Graph Theoretical Network Analysis Research Articles

Deteriorated functional and structural brain networks and normally appearing functional-structural coupling in diabetic kidney disease: a graph theory-based magnetic resonance imaging study.

This study was conducted in order to investigate the topological organization of functional and structural brain networks in diabetic kidney disease (DKD) and its potential clinical relevance. Two hundred two subjects (62 DKD patients, 60 diabetes mellitus [DM] patients, and 80 healthy controls) underwent laboratory examination, neuropsychological test, and magnetic resonance imaging (MRI). Large-scale functional and structural brain networks were constructed and graph theoretical network analyses were performed. The effect of renal function on brain functional and structural networks in DKD patients was further evaluated. Correlations were performed between network properties and neuropsychological scores and clinical variables. Progressing deteriorated global and local network topology organizations (especially for functional network) were observed for DKD patients compared with control subjects (all p < 0.05, Bonferroni-corrected), with intermediate values for the patients with DM. DKD patients showed normally appearing functional-structural coupling compared with controls, while DM patients manifested functional-structural decoupling (p < 0.05, Bonferroni-corrected). Impaired kidney function markedly affected functional and structural network organization in DKD patients (all p < 0.05). Urea nitrogen correlated with global and local efficiency in the structural networks (r = - 0.551, p < 0.001; r = - 0.476, p < 0.001, respectively). Global and local efficiency in the structural networks and normalized characteristic path length in the functional networks were associated with information processing speed and/or psychomotor speed. DKD patients showed enhanced functional and structural brain network disruption and normally appearing functional-structural coupling compared with DM patients, which correlated with kidney function, renal toxins, and cognitive performance. • DKD patients showed markedly disrupted functional and structural brain network efficiency measures compared with DM patients and healthy controls. • Reduced kidney function clearly deteriorated functional and structural brain networks in DKD patients. • DKD patients displayed normally appearing functional-structural coupling compared with DM patients.

(264) Altered Brain Network Topology in Chronic Low Back Pain Patients on Prescription Opioid Analgesics

Opioid prescribing for chronic pain conditions such as Chronic Low Back Pain (CLBP) in the United States has increased substantially in the past two decades. However, the effects of opioid analgesics on the brain network topology in CLBP remains unknown. The present study therefore provides the first test of the hypothesis that opioid status impacts the activity of the whole-brain network using graph theory methods. Resting state fMRI data were collected on a 3T scanner from 10 CLBP patients on long-term opioid regimens (CLBP+; 5 males, mean age ± SD = 48.5 ± 14.8 years) and 10 matched opioid-naive CLBP patients (CLBP-, 5 males, mean age ± SD = 43.6 ± 12.6 years) according to a protocol approved by the Stanford IRB. Following image quality assurance in MRIQC, and preprocessing in SPM12, we performed graph theoretical network analysis using CONN toolbox. For each participant, global network efficiency — a graph theory measure for integrative capacity of complex systems, was calculated and correlated with individual differences in sensory pain scores from Short Form McGill Pain Questionnaire (SF-MPQ). We focused on global efficiency as it reflects effective information transfer (i.e., small-worldness) within a network of nodes (i.e., regions of interests) and edges (i.e., correlation). Results revealed that the global efficiency values were positively correlated with pain in CLBP- but not in CLBP+ (r = 0.49 vs r = - 0.06, p = 0.05). This suggests that as sensory dimension of the pain intensity increased, CLBP- exhibited more efficient information transfer across a network of brain regions, including the core nodes of the salience network (anterior insula), frontoparietal central executive network (dorsolateral prefrontal cortices), and bilateral sensorimotor networks. Follow up studies with larger sample size are required to corroborate these observations and to formulate appropriate strategies for opioid prescribing guidelines, accordingly. Supported by NIH P01AT006651, and NIH T32DA035165.

