Differences In Functional Networks Research Articles

Functional network characteristics in anxiety- and mania-based subgroups of bipolar I disorder

BackgroundBipolar disorder I (BD-I) is a heterogeneous disorder with a high prevalence of comorbid anxiety. The aim of this study was to investigate whether anxiety and mania symptoms define distinct subgroups within BD-I and to explore potential differences in functional network characteristics between these subgroups. MethodsSubgroups were identified using scores from clinical anxiety and mania scales. After dimension reduction of these scores, data-driven clustering analysis with cross-validation was employed to reveal the existence of subgroups. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were pre-processed using fMRIPrep. After parcellation and network construction, global and regional graph theoretical measures were calculated per subgroup. ResultsClustering results revealed that, based on anxiety symptomatology, subjects fell into two distinct subgroups, whereas mania symptoms divided subjects into four unique subgroups. These subgroups varied notably on several symptom scales. Network assortativity was significantly associated with anxiety subgroups. Post-hoc pairwise comparisons did not reveal significant global functional network differences between the anxiety subgroups or between mania subgroups. Regional network differences between clinical subgroups were especially apparent for strength and degree in the temporal and frontal lobes. LimitationsSmall sample size of some subgroups is a limitation of this study as is the categorical rather than continuous representation of anxiety and mania symptoms. ConclusionsBD-I populations may be stratified into robust subgroups based on anxiety and mania symptoms, showing differences in functional network connectivity. Our findings highlight new avenues of research for investigating heterogeneity in psychiatric populations.

Dysconnectivity of the brain functional network and abnormally expressed peripheral transcriptional profiles in patients with anxious depression

BackgroundMajor depressive disorder (MDD) is a heterogeneous mental disorder, and accompanying anxiety symptoms, known as anxious depression (AD), are the most common subtype. However, the pathophysiology of AD may be distinct in depressed patients without anxiety (NAD) and remains unknown. This study aimed to investigate the relationship between functional connectivity and peripheral transcriptional profiles in patients with AD and NAD. MethodsFunctional imaging data were collected to identify differences in functional networks among patients with AD (n = 66), patients with NAD (n = 115), and healthy controls (HC, n = 200). The peripheral transcriptional data were clustered as co-expression modules, and their associations with AD, AND, and HC were analyzed. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the genes in the significant module were performed. Correlation analysis was performed to identify functional network-associated gene co-expression modules. ResultsA network was identified which consisted of 23 nodes and 28 edges that were significantly different among three sample groups. The regions of the network were located in temporal and occipital lobe. Two gene co-expression modules were shown to be associated with NAD, and one of which was correlated with the disrupted network in the AD group. The biological function of this module was enriched in immune regulation pathways. ConclusionThe results suggested that immune-related mechanisms were associated with functional networks in AD.

Individual differences in the language task-evoked and resting-state functional networks.

The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.

Identifying Patients with Epilepsy Having Depression/Anxiety Disorder Using Common Spatial Patterns of Functional EEG Networks

PurposeThe presentation of epilepsy with depression/anxiety disorder (E-AD) is a complex comorbidity, and a systematic psychiatric screen poses a heavy economic burden on the patients. Typically, distinguishing between E-AD patients and epilepsy without depression/anxiety disorder (E-no-AD) needs multiple evaluations, which is incredibly difficult owing to the high costs. Therefore, there is an urgent need for a reliable assessment protocol to preemptively distinguish between the two types of patients.MethodsWe collected the resting-state electroencephalography (EEG) signals from E-AD patients and E-no-AD patients and constructed the functional networks for the two groups of. The connectivity strength and the corresponding network properties of different frequency bands were statistically compared to explore the inherent differences. Based on the differences in functional networks, we evaluated the classification performance using different network features, including the network properties, principal component analysis (PCA) and spatial patterns of networks (SPN).ResultsUsing the support vector machine, the differentiation of the 4416 EEG segments from E-no-AD patients and 6346 EEG segments from E-AD patients was realized using three different network features. The results revealed that the SPN feature of functional network connectivity was remarkably superior to the traditional network feature for distinguishing between the two patient groups, with an appreciable classification accuracy of 89.90% and sensitivity of 87.37% and specificity of 91.67%.ConclusionThese findings demonstrate the superiority of functional SPN features as a reliable way to characterize the differences between E-AD and E-no-AD patients and potentially provide insights regarding the complex mechanisms causing E-AD in patients.

Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis.

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Classification of partial seizures based on functional connectivity: A MEG study with support vector machine

Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification.

Alterations of brain activity in multiple system atrophy patients with freezing of gait: A resting-state fMRI study.

BackgroundFreezing of gait (FOG) in multiple system atrophy (MSA) is characterized by a higher risk of falls and a reduced quality of life; however, the mechanisms underlying these effects have yet to be identified by neuroimaging. The aim of this study was to investigate the differences in functional network when compared between MSA patients with and without freezing.MethodsDegree centrality (DC) based on the resting-state functional magnetic resonance imaging was computed in 65 patients with MSA and 36 healthy controls. Brain regions with statistically different DC values between groups were selected as seed points for a second seed-based functional connectivity (FC) analysis. The relationships between brain activity (DC and FC alterations) and the severity of freezing symptoms were then investigated in the two groups of patients with MSA.ResultsCompared to MSA patients without FOG symptoms (MSA-nFOG), patients with MSA-FOG showed an increased DC in the left middle temporal gyrus but a reduced DC in the right superior pole temporal gyrus, left anterior cingulum cortex, left thalamus, and right middle frontal gyrus. Furthermore, in patients with MSA-FOG, the DC in the left thalamus was negatively correlated with FOG scores. Using the left thalamus as a seed, secondary seed-based functional connectivity analysis revealed that patients with MSA-FOG commonly showed the left thalamus-based FC abnormalities in regions related to cognition and emotion. In contrast to the patients with MSA-nFOG, patients with MSA-FOG showed an increased FC between the left thalamus and the left middle temporal gyrus (MTG), right inferior parietal lobule (IPL), bilateral cerebellum_8, and left precuneus.ConclusionFreezing of gait is associated with centrality of the impaired thalamus network. Abnormal FC between the thalamus and left MTG, right IPL, bilateral cerebellum_8, and left precuneus was involved in FOG. These results provide new insight into the pathophysiological mechanism of FOG in MSA.

Differential structure-function network coupling in the inattentive and combined types of attention deficit hyperactivity disorder.

The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery

Studies reporting significant associations between maternal prenatal stress and child outcomes are frequently confounded by correlates of prenatal stress that influence the postnatal rearing environment. The major objective of this study is to identify whether maternal prenatal stress is associated with variation in human brain functional connectivity prior to birth. We utilized fetal fMRI in 118 fetuses [48 female; mean age 32.9 weeks (SD = 3.87)] to evaluate this association and further addressed whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Community detection was used to empirically define networks and enrichment was used to isolate differential within- or between-network connectivity effects. Significance for χ2 enrichment was determined by randomly permuting the subject pairing of fetal brain connectivity and maternal stress values 10,000 times. Mixtures modelling was used to test whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Increased maternal prenatal negative affect/stress was associated with alterations in fetal frontoparietal, striatal, and temporoparietal connectivity (β = 0.82, p < 0.001). Follow-up analysis demonstrated that these associations were stronger in women with better health behaviors, more positive interpersonal support, and lower overall stress (β = 0.16, p = 0.02). Additionally, magnitude of stress-related differences in neural connectivity was marginally correlated with younger gestational age at delivery (β = −0.18, p = 0.05). This is the first evidence that negative affect/stress during pregnancy is reflected in functional network differences in the human brain in utero, and also provides information about how positive interpersonal and health behaviors could mitigate prenatal brain programming.

Brain networks in patients with isolated or recurrent transient global amnesia.

