Global Topological Properties Research Articles

Time Series Clustering with Topological and Geometric Mixed Distance

Time series clustering is an essential ingredient of unsupervised learning techniques. It provides an understanding of the intrinsic properties of data upon exploiting similarity measures. Traditional similarity-based methods usually consider local geometric properties of raw time series or the global topological properties of time series in the phase space. In order to overcome their limitations, we put forward a time series clustering framework, referred to as time series clustering with Topological-Geometric Mixed Distance (TGMD), which jointly considers local geometric features and global topological characteristics of time series data. More specifically, persistent homology is employed to extract topological features of time series and to compute topological similarities among persistence diagrams. The geometric properties of raw time series are captured by using shape-based similarity measures such as Euclidean distance and dynamic time warping. The effectiveness of the proposed TGMD method is assessed by extensive experiments on synthetic noisy biological and real time series data. The results reveal that the proposed mixed distance-based similarity measure can lead to promising results and that it performs better than standard time series analysis techniques that consider only topological or geometrical similarity.

Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients

Suicide ideation (SI) is a most high-risk clinical sign for major depressive disorder (MDD). However, whether the rich-club network organization as a core structural network is associated with SI and how the related neural circuits are distributed in MDD patients remain unknown. Total 177 participants including 69 MDD patients with SI (MDDSI), 58 MDD without SI (MDDNSI) and 50 cognitively normal (CN) subjects were recruited and completed neuropsychological tests and diffusion-tensor imaging scan. The rich-club organization was identified and the global and regional topological properties of structural networks, together with the brain connectivity of specific neural circuit architectures, were analyzed. Further, the support vector machine (SVM) learning was applied in classifying MDDSI or MDDNSI from CN subjects. MDDSI and MDDNSI patients both exhibited disrupted rich-club organizations. However, MDDSI patients showed that the differential network was concentrated on the non-core low-level network and significantly destroyed betweeness centrality was primarily located in the regional non-hub regions relative to MDDNSI patients. The differential structural network connections involved the superior longitudinal fasciculus and the corpus callosum were incorporated in the cognitive control circuit and default mode network. Finally, the feeder serves as a potentially powerful indicator for distinguishing MDDSI patients from MDDNSI or CN subjects. The altered rich-club organization provides new clues to understand the underlying pathogenesis of MDD patients, and the feeder was useful as a diagnostic neuroimaging biomarker for differentiating MDD patients with or without SI.

Functional Connectomics and Disease Progression in Drug-Naïve Parkinson's Disease Patients.

Functional brain connectivity alterations may be detectable even before the occurrence of brain atrophy, indicating their potential as early markers of pathological processes. We aimed to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naïve Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging and to explore whether baseline connectivity changes may predict clinical progression. One hundred and forty-seven drug-naïve, cognitively unimpaired PD patients were enrolled in the study at baseline and compared to 38 age- and gender-matched controls. Non-hierarchical cluster analysis using motor and non-motor data was applied to stratify PD patients into two subtypes: 77 early/mild and 70 early/severe. Graph theory analysis and connectomics were used to assess global and local topological network properties and regional functional connectivity at baseline. Stepwise multivariate regression analysis investigated whether baseline functional imaging data were predictors of clinical progression over 2 years. At baseline, widespread functional connectivity abnormalities were detected in the basal ganglia, sensorimotor, frontal, and occipital networks in PD patients compared to controls. Decreased regional functional connectivity involving mostly striato-frontal, temporal, occipital, and limbic connections differentiated early/mild from early/severe PD patients. Connectivity changes were found to be independent predictors of cognitive progression at 2-year follow-up. Our findings revealed that functional reorganization of the brain connectome occurs early in PD and underlies crucial involvement of striatal projections. Connectomic measures may be helpful to identify a specific PD patient subtype, characterized by severe motor and non-motor clinical burden as well as widespread functional connectivity abnormalities. © 2021 International Parkinson and Movement Disorder Society.

