Efficient Node Research Articles

Nodal Global Efficiency in Front-Parietal Lobe Mediated Periventricular White Matter Hyperintensity (PWMH)-Related Cognitive Impairment.

White matter hyperintensity (WMH) is widely observed in the elderly population and serves as a key indicator of cognitive impairment (CI). However, the underlying mechanism remains to be elucidated. Herein, we investigated the topological patterns of resting state functional networks in WMH subjects and the relationship between the topological measures and CI. A graph theory-based analysis was employed in the resting-state functional magnetic resonance scans of 112 subjects (38 WMH subjects with cognitive impairment without dementia (CIND), 36 WMH subjects with normal cognition and 38 healthy controls (HCs), and we found that WMH-CIND subjects displayed decreased global efficiency at the levels of the whole brain, specific subnetworks [fronto-parietal network (FPN) and cingulo-opercular network (CON)] and certain nodes located in the FPN and CON, as well as decreased local efficiency in subnetworks. Our results demonstrated that nodal global efficiency in frontal and parietal regions mediated the impairment of information processing speed related to periventricular WMH (PWMH). Additionally, we performed support vector machine (SVM) analysis and found that altered functional efficiency can identify WMH-CIND subjects with high accuracy, sensitivity and specificity. These findings suggest impaired functional networks in WMH-CIND individuals and that decreased functional efficiency may be a feature of CI in WMH subjects.

Brain gray matter network organization in psychotic disorders

Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.

Changes of brain structural network connection in Parkinson's disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging.

Previous studies have found that white matter (WM) alterations might be correlated in Parkinson's disease (PD) patients with cognitive impairment. This study aimed to investigate WM structural network connectome alterations in PD patients with mild cognitive impairment (PD-MCI) and assess the relationship between cognitive impairment and structural topological network changes in PD patients. All 31 healthy controls (HCs) and 71 PD patients (43 PD-NC and 28 PD-MCI) matched for age, sex and education underwent 3.0 T MRI and diffusion tensor imaging (DTI) scan. Graph theoretical analyses and network-based statistical (NBS) analyses were performed to identify the structural WM networks and subnetwork changes in PD-MCI. PD-MCI patients showed significantly decreased global efficiency (Eglob) and increased shortest path length (Lp) compared with the HC group. Several nodal efficiencies showed significant differences in multiple brain regions among the three groups. The nodal efficiency of the orbitofrontal part was closely related to the overall cognitive ability and multiple sub-cognitive domains. Moreover, NBS analyses identified eight one-connect subnetworks, three two-connect subnetworks and two multi-connect subnetworks with reduced connectivity that characterizes the WM structural organization in PD-MCI patients. The two multi-connect subnetworks were located on the bilateral lobe, and both were centered on the orbitofrontal part. This study provided new evidence that PD with cognitive dysfunction is associated with WM structural alterations. The nodal efficiency and sub-network analyses focusing on the orbitofrontal part might provide new ideas to explore the physiological mechanism of PD-MCI.

Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification

In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.

Altered Functional Connectivity and Brain Network Property in Pregnant Women With Cleft Fetuses.

Non-syndromic clefts of the lip and/or palate (NSCLP) is the most common congenital anomaly in the craniofacial region. NSCLP is a highly gene-associated malformation. We speculate that pregnant women with NSCLP fetuses (pregnancies with NSCLP) may have specific brain changes during pregnancy. To explore characteristic brain function changes of pregnancies with NSCLP, we analyzed resting-state fMRI (rs-fMRI) data of 42 pregnant women (21 pregnancies with NSCLP and 21 pregnancies with normal fetuses) to compare intergroup differences of (fractional) amplitude of low frequency fluctuations (fALFF/ALFF), regional homogeneity (Reho), functional connectivity (FC) and network topological properties. Compared with the control group, increased ALFF in the left hippocampus, the right fusiform and the left anterior cingulate (ACG), increased Reho in left middle occipital gyrus (MOG) and right medial frontal gyrus (MFG) were found for pregnancies with NSCLP. Meanwhile, FC between the left supramarginal gyrus (SMG) and bilateral olfactory cortex (OLF), FC between left precentral gyrus (PreCG) and right MFG, FC between right inferior frontal gyrus (IFG) and left inferior temporal gyrus (ITG) were enhanced in pregnancies with NSCLP. Besides, FC between left PreCG and left amygdala, bilateral para-hippocampal gyrus, FC between left amygdala and left MFG, right IFG were decreased. Graph theory-based analysis explored increased degree centrality (DC), betweenness centrality (BC) and nodal efficiency (Ne) in the left ITG and left SMG for pregnancies with NSCLP. Pregnancies with NSCLP has widespread decreased FC within neural networks of speech and language, which indicated that they were more likely to be associated with defects in speech and language skills. At the same time, increased topological indices showed that speech and language related regions played dominant role in their brain networks. These findings may provide clues for early detection of NSCLP fetuses.

