Graph Theoretical Network Analysis Research Articles

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome. Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001). Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

Dissociative Experiences, Borderline Personality Disorder Features, and Childhood Trauma: Generating Hypotheses from Data-Driven Network Analysis in an International Sample

ABSTRACT Dissociation is a multifaceted phenomenon that occurs in various mental disorders, including borderline personality disorder (BPD), but also in non-clinical populations. Severity of childhood trauma (abuse, neglect) plays an important role in the development of dissociation and BPD. However, the complex interplay of different dissociative symptoms, BPD features, and self-reported childhood trauma experiences is not yet fully understood. Graph-theoretical network analysis can help to better understand such multivariate interrelations. Objective: This study aimed to investigate associations between self-reported dissociation, BPD features, and childhood trauma experiences using a graph-theoretical approach. Data was collected online via international mental health platforms and research sites. N = 921 individuals (77.4% female) were included; 40% reported pathological levels of dissociation. Variables were assessed with established psychometric scales (Dissociative Experiences Scale; Personality Assessment Inventory Borderline Features Scale; Childhood Trauma Questionnaire) and analyzed within a partial correlation network. Positive bivariate correlations between all variables were found. When accounting for their mutual influence on each other, dissociation was predominantly connected to BPD features with effect sizes between rp = .028 and rp = .126, while still showing a slight unique relationship with physical neglect (rp = .044). Findings suggest close associations between dissociative experiences and BPD features. While childhood trauma plays an important role in the development of dissociation and BPD, its recall may not fully explain their current co-occurrence. Prospective studies are needed to shed more light on causal pathways to better understand which factors contribute to dissociation and its link to BPD (features).

Aging effects on dual-route speech processing networks during speech perception in noise.

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis.

Abstract JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Graph Theoretical Network Analysis and Pharmacoinformatics-Based Investigation of Bioactive Compounds from Semecarpus anacardium Linn. for Alzheimer’s Disease

Alzheimer’s disease is a highly prevalent disease that has affected over 50 million people worldwide. It is mainly caused by the continuous deposition of amyloid-[Formula: see text] peptide (A[Formula: see text]. Acetylcholinesterase (AChE), N-methyl D-aspartate (NMDA), Tau-Tubulin kinase 1 (TTBK1) and [Formula: see text]-secretase 1 (BACE-1) are potential targets to treat Alzheimer’s disease and to reduce the pathogenesis; amongst, BACE-1 is preferred owing to its capacity in minimizing the accumulation of A[Formula: see text]. This study explored Semecarpus anacardium Linn.-derived phytocompounds and their interaction with BACE-1. The phytochemicals of S. anacardiumLinn. were retrieved from the IMPPAT database. The factual targets (AChE, BACE-1, NMDA and TTBK1) of Alzheimer’s disease are identified by the graph theoretical network analysis, and are docked with a total of eight bioactive compounds from S. anacardium Linn. Interestingly, the docking score was significant (−12 kcal × mol[Formula: see text] for the interactions of amentoflavone with BACE-1. The co-crystallized ligand of BACE-1 (2-imino-3-methyl-5,5-diphenylimidazolidin-4-one) was considered for further studies and revealed a superior binding affinity (−8.7 kcal × mol[Formula: see text]. Molecular dynamics (MD) simulation and Density Functional Theory (DFT) analyzed the conformational stability of amentoflavone and BACE-1 complex. Furthermore, S. anacardium Linn. seemed to be nontoxic against U87-MG cells in vitro. These results from in silico prediction suggest that the derivatives of S. anacardium Linn. possess a significant interaction with the BACE-1 and provide a favorable direction to prevent aggregation of A[Formula: see text] peptides.