Design, graph theoretical analysis and bioinformatic studies of proanthocyanidins encapsulated ethyl cellulose nanoparticles for effective anticancer activity

Phytoconstituent’s has significant antioxidant and anticancer activity against various types of cancer cells. But these water soluble phytoconstituent’s has poor absorption due to their huge molecular size as well as it cannot be reach at the targeted disease site, thus targeted drug delivery system is one of the effective approaches to enhancing the bioavailability and decreasing drug toxicity. To achieve this target, the Proanthocyanidins (Pro) and ethyl cellulose (EC) was used to formulate nanoparticles (NPs) for effective drug delivery system. The Pro encapsulated ECNPs were formulated by simple anti-solvent particle induction method and its physico-chemical characteristics were examined by FTIR, XRD, Zeta size analyzer, SEM, and TEM. The graph theoretical network analysis was implemented to identify the drug target and subsequently molecular docking studies were screened. The identified target using graph theoretical analysis and molecular docking study reported strong interaction of Pro encapsulated ECNPs in binding pockets of MAPK. The formulated ECNPs has been very effective in arresting the proliferation of colon cancer (HT-29) cells with an IC50 value of 33.4 μg ml−1 after 24 h treatment. Further, HT-29 cells exposure with Pro encapsulated ECNPs resulted in typical apoptotic through necrotic cellular morphology, loss of mitochondrial transmembrane potential, and high ROS generation were also observed. The observed results highlight that EC as a potential carrier material for enhancing the bioavailability of anti-cancer drugs.

Structural Correlations and Percolation in Twisted Perylene Diimides Using a Simple Anisotropic Coarse-Grained Model.

Large, twisted, and fused conjugated molecular architectures have begun to appear more prominently in the organic semiconductor literature. From a modeling perspective, such structures present a challenge to conventional simulation techniques; atomistic resolutions are computationally inefficient, while traditional isotropic coarse-grained models do not capture the inherent anisotropies of the molecules. In this work, we develop a simple coarse-grained model that explicitly incorporates the anisotropy of these molecular architectures, thereby providing a route toward analyzing π-stacking, and thus qualitative electronic structure, at a computationally efficient coarse-grained resolution. Our simple coarse-grained model maintains relative orientations of conjugated rings, as well as inter-ring dihedrals, that are critical for understanding electronic and excitonic transport in bulk systems. We apply this model to understand structural correlations in several recently synthesized perylene diimide (PDI)-based organic semiconductors. Twisted and nonplanar molecular architectures are found to promote amorphous morphologies while maintaining local π-stacking. A graph theoretical network analysis demonstrates that these twisted molecules are more likely to form percolating three-dimensional pathways for charge motion than strictly planar molecules, which show connectivity in only one dimension.

Unaltered intrinsic functional brain architecture in young women with primary dysmenorrhea

Primary dysmenorrhea (PDM), painful menstruation without organic causes, is the most prevalent gynecological problem in women of reproductive age. Dysmenorrhea later in life often co-occurs with many chronic functional pain disorders, and chronic functional pain disorders exhibit altered large-scale connectedness between distributed brain regions. It is unknown whether the young PDM females exhibit alterations in the global and local connectivity properties of brain functional networks. Fifty-seven otherwise healthy young PDM females and 62 age- and education-matched control females participated in the present resting-state functional magnetic resonance imaging study. We used graph theoretical network analysis to investigate the global and regional network metrics and modular structure of the resting-state brain functional networks in young PDM females. The functional network was constructed by the interregional functional connectivity among parcellated brain regions. The global and regional network metrics and modular structure of the resting-state brain functional networks were not altered in young PDM females at our detection threshold (medium to large effect size differences [Cohen’s d ≥ 0.52]). It is plausible that the absence of significant changes in the intrinsic functional brain architecture allows young PDM females to maintain normal psychosocial outcomes during the pain-free follicular phase.

The interpreter's brain during rest - Hyperconnectivity in the frontal lobe.

