Brain Functional Connectivity Networks Research Articles

Effects and mechanisms of computerized cognitive training in Huntington's disease: protocol for a pilot study

Huntington's disease (HD) causes progressive cognitive decline, with no available treatments. Computerized cognitive training (CCT) has shown efficacy in other populations, but its effects in HD are largely unknown. This pilot study will explore the effects and neural mechanisms of CCT in HD. The intervention group participants will complete 12 weeks of multidomain CCT. Control group participants will receive lifestyle education and access to CCT after the study. The primary outcome is change in processing speed. Secondary outcomes include – change in other cognitive domains, functional brain network connectivity (derived from MRI) and psychosocial function. Feasibility outcomes include rates of recruitment, adherence and retention. This study may provide insights into the effects of CCT in HD and guide future trials. Clinical Trial Registration: ACTRN12622000908730 ( ClinicalTrials.gov )

Educational Attainment Moderates Task-state Control Network Connectivity Relations to Response Conflict Among Healthy Older Adults.

Older adult executive function varies widely due to brain and cognitive aging. Variance in older adult executive function is linked to increased response conflict from cognitive and brain aging. Cognitive Reserve (CR) is a theoretical protective mechanism that lessens brain aging's impact on cognition and is associated with greater educational attainment. Recent work in rest-state fMRI suggests CR proxies moderate the relationship between functional connectivity (FC) and cognitive performance. Brain network FC in "control networks", including the salience (SN), dorsal attention (DAN) and frontoparietal (FPN) networks, are associated with cognitive processes in older adults. CR is hypothesized to maintain cognitive processing in part through changes in how brain networks respond to cognitive demands. However, it is unclear how CR proxies like educational attainment are related to control network FC during performance when cognitive demands are increased relative to rest. Because CR is expressed more in those with higher education, we hypothesized stronger control network FC would relate to better performance, where this relationship would be strongest among the most educated. We collected flanker task data during fMRI to assess the impact of a CR-proxy (i.e., educational attainment) on response conflict among older adult subjects (n=42, age=65-80). Linear mixed effects models showed more educated older adults with greater SN-FC had a smaller flanker effect (i.e., less influence of distractors; p<.001) during task performance. For the first time, we show that educational attainment moderates the relationship between task-state SN-FC and executive function among older adults.

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.

Reconfigurations of Dynamic Functional Network Connectivity in Large-scale Brain Network after Prolonged Abstinence in Heroin Users.

Brain recovery phenomenon after long-term abstinence had been reported in substance use disorders. Yet, few longitudinal studies have been conducted to observe the abnormal dynamic functional connectivity (dFNC) of large-scale brain networks and recovery after prolonged abstinence in heroin users. The current study will explore the brain network dynamic connection reconfigurations after prolonged abstinence in heroin users (HUs). The 10-month longitudinal design was carried out for 40 HUs. The 40 healthy controls (HCs) were also enrolled. Group independent component analysis (GICA) and dFNC analysis were employed to detect the different dFNC patterns of addiction-related ICNs between HUs and HCs. The temporal properties and the graph-theoretical properties were calculated. Whether the abnormalities would be reconfigured in HUs after prolonged abstinence was then investigated. Based on eight functional networks extracted from GICA, four states were identified by the dFNC analysis. Lower mean dwell time and fraction rate in state4 were found for HUs, which were increased toward HCs after prolonged abstinence. In this state, HUs at baseline showed higher dFNC of RECN-aSN, aSN- aSN and dDMN-pSN, which decreased after protracted abstinence. A similar recovery phenomenon was found for the global efficiency and path length in abstinence HUs. Mean while, the abnormal dFNC strength was correlated with craving both at baseline and after abstinence. Our longitudinal study observed the large-scale brain network reconfiguration from the dynamic perspective in HUs after prolonged abstinence and improved the understanding of the neurobiology of prolonged abstinence in HUs.

Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia.

This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.

Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients

Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101. Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253.

Aberrant Brain Triple-Network Effective Connectivity Patterns in Type2 Diabetes Mellitus.

Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links through Daily Activity Patterns and Brain Connectivity Analysis

Better understanding apathy in late-life depression (LLD) would help predicting poor prognosis of the disease such as dementia. Actimetry provides an objective and ecological measure of apathy from patients' daily motor activity. We aimed to determine if patterns of motor activity were associated with apathy and brain connectivity in networks underlying goal-directed behaviors. Resting-state functional MRI and diffusion MRI were collected from 38 non-demented LLD subjects. Apathy was evaluated using the diagnostic criteria for apathy, the apathy evaluation scale (AES) and the apathy motivation index (AMI). Functional principal components (fPC) of motor activity were derived from actimetry recordings of 72 hours. Associations between fPC and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPC were identified via the threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry. We found two fPC associated with apathy: mean diurnal activity, negatively associated with AES, and an early chronotype, negatively associated with AMI. Mean diurnal activity was associated with increased connectivity in the default-mode, the cingulo-opercular and the frontoparietal networks, while chronotype was associated with a more heterogenous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs. Our findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in LLD, associated with modified functional connectivity of brain networks underlying goal-directed behaviors.

Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery.

Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.

Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition. We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency. We subsequently tested large-scale brain network FC as a potential mediator of the beneficial relationship between social engagement and cognitive performance. 112 older adults (70.7±7.3 years, range 54.6-89.7; 84 women) completed the Lubben Social Network Scale 6 (LSNS-6), National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3 (UDS-3) cognitive battery, and resting state fMRI. We completed seed-based correlational analysis in the default mode and salience networks. Significant associations between social engagement scores and cognitive performance, as well as between social engagement and FC of brain networks, informed the construction of mediation models. Social engagement was significantly associated with executive function and global cognition, with greater social engagement associated with better cognitive performance. Social engagement was significantly associated with salience network FC, with greater social engagement associated with higher connectivity. Salience network FC partially mediated associations between social engagement and both executive function and global cognition. Our results suggest that the salience network is a key mediator of the beneficial relationship between social engagement and cognition in older adults.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body.Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15years, 25 to 35years, and 45 to 75years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Large-scale correlation screening under dependence for brain functional connectivity network inference

Large-scale correlation screening under dependence for brain functional connectivity network inference

Graph theoretical analysis and independent component analysis of diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study.

This study aimed to investigate the resting-state functional connectivity and topologic characteristics of brain networks in patients with diabetic optic neuropathy (DON). Resting-state functional magnetic resonance imaging scans were performed on 23 patients and 41 healthy control (HC) subjects. We used independent component analysis and graph theoretical analysis to determine the topologic characteristics of the brain and as well as functional network connectivity (FNC) and topologic properties of brain networks. Compared with HCs, patients with DON showed altered global characteristics. At the nodal level, the DON group had fewer nodal degrees in the thalamus and insula, and a greater number in the right rolandic operculum, right postcentral gyrus, and right superior temporal gyrus. In the internetwork comparison, DON patients showed significantly increased FNC between the left frontoparietal network (FPN-L) and ventral attention network (VAN). Additionally, in the intranetwork comparison, connectivity between the left medial superior frontal gyrus (MSFG) of the default network (DMN) and left putamen of auditory network was decreased in the DON group. DON patients altered node properties and connectivity in the DMN, auditory network, FPN-L, and VAN. These results provide evidence of the involvement of specific brain networks in the pathophysiology of DON.

Sex differences in fetal brain functional network topology.

The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.

Measurement of Functional Brain Network Connectivity in People with Orthostatic Tremor.

Orthostatic tremor is a rare movement disorder characterized by a sensation of unsteadiness and leg tremor while standing. It has been hypothesized that the disorder is attributable to dysregulation of a central oscillatory network in the brain. This putative network includes primary motor cortex, supplementary motor area, cerebellum, thalamus, and pontine tegmentum. We studied this brain network by recording resting-state functional MRI data from individuals with orthostatic tremor. For each participant, we measured resting-state functional connectivity using a seed-based approach. Regions of interest included were components of the putative central oscillatory network and a primary motor thumb region (identified via transcranial magnetic stimulation). A non-central oscillatory network region of interest-posterior cingulate cortex-was included for comparative analysis of a well-characterized intrinsic network, the default mode network. Demographic information, medical history, and tremor characteristics were collected to test associations with functional connectivity. For normative context, data from the 1000 Functional Connectomes Project were analyzed using an identical approach. We observed that tremor and demographic variables were correlated with functional connectivity of central oscillatory network components. Furthermore, relative to healthy comparison participants, patients with orthostatic tremor exhibited qualitatively different patterns of cerebellar resting state functional connectivity. Our study enhances the current understanding of brain network differences related to orthostatic tremor and is consistent with a hypothesized selective decoupling of cerebellum. Additionally, associations observed between functional connectivity and factors including medical history and tremor features may suggest targets for treatment of orthostatic tremor.

