Graph Theoretical Metrics Research Articles

Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network.

Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13–30) Hz) and gamma ((30–40) Hz) frequency bands.

Brain structural connectome in neonates with prenatal opioid exposure.

IntroductionInfants with prenatal opioid exposure (POE) are shown to be at risk for poor long-term neurobehavioral and cognitive outcomes. Early detection of brain developmental alterations on neuroimaging could help in understanding the effect of opioids on the developing brain. Recent studies have shown altered brain functional network connectivity through the application of graph theoretical modeling, in infants with POE. In this study, we assess global brain structural connectivity through diffusion tensor imaging (DTI) metrics and apply graph theoretical modeling to brain structural connectivity in infants with POE.MethodsIn this prospective observational study in infants with POE and control infants, brain MRI including DTI was performed before completion of 3 months corrected postmenstrual age. Tractography was performed on the whole brain using a deterministic fiber tracking algorithm. Pairwise connectivity and network measure were calculated based on fiber count and fractional anisotropy (FA) values. Graph theoretical metrics were also derived.ResultsThere were 11 POE and 18 unexposed infants included in the analysis. Pairwise connectivity based on fiber count showed alterations in 32 connections. Pairwise connectivity based on FA values showed alterations in 24 connections. Connections between the right superior frontal gyrus and right paracentral lobule and between the right superior occipital gyrus and right fusiform gyrus were significantly different after adjusting for multiple comparisons between POE infants and unexposed controls. Additionally, alterations in graph theoretical network metrics were identified with fiber count and FA value derived tracts.ConclusionComparisons show significant differences in fiber count in two structural connections. The long-term clinical outcomes related to these findings may be assessed in longitudinal follow-up studies.

Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes.

Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n= 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI]=0.68[0.64-0.73], accuracy[CI]=68.34%[63-73.2%]; RF: AUC-ROC[CI]=0.7[0.66-0.73], accuracy[CI]=64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI]=0.69[0.53-0.84], accuracy[CI]=68.09[53.2-80.9]; RF: AUC-ROC[CI]=0.68[0.53-0.84], accuracy[CI]=68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.

Brain Functional Connectivity in Low- and High-Grade Gliomas: Differences in Network Dynamics Associated with Tumor Grade and Location.

Simple SummaryThis study investigates brain network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging and graph theory. We demonstrated that low-grade gliomas (LGG) lead to increased efficiency of the surrounding functional network, while high-grade gliomas (HGG) seem to disrupt brain connectivity in remote areas. Tumor location appears to influence the pattern of reorganization, including the recruitment of the contralateral hemisphere. Overall, LGG may show more favorable connectivity changes than HGG. If confirmed by future studies, the ability to discriminate between ‘maladaptive’ (detrimental) and ‘adaptive’ (beneficial) functional reorganization based on graph theory metrics may provide biomarkers to select patients for surgery and monitor recovery. Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann–Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.

Forecasting the influence of conservation strategies on landscape connectivity.

Maintaining and enhancing landscape connectivity reduces biodiversity declines due to habitat fragmentation. Uncertainty remains, however, about the effectiveness of conservation for enhancing connectivity for multiple species on dynamic landscapes, especially over long time horizons. We forecasted landscape connectivity from 2020 to 2100 under four common conservation land-acquisition strategies: acquiring the lowest cost land, acquiring land clustered around already established conservation areas, acquiring land with high geodiversity characteristics, and acquiring land opportunistically. We used graph theoretic metrics to quantify landscape connectivity across these four strategies, evaluating connectivity for four ecologically relevant species guilds that represent endpoints along a spectrum of vagility and habitat specificity: long- versus short-distance dispersal ability and habitat specialists versus generalists. We applied our method to central North Carolina and incorporated landscape dynamics, including forest growth, succession, disturbance, and management. Landscape connectivity improved for specialist species under all conservation strategies employed, although increases were highly variable across strategies. For generalist species, connectivity improvements were negligible. Overall, clustering the development of new protected areas around land already designated for conservation yielded the largest improvements in connectivity; increases were several orders of magnitude beyond current landscape connectivity for long- and short-distance dispersing specialist species. Conserving the lowest cost land contributed the least to connectivity. Our approach provides insight into the connectivity contributions of a suite of conservation alternatives prior to on-the-ground implementation and, therefore, can inform connectivity planning to maximize conservation benefit.