Abnormal Alpha Rhythm During Self-Referential Processing in Schizophrenia Patients.

Schizophrenia patients exhibited a psychological abnormal appearance when they recognized objects related to themselves. This cognitive process is associated with self-referential processing. In this study, the self-referential memory (SRM) task was performed by 18 schizophrenia patients and 18 healthy controls. In the encoding stage of the SRM task, the behavioral experiment data and electroencephalogram (EEG) data were recorded in three experimental conditions (self-referential condition, other-referential condition, and physical condition). For data analysis, the electrophysiological performance of the time-frequency distribution, phase lag index (PLI) strengths, phase synchronization connectivity, and brain-network properties were assessed in schizophrenia patients compared to healthy controls. We found that schizophrenia patients exhibited abnormal alpha oscillation characteristics at the time of 100–300 ms poststimulus during the self-referential condition, which consisted of diminished time-frequency distributions over the prefrontal, parietal, and occipital regions; lower functional connectivity strengths of the PLI in the parietal and occipital areas; higher global efficiency and the lower characteristic path length; and nodal efficiency of local areas (increased nodal efficiency in temporal regions and decreased nodal efficiency in occipital region) for dynamic network topology properties. Furthermore, the evoked power of the alpha band during the self-referential condition was significantly correlated with the SRM bias score in the patients (r = 0.595, p = 0.009). These results provided electrophysiological evidence and supported the hypothesis that an abnormal alpha rhythm might be the principal factor of dysfunctional self-referential processing in schizophrenia patients.

Altered brain structural topological properties in Parkinson's disease with levodopa-induced dyskinesias

ObjectivesIn this study, the alterations of structural topological properties in Parkinson's disease (PD) patients with levodopa-induced dyskinesias (LIDs) were explored using white matter structural network connectome derived from diffusion tensor imaging (DTI). Methods21 dyskinetic PD patients, 21 non-dyskinetic PD patients and 25 healthy controls were studied in global and nodal topological properties of structural networks after controlling age, gender and education. Afterwards, post hoc analyses were performed to explore further differences. Finally, multiple linear regression analysis was employed to test the associations between significant different properties and the severity of dyskinesias in PD. ResultsDyskinetic PD patients exhibited significant increased global efficiency, local efficiency, clustering coefficient, but decreased shortest path length compared with the non-dyskinetic. Additionally, increased nodal efficiency in bilateral inferior frontal gyrus (IFG), right putamen, right thalamus, and decreased nodal shortest path length in bilateral IFG and right thalamus, were discovered in dyskinetic PD in comparison with non-dyskinetic PD. Notably, a negative correlation between the Abnormal Involuntary Movement Scale (AIMS) scores and shortest path length of whole-brain network was found in PD with LIDs. ConclusionsOur results indicated excessively optimized topological organization of whole-brain structural connectome in PD patients with LIDs. These findings also illustrated that excessively strengthened basal ganglia-thalamocortical nodal structural connections played an important role in the presence of LIDs.

Brain Function Network and Young Adult Smokers: A Graph Theory Analysis Study.