Language in its highest complexity is a unique human faculty with simultaneous translation being among the most demanding language task involving both linguistic and executive functions. In this context, bilingually grown up individuals as well as simultaneous interpreters (SIs) represent appropriate groups for studying expertise-related neural adaptations in the human brain. The present study was performed to examine if a domain-specific neural network activation pattern, constituted by brain regions involved in speech processing as well as cognitive control mechanisms can be detected during a task-free resting state condition. To investigate this, electroencephalographic (EEG) data were recorded from 16 SIs and 16 age and gender-matched multilingual control subjects. Graph-theoretical network analyses revealed interhemispheric hyperconnectivity between the ventral part of the prefrontal cortex (pars opercularis and pars triangularis) and the dorsolateral prefrontal cortex (DLPFC) in language experts compared to multilingual controls in the alpha frequency range. This finding suggests that the high cognitive demands placed on simultaneous interpreting lead to an increased neural communication between prefrontal brain regions essentially engaged in supporting executive control—a neural fingerprint that is even detectable during rest.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing.

Altered intrinsic functional connectivity in the latent period of epileptogenesis in a temporal lobe epilepsy model

The latent period, a seizure-free phase, is the duration between brain injury and the onset of spontaneous recurrent seizures (SRSs) during epileptogenesis. The latent period is thought to involve several progressive pathophysiological events that lead to the evolution of the chronic epilepsy phase. Hence, it is vital to investigate the changes in the latent period during epileptogenesis in order to better understand temporal lobe epilepsy (TLE), and to achieve early diagnosis and appropriate management of the condition. Accordingly, recent studies with patients with TLE using resting-state functional magnetic resonance imaging (rs-fMRI) have reported that alterations of resting-state functional connectivity (rsFC) during the chronic period are associated with some clinical manifestations, including learning and memory impairments, emotional instability, and social behavior deficits, in addition to repetitive seizure episodes. In contrast, the changes in the intrinsic rsFC during epileptogenesis, particularly during the latent period, remain unclear. In this study, we investigated the alterations in intrinsic rsFC during the latent and chronic periods in a pilocarpine-induced TLE mouse model using intrinsic optical signal imaging (IOSI). This technique can monitor the changes in the local hemoglobin concentration according to neuronal activity and can help investigate large-scale brain intrinsic networks. After seeding on the anatomical regions of interest (ROIs) and calculating the correlation coefficients between each ROI, we established and compared functional correlation matrices and functional connectivity maps during the latent and chronic periods of epilepsy. We found a decrease in the interhemispheric rsFC at the frontal and temporal regions during both the latent and chronic periods. Furthermore, a significant decrease in the interhemispheric rsFC was observed in the somatosensory area during the chronic period. Changes in network configurations during epileptogenesis were examined by graph theoretical network analysis. Interestingly, increase in the power of low frequency oscillations was observed during the latent period. These results suggest that, even if there are no apparent ictal seizure events during the latent period, there are ongoing changes in the rsFC in the epileptic brain. Furthermore, these results suggest that the pathophysiology of epilepsy may be related to widespread altered intrinsic functional connectivity. These findings can help enhance our understanding of epileptogenesis, and accordingly, changes in intrinsic functional connectivity can serve as an early diagnosis.

Chronic antiepileptic drug use and functional network efficiency: A functional magnetic resonance imaging study

AIMTo increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment.METHODSThe relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A “low risk” category (lamotrigine or levetiracetam, n = 16), an “intermediate risk” category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a “high risk” category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment.RESULTSCentral information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant (P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category (P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed.CONCLUSIONOnly the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.

Hyperglycemia Reduces Efficiency of Brain Networks in Subjects with Type 2 Diabetes.