In-scanner head motion and structural covariance networks.

In‐scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion‐affected and low‐motion whole brain T1‐weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel‐based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion‐affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion‐affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in‐scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

River networks: An analysis of simulating algorithms and graph metrics used to quantify topology

Abstract River networks are frequently simulated for use in the development and testing of ecological theory. Currently, two main algorithms are used, stochastic branching networks (SBNs) and optimal channel networks (OCNs). The topology of these simulated networks and ‘real’ rivers is often quantified using graph theoretic metrics; however, to date, there has not been a comprehensive analysis of how these algorithms compare regarding graph theoretic metrics, or an analysis of metric redundancy and variability across dendritic ecological networks. We aim to provide guidance as to which algorithm and metrics should be used, and under what circumstances. We performed an extensive simulation study in which we (a) identified orthogonal sets of metrics that describe the topology of real and simulated river networks, (b) analysed the relationship between algorithm hyper‐parameters and node topology metrics, (c) determined whether simulated and real rivers are indistinguishable in their graph metric scores and (d) examined how patterns of species abundances compare across the three network types. We identified two orthogonal sets of node metrics; those that describe centrality and those that describe neighbourhood characteristics. Both stochastic branching networks and optimal channel networks can reproduce network topology metric scores of real rivers, but this relationship is dependent on the algorithm hyper‐parameters used. Finally, using a metapopulation model, we show that both SBNs and OCNs can reproduce ecological patterns of species abundances similar to those of real rivers. SBNs and OCNs can replicate the node topology of real rivers. The choice of which algorithm to use will depend on the research aims, SBNs are faster to generate and more tractable, whereas OCNs can reproduce a wider variety of the characteristics of real rivers, but are more time‐consuming to generate. When quantifying node topology in river networks, we recommend the orthogonal node metrics eccentricity, when interested in network centrality, and mean neighbour degree, when interested in local node importance.

Studying the Wikipedia Math Essential Pages using Graph Theory Metrics

Abstract COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. Keywords: Graph metrics, Math essentials, Gephi, Small world phenomenon, Directed graph

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation.

There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.

Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks.

Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling. We then introduce multilayer degree, participation coefficient and clustering coefficient to quantify the centrality of nodes across frequency layers and to identify the importance of different frequency bands. The proposed framework is applied to electroencephalogram data collected during a study of error monitoring in the human brain.

Tipping the scales: how clinical assessment shapes the neural correlates of Parkinson's disease mild cognitive impairment.

Mild cognitive impairment in Parkinson's disease (PD-MCI) is associated with consistent structural and functional brain changes. Whether different approaches for diagnosing PD-MCI are equivalent in their neural correlates is presently unknown. We aimed to profile the neuroimaging changes associated with the two endorsed methods of diagnosing PD-MCI. We recruited 53 consecutive non-demented PD patients and classified them as PD-MCI according to comprehensive neuropsychological examination as operationalized by the Movement Disorders Task Force. Voxel-based morphometry, cortical thickness, functional connectivity and graph theoretical measures were obtained on a 3-Tesla MRI scanner. 18 patients (32%) were classified as PD-MCI with Level-II criteria, 19 (33%) with the Parkinson's disease Cognitive Rating Scale (PD-CRS) and 32 (60%) with the Montreal Cognitive Assessment (MoCA) scale. Though regions of atrophy differed across classifications, reduced gray matter in the precuneus was found using both Level-II and PD-CRS classifications in PD-MCI patients. Patients diagnosed with the PD-CRS also showed extensive changes in cortical thickness, concurring with the MoCA in regions of the cingulate cortex, and again with Level-II regarding cortical thinning in the precuneus. Functional connectivity analysis found higher coherence within salience network regions of interest, and decreased anticorrelations between salience/central executive and default-mode networks in the PD-CRS classification for PD-MCI patients. Graph theoretical metrics showed a widespread decrease in node degree for the three classifications in PD-MCI, whereas betweenness centrality was increased in select nodes of the default mode network (DMN). Clinical and neuroimaging commonalities between the endorsed methods of cognitive assessment suggest a corresponding set of neural correlates in PD-MCI: loss of structural integrity in DMN structures, mainly the precuneus, and a loss of weighted connections in the salience network that might be counterbalanced by increased centrality in the DMN. Furthermore, the similarity of the results between exhaustive Level-II and screening Level-I tools might have practical implications in the search for neuroimaging biomarkers of cognitive impairment in Parkinson's disease.

Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis.

Background: Alzheimer's disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional magnetic resonance imaging (MRI) measurements have identified connectivity changes in AD and individuals with mild cognitive impairment. However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood.Methods: We analyzed diffusion-weighted MRI data from 161 asymptomatic individuals (38–71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default mode network (DMN) and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E ɛ4 (APOE4) genotype, family history of dementia (FH), and central obesity (Waist-Hip-Ratio [WHR]), on graph theoretical measures and hubs.Results: We observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter, and radius across the whole-brain, DMN or visual system. However, a hub in the right paracentral lobule was present in all the high-risk groups (FH, APOE4, obese), but absent in low-risk groups (no FH, APOE4-ve, healthy WHR).Discussion: We identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high risk was associated with a hub in the right paracentral lobule, a medial fronto-parietal cortical area with motor and sensory functions. This finding is consistent with accumulating evidence for right parietal cortex contributions in AD. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD.Impact statementAlzheimer's disease (AD) is a common form of dementia that to date has no cure. Identifying early biomarkers will aid the discovery and development of treatments that may slow AD progression in the future. In this article, we report that asymptomatic individuals at heightened risk of dementia due to their family history, Apolipoprotein-E ɛ4 genotype, and central adiposity have a hub in the right paracentral lobule, which is absent in low-risk groups. If this phenotype were to predict the development of symptoms in a longitudinal study of the same cohort, it could provide an early biomarker of disease progression.

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study.

Growing evidence indicates that disruptions in the brain’s functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Brain connectivity alterations during sleep by closed-loop transcranial neurostimulation predict metamemory sensitivity.

Metamemory involves the ability to correctly judge the accuracy of our memories. The retrieval of memories can be improved using transcranial electrical stimulation (tES) during sleep, but evidence for improvements to metamemory sensitivity is limited. Applying tES can enhance sleep-dependent memory consolidation, which along with metamemory requires the coordination of activity across distributed neural systems, suggesting that examining functional connectivity is important for understanding these processes. Nevertheless, little research has examined how functional connectivity modulations relate to overnight changes in metamemory sensitivity. Here, we developed a closed-loop short-duration tES method, time-locked to up-states of ongoing slow-wave oscillations, to cue specific memory replays in humans. We measured electroencephalographic (EEG) coherence changes following stimulation pulses, and characterized network alterations with graph theoretic metrics. Using machine learning techniques, we show that pulsed tES elicited network changes in multiple frequency bands, including increased connectivity in the theta band and increased efficiency in the spindle band. Additionally, stimulation-induced changes in beta-band path length were predictive of overnight changes in metamemory sensitivity. These findings add new insights into the growing literature investigating increases in memory performance through brain stimulation during sleep, and highlight the importance of examining functional connectivity to explain its effects.

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42–78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl’s gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl’s gyrus when characterizing autism.

Disentangling age- and disease-related alterations in schizophrenia brain network using structural equation modeling: A graph theoretical study based on minimum spanning tree.

Functional brain networks have been shown to undergo fundamental changes associated with aging or schizophrenia. However, the mechanism of how these factors exert influences jointly or interactively on brain networks remains elusive. A unified recognition of connectomic alteration patterns was also hampered by heterogeneities in network construction and thresholding methods. Recently, an unbiased network representation method regardless of network thresholding, so called minimal spanning tree algorithm, has been applied to study the critical skeleton of the brain network. In this study, we aimed to use minimum spanning tree (MST) as an unbiased network reconstruction and employed structural equation modeling (SEM) to unravel intertwined relationships among multiple phenotypic and connectomic variables in schizophrenia. First, we examined global and local brain network properties in 40 healthy subjects and 40 schizophrenic patients aged 21–55 using resting‐state functional magnetic resonance imaging (rs‐fMRI). Global network alterations are measured by graph theoretical metrics of MSTs and a connectivity‐transitivity two‐dimensional approach was proposed to characterize nodal roles. We found that networks of schizophrenic patients exhibited a more star‐like global structure compared to controls, indicating excessive integration, and a loss of regional transitivity in the dorsal frontal cortex (corrected p <.05). Regional analysis of MST network topology revealed that schizophrenia patients had more network hubs in frontal regions, which may be linked to the “overloading” hypothesis. Furthermore, using SEM, we found that the level of MST integration mediated the influence of age on negative symptom severity (indirect effect 95% CI [0.026, 0.449]). These findings highlighted an altered network skeleton in schizophrenia and suggested that aging‐related enhancement of network integration may undermine functional specialization of distinct neural systems and result in aggravated schizophrenic symptoms.