Cigarette smoking is associated with abnormalities in the widespread inter-regional functional connectivity of the brain. However, few studies focused on the abnormalities in the topological organization of brain functional networks in young smokers. In the current study, resting-state functional magnetic resonance images were acquired from 30 young male smokers and 32 age-, gender-, and education-matched healthy male nonsmokers. A functional network was constructed by calculating the Pearson correlation coefficients among 246 subregions in the human Brainnetome Atlas. The topological parameters were compared between smokers and nonsmokers. The results showed that the functional network of both young smokers and nonsmokers had small-world topology. Compared to nonsmokers, young smokers exhibited a decreased clustering coefficient (Cp) and local network efficiency (Elocal). Cp and Elocal were negatively correlated with the duration of cigarette use. In addition, increased nodal efficiency (Enodal) was mainly located in the prefrontal cortex (PFC), cingulate gyrus, insula, and caudate. Decreased connectivities among the PFC, cingulate gyrus, insula, basal ganglia (of specific node), and thalamus were also observed. In sum, we revealed the abnormal topological organization of brain functional networks in young smokers, which may improve our understanding of the neural mechanism of young smokers from a brain functional network topological organization perspective.

Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease

Previous studies have found widespread impairment of white matter (WM) integrity and disruption of structural network connectivity in cerebral small-vessel disease, but have not evaluated these changes jointly in nondemented patients. To jointly investigate the microstructural impairment of WM and the related alterations of structural network topology in nondemented cerebral small-vessel disease (CSVD-ND). Prospective. Thirty-seven CSVD-ND patients and 34 elderly controls, who were age-, sex-, and education-matched. 3.0T/diffusion tensor imaging (DTI). Clinical characteristics, lacunar infarct, and white matter hyperintensity (WMH) was assessed. A multiatlas likelihood fusion (MALF) algorithm was used for DTI-based brain segmentation and network node defining. Then the alterations of WM integrity and structural network topology were investigated jointly. Student's t-test, chi-square test, Mann-Whitney U-test, linear regression, Pearson correlation, and multiple comparison correction. Decreased fractional anisotropy and increased trace values were observed in predefined structures (P < 0.05, familywise error rate-corrected), including major commissural fibers, projection fibers, and some association fibers. Topologically, both groups showed small-worldness. CSVD-ND patients showed reduced global and local efficiency (P < 0.001). Despite widespread impairment of WM integrity, CSVD-ND patients only showed reduced nodal efficiency in the right superior occipital gyrus and the right lingual gyrus (P < 0.05, familywise error rate-corrected). The nodal local efficiency of the right precuneus was associated with the processing speed after adjusting the effect of lacunar infarct and WMH (r = -0.499, P = 0.038). WM integrity was widely impaired in nondemented CSVD patients but structural network connectivity was relatively preserved. DTI may potentially provide information for the pathophysiology of CSVD in the nondemented phase. 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1162-1169.

Cortical network underlying audiovisual semantic integration and modulation of attention: An fMRI and graph-based study

Many neuroimaging and electrophysiology studies have suggested that semantic integration as a high-level cognitive process involves various cortical regions and is modulated by attention. However, the cortical network specific to semantic integration and the modulatory mechanism of attention remain unclear. Here, we designed an fMRI experiment using “bimodal stimulus” to extract information regarding the cortical activation related to the effects of semantic integration with and without attention, and then analyzed the characteristics of the cortical network and the modulating effect of attention on semantic integration. To further investigate the related cortical regions, we constructed a functional brain network for processing attended AV stimuli to evaluate the nodal properties using a graph-based method. The results of the fMRI and graph-based analyses showed that the semantic integration with attention activated the anterior temporal lobe (ATL), temporoparietal junction (TPJ), and frontoparietal cortex, with the ATL showing the highest nodal degree and efficiency; in contrast, semantic integration without attention involved a relatively small cortical network, including the posterior superior temporal gyrus (STG), Heschl’s gyrus (HG), and precentral gyrus. These results indicated that semantic integration is a complex cognitive process that occurs not only in the attended condition but also in the unattended condition, and that attention could modulate the distribution of cortical networks related to semantic integration. We suggest that semantic integration with attention is a conscious process and needs a wide cortical network working together, in which the ATL plays the role of a central hub; in contrast, semantic integration without attention is a pre-attentive process and involves a relatively smaller cortical network, in which the HG may play an important role. Our study will provide valuable insights into semantic integration and will be useful for investigations on multisensory integration and attention mechanism at multiple processing stages and levels within the cortical hierarchy.

