Organization Of Functional Brain Networks Research Articles

Impaired Topographical Organization of Functional Brain Networks in Parkinson's Disease Patients With Freezing of Gait.

Objective: This study aimed to explore alterations in the topological properties of the functional brain network in primary Parkinson’s disease (PD) patients with freezing of gait (PD-FOG).Methods: Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 23 PD-FOG patients, 33 PD patients without FOG (PD-nFOG), and 24 healthy control (HC) participants. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 90 brain regions, and topological properties were analyzed by using graph theory approaches. The network-based statistics (NBS) method was used to determine the suprathreshold connected edges (P < 0.05; threshold T = 2.725), and statistical significance was estimated by using the non-parametric permutation method (5,000 permutations). Statistically significant topological properties were further evaluated for their relationship with clinical neurological scales.Results: The topological properties of the functional brain network in PD-FOG and PD-nFOG showed no abnormalities at the global level. However, compared with HCs, PD-FOG patients showed decreased nodal local efficiency in several brain regions, including the bilateral striatum, frontoparietal areas, visual cortex, and bilateral superior temporal gyrus, increased nodal local efficiency in the left gyrus rectus. When compared with PD-nFOG patients and HCs, PD-FOG showed increased betweenness centrality in the left hippocampus. Moreover, compared to HCs, both PD-FOG and PD-nFOG patients displayed reduced network connections by using the NBS method, mainly involving the sensorimotor cortex (SM), visual network (VN), default mode network (DMN), auditory network (AN), dorsal attention network (DAN), subcortical regions, and limbic network (LIM). The local node efficiency of the right temporal pole: superior temporal gyrus in PD-FOG patients was positively correlated with the Freezing of Gait Questionnaire (FOGQ) scores.Conclusions: This study demonstrates the disrupted regional topological organization in PD-FOG patients, especially associated with damage to the subcortical regions and multiple cortical regions. Our results provide insights into the dysfunctional mechanisms of the relevant networks and indicate potential neuroimaging biomarkers of PD-FOG.

Altered electroencephalographic networks in developmental dyslexia after remedial training: a prospective case-control study.

Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia. However, how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear. We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children (8-9 years old) before and after training with visual tasks in this prospective case-control study. The minimum spanning tree method was used to construct the subjects' brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task. We found group differences in the theta, alpha, beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls. After training, the network topology of dyslexic children had become more segregated and similar to that of the controls. In the θ, α and β1-frequency bands, compared to the controls, the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions. The simultaneous appearance in the left hemisphere of hubs in temporal and parietal (α, β1), temporal and superior frontal cortex (θ, α), parietal and occipitotemporal cortices (β1), identified in the networks of normally developing children was not present in the brain networks of dyslexics. After training, the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls. In summary, our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls. This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children. Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies, Bulgarian Academy of Sciences (approval No. 02-41/12.07.2019) on March 28, 2017, and the State Logopedic Center and the Ministry of Education and Science (approval No. 09-69/14.03.2017) on July 12, 2019.

Sex differences in brain modular organization in chronic pain.

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

Testing nonlinearity in topological organization of functional brain networks.

Aiming to provide an argumentation on the underlying nonlinearity of the overall functional brain network via surrogate data method and graph theory. Taking the functional magnetic resonance imaging data as original data set and then shuffled the time series of each region of interest to generate surrogate data sets, corresponding original network and its 400 surrogates were obtained via computing connectivity matrixes. The results show that both the global correlation level and corresponding small-world topological characters exhibited obvious differences between the original network and its surrogates. And the following statistical testing results demonstrate their significant distinction, and this topological difference has been proved to be caused by the intrinsic nonlinear dynamics. Accordingly, the nonlinearity of the original functional network and its superior dynamical complexity have been confirmed. The results of this study could provide a novel angle into exploring the underlying mechanism of the neural brain system and offer an essential evidence in explaining complex brain activities.

Relationships between intrinsic functional connectivity, cognitive control, and reading achievement across development

There are vast individual differences in reading achievement between students. Besides structural and functional variability in domain-specific brain regions, these differences may partially be explained by the organization of domain-general functional brain networks. In the current study we used resting-state functional MRI data from the Philadelphia Neurodevelopmental Cohort (PNC; N = 553; ages 8–22) to examine the relation between performance on a well-validated reading assessment task, the Wide Range Achievement Word Reading Test (WRAT-Reading) and patterns of functional connectivity. We focused specifically on functional connectivity within and between networks associated with cognitive control, and investigated whether the relationship with academic test performance was mediated by cognitive control abilities. We show that individuals with higher scores on the WRAT-Reading, have stronger lateralization in frontoparietal networks, increased functional connectivity between dorsal striatum and the dorsal attention network, and reduced functional connectivity between dorsal and ventral striatum. The relationship between functional connectivity and reading performance was mediated by cognitive control abilities (i.e., performance on a composite measure of executive function and complex cognition), but not by abilities in other domains, demonstrating the specificity of our findings. Finally, there were no significant interactions with age, suggesting that the observed brain-behavior relationships stay relatively stable over the course of development. Our findings provide important insights into the functional significance of inter-individual variability in the network architecture of the developing brain, showing that functional connectivity in domain-general control networks is relevant to academic achievement in the reading domain.

Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI

Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.

Synergistic effects of HIV and marijuana use on functional brain network organization

HIV is associated with disruptions in cognition and brain function. Marijuana use is highly prevalent in HIV but its effects on resting brain function in HIV are unknown. Brain function can be characterized by brain activity that is correlated between regions over time, called functional connectivity. Neuropsychiatric disorders are increasingly being characterized by disruptions in such connectivity. We examined the synergistic effects of HIV and marijuana use on functional whole-brain network organization during resting state. Our sample included 78 adults who differed on HIV and marijuana status (19 with co-occurring HIV and marijuana use, 20 HIV-only, 17 marijuana-only, and 22 controls). We examined differences in local and long-range brain network organization using eight graph theoretical metrics: transitivity, local efficiency, within-module degree, modularity, global efficiency, strength, betweenness, and participation coefficient. Local and long-range connectivity were similar between the co-occurring HIV and marijuana use and control groups. In contrast, the HIV-only and marijuana-only groups were both associated with disruptions in brain network organization. These results suggest that marijuana use in HIV may normalize disruptions in brain network organization observed in persons with HIV. However, future work is needed to determine whether this normalization is suggestive of a beneficial or detrimental effect of marijuana on cognitive functioning in HIV.

Trait-related changes in brain network topology in premenstrual dysphoric disorder

The female predominance in the prevalence of depression is partially accounted by reactivity to hormonal fluctuations. Premenstrual dysphoric disorder (PMDD) is a reproductive subtype of depression characterized by cyclic emotional and somatic symptoms that recur before menstruation. Despite the growing understanding that most psychiatric disorders arise from dysfunctions in distributed brain circuits, the brain's functional connectome and its network properties of segregation and integration were not investigated in PMDD. To this end, we examined the brain's functional network organization in PMDD using graph theoretical analysis. 24 drug naïve women with PMDD and 27 controls without premenstrual symptoms underwent 2 resting-state fMRI scans, during the mid-follicular and late-luteal menstrual cycle phases. Functional connectivity MRI, graph theory metrics, and levels of sex hormones were computed during each menstrual phase. Altered network topology was found in PMDD across symptomatic and remitted stages in major graph metrics (characteristic path length, clustering coefficient, transitivity, local and global efficiency, centrality), indicating decreased functional network segregation and increased functional network integration. In addition, PMDD patients exhibited hypoconnectivity of the anterior temporal lobe and hyperconnectivity of the basal ganglia and thalamus, across menstrual phases. Furthermore, the relationship between difficulties in emotion regulation and PMDD was mediated by specific patterns of functional connectivity, including connections of the striatum, thalamus, and prefrontal cortex. The shifts in the functional connectome and its topology in PMDD may suggest trait vulnerability markers of the disorder.

Modeling task-based fMRI data via deep belief network with neural architecture search

It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and not optimal. To tackle this problem, we proposed an unsupervised neural architecture search (NAS) framework on a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The NAS-DBN framework is based on Particle Swarm Optimization (PSO) where the swarms of neural architectures can evolve and converge to a feasible optimal solution. The experiments showed that the proposed NAS-DBN framework can quickly find a robust architecture of DBN, yielding a hierarchy organization of functional brain networks (FBNs) and temporal responses. Compared with 3 manually designed DBNs, the proposed NAS-DBN has the lowest testing loss of 0.0197, suggesting an overall performance improvement of up to 47.9 %. For each task, the NAS-DBN identified 260 FBNs, including task-specific FBNs and resting state networks (RSN), which have high overlap rates to general linear model (GLM) derived templates and independent component analysis (ICA) derived RSN templates. The average overlap rate of NAS-DBN to GLM on 20 task-specific FBNs is as high as 0.536, indicating a performance improvement of up to 63.9 % in respect of network modeling. Besides, we showed that the NAS-DBN can simultaneously generate temporal responses that resemble the task designs very well, and it was observed that widespread overlaps between FBNs from different layers of NAS-DBN model form a hierarchical organization of FBNs. Our NAS-DBN framework contributes an effective, unsupervised NAS method for modeling volumetric task fMRI data.

Greater functional connectivity between sensory networks is related to symptom severity in toddlers with autism spectrum disorder.

Symptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood. Resting-state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17-45months. Intrinsic functional connectivity (iFC) within and between resting-state functional networks was derived with independent component analysis (ICA). Increased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age-related weakening of the visual-auditory between-network connectivity was observed in the ASD but not the TD group. Taken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual-sensorimotor between-network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.

Scale-free functional brain dynamics during recovery from sport-related concussion.

Studies using blood‐oxygenation‐level‐dependent functional magnetic resonance imaging (BOLD fMRI) have characterized how the resting brain is affected by concussion. The literature to date, however, has largely focused on measuring changes in the spatial organization of functional brain networks. In the present study, changes in the temporal dynamics of BOLD signals are examined throughout concussion recovery using scaling (or fractal) analysis. Imaging data were collected for 228 university‐level athletes, 61 with concussion and 167 athletic controls. Concussed athletes were scanned at the acute phase of injury (1–7 days postinjury), the subacute phase (8–14 days postinjury), medical clearance to return to sport (RTS), 1 month post‐RTS and 1 year post‐RTS. The wavelet leader multifractal approach was used to assess scaling () and multifractal () behavior. Significant longitudinal changes were identified for , which was lowest at acute injury, became significantly elevated at RTS, and returned near control levels by 1 year post‐RTS. No longitudinal changes were identified for . Secondary analyses showed that clinical measures of acute symptom severity and time to RTP were related to longitudinal changes in . Athletes with both higher symptoms and prolonged recovery had elevated values at RTS, while athletes with higher symptoms but rapid recovery had reduced at acute injury. This study provides the first evidence for long‐term recovery of BOLD scale‐free brain dynamics after a concussion.

Structural and functional brain network alterations in prenatal alcohol exposed neonates

Prenatal alcohol exposure leads to alterations in cognition, behavior and underlying brain architecture. However, prior studies have not integrated structural and functional imaging data in children with prenatal alcohol exposure. The aim of this study was to characterize disruptions in both structural and functional brain network organization after prenatal alcohol exposure in very early life. A group of 11 neonates with prenatal alcohol exposure and 14 unexposed controls were investigated using diffusion weighted structural and resting state functional magnetic resonance imaging. Covariance networks were created using graph theoretical analyses for each data set, controlling for age and sex. Group differences in global hub arrangement and regional connectivity were determined using nonparametric permutation tests. Neonates with prenatal alcohol exposure and controls exhibited similar global structural network organization. However, global functional networks of neonates with prenatal alcohol exposure comprised of temporal and limbic hubs, while hubs were more distributed in controls representing an early default mode network. On a regional level, controls showed prominent structural and functional connectivity in parietal and occipital regions. Neonates with prenatal alcohol exposure showed regionally, predominant structural and functional connectivity in several subcortical regions and occipital regions. The findings suggest early functional disruption on a global and regional level after prenatal alcohol exposure and indicate suboptimal organization of functional networks. These differences likely underlie sensory dysregulation and behavioral difficulties in prenatal alcohol exposure.

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning.

Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.

Altered Functional Connectivity in the Motor and Prefrontal Cortex for Children With Down's Syndrome: An fNIRS Study.

Children with Down's syndrome (DS) might exhibit disrupted brain functional connectivity in the motor and prefrontal cortex. To inspect the alterations in brain activation and functional connectivity for children with DS, the functional near-infrared spectroscopy (fNIRS) method was applied to examine the brain activation difference in the motor and prefrontal cortex between the DS and typically developing (TD) groups during a fine motor task. In addition, small-world analysis based on graph theory was also carried out to characterize the topological organization of functional brain networks. Interestingly, behavior data demonstrated that the DS group showed significantly long reaction time and low accuracy as compared to the TD group (p < 0.05). More importantly, significantly reduced brain activations in the frontopolar area, the pre-motor, and the supplementary motor cortex (p < 0.05) were identified in the DS group compared with the TD group. Meanwhile, significantly high global efficiency (Eg) and short average path length (Lp) were also detected for the DS group. This pilot study illustrated that the disrupted connectivity of frontopolar area, pre-motor, and supplementary motor cortex might be one of the core mechanisms associated with motor and cognitive impairments for children with DS. Therefore, the combination of the fNIRS technique with functional network analysis may pave a new avenue for improving our understanding of the neural mechanisms of DS.

Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences

More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.