Network Analysis and In Silico Molecular Modeling of Bioactive Compounds from Sida cordifolia Against NMDA Receptor

Epilepsy, one of the most common neurological disorders, is characterized by continuing predisposition of epileptic seizures. Abnormal discharges of seizures burst the electrical functions in the brain leading to unprompted behaviors of brain activity. Recently, more attention has been focused on bioactive substances from plants as therapeutic agents for neurodegenerative disorders because of their efficacy, low toxicity, and less side-effects. This study has utilized in silico molecular modeling as a tool for screening effective antiepileptic compounds from Sida cordifolia which could be further explored for the treatment of epilepsy. Twenty-six bioactive compounds were identified through data repository and their structures were retrieved from PubChem database. Based on the Graph theoretical network analysis, NMDA protein was identified as an ideal drug target. The effective compound was chosen based on the docking scores and its drug-likeness properties were analyzed by in silico prediction of pharmacokinetic and physicochemical properties. Based on molecular docking, 5,7-dihydroxy-3-isoprenyl flavone was identified as a better binder as this exhibited higher binding towards the target with lower binding energy (−10.5 kcal × mol[Formula: see text]. The stability of the protein–ligand complex was confirmed by molecular dynamics simulation study. The intermolecular interaction assessed during dynamic conditions indicated that the bioactive compound, 5,7-dihydroxy-3-isoprenyl flavone, from Sida cordifolia could be a potential lead molecule that can be developed as a candidate drug for anti-epileptic therapy.

Functional connectivity varies across scales in a fragmented landscape.

Species of different sizes interact with the landscape differently because ecological structure varies with scale, as do species movement capabilities and habitat requirements. As such, landscape connectivity is dependent upon the scale at which an animal interacts with its environment. Analyses of landscape connectivity must incorporate ecologically relevant scales to address scale-specific differences. Many evaluations of landscape connectivity utilize incrementally increasing buffer distances or other arbitrary spatial delineations as scales of analysis. Instead, we used a mammalian body mass discontinuity analysis to objectively identify scales in the Central Platte River Valley (CPRV) of Nebraska, U.S.A. We implemented a graph-theoretic network analysis to evaluate the connectivity of two wetland land cover types in the CPRV, wet meadow and emergent marsh, at multiple scales represented by groupings of species with similar body mass. Body mass is allometric with multiple traits of species, including dispersal distances. The landscape was highly connected at larger scales but relatively unconnected at smaller scales. We identified a threshold at which the landscape becomes highly connected between 500 m and 6,500 m dispersal distances. The presence of a connectivity threshold suggests that species with dispersal distances close to the threshold may be most vulnerable to habitat loss or reconfiguration and management should account for the connectivity threshold. Furthermore, we propose that a multiscale approach to management will be necessary to ensure landscape connectivity for diverse species.

A network perspective on real-life threat, anxiety, and avoidance.

Anxiety, approach, and avoidance motivation crucially influence mental and physical health, especially when environments are stressful. The interplay between anxiety and behavioral motivation is modulated by multiple individual factors. This proof-of-concept study applies graph-theoretical network analysis to explore complex associations between self-reported trait anxiety, approach and avoidance motivation, situational anxiety, stress symptoms, perceived threat, perceived positive consequences of approach, and self-reported avoidance behavior in real-life threat situations. A total of 436 participants who were matched on age and gender (218 psychotherapy patients, 218 online-recruited nonpatients) completed an online survey assessing these factors in response to the COVID-19 pandemic. The resulting cross-sectional psychological network revealed a complex pattern with multiple positive (e.g., between trait anxiety, avoidance motivation, and avoidance behavior) and negative associations (e.g., between approach and avoidance motivation). The patient and online subsample networks did not differ significantly, however, descriptive differences may inform future research.

Psychological Factors Linked to Intimate Partner Violence and Childhood Maltreatment: On Dissociation as a Possible Bridge Symptom.

Intimate partner violence (IPV) is a serious health concern, occurring worldwide in various forms and settings. Over the past years, multiple sources reported an increase of IPV globally, partly related to COVID-19 restrictions. Childhood maltreatment enhances the risk of IPV, possibly via alterations in emotion regulation, attachment, maladaptive core beliefs, dissociation, and psychopathological symptoms. However, studies investigating these associations simultaneously are still needed. This study aimed to investigate association between IPV, childhood maltreatment severity, maladaptive schemata (mistrust, alienation, enmeshment), attachment anxiety, social support, emotion regulation, dissociation, posttraumatic stress disorder (PTSD), and borderline personality disorder (BPD) symptoms. We further explored the complex interplay of all factors, accounting for their shared associations. An anonymous online survey was posted on international online platforms for people experiencing domestic violence and on research platforms. Regression analyses and graph-theoretical network analysis were used to explore associations between all variables. N = 434 participants (40% in treatment) completed the survey. IPV perpetration and victimization were highly correlated. Both were significantly associated with childhood maltreatment severity, early maladaptive schemata, dissociation, BPD features, and PTSD symptoms. When including all variables in one model, IPV was associated with dissociation, which indirectly linked it to childhood maltreatment experiences, PTSD symptoms, withdrawal, and self-blame. Our findings suggest that IPV perpetration and victimization often co-occur. Dissociation may be an important bridge symptom, linking IPV to childhood maltreatment experiences, PTSD symptoms, and maladaptive coping. Prospective studies are needed to corroborate these findings and to establish psychological mechanisms underlying IPV.