Language First, Cognition Later: Different Trajectories of Subcomponents of the Future-Reading Network in Processing Narratives from Kindergarten to Adolescence.

Narrative comprehension is a linguistic ability that emerges early in life and has a critical role in language development, reading acquisition, and comprehension. According to the Simple View of Reading model, reading is acquired through word decoding and linguistic comprehension. Here, within and between networks, functional connectivity in several brain networks supporting both language and reading abilities was examined from prereading to proficient reading age in 32 healthy children, ages 5-18 years, scanned annually while listening to stories over 12 years. Functional connectivity changes within and between the networks were assessed and compared between the years using hierarchical linear regression and were related to reading abilities. At prereading age, the networks related to basic language processing accounted for 32.5% of the variation of reading ability at reading age (at 12-14 years) (R2 = 0.325, p = 0.05). At age 17, more complex cognitive networks were involved and accounted for 97.4% of the variation in reading ability (R2 = 0.974, p = 0.022). Overall, networks composing the future-reading network are highly involved in processing narratives along development; however, networks related to semantic, phonological, and syntactic processing predict reading ability earlier in life, and more complex networks predict reading proficiency later in life.

Effects of aripiprazole on resting-state functional connectivity of large-scale brain networks in first-episode drug-naïve schizophrenia patients

Aripiprazole modulates functional connectivity (FC) between several brain regions in first-episode schizophrenia patients, contributing to improvement in clinical symptoms. However, the effects of aripiprazole on abnormal connections among extensive brain networks in schizophrenia patients remain unclear. We aimed to investigate the effects of 12 weeks of aripiprazole treatment on the FC of large-scale brain networks. Forty-five first-episode drug-naïve schizophrenia patients and 45 healthy controls were recruited for this longitudinal study. Resting-state functional magnetic resonance imaging (fMRI) data were collected at baseline and after 12 weeks of aripiprazole treatment. The patients were classified into those in response (SCHr group) and non-response (SCHnr group) according to the improvement of clinical symptoms after 12-weeks treatment. The FC were evaluated for seven large-scale brain networks. In addition, correlation analysis was performed to investigate associations between changes FC of large-scale brain networks and clinical symptoms. Before aripiprazole treatment, schizophrenia patients showed decreased FC of extensive brain networks compared to healthy controls. The 12-week aripiprazole treatment significantly prevented the constantly decreased FC of subcortical network, default mode network and other brain networks in patients with SCHr, in association with the improvement of clinical symptoms. Taken together, these findings have revealed the effects of aripiprazole on FC in large-scale networks in schizophrenia patients, which could provide new insight on interpreting symptom improvement in SCH.

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study.

Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI.

Abnormal dynamics of functional brain network in Apolipoprotein E ε4 carriers with mild cognitive impairment

&lt;abstract&gt; &lt;p&gt;As is well known, the Apolipoprotein E (APOE) ε4 allele is the most pertinent genetic hazardous element for Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered a prodromal stage of AD. How the APOE ε4 allele modulates functional connectivity of brain network in MCI group is a question worth exploring. At present, some studies have evaluated the relationship between APOE ε4 allele and static functional network connectivity (sFNC) for MCI individuals, while the relationship of dynamic FNC (dFNC) with APOE ε4 allele still remained puzzled. Thus, we aim to detect aberrant dFNC for APOE ε4 carriers in the MCI group. On the basis of the resting-state functional magnetic resonance imaging (rs-fMRI) data, seven intrinsic brain functional networks were first recognized by the group independent component analysis. Then, the technique of sliding window was employed to determine the dFNC, and two dFNC states were detected by the k-means clustering algorithm. Finally, three temporal properties of fraction time, mean dwell time as well as transition numbers in the dFNC states were investigated. The results found that the dFNC and temporal properties in APOE ε4 carriers were abnormal compared with those in APOE ε4 noncarriers. In detail, in the MCI group, compared with APOE ε4 noncarriers, carriers had 9 pairs of abnormal dFNC and had significant differences in all the three temporal properties of the two dFNC states. In addition, two pairs of dFNC were found significantly correlated with clinical measure. This detected abnormal dynamics of temporal properties and dFNC in APOE ε4 carriers were similar with that reported for AD patients in previous studies. These results may suggest that in the MCI group, APOE carriers are more at risk for AD compared to noncarriers. Our findings may offer novel insights into the mechanisms of abnormal brain reconfiguration for individuals at genetic risk for AD, which could also be regarded as biomarkers for the early identification of AD.&lt;/p&gt; &lt;/abstract&gt;

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.