A graph theory study of resting-state functional connectivity in children with Tourette syndrome

Little is known about the brain's functional organization during resting-state in children with Tourette syndrome (TS). We aimed to investigate this with a specific focus on the role of comorbid attention-deficit/hyperactivity disorder (ADHD). We applied graph theoretical analysis to resting-state functional magnetic resonance imaging data of 109 8-to-12-year-old children with TS (n = 46), ADHD without tics (n = 23), and healthy controls (n = 40). First, we compared these three groups, and in a second comparison four groups, distinguishing TS with (TS + ADHD, n = 19) and without comorbid ADHD (TS−ADHD, n = 27). Weighted brain graphs were constructed for both comparisons to investigate global efficiency, local efficiency, and clustering coefficient per acquired network. Local efficiency and clustering coefficient were significantly lower in children with TS−ADHD in the default mode network compared with healthy controls, and in the frontoparietal network compared with ADHD; we also found associations with higher tic severity. Our study supports a different functional brain network organization in children with TS−ADHD, compared with healthy controls and children with ADHD.

Altered topological organization of functional brain networks in drug-naive patients with paroxysmal kinesigenic dyskinesia

BackgroundPrevious neuroimaging studies have revealed aberrant basal ganglia-thalamocortical circuit in patients with paroxysmal kinesigenic dyskinesia (PKD) with drug treatment. This study aims to investigate the topological organization of functional networks in drug-naive PKD. MethodsResting-state functional magnetic resonance imaging (rs-fMRI) was performed in 24 drug-naive PKD patients and 24 age, gender and mean framewise displacement (FD)-matched healthy controls (HCs). The network topological properties (including global and nodal measures) were analyzed between two groups by using graph-based theoretical approaches. Pearson's correlation analysis was performed between significant metrics and duration of disease and the age of onset of patients with PKD. ResultsCompare to HCs, the drug-naïve PKD patients showed increased nodal centralities mainly in left precentral gyrus, basal ganglia and limbic regions and decreased nodal centralities in the temporal pole. Our results showed that drug-naïve PKD patients presented the small-world topology and at the global level no significant differences were found between PKD and HCs. In the correlation analysis, the increased nodal efficiency in the left pallidum was positively correlated with the onset of age. ConclusionsOur findings supported the previous observation of the disruptive cortical-basal ganglia circuitry in PKD patients, but difference in that the prominent change of precentral area and temporal pole were also observed in our study when the potential impact of drug was excluded. These findings may provide a novel insight into further delineation of the pathophysiological genesis and possible target for PKD.

Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks.

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.

In silico exploration of mouse brain dynamics by focal stimulation reflects the organization of functional networks and sensory processing.

Resting-state functional networks such as the default mode network (DMN) dominate spontaneous brain dynamics. To date, the mechanisms linking brain structure and brain dynamics and functions in cognition, perception, and action remain unknown, mainly due to the uncontrolled and erratic nature of the resting state. Here we used a stimulation paradigm to probe the brain’s resting behavior, providing insights on state-space stability and multiplicity of network trajectories after stimulation. We performed explorations on a mouse model to map spatiotemporal brain dynamics as a function of the stimulation site. We demonstrated the emergence of known functional networks in brain responses. Several responses heavily relied on the DMN and were suggestive of the DMN playing a mechanistic role between functional networks. We probed the simulated brain responses to the stimulation of regions along the information processing chains of sensory systems from periphery up to primary sensory cortices. Moreover, we compared simulated dynamics against in vivo brain responses to optogenetic stimulation. Our results underwrite the importance of anatomical connectivity in the functional organization of brain networks and demonstrate how functionally differentiated information processing chains arise from the same system.

Topological reorganization of brain functional networks in patients with mitochondrial encephalomyopathy with lactic acidosis and stroke‐like episodes

Mitochondrial encephalomyopathy with lactic acidosis and stroke‐like episodes (MELAS) is a rare maternally inherited genetic disease; however, little is known about its underlying brain basis. Furthermore, the topological organization of brain functional network in MELAS has not been explored. Here, 45 patients with MELAS (22 at acute stage, 23 at chronic stage) and 22 normal controls were studied using resting- state functional magnetic resonance imaging and graph theory analysis approaches. Topological properties of brain functional networks including global and nodal metrics, rich club organization and modularity were analyzed. At the global level, MELAS patients exhibited reduced clustering coefficient, normalized clustering coefficient, normalized characteristic path length and local network efficiency compared with the controls. At the nodal level, several nodes with abnormal degree centrality and nodal efficiency were detected in MELAS patients, and the distribution of these nodes was partly consistent with the stroke-like lesions. For rich club organization, rich club nodes were reorganized and the connections among them were decreased in MELAS patients. Modularity analysis revealed that MELAS patents had altered intra- or inter-modular connections in default mode network, fronto-parietal network, sensorimotor network, occipital network and cerebellum network. Notably, the patients at acute stage showed more obvious changes in these topological properties than the patients at chronic stage. These findings indicated that MELAS patients, particularly those at acute stage, exhibited topological reorganization of the whole-brain functional network. This study may help us to understand the neuropathological mechanisms of MELAS.