional connectivity in the right mPFC. Resting-state functional connectivity of the IPL and DLPFC in the CEN correlated with psychosocial functioning.ConclusionsAt six-month follow-up, the CEN still showed abnormal functional connectivity in those with rMDD, while anomalies in the SN and DMN has disappeared. Resting-state functional connectivity of the CEN during early rMDD is associated with psychosocial function.Clinical trials RegistrationPharmacotherapy and Psychotherapy for MDD after Remission on Psychology and Neuroimaging. https://www.clinicaltrials.gov/, registration number: NCT01831440 (15/4/2013).

Relationship between carotid intima-media thickness (cIMT) and dual-system imbalance in tobacco dependence: An rs-fMRI research.

According to the classic cognitive behavioral theory proposes, dysfunctional goal-directed and habit control systems are considered central to the pathogenesis of dependent behavior and impair recovery from addictions. The functional connectivity (FC) of the brain circuits for goal-directed or habitual behavior has not been clearly reported in tobacco-dependent groups. Smoking is one of the factors in the formation of atherosclerosis. Studies have shown that the thickness of carotid intima-media (cIMT) is associated with attention-executive-psychomotor functioning. Therefore, we hypothesized whether cIMT in tobacco-dependent individuals is associated with changes in the FC of the dual-system network. A total of 29 male tobacco-dependent subjects (tobacco-dependent group) (mean age: 64.20 years, standard deviation [SD]: 4.81 years) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Exactly 28 male nonsmokers (control group) (mean age: 61.95 years, SD: 5.52 years) were also recruited to undergo rs-fMRI. We used the dorsolateral striatum (putamen) and dorsomedial striatum (caudate) as regions of interest for whole-brain resting-state connectivity to construct habitual and goal-directed brain networks, respectively. In addition, all participants were evaluated by carotid artery ultrasound to obtain the cIMT values. Then, we compared the dual-system brain networks between the tobacco dependence and control groups and the relationship between cIMT and imbalance of dual-system brain networks in tobacco dependence. The results showed a reduction in the connection between the caudate and precuneus and an increased connection between the putamen and prefrontal cortex; and supplementary motor area. The bilateral connectivity between the caudate and inferior frontal gyrus showed a significant negative correlation with the cIMT, and no positive correlation was observed with cIMT in the brain region that connects to the caudate. However, for the putamen, increased connectivity with the inferior temporal and medial frontal gyri was strongly associated with a high cIMT. The results indicate that the formation of tobacco dependence behavior is related to changes in the dual-system brain network. Carotid sclerosis is associated with the weakening of the goal-directed network and enhancement of the habit network in tobacco dependence. This finding suggests that tobacco dependence behavior and clinical vascular diseases are related to changes in brain functional networks.

Brain functional network changes associated with psychological symptoms in emergency psychological responding professionals after the first wave of COVID-19 pandemic.

Emergency psychological responding professionals are recruited to help deal with psychological issues as the Corona Virus Disease 2019 (COVID-19) continues. We aimed to study the neural correlates of psychological states in these emergency psychological responding professionals after exposure to COVID-19 related trauma at baseline and after 1-year self-adjustment. Resting-state functional MRI (rs-fMRI) and multiscale network approaches were utilized to evaluate the functional brain activities in emergency psychological professionals after trauma. Temporal (baseline vs. follow-up) and cross-sectional (emergency psychological professionals vs. healthy controls) differences were studied using appropriate t-tests. The brain functional network correlates of psychological symptoms were explored. At either time-point, significant changes in the ventral attention (VEN) and the default mode network (DMN) were associated with psychological symptoms in emergency psychological professionals. In addition, the emergency psychological professionals whose mental states improved after 1 year demonstrated altered intermodular connectivity strength between several modules in the functional network, mainly linking the DMN, VEN, limbic, and frontoparietal control modules. Brain functional network alterations and their longitudinal changes varied across groups of EPRT with distinctive clinical features. Exposure to emergent trauma does cause psychological professionals to produce DMN and VEN network changes related to psychological symptoms. About 65% of them will gradually adjust mental states, and the network tends to be rebalanced after a year.