Parkinson's disease speech production network as determined by graph-theoretical network analysis.

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

Graph theoretical network analysis, in silico exploration, and validation of bioactive compounds from Cynodon dactylon as potential neuroprotective agents against α-synuclein.

Introduction: Parkinson's disease (PD) is a chronic, devastating neurodegenerative disorder marked by the death of dopaminergic neurons in the midbrain's substantia nigra pars compacta (Snpc). In alpha-synuclein (α-Syn) self-aggregation, the existence of intracytoplasmic inclusion bodies called Lewy bodies (LBs) and Lewy neurites (LNs) causes PD, which is a cause of neuronal death. Methods: The present study is aimed at finding potential bioactive compounds from Cynodon dectylon that can degrade α-Syn aggregation in the brain, through in silico molecular docking investigations. Graph theoretical network analysis was used to identify the bioactive compounds that target α-Syn and decipher their network as a graph. From the data repository, twenty-nine bioactive chemicals from C. dactylon were chosen and their structures were retrieved from Pubchem. On the basis of their docking scores and binding energies, significant compounds were chosen for future investigation. The in silico prediction of chosen compounds, and their pharmacokinetic and physicochemical parameters were utilized to confirm their drug-likeness profile. Results: During molecular docking investigation the bioactive compounds vitexin (-7.3 kcal.mol-1) and homoorientin (-7.1 kcal.mol-1) showed significant binding energy against the α-Syn target protein. A computer investigation of molecular dynamics simulation study verifies the stability of the α-Syn-ligand complex. The intermolecular interactions assessed by the dynamic conditions indicate that the bioactive compound vitexin has the potency to prevent α-Syn aggregation. Conclusion: Interestingly, the observed results indicate that vitexin is a potential lead compound against α-Syn aggregation, and in vitro and in vivo studies are warranted to confirm the promising therapeutic capability.

Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.

From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation. We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach. We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms. We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

Functional Connectivity as an Index of Brain Changes Following a Unicycle Intervention: A Graph-Theoretical Network Analysis.

Grey matter volume reductions in the right superior temporal gyrus (rSTG) were observed in young adults who learned to ride a unicycle. As these decreases were correlated with the acquired ability in unicycling, the authors interpreted the change as a brain tissue reorganization to increase postural control’s automated and efficient coordination. The current study aims to further corroborate this interpretation by looking at changes in the functional brain network in the very same sample of participants. For this reason, we applied graph theory, a mathematics field used to study network structure functionality. Four global and two local graph-theoretical parameters were calculated to measure whole brain and rSTG specific changes in functional network activity following the three-week-unicycle training. Findings revealed that the Local Efficiency of the rSTG was significantly elevated after the intervention indicating an increase in fault tolerance of the rSTG, possibly reflecting decentralisation of rSTG specific functions to neighbouring nodes. Thus, the increased Local Efficiency may indicate increased task efficiency by decentralising the processing of functions crucial for balance.