The aim of this study was to investigate differences in cerebral blood flow (CBF) and functional networks between transient global amnesia (TGA) patients with a single event and those with recurrent events using arterial spin labeling (ASL) MRI. We enrolled patients with TGA and classified them into two groups according to the number of TGA events: TGA patients with a single event and those with recurrent events. MRI scans were performed within 24h after TGA ictal onset in all patients. We quantified CBF and analyzed the functional network based on CBF using graph theory, and determined the differences in CBF and functional networks between the groups. We enrolled 44 patients with TGA. Among them, 6 patients had recurrent TGA events, whereas 38 patients had a single TGA event. No regions had significantly different CBFs between TGA patients with recurrent events and those with a single event. The global functional network analysis found that the eccentricity was significantly higher in TGA patients with recurrent events than in those with a single event (5.829 vs. 4.657, p=.001). The local functional network analysis showed that several regions had significantly different betweenness centrality and eccentricity measures between TGA patients with recurrent events and those with a single event. We demonstrated the differences in the functional network based on CBF using graph theory according to recurrence in patients with TGA. These findings suggest that TGA is a network disease, and functional network alterations in TGA are related to clinical symptoms.

Connectome Wide Study of Intrinsic Functional Connectivity Associated With Impulsive Choice in Adolescence

Impulsive risk-taking behavior is associated with psychopathology and morbidity and mortality in adolescence. However, individual differences in functional networks that underlie impulsive choice in adolescence remain poorly understood.

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia.

The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease. Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance.

Differences in network centrality between high and low myopia: a voxel-level degree centrality study.

Previous studies have linked high myopia (HM) to brain activity, and the difference between HM and low myopia (LM) can be assessed. To study the differences in functional networks of brain activity between HM and LM by the voxel-level degree centrality (DC) method. Twenty-eight patients with HM (10 men, 18 women), 18 patients with LM (4 men, 14 women), and 59 healthy controls (27 men, 32 women) were enrolled in this study. The voxel-level DC method was used to assess spontaneous brain activity. Correlation analysis was used to explore the change of average DC value in different brain regions, in order to analyze differences in brain activity between HM and LM. DC values of the right cerebellum anterior lobe/brainstem, right parahippocampal gyrus, and left caudate in HM patients were significantly higher than those in LM patients (P < 0.05). In contrast, DC values of the left medial frontal gyrus, right inferior frontal gyrus, left middle frontal gyrus, and left inferior parietal lobule were significantly lower in patients with HM (P < 0.05). However, there was no correlation between behavior and average DC values in different brain regions (P < 0.05). Different changes in brain regions between HM and LM may indicate differences in neural mechanisms between HM and LM. DC values could be useful as biomarkers for differences in brain activity between patients with HM and LM. This study provides a new method to assess differences in functional networks of brain activity between patients with HM and LM.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.

Individual identification for different age groups using functional connectivity strength.

Many studies demonstrate individual differences in functional network, especially those with age. Meanwhile, aging is one of the potential risk factors for Alzheimer's disease. Therefore, it is important to explore the discrepant pattern in aging population. Most existing methods mostly target ancient atlas for the extraction of the classification features and not consider the effect of global signal. We use two novel atlases for the extraction of classification features and then use the whole and intra-hemispheric functional connectivity strength (FCS) as classification parameters to classify different age groups, respectively. Meanwhile, the regression of global signal or not during the preprocessing has been considered. Next, the support vector machine-recursive feature elimination (SVM-RFE) method is applied for feature selection and the SVM method is applied for classification. In addition, the receiver operating characteristic curve and area under the curve are drawn to evaluate the robustness of classifier. Finally, the discriminative features are related to the physiological mechanism of aging. The promising classification performance exhibits that the FCS can effectively distinguish different age groups. Moreover, the SVM-RFE method can increase the accuracy and extract the discriminative features. The classifiers constructed by the features derived from different atlas receive similar classification performance. This study successfully distinguishes the young group, middle-aged group, and elderly group through FCS parameter, indicating the functional pattern of the network exists difference between three groups. Moreover, the results received by the SVM-RFE method and SVM classifier have the very good robustness and not specific to particular atlas and not affected by global signal and appropriate for the FCS of the whole brain or intra-hemisphere, which suggests that we can apply them to disease diagnosis in the future.