Disrupted brain functional networks in drug-naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis.

Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting-state functional magnetic resonance imaging was performed on 51 drug-naïve children with ADHD and 55 age-matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q < 0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p <0 .05). Alterations in network topologies in drug-naïve ADHD patients indicate weaker small-worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo-fronto-parietal attention network were associated with inhibitory control deficits.

Topological Disruption of Structural Brain Networks in Patients With Cognitive Impairment Following Cerebellar Infarction.

Cerebellar lesions can lead to a series of cognitive and emotional disorders by influencing cerebral activity via cerebro-cerebellar loops. To explore changes in cognitive function and structural brain networks in patients with posterior cerebellar infarction, we conducted the current study using diffusion-weighted MRI (32 cerebellar infarction patients, 29 controls). Moreover, a series of neuropsychological tests were used to assess the subject's cognitive performance. We found cognitive impairment following cerebellar infarction involving multiple cognitive domains, including memory, executive functions, visuospatial abilities, processing speed and language functions, and brain topological abnormalities, including changes in clustering coefficients, shortest path lengths, global efficiency, local efficiencies, betweenness centrality and nodal efficiencies. Our results indicated that measures of local efficiency, mainly in the precuneus, cingulate gyrus and frontal-temporal cortex, were significantly reduced with posterior cerebellar infarction. At the same time, The correlation analysis suggested thatthe abnormal alterations in the right PCG, bilateral DCG, right PCUN may play a core role in the cognitive impairment following cerebellar infarctions. The differences in topological features of the structural brain networks within the cerebro-cerebellar circuits may provide a new approach to explore the pathophysiological mechanisms of cognitive impairment following cerebellar infarction.

Predicting the Post-therapy Severity Level (UPDRS-III) of Patients With Parkinson's Disease After Drug Therapy by Using the Dynamic Connectivity Efficiency of fMRI.

Parkinson's disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficiency changes of topological network are detectable in PD patients, and to explore whether the severity level (UPDRS-III) after drug treatment can be predicted by the pre-treatment resting-state fMRI (rs-fMRI). Here, we recruited 62 Parkinson's disease patients and calculated the dynamic nodal efficiency networks based on rs-fMRI. With connectome-based predictive models using the least absolute shrinkage and selection operator, we demonstrated that the dynamic nodal efficiency properties predict drug therapy effect well. The contributed regions for the prediction include hippocampus, post-central gyrus, cingulate gyrus, and orbital gyrus. Specifically, the connections between hippocampus and cingulate gyrus, hippocampus and insular gyrus, insular gyrus, and orbital gyrus are positively related to the recovery (post-therapy severity level) after drug therapy. The analysis of these connection features may provide important information for clinical treatment of PD patients.

Effects of brain network construction on individual differences of topological properties