Structural network alterations induced by ART-naive and ART-treated subjects infected with HIV

ObjectiveTo investigate how the structural connectivity altered in combined antiretroviral therapy-treated (cART+) HIV patients and cART-naive (cART-) HIV patients by conducting Network analysis of Diffusion Tensor Imaging (DTI) data. MethodsWe enrolled 22 cART-, 23 cART+ and 28 normal controls (NC) in our current study. Firstly, the DTI imaging data pre-processing was conducted and the asymmetric 90 × 90 matrix for each participant from their DTI data was obtained with the use of PANDA. Then, we applied a graph-theoretical network analysis toolkit, GRETNA v2.0, to calculate metrics such as small-“worldness,” characteristic path length, clustering coefficient, global efficiency, local efficiency, and nodal “betweenness”. Finally, we took comparisons among the three groups to investigate topological alterations. ResultsResults (1) the regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical regions; (2) changes in various network properties in cART+treat and cART-patients were associated with the performance of behavior functions; (3) Hubs redistributed in HIV subjects especially in cART+ patients. ConclusionThe regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical region, and changes in various network properties in cART- and cART+ patients were associated with the performance of behavior functions. In addition, Hubs redistributed in HIV subjects especially in cART+ patients.

Imaging the influence of peripheral TRPV1-signaling on cerebral nociceptive processing applying fMRI-based graph theory in a resiniferatoxin rat model.

Resiniferatoxin (RTX), an extract from the spurge plant Euphorbia resinifera, is a potent agonist of the transient receptor potential cation channel subfamily V member 1 (TRPV1), mainly expressed on peripheral nociceptors—a prerequisite for nociceptive heat perception. Systemic overdosing of RTX can be used to desensitize specifically TRPV1-expressing neurons, and was therefore utilized here to selectively characterize the influence of TRPV1-signaling on central nervous system (CNS) temperature processing. Resting state and CNS temperature processing of male rats were assessed via functional magnetic resonance imaging before and after RTX injection. General linear model-based and graph-theoretical network analyses disentangled the underlying distinct CNS circuitries. At baseline, rats displayed an increase of nociception-related response amplitude and activated brain volume that correlated highly with increasing stimulation temperatures. In contrast, RTX-treated rats showed a clear disruption of thermal nociception, reflected in a missing increase of CNS responses to temperatures above 48°C. Graph-theoretical analyses revealed two distinct brain subnetworks affected by RTX: one subcortical (brainstem, lateral and medial thalamus, hippocampus, basal ganglia and amygdala), and one cortical (primary sensory, motor and association cortices). Resting state analysis revealed first, that peripheral desensitization of TRPV1-expressing neurons did not disrupt the basic resting-state-network of the brain. Second, only at baseline, but not after RTX, noxious stimulation modulated the RS-network in regions associated with memory formation (e.g. hippocampus). Altogether, the combination of whole-brain functional magnetic resonance imaging and RTX-mediated desensitization of TRPV1-signaling provided further detailed insight into cerebral processing of noxious temperatures.

Altered topological properties of the intrinsic functional brain network in patients with right-sided unilateral hearing loss caused by acoustic neuroma.

Neuroimaging studies have identified alterations in functional connectivity between specific brain regions in patients with unilateral hearing loss (UHL) and different influence of the side of UHL on neural plasticity. However, little is known about changes of whole-brain functional networks in patients with UHL and whether differences exist in topological organization between right-sided UHL (RUHL) and left-sided UHL (LUHL). To address this issue, we employed resting-state fMRI (rs-fMRI) and graph-theoretical approaches to investigate the topological alterations of brain functional connectomes in patients with RUHL and LUHL. Data from 44 patients with UHL (including 22 RUHL patients and 22 LUHL patients) and 37 healthy control subjects (HCs) were collected. Functional brain networks were constructed for each participant, following by graph-theoretical network analyses at connectional and global (e.g., small-worldness) levels. The correlations between brain network topologies and clinical variables were further studied. Using network-based analysis, we found a subnetwork in the visual cortex which had significantly lower connectivity strength in patients with RUHL as compared to HCs. At global level, all participants showed small-world architecture in functional brain networks, however, significantly lower normalized clustering coefficient and small-worldness were observed in patients with RUHL than in HCs. Moreover, these abnormal network metrics were demonstrated to be correlated with the clinical variables and cognitive performance of patients with RUHL. Notably, no significant alterations in the functional brain networks were found in patients with LUHL. Our findings demonstrate that RUHL (rather than LUHL) is accompanied with aberrant topological organization of the functional brain connectome, indicating different pathophysiological mechanisms between RUHL and LUHL from a viewpoint of network topology.

The Severity of Sensorimotor Tracts Degeneration May Predict Motor Performance in Chronic Stroke Patients, While Brain Structural Network Dysfunction May Not.