Emotional face processing in autism spectrum disorder: Effects in gamma connectivity

Impairments in social functioning are characteristic of autism spectrum disorder (ASD). Differences in functional networks during face processing in ASD compared to controls have been reported; however, the spatial-temporal dynamics of networks underlying affective processing are still not well understood. The current magnetoencephalography study examined whole-brain functional connectivity to implicit happy and angry faces in 104 adults with and without ASD. A network of reduced gamma band (30–55 Hz) phase synchrony occurring 80–308 ms following angry face presentation was found in adults with ASD compared to controls. The network involved widespread connections primarily anchored in frontal regions, including bilateral orbitofrontal areas, bilateral inferior frontal gyri, and left middle frontal gyrus extending to occipital, temporal, parietal, and subcortical regions. This finding suggests disrupted long-range neuronal communication to angry faces. Additionally, reduced gamma band-specific connectivity may reflect altered E/I balance in brain regions critical for emotional face processing in ASD.

FMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application.

Dynamic causal modeling (DCM)—a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation—was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics—particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).

Network substrates of cognitive impairment in Alzheimer’s Disease

ObjectivesFunctional and structural disconnection of the brain is a prevailing hypothesis to explain cognitive impairment in Alzheimer’s Disease (AD). We aim to understand the link between alterations to networks and cognitive impairment using functional connectivity analysis and modelling. MethodsEEG was recorded from 21 AD patients and 26 controls, mapped into source space using eLORETA, and functional connectivity was calculated using phase locking factor. The mini-mental state exam (MMSE) was used to assess cognitive impairment. A computational model was used to uncover mechanisms of altered functional connectivity. ResultsSmall-worldness (SW) of functional networks decreased in AD and was positively correlated with MMSE score and the language sub-score. Reduced SW was a result of increased path lengths, predominantly localized to the temporal lobes. Combining observed differences in local oscillation frequency with reduced temporal lobe effective connectivity in the model could account for observed functional network differences. ConclusionsTemporal lobe disconnection plays a key role in cognitive impairment in AD. SignificanceWe combine electrophysiology, neuropsychological scores, and computational modelling to provide novel insight into the relationships between the disconnection hypothesis and cognitive decline in AD.

Sex- and Age-dependent Effects of Orexin 1 Receptor Blockade on Open-Field Behavior and Neuronal Activity

Adolescence is a sensitive and critical period in brain development where psychiatric disorders such as anxiety, depression and post-traumatic stress disorder are more likely to emerge following a stressful life event. Females are two times more likely to suffer from psychiatric disorders than males. Patients with these disorders show alterations in orexins (also called hypocretins), important neuropeptides that regulate arousal, wakefulness and the hypothalamic–pituitary–adrenal axis activity. Little is known on the role of orexins in mediating arousal behaviors in male and female rats during adolescence or adulthood. Here, we examine the influence of orexin 1 receptor blockade by SB334867 in open-field behavior in male and female rats during early adolescence (PND 31–33) or adulthood (PND 75–77). Animals were injected with 0 (vehicle), 1, 10, or 30 mg/kg SB334867 (i.p.). Thirty minutes later, they were placed in an open field, and behavior and neuronal activity (c-Fos) were assessed. In adolescent males, SB334867 significantly increased immobility in the 10 mg/kg group compared to vehicle. However, this increase in immobility in adolescent males was not observed in adolescent females. In contrast to adolescent males, adult males in the 10 mg/kg dose group showed the opposite effect on immobility compared to vehicle. These results indicate that 10 mg/kg dose of SB334867 has opposing effects in adolescent and adult males, but few effects in adolescent and adult females. Differences in functional networks between limbic regions may underlie these effects of orexin receptor blockade that are sex- and age-dependent in rats.