Data-driven parcellation and graph theory analyses to study adolescent mood and anxiety symptoms

Adolescence is a period of rapid brain development when psychiatric symptoms often first emerge. Studying adolescents may therefore facilitate the identification of neural alterations early in the course of psychiatric conditions. Here, we sought to utilize new, high-quality brain parcellations and data-driven graph theory approaches to characterize associations between resting-state networks and the severity of depression, anxiety, and anhedonia symptoms—salient features across psychiatric conditions. As reward circuitry matures considerably during adolescence, we examined both Whole Brain and three task-derived reward networks. Subjects were 87 psychotropic-medication-free adolescents (age = 12–20) with diverse psychiatric conditions (n = 68) and healthy controls (n = 19). All completed diagnostic interviews, dimensional clinical assessments, and 3T resting-state fMRI (10 min/2.3 mm/TR = 1 s). Following high-quality Human Connectome Project-style preprocessing, multimodal surface matching (MSMAll) alignment, and parcellation via the Cole-Anticevic Brain-wide Network Partition, weighted graph theoretical metrics (Strength Centrality = CStr; Eigenvector Centrality = CEig; Local Efficiency = ELoc) were estimated within each network. Associations with symptom severity and clinical status were assessed non-parametrically (two-tailed pFWE < 0.05). Across subjects, depression scores correlated with ventral striatum CStr within the Reward Attainment network, while anticipatory anhedonia correlated with CStr and ELoc in the subgenual anterior cingulate, dorsal anterior cingulate, orbitofrontal cortex, caudate, and ventral striatum across multiple networks. Group differences and associations with anxiety were not detected. Using detailed functional and clinical measures, we found that adolescent depression and anhedonia involve increased influence and communication efficiency in prefrontal and limbic reward areas. Resting-state network properties thus reflect positive valence system anomalies related to discrete reward sub-systems and processing phases early in the course of illness.

Functional brain network reorganization in HIV infection.

To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection. Forty HIV-positive patients without neurological impairment and 20 age- and sex-matched healthy controls underwent rs-fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T-lymphocytes value and plasmatic HIV-RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph-theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART). Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV-RNA and local graph-theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups. Our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients.

Functional connectivity of the attention networks is altered and relates to neuropsychological outcomes in children with prenatal alcohol exposure

Cognitive and functional brain alterations can occur in children with prenatal alcohol exposure (PAE). We examined the functional connectivity (FC) among regions within and between attention networks, and whether inter- and intranetwork FC moderated cognition in children with PAE (n = 37; age 12.8 ± 2.8 years) and nonexposed controls (n = 40; age 13.2 ± 2.8 years). Participants completed standardized attention and executive functioning tasks and resting state functional MRI. Inter- and intra-network FC and graph-theoretical metrics were calculated among attention network regions. Relative to controls, PAE was associated with reduced FC between the left temporoparietal junction and left ventral frontal cortex and anterior insula/frontal operculum (aI/fO), and between the left intraparietal sulcus and bilateral aI/fO. PAE was associated with increased FC between the right precuneus and intraparietal lobes, the right anterior prefrontal cortex and left ventral frontal cortex and aI/fO, and the left thalamus and dorsal frontal cortex. Graph-theoretical metrics did not differ by group. FC predicted cognitive performance, negatively in the children with PAE and positively in controls. Increased intra-network together with reduced internetwork FC suggests inefficient network specialization and impaired long-range FC among attention network regions after PAE. Results further suggest that those alterations may underlie attention and executive dysfunction in children with PAE.