Previous research has shown that the brain is an important target of diabetic complications. Since brain regions are interconnected to form a large-scale neural network, we investigated whether severe hyperglycemia affects the topology of the brain network in people with type 2 diabetes. Twenty middle-aged (average age: 54 years) individuals with poorly controlled diabetes (HbA1c: 8.9−14.6%, 74−136 mmol/mol) and 20 age-, sex-, and education-matched healthy volunteers were recruited. Graph theoretic network analysis was performed with axonal fiber tractography and tract-based spatial statistics (TBSS) using diffusion tensor imaging. Associations between the blood glucose level and white matter network characteristics were investigated. Individuals with diabetes had lower white matter network efficiency (P<0.001) and longer white matter path length (P<0.05) compared to healthy individuals. Higher HbA1c was associated with lower network efficiency (r = −0.53, P = 0.001) and longer network path length (r = 0.40, P<0.05). A disruption in local microstructural integrity was found in the multiple white matter regions and associated with higher HbA1c and fasting plasma glucose levels (corrected P<0.05). Poorer glycemic control is associated with lower efficiency and longer connection paths of the global brain network in individuals with diabetes. Chronic hyperglycemia in people with diabetes may disrupt the brain’s topological integration, and lead to mental slowing and cognitive impairment.

Impaired functional default mode network in patients with mild neurological Wilson’s disease

Wilson’s disease (WD) is an autosomal recessive metabolic disorder characterized by cognitive, psychiatric and motor signs and symptoms that are associated with structural and pathological brain abnormalities, in addition to liver changes. However, functional brain connectivity pattern of WD patients remains largely unknown. In the present study, we investigated functional brain connectivity pattern of WD patients using resting state functional magnetic resonance imaging. Particularly, we studied default mode network (DMN) using posterior cingulate cortex (PCC) based seed functional connectivity analysis and graph theoretic functional brain network analysis tools, and investigated the relationship between the DMN’s functional connectivity pattern of WD patients and their attention functions examined using the attention network test (ANT). Our results demonstrated that WD patients had altered DMN’s functional connectivity and lower local and global network efficiency compared with normal controls (NCs). In addition, the functional connectivity between left inferior temporal cortex and right lateral parietal cortex was correlated with altering function, one of the attention functions, across WD and NC subjects. These findings indicated that the DMN’s functional connectivity was altered in WD patients, which might be correlated with their attention dysfunction.

Mnemonic convergence in the human hippocampus.

The ability to form associations between a multitude of events is the hallmark of episodic memory. Computational models have espoused the importance of the hippocampus as convergence zone, binding different aspects of an episode into a coherent representation, by integrating information from multiple brain regions. However, evidence for this long-held hypothesis is limited, since previous work has largely focused on representational and network properties of the hippocampus in isolation. Here we identify the hippocampus as mnemonic convergence zone, using a combination of multivariate pattern and graph-theoretical network analyses of functional magnetic resonance imaging data from humans performing an associative memory task. We observe overlap of conjunctive coding and hub-like network attributes in the hippocampus. These results provide evidence for mnemonic convergence in the hippocampus, underlying the integration of distributed information into episodic memory representations.

Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study.

Type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and increased risk for dementia. Patients with the metabolic syndrome, a major risk factor for diabetes, may display comparable cognitive decrements as seen in type 2 diabetes. Currently, the impact of diabetes and prediabetes on cognition and the underlying organization of functional brain networks still remain to be elucidated. This study investigated whether functional brain networks are affected in type 2 diabetes and prediabetes. Forty-seven participants with diabetes, 47 participants with prediabetes, and 45 control participants underwent detailed cognitive testing and 3-Tesla resting state functional MRI. Graph theoretical network analysis was performed to investigate alterations in functional cerebral networks. Participants with diabetes displayed altered network measures, characterized by a higher normalized cluster coefficient and higher local efficiency, compared with control participants. The network measures of the participants with prediabetes fell between those with diabetes and control participants. Lower processing speed was associated with shorter path length and higher global efficiency. Participants with type 2 diabetes have altered functional brain networks. This alteration is already apparent in the prediabetic stage to a somewhat lower level, hinting at functional reorganization of the cerebral networks as a compensatory mechanism for cognitive decrements.