Effects of chronic caffeine intake and withdrawal on neural activity assessed via resting-state functional magnetic resonance imaging in mice

Caffeine is a psychoactive substance that not only improves wakefulness, but also slows the cognitive decline caused by aging. However, at present, there are no reports about the effects of caffeine withdrawal, including headaches and changes in brain functional networks (nerve activity). Headache may occur approximately 24 h after discontinuing caffeine intake in chronic caffeine drinkers.The current study aimed to examine the brain functional activity via resting-state functional magnetic resonance imaging in chronically caffeinated and decaffeinated groups to investigate changes in brain activity caused by caffeine.C57BL/6J mice were included in the analysis, and they underwent 9.4-T ultrahigh-field magnetic resonance imaging. The mice were classified into the control, chronic caffeinated, and caffeine withdrawal grsoups. Mice were divided into three groups: 1) not exposed to caffeine (control); 2) treated with caffeine at a concentration of 0.3 mg/mL for 4 weeks (chronic caffeinated); and 3) treated as before with caffeine and withdrawn from caffeine for 24 h. After the three groups were examined, functional connectivity matrices were calculated using brain imaging analysis tools, and independent component analysis was performed.The results showed that caffeine administration activated neural activity areas in the stress response system. Furthermore, 24h after caffeine withdrawal, the results showed an increase in pain-related neural activity. In addition, caffeine administration was shown to activate the dentate gyrus, one of the hippocampal regions, and to decrease the neural activity in the olfactory bulb and anterior cingulate cortex.In the current research, the neural activity of specific brain regions changed after chronic caffeine administration and withdrawal.

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke.

Prior studies have shown abnormal brain functional network changes in patients with acute ischemic stroke. However, the alterations of dynamic functional network connectivity (FNC) in brainstem strokes have not been elucidated. To assess alterations of static and dynamic FNCs and determine the relationships between these and upper limb movement performance in patients with acute brainstem ischemic stroke. In total, 50 patients with acute brainstem ischemic stroke and 50 age- and sex-matched healthy controls were enrolled in the present study and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis was conducted to assess static and dynamic FNC patterns based on seven resting-state networks, namely, the default mode network (DMN), executive control network (ECN), attention network (AN), somatomotor network (SMN), visual network (VN), auditory network (AUN), and cerebellum network (CN). Compared with controls, patients with acute brainstem ischemic stroke exhibited wide aberrations of static FNC, including increased FNC in DMN-ECN, DMN-VN, ECN-VN, ECN-AN and AN-AUN pairs. Patients with acute brainstem ischemic stroke showed aberrant dynamic FNC in State 1, involving increased FNC aberrance in the DMN with AN, DMN with ECN, and reduced FNC in SMN-VN pairs. In State 5, patients with acute brainstem ischemic stroke showed increased FNC in DMN-VN and AN-AUN, and decreased FNC in AN-SMN pairs. This study suggests that static and dynamic FNC impairment and aberrant connections exist in acute brainstem ischemic stroke, which expands what is known regarding the relationship between stroke and FNC from static and dynamic perspectives.

Evolution of brain functional plasticity associated with increasing symptom severity in degenerative cervical myelopathy.

SummaryBackgroundAdvanced imaging modalities have helped elucidate the cerebral alterations associated with neurological impairment caused by degenerative cervical myelopathy (DCM), but it remains unknown how brain functional network changes at different stages of myelopathy severity in DCM patients, and if patterns in network connectivity can be used to predict transition to more myelopathic stages of DCM.MethodsThis pilot cross-sectional study, which involves the collection of resting-state functional MRI (rs-fMRI) images and the modified Japanese Orthopedic Association (mJOA) score, enrolled 116 participants (99 patients and 17 healthy controls) from 2016 to 2021. The patient cohort included 21patients with asymptomatic spinal cord compression, 48 mild DCM patients, and 20 moderate or severe DCM patients. Functional connectivity networks were quantified for all participants, and the transition matrices were quantified to determine the differences in network connectivity through increasingly myelopathic stages of DCM. Additionally, a link prediction model was used to determine whether more severe stages of DCM can be predicted from less symptomatic stages using the transition matrices.FindingsResults indicated interruptions in most connections within the sensorimotor network in conjunction with spinal cord compression, while compensatory connectivity was observed within and between primary and secondary sensorimotor regions, subcortical regions, visuospatial regions including the cuneus, as well as the brainstem and cerebellum. A link prediction model achieved an excellent predictive performance in estimating connectivity of more severe myelopathic stages of DCM, with the highest area under the receiver operator curve (AUC) of 0.927 for predicting mild DCM from patients with asymptomatic spinal cord compression.InterpretationA series of predictable changes in functional connectivity occur throughout the stages of DCM pathogenesis. The brainstem and cerebellum appear highly influential in optimizing sensorimotor function during worsening myelopathy. The link predication model can inclusively estimate brain alterations associated with myelopathy severity.FundingNIH/NINDS grants (1R01NS078494-01A1, and 2R01NS078494).