Advances in diffusion MRI techniques have made it possible to virtually reconstruct white matter tracts, which further allows the modeling of the human brain as a complex network in vivo . Graph theoretical approaches have been applied to characterize topological architectures of whole-brain white matter networks. By using these techniques, many important topological properties for the whole-brain white matter network of human brain have been revealed in many previous works, while the construction method for white matter networks differs across studies. At present, a few studies have assessed the effects of network construction methods on specific white matter network properties and substantial influences of the construction methods were observed. However, the previous research mainly focuses on the effects of the constructed methods on the same subjects for their network properties. Up to now, how the topological properties are affected by construction methods for the individual differences is still unknown, even it is the key for the investigation of white matter networks in a cross-sectional manner, including group comparison, correlational analysis, etc. Therefore, it is necessary but still insufficient to make a systematic evaluation for the network construction methods on the individual differences. In the present work, the influence of different network construction methods on the network properties is desired to be assessed individual differences. Thirty-two methods were adopted to construct the whole-brain white matter networks of 80 healthy adult subjects, which includes two node definitions (low-resolution and high-resolution), four deterministic tractography algorithms (Fiber Assignment by Continuous Tracking, Tensorline Tracking, Interpolated streamline method and 2nd-order Runge-Kutta method) and four weighted edge definitions (binary, FA weighted, fiber-length weighted, and fiber-number weighted). For these white matter networks, individual differences were analyzed by graph theory and hierarchical clustering in three topological parameters including the global efficiency, the local efficiency and the nodal efficiency of the network. The results can be summarized that, firstly, the nodal efficiency with the same nodal definition exhibits convergent individual differences, which indicated that the individual differences of network nodal properties are divergent across network resolutions. Secondly, the individual differences of global and local efficiency of white matter networks constructed by different deterministic fiber tracking algorithms do not differ significantly. However, the constructed networks based on the same deterministic fiber tracking algorithm exhibited the same individual differences with the different node efficiency, which indicated that the effect of fiber tracking algorithm should be emphasized in the comparison and analysis of nodal properties in different studies. Finally, the method clustering analysis for the network global efficiency and local efficiency indicates that the networks with both low-resolution and high-resolution will compose to be the same module with the definition of the same edges, which indicated the high convergent of individual differences for the network efficiencies with different resolutions. It can be concluded that the difference of network construction methods depends on the network properties when it is used to reflexing the individual difference. These findings may provide valuable implications for understanding the intersubject variability of white matter networks and comparing results across different studies.

Altered topological properties in white matter structural networks in patients with schizophrenia

Objective To explore the changes of whole brain white matter (WM) structural network topological property in patients with schizophrenia (SP) and the associations between WM networks topological efficiency and clinical variables in patients. Methods Deterministic tractography was used to construct the WM networks of 59 patients with SP (patients group) and 41 age-, handedness-, and gender-matched healthy controls (HCs), and graph theoretical methods were applied to investigate abnormalities in the global and nodal properties of the WM network in these patients.Partial correlation analysis was used to analyze the relationship between global and nodal properties of the WM network and clinical variables in patients with SP. Results Both the patients with SP and HCs showed small-world organization of the WM networks.However, compared with HCs, the patients with SP exhibited significant abnormal global topology, including increased shortest path length (t=7.95, P=0.0001) and decreased global efficiency (30.83±16.08, 8.25±6.13, t=-9.81, P=0.002), clustering coefficiency (0.03±0.01, 0.02±0.01, t=-4.48, P=0.0003), the average clustering coefficiency (t=-8.28, P=0.002), the small-worldness (3.92±0.79, 2.79±0.56, t=-7.82, P=0.001) of their WM structural networks(all P<0.005, FDR corrected). Further, the patients with SP showed a reduction in nodal efficiency predominately in the cingulate gyrus (t=-4.11, P=0.000), superior occipital gyrus (t=-6.05, P=0.002), superior temporal gyrus (t=-10.46, P=0.001), middle temporal gyrus (t=-10.38, P=0.000), thalamus (t=-6.10, P=0.000) and putamen (t=-8.38, P=0.000) (P<0.005, FDR corrected). Partial correlation results showed that there was no significant correlation between global topological properties, node efficiency and clinical symptoms in patients group(Eglob: r=-0.14, P=0.279; Eloc: r=-0.06, P=0.628; Lp: r=0.28, P=0.031; Cp: r=0.27, P=0.043; λ: r=-0.18, P=0.166; γ: r=-0.29, P=0.026; σ: r=0.26, P=0.048; nEglob: r=0.36, P=0.005; nEloc: r=0.02, P=0.901). Conclusions The patients with SP exhibit the abnormal of whole brain WM structural network topological property and the node efficiencies of cortico-striato-thalamo circuitry are significantly reduced. Key words: Schizophrenia; White matter; Topological properties; Graph theoretical

Functional near-infrared spectroscopy evidence for the development of topological asymmetry between hemispheric brain networks from childhood to adulthood.