Disrupted functional brain connectome in unilateral sudden sensorineural hearing loss

Sudden sensorineural hearing loss (SSNHL) is generally defined as sensorineural hearing loss of 30 dB or greater over at least three contiguous audiometric frequencies and within a three-day period. This hearing loss is usually unilateral and can be associated with tinnitus and vertigo. The pathogenesis of unilateral sudden sensorineural hearing loss is still unknown, and the alterations in the functional connectivity are suspected to involve one possible pathogenesis. Despite scarce findings with respect to alterations in brain functional networks in unilateral sudden sensorineural hearing loss, the alterations of the whole brain functional connectome and whether these alterations were already in existence in the acute period remains unknown. The aim of this study was to investigate the alterations of brain functional connectome in two large samples of unilateral sudden sensorineural hearing loss patients and to investigate the correlation between unilateral sudden sensorineural hearing loss characteristics and changes in the functional network properties. Pure tone audiometry was performed to assess hearing ability. Abnormal changes in the peripheral auditory system were examined using conventional magnetic resonance imaging. The graph theoretical network analysis method was used to detect brain connectome alterations in unilateral sudden sensorineural hearing loss. Compared with the control groups, both groups of unilateral SSNHL patients exhibited a significantly increased clustering coefficient, global efficiency, and local efficiency but a significantly decreased characteristic path length. In addition, the primary increased nodal strength (e.g., nodal betweenness, hubs) was observed in several regions primarily, including the limbic and paralimbic systems, and in the auditory network brain areas. These findings suggest that the alteration of network organization already exists in unilateral sudden sensorineural hearing loss patients within the acute period and that the functional connectome of unilateral SSNHL patients is characterized by a shift toward small-worldization. Additionally, we hope that these findings will help to elucidate unilateral SSNHL through a new research perspective and provide insight for the potential pathophysiology of unilateral SSNHL.

Graph theoretic network analysis reveals protein pathways underlying cell death following neurotropic viral infection.

Complex protein networks underlie any cellular function. Certain proteins play a pivotal role in many network configurations, disruption of whose expression proves fatal to the cell. An efficient method to tease out such key proteins in a network is still unavailable. Here, we used graph-theoretic measures on protein-protein interaction data (interactome) to extract biophysically relevant information about individual protein regulation and network properties such as formation of function specific modules (sub-networks) of proteins. We took 5 major proteins that are involved in neuronal apoptosis post Chandipura Virus (CHPV) infection as seed proteins in a database to create a meta-network of immediately interacting proteins (1st order network). Graph theoretic measures were employed to rank the proteins in terms of their connectivity and the degree upto which they can be organized into smaller modules (hubs). We repeated the analysis on 2nd order interactome that includes proteins connected directly with proteins of 1st order. FADD and Casp-3 were connected maximally to other proteins in both analyses, thus indicating their importance in neuronal apoptosis. Thus, our analysis provides a blueprint for the detection and validation of protein networks disrupted by viral infections.

V3. Idiopathic generalized epilepsy shows widespread increased network connectivity