Changes in intrinsic functional brain connectivity related to occupational stress of firefighters

Firefighters are exposed to physical and mental threats in the working environment that put them at risk for occupational stress and burnout. Stress responses can impact the functional interactions between brain regions involved in emotional and cognitive regulation. The objective of this study was to investigate brain functional connectivity (FC) related to occupational stress in firefighters. Male firefighters (n = 77) completed the Korean Occupational Stress Scale questionnaire on occupational stress and underwent brain magnetic resonance imaging. Seed-based FC analyses were conducted by setting core regions of the large-scale functional networks as seeds. Subsequent correlational analyses detected relationships between occupational stress scale scores and brain FC. The results showed that occupational stress was negatively correlated with FC between the central executive network (CEN)-related brain regions and seed regions of other networks. Additionally, occupational stress was negatively correlated with FC within the default mode network (DMN), but positively correlated with FC between the salience network (SN) and the DMN. Changes in FC in large-scale neural networks are likely involved in occupational stress responses. Taken together, these results suggest that proper management of occupational stress may help prevent the occurrence of clinical problems caused by changes in brain functional networks.

Age-related changes in brain functional networks under multisensory-guided hand movements assessed by the functional near – Infrared spectroscopy

ObjectivesThis study aims to explore the age-related effects of hand rehabilitation training under multisensory stimulation interaction on brain functional networks. MethodsA multisensory stimulation training glove (MSTG) was designed to realize 3 sensory guidance modes, namely audio-visual guidance (AVG), visual guidance (VG) and no guidance (NG). This study recruited 20 older subjects as the experimental group and 22 young people as the control group. Functional near-infrared spectroscopy (fNIRS) was used to monitor haemoglobin concentration in the motor cortex (MC), prefrontal cortex (PFC), temporary lo be (TL) and occipital lobe (OL) under three different guidance stages, and further analysed the cortical activation and functional connectivity (FC). ResultsMultisensory guidance stage showed more activation and higher FC in all subjects. The activated brain regions of the older subjects showed bilateral activation, which is consistent with the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) model. In terms of brain region coordination, older people have a more balanced and denser functional network in the left and right hemispheres compared to younger people. Meanwhile, multisensory stimulation produced a positive training effect on the number of training and reaction time. ConclusionAudio-visual combined stimulation had a significant gain effect on hand training at different ages. However, older adults induce a wider range of cortical activations. At the same time, young and older people have different intercortical coordination networks. All these results provide theoretically and applied references for multisensory stimulation in the prevention and rehabilitation of ageing and brain neurological disorders.

The measurements of the similarity of dynamic brain functional network

Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.

Altered Intra- and Inter-Network Functional Connectivity in Patients With Crohn's Disease: An Independent Component Analysis-Based Resting-State Functional Magnetic Resonance Imaging Study.

BackgroundMany studies have reported changes in the structure and function of several brain areas in patients with Crohn’s disease (CD). However, little is known about whether the possible functional connectivity of resting-state networks (RSNs) is altered in CD patients.PurposeAim to investigate the intra- and inter-network alterations between related RSNs in patients with CD and the potential relationships between altered neuroimaging and CD clinical indices.Materials and MethodsIn this study, 20 CD patients and 22 age- and sex-matched healthy controls were included. All participants underwent functional magnetic resonance imaging examination. We used independent component analysis (ICA) to explore the changes in RSNs and evaluated functional connectivity between different RSNs using functional network connectivity (FNC) analysis, and Pearson correlation analysis was performed between altered intra- and inter-network functional connectivity and CD clinical index.ResultsSix CD-related RSNs were identified via ICA, namely the high visual, prime visual, language, dorsal default mode, posterior insula, and precuneus networks. Compared to healthy controls, patients with CD showed significant changes in prime visual and language networks. Additionally, the functional connectivity (FC) values of the left calcarine within the prime visual network were negatively correlated with CD duration. The inter-alterations showed that a significantly increased FNC existed between the language and dorsal default mode networks.ConclusionThe results showed CD-related changes in brain functional networks. This evidence provides more insights into the pathophysiological mechanisms of brain plasticity in CD.

Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering

This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.

Amyloid and cerebrovascular burden divergently influence brain functional network changes over time.

Amyloid and cerebrovascular burden divergently influence brain functional network changes over time.

Amyloid and cerebrovascular burden divergently influence brain functional network changes over time.