Energetics and magnetism of topological graphene nanoribbons

The topological properties of graphene nanoribbons (GNRs) have received a significant amount of attention in emerging fields such as spintronics and quantum computing. This study is focused on the energetics and magnetism of symmetry-protected junction state arrays, which are realized in the alternating periodic structures of two topologically different armchair GNRs. We found that the antiferromagnetic states require at least eight unit cells for each segment of the periodic armchair GNRs, where the armchair GNRs whose numbers of carbon atoms in a row are seven and nine are connected with a junction structure. We also found the junction structure that provides more stable antiferromagnetic states. Furthermore, we propose an end (armchair GNRs/vacuum interface) structure to avoid disturbing the global topological properties of the junction state array. This means that if the topological end states (non-trivial phases of the Su, Schrieffer, and Heeger model or Majorana fermions) exist, they are properly formed at the endmost junctions without the requirement for extra effort such as long end extension. We believe that this study can add new guidelines and challenges for realizing graphene-based quantum computing.

A study of the U.S. domestic air transportation network: temporal evolution of network topology and robustness from 2001 to 2016

The U.S. air transportation network (ATN) is critical to the mobility and the functioning of the United States. It is thus necessary to ensure that it is well-connected, efficient, robust, and secure. Despite extensive research on its topology, the temporal evolution of the network’s robustness remains largely unexplored. In the present paper, a methodology is proposed to identify long-term trends in the evolution of the network’s topology and robustness over time. The study of the U.S. domestic ATN’s robustness was performed based on annual flight data from 1996 to 2016 and network analytics were used to examine the effects of restructuring that followed the 9/11 events. Centrality measures were computed and a node deletion method was applied to assess the network’s tolerance to a targeted attack scenario. The outcome of this study indicated that the 9/11 terrorist attacks triggered vast restructuring of the network, in terms of efficiency and security. Air traffic expanded, as new airports and air routes were introduced, allowing the network to recover rapidly and become more efficient. Security concerns resulted in significant improvement of the network’s robustness. Since 2001, the global traffic and topological properties of the U.S. ATN have displayed continuous growth, due to the network’s expansion. On the other hand, the results suggest that although the system’s ability to sustain its operational level under extreme circumstances has lately improved, its tolerance to targeted attacks has deteriorated. The presented methodology has shown its potential to be applied on different network levels or different transportation networks, in order to provide a general perspective of the system’s vulnerabilities.

Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer’s disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive (‘Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.

Alteration of single-subject gray matter networks in major depressed patients with suicidality.

While regional brain alterations and functional connectivity in depressed suicidal patients have previously been reported, knowledge about gray matter (GM) structural networks is limited. The aim of this study was to explore the GM of depressed suicidal brains from the single-subject structural network level. This was a cross-sectional study, in which 50 healthy controls (HC, 31 ± 9 years), 50 major depressed patients without suicidality (NSD, 29 ± 10 years), and 50 major depressed patients with suicidality (SU, 29 ± 12 years) were enrolled. T1 -weighted images (T1 WI) were acquired with three-dimensional-magnetization prepared rapid gradient echo sequence in 3.0 T magnetic resonance. The analysis was performed using the automated Computational Anatomy Toolbox (CAT12) within Statistical Parametric Mapping while running MATLAB. The T1 images were segmented into GM, white matter, and cerebrospinal fluid. Then single-subject structural networks were constructed based on the morphological similarity of GM regions. Global network topological properties, including clustering coefficient (Cp ), characterpath length (Lp ), normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), global efficiency (Eglob ), local efficiency (Eloc ), and nodal network topological properties, including nodal efficiency, degree, and betweenness centrality, were measured using graph theory analysis. Statistical tests performed were analysis of variance, Pearson correlation analysis, and multiple linear regression analysis. Decreased Eglob and increased shortest Lp were observed in SU group compared to HC and NSD groups (p < 0.05). The NSD and SU groups had an increased λ and decreased Eloc compared to the HC group (p < 0.05). Altered nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in the SU group compared with the HC and NSD groups (all p < 0.05). The GM network in the SU group showed decreased segregation and weaker integration, that is weaker small-worldness, compared to the NSD and HC groups. Abnormal nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in suicidal brains. This study provides new evidence for therapeutic targets for patients with depression and suicidality. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Visibility Graph Complex Network Analysis on Seismic-Induced Variation in Groundwater Level of Nanxi Well, China