Cerebral asymmetry is considered an important marker of the successful development of the human brain. Recent studies have demonstrated topological asymmetries between structurally hemispheric networks in the human brain. However, it remains largely unknown whether and how the functionally topological asymmetries evolve from childhood to adulthood, a critical period that constitutes the primary peak of human brain and cognitive development. Here, we adopted resting-state functional near-infrared spectroscopy imaging data to construct hemispheric functional networks and then applied graph theory analysis to quantify the topological characteristics of the hemispheric networks. We found that the adult group exhibited consistent leftward hemispheric asymmetries in both global and local network efficiency, and the degree of leftward asymmetry in local network efficiency was significantly increased with development from childhood to adulthood. At the nodal level, the degree of leftward asymmetry in nodal efficiency, mainly involving the frontal, parietal-occipital junction, and occipital regions, increased with development. These developmental patterns of topological asymmetries suggest that the protracted maturation of functional segregation in the left hemisphere could underlie language development from childhood to adulthood and provide insight into the development of human brain functional networks.

The negative correlation between energy consumption and communication efficiency in motor network.

Motor network plays an important role in people's daily lives. However, until now, the energy consumption mechanism of motor network remains unclear. In this study, we aimed to investigate the energy consumption of motor network. Fluorine-18-fluorodeoxyglucose PET ([F]FDG PET) data of 81 healthy male Sprague-Dawley rats were included in this study. Metabolic motor network was constructed on the basis of group independent component analysis. Properties of motor network such as degree and nodal efficiency were investigated using graph theory-based analysis. Furthermore, the relationships between [F]FDG standardized uptake value ratio and these properties of each node were investigated. A motor network comprising of the following 11 regions were found: left primary motor cortex, right primary motor cortex, left secondary motor cortex, right secondary motor cortex, left primary somatosensory cortex, right primary somatosensory cortex, left secondary somatosensory cortex, right secondary somatosensory cortex, left insular cortex, right insular cortex, and left orbital cortex. Graph theory-based analysis indicated that right primary somatosensory cortex and left secondary somatosensory cortex were the hubs of motor network, and the nodal efficiency and nodal degree share the same order. Further investigation found a significantly negative correlation between nodal efficiency and [F]FDG standardized uptake value ratios. This study investigated the energy consumption of motor network and found a relationship between energy consumption and communication efficiency. These results may provide insights into the understanding of energy consumption mechanism underlying motor network.Video abstract: http://links.lww.com/NMC/A142.

Reliability evaluation on weighted graph metrics of fNIRS brain networks.

Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown. Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics. We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness. Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.

Disrupted structural brain connectome underlying the cognitive deficits in remitted late-onset depression.

Cognitive deficit is a key feature of late-onset depression (LOD) and remains after clinical recovery. LOD has been associated with widespread neurobiological difficulties, including atrophy in gray and white matter (WM) tissue in areas distributed throughout the brain. However, little is known about the topological pattern changes of WM structural networks in LOD in the remitted state and its relationship to cognitive deficits. We acquired diffusion tensor images in 37 remitted LOD (rLOD) patients and 30 healthy controls. The tract-based spatial statistics method was employed to investigate WM tract integrity in rLOD. Graph-theory based network models were further used to characterize the topological organization of WM structural networks between the two groups. Compared with controls, rLOD patients showed decreased fractional anisotropy values in the left posterior cingulate bundle, right inferior fronto-occipital fasciculus and superior longitudinal fasciculus. Moreover, rLOD patients showed abnormal small-world architecture (i.e., increased path length and decreased network efficiency) in the WM structural networks. rLOD patients also showed reduced nodal efficiencies predominantly in the frontal-striatal-occipital and posterior default-mode regions. Importantly, these structural connectomic changes significantly correlated with cognitive deficits in the rLOD patients. Finally, rLOD networks showed more vulnerable to targeted attacks compared with healthy controls. These findings provide structural evidence to support the concept of rLOD that the core aspects of the pathophysiology of this disease are associated with disruptive alterations in the coordination of large-scale brain networks and advance our understanding of the neurobiological mechanism underlying cognitive deficits in the rLOD patients.

Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity

BackgroundRecent studies showed that major depressive disorder (MDD) has been involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited. MethodsFunctional network (FN) was constructed from resting-state functional MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN. ResultsThe losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients. LimitationsThe samples size is small and most of the MDD patients take different antidepressant medications. ConclusionsAlterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.