To examine the effects of baseline Alzheimer disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connectivity (FC) changes in mild cognitive impairment (MCI). We studied 30 patients with amnestic MCI (aMCI) and 55 patients with subcortical vascular MCI (svMCI) with baseline Pittsburgh Compound B (PiB)-PET scans and longitudinal MRI scans. Participants were followed up clinically with annual MRI for up to 4 years (aMCI: 26 with 2 timepoints, 4 with 3 timepoints; svMCI: 13 with 2 timepoints, 16 with 3 timepoints, 26 with 4 timepoints). β-Amyloid (Aβ) burden was associated with longitudinal DMN FC declines, while cerebrovascular burden was associated with longitudinal ECN FC changes. When patients were divided into PiB+ and PiB- groups, PiB+ patients showed longitudinal DMN FC declines, while patients with svMCI showed longitudinal ECN FC increases. Direct comparisons between the 2 groups without mixed pathology (aMCI PiB+ and svMCI PiB-) recapitulated this divergent pattern: aMCI PiB+ patients showed steeper longitudinal DMN FC declines, while svMCI PiB- patients showed steeper longitudinal ECN FC increases. Finally, using baseline PiB uptake and lacune numbers as continuous variables, baseline PiB uptake showed inverse U-shape associations with longitudinal DMN FC changes in both MCI subtypes, while baseline lacune numbers showed mainly inverse U-shape relationships with longitudinal ECN FC changes in patients with svMCI. Our findings underscore the divergent effects of Aβ and cerebrovascular burden on longitudinal FC changes in the DMN and ECN in the predementia stage, which reflect the underlying pathology and may be used to track early changes in Alzheimer disease and cerebrovascular disease.

Predicting Male vs. Female from Task-fMRI Brain Connectivity.

A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean time-series from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.

Changes in Brain Functional Networks of Insomniacs Induced by Magnetic Stimulation at Acupoints

Insomnia and the tension, anxiety, and neurasthenia caused by it are a prevalent sleep disorder. Acupuncture is a common method of treating insomnia. Magnetic stimulation has a similar mechanism as acupuncture. In this paper, 18 (nine insomnia and nine healthy) subjects were selected, Neiguan (PC6), Shenmen (HT7), and Sanyinjiao (SP6) acupuncture points were stimulated and 32-channel electroencephalography (EEG) signals were recorded before and after stimulation. Granger causality of EEG signals between each two channels was calculated. Based on the EEG causal coefficient matrix, brain functional networks were constructed to determine whether magnetic stimulation at acupoints could improve the brain network of insomniacs or not. The results suggested that the magnetic stimulation of the acupoints might be an effective treatment for insomnia.

A hierarchical independent component analysis model for longitudinal neuroimaging studies

In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions, to study neurodevelopment or to evaluate treatment effects on neural processing. One of the important goals in longitudinal imaging analysis is to study changes in brain functional networks across time and how the changes are modulated by subjects' clinical or demographic variables. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA), which separates multivariate signals into linear mixture of independent components. However, existing ICA methods are only applicable to cross-sectional studies and not suited for modeling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of changes in brain functional networks on both the population- and individual-level, borrow information across repeated scans within the same subject to increase statistical power in detecting covariate effects on the networks, and allow for model-based prediction for brain networks changes caused by disease progression, treatment or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain maximum likelihood estimates of L-ICA. We further develop a subspace-based approximate EM algorithm which greatly reduce the computation time while still retaining high accuracy. Moreover, we present a statistical testing procedure for examining covariate effects on brain network changes. Simulation results demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2 study to investigate changes in brain functional networks in Alzheimer disease. Results from the L-ICA provide biologically insightful findings which are not revealed using existing methods.

Developmental Changes in Backbone of Brain Functional Network During the Infancy Period

In a normal development, structure and functions of the infant’s brain change efficiently to enable one’s communication with the external world. However, it is still a mystery that how dominant connections of the brain network called “backbone” change in the infancy period. In this study developmental changes in the backbone of functional brain network are investigated. Therefore, resting state EEG of 15 infants (7 girls, full-term) with no family health problem were recorded at the ages of 6 and 18 months. Subsequently, the brain functional network was estimated from the cleaned EEG using Weighted Phase Lag Index (WPLI) algorithm. The WPLI network was then explored using the graph theory by Minimum Spanning Tree. Parameters of the MST including diameter, betweenness, leaf number, eccentricity, and hierarchy were calculated. Subsequently, a pairwise t-test was performed based on the MST parameters to compare backbone of the brain network between both age groups. Based on the Power Spectral Analysis, four frequency bands including delta, lower alpha, lower beta, and gamma bands were selected, each of them investigated separately. The results showed an increase of eccentricity and diameter, and a reduction in leaf numbers, betweenness and hierarchy (lower complexity) in the functional networks of the frequencies with enhanced power (eg. gamma). In addition, opposite results were observed in the networks related to the frequencies with declined power (eg. delta). Our findings indicate that backbone of the brain functional network changes in a frequency specific manner, and a reverse order follows the changes in oscillatory pattern.