Although the relationship between corticospinal tract (CST) fiber degeneration and motor outcome after stroke has been established, the relationship of sensorimotor cortical areas with CST fibers has not been clarified. Also limited research has been conducted on how abnormalities in brain structural networks are related to motor recovery. To address these gaps in knowledge, we conducted a diffusion tensor imaging (DTI) study with 12 chronic stroke patients (CSPs) and 12 age-matched healthy controls (HCs). We compared fractional anisotropy (FA) and mean diffusivity (MD) in 60 CST segments using the probabilistic sensorimotor area tract template (SMATT). Least Absolute Shrinkage and Selection Operator (LASSO) regressions were used to select independent predictors of Fugl-Meyer upper extremity (FM-UE) scores among FA and MD values of SMATT regions. The Graph Theoretical Network Analysis Toolbox was used to assess the structural network of each subject's brain. Global and nodal metrics were calculated, compared between the groups, and correlated with FM-UE scores. Mann–Whitney U-tests revealed reduced FA values in CSPs, compared to HCs, in many ipsilesional SMATT regions and in two contralesional regions. Mean FA value of the left (L.) primary motor cortex (M1)/supplementary motor area (SMA) region was predictive of FM-UE score (P = 0.004). Mean MD values for the L. M1/ventral premotor cortex (PMv) region (P = 0.001) and L. PMv/SMA region (P = 0.001) were found to be significant predictors of FM-UE scores. Network efficiency was the only global metric found to be reduced in CSPs (P = 0.006 vs. HCs). Nodal efficiency of the L. hippocampus, L. parahippocampal gyrus, L. fusiform gyrus (P = 0.001), and nodal local efficiency of the L. supramarginal gyrus (P < 0.001) were reduced in CSPs relative to HCs. No graph metric was associated with FM-UE scores. In conclusion, the integrity of CSTs connected to M1, SMA, and PMv were shown to be independent predictors of motor performance in CSPs, while stroke-induced topological changes in the brain's structural connectome may not be. A sensorimotor cortex-specific tract template can refine CST degeneration data and the relationship of CST degeneration with motor performance.

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

BackgroundGraph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling.MethodsThe study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling.ResultsArtefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients.ConclusionLesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight.

Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin.

Neurobehavioral mechanisms underlying the effects of physical exercise break on episodic memory during prolonged sitting

Background and objectiveEpisodic memory is the ability that enables individuals to recall and re-experience previous events and usually includes information concerning the spatial and temporal context. This study examined the effects of a physical exercise break during a period of prolonged sitting on episodic memory. Furthermore, we aimed to investigate whether alterations of functional connectivity patterns might contribute to the exercise-induced changes in episodic memory. MethodsSixty healthy male college students were randomly assigned (1:1 ratio) to a prolonged-sitting group (PS group) or a physical-exercise-break group (PE group). The face-name paired-associate learning task was used to probe episodic memory. During the task, cortical hemodynamics in the prefrontal cortex were recorded using functional near-infrared spectroscopy (fNIRS). Changes in cortical hemodynamics were used to determine functional connectivity using graph-theoretical network analysis. ResultsThere was no between-group difference in neurobehavioral outcomes at the pretest assessment. During the posttest assessment, compared with the PS group, higher nodal efficiency in the anterior prefrontal cortex (orbitofrontal and frontopolar cortices) was observed during the encoding phase (FDR corrected p values = 0.039), and higher nodal efficiency and degree centrality of orbitofrontal cortex were observed in the retrieval phase in the PE group (FDR corrected p values = 0.035). Moreover, the PE group showed closer temporal correlational interactions between the dorsolateral prefrontal cortex and the anterior prefrontal cortex in the left hemisphere during the episodic memory encoding phase (FDR corrected p values = 0.043), when compared to the PS group. Neither significant between-group difference in accuracy nor correlations between neural and behavioral outcomes were observed after the intervention. ConclusionOur findings suggest that a physical exercise break during a prolonged sitting period has neither a beneficial nor a detrimental effect on behavioral performance concerning episodic memory. However, physical breaks do facilitate functional connectivity patterns of the prefrontal cortex while performing a episodic memory task.