Introduction Idiopathic generalized epilepsy (IGE) is a subtype of epilepsy presumed to have a genetic etiology and characterized by generalized seizures, i.e. seizures starting and rapidly engaging distributed networks. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in idiopathic generalized epilepsy patients using diffusion tensor imaging and voxel based morphometry. Changes were also reported in functional networks during generalized spike wave discharges. In this study we wanted to investigate the IGE brain networks during the discharge-free intervals using Magnetoencephalography (MEG). To study the characteristics of the network, we used a graph theoretical approach. Methods We recorded resting state MEG data from 13 (9 females, mean age 38.6 years) IGE patients and 19 (11 females, mean age 38.5 years) healthy controls. Epileptic discharges were identified and marked by a neurologist and excluded from the analysis. Source localization of the resting state activity was performed using dynamic imaging of coherent sources (DICS) beamforming. Connectivity was estimated between all sources in the gray matter using the imaginary part of coherence. Another low-resolution network was generated by averaging connections between sources grouped by anatomical labeling. Graph measures were calculated on both the high resolution and low resolution networks. Results Network connectivity was significantly increased in IGE patients in the high-resolution networks in beta1 ( p = 0.003) and beta2 ( p = 0.0003) bands as compared to controls. In the low-resolution networks, connectivity in beta1 and beta2 was significantly higher in patients than in control subjects ( p = 0.005 and p = 0.0003 respectively). The further analysis of nodal strength using cluster-based statistics yielded significant clusters in the beta1 and beta2 bands. In the beta1 band, three significant clusters were found with higher nodal strength in IGE patients than in controls ( p = 0.004, p = 0.011 and p = 0.029 respectively). In the beta2 band, four significant clusters were found ( p = 0.0004, p = 0.002, p = 0.005 and p = 0.034 respectively) (see Fig. 1 ). We found no significant clusters of higher connectivity in controls compared to IGE patients in any of the frequency bands. Using Network Based Statistics, we found significant subnetworks with higher connectivity in IGE patients. The differences were found in alpha, beta1 and beta2 bands (see Fig. 2 ). We did not find any significant correlation between global connectivity and the age of participants. Conclusions Using graph theoretical network analysis, we found a widespread increase in connectivity in IGE patients compared to controls. These changes were more pronounced in the motor network and the mesio-frontal cortex. We did not, however, find any significant difference in clustering coefficient and characteristic path length which are measures that represent architectural differences in the network. Our findings suggest that an increased resting state connectivity in idiopathic generalized epilepsy could be a mechanism of seizure generation and represents a functional brain imaging endophenotype of the disease.

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics.

Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.1

Persistency and flexibility of complex brain networks underlie dual-task interference.

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules.

Disrupted Structural Connectome Is Associated with Both Psychometric and Real-World Neuropsychological Impairment in Diffuse Traumatic Brain Injury

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment.

Disturbed Small-World Networks and Neurocognitive Function in Frontal Lobe Low-Grade Glioma Patients

BackgroundBrain tumor patients often associated with losses of the small-world configuration and neurocognitive functions before operations. However, few studies were performed on the impairments of frontal lobe low-grade gliomas (LGG) after tumor resection using small-world network features.Methodology/Principal FindingsTo detect differences in the whole brain topology among LGG patients before and after operation, a combined study of neurocognitive assessment and graph theoretical network analysis of fMRI data was performed. We collected resting-state fMRI data of 12 carefully selected frontal lobe LGG patients before and after operation. We calculated the topological properties of brain functional networks in the 12 LGG, and compared with 12 healthy controls (HCs). We also applied Montreal Cognitive Assessment (MoCA) in a subset of patients (n = 12, including before and after operation groups) and HCs (n = 12). The resulting functional connectivity matrices were constructed for all 12 patients, and binary network analysis was performed. In the range of , the functional networks in preoperative LGG and postoperative one both fitted the definition of small-worldness. We proposed as small-world network interval, and the results showed that the topological properties were found to be disrupted in the two LGG groups, meanwhile the global efficiency increased and the local efficiency decreased. in the two LGG groups both were longer than HCs. in the LGG groups were smaller than HCs. Compared with the Hcs, MoCA in the two LGG groups were lower than HCs with significant difference, and the disturbed networks in the LGG were negatively related to worse MoCA scores.ConclusionsDisturbed small-worldness preperty in the two LGG groups was found and widely spread in the strength and spatial organization of brain networks, and the alterated small-world network may be responsible for cognitive dysfunction in frontal lobe LGG patients.