Groundwater level in wells, i.e., well water level (WWL) is an important index in hydrological monitoring during earthquakes. Due to the complex dynamics of groundwater, the WWL might change under seismic actions. This paper attempts to identify the long-term correlation between WWL and earthquakes, and disclose the topological features of groundwater dynamics. Taking Nanxi Well as an example, the authors conducted state space analysis on the raw series and trend of WWL to eliminate interferences like barometric pressure, rainfall, and solid tide, creating the trend time series. Then, the raw series and trend time series were converted into the raw visible graph (VG) network and trend VG network, respectively. Further, the global period was divided into five local time windows, and the two VG networks were compared by global aspect, local aspect, and topological properties of complex networks. The results show that the nodes of high degrees are closely related to the seismic response of the WWL in Nanxi Well; all VG networks are scale free and hierarchical; the seismic response of the WWL in the well is reflected by degree correlation; the community division of raw VG network was basically the same as that of trend VG network. The research findings provide insights to the seismic response of WWL and the dynamic fluctuation of groundwater level.

Reorganization of functional brain network architecture in chronic osteoarthritis pain.

Osteoarthritis (OA) manifests with chronic pain, motor impairment, and proprioceptive changes. However, the role of the brain in the disease is largely unknown. Here, we studied brain networks using the mathematical properties of graphs in a large sample of knee and hip OA (KOA, n = 91; HOA, n = 23) patients. We used a robust validation strategy by subdividing the KOA data into discovery and testing groups and tested the generalizability of our findings in HOA. Despite brain global topological properties being conserved in OA, we show there is a network wide pattern of reorganization that can be captured at the subject‐level by a single measure, the hub disruption index. We localized reorganization patterns and uncovered a shift in the hierarchy of network hubs in OA: primary sensory and motor regions and parahippocampal gyrus behave as hubs and insular cortex loses its central placement. At an intermediate level of network structure, frontoparietal and cingulo‐opercular modules showed preferential reorganization. We examined the association between network properties and clinical correlates: global disruption indices and isolated degree properties did not reflect clinical parameters; however, by modeling whole brain nodal degree properties, we identified a distributed set of regions that reliably predicted pain intensity in KOA and generalized to hip OA. Together, our findings reveal that while conserving global topological properties, brain network architecture reorganizes in OA, at both global and local scale. Network connectivity related to OA pain intensity is dissociated from the major hub disruptions, challenging the extent of dependence of OA pain on nociceptive signaling.

Reading-Network in Developmental Dyslexia before and after Visual Training

Electroencephalographic studies using graph-theoretic analysis have found aberrations in functional connectivity in dyslexics. How visual nonverbal training (VT) can change the functional connectivity of the reading network in developmental dyslexia is still unclear. We studied differences in the local and global topological properties of functional reading networks between controls and dyslexic children before and after VT. The minimum spanning tree method was used to construct the reading networks in multiple electroencephalogram (EEG) frequency bands. Compared to controls, pre-training dyslexics had a higher leaf fraction, tree hierarchy, kappa, and smaller diameter (θ—γ-frequency bands), and therefore, they had a less segregated neural network than controls. After training, the reading-network metrics of dyslexics became similar to controls. In β1 and γ-frequency bands, pre-training dyslexics exhibited a reduced degree and betweenness centrality of hubs in superior, middle, and inferior frontal areas in both brain hemispheres compared to the controls. Dyslexics relied on the left anterior temporal (β1, γ1) and dorsolateral prefrontal cortex (γ1), while in the right hemisphere, they relied on the occipitotemporal, parietal, (β1), motor (β2, γ1), and somatosensory cortices (γ1). After training, hubs appeared in both hemispheres at the middle occipital (β), parietal (β1), somatosensory (γ1), and dorsolateral prefrontal cortices (γ2), while in the left hemisphere, they appeared at the middle temporal, motor (β1), intermediate (γ2), and inferior frontal cortices (γ1, β2). Language-related brain regions were more active after visual training. They contribute to an understanding of lexical and sublexical representation. The same role has areas important for articulatory processes of reading.

White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression

Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective—WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 anatomical regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (Hamilton Depression Rating Scale) in discovery sample (r = 0.34, p = 0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a potential neuromarker for the classification and prediction of MDD patients.

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

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

Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia.

Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

White-Matter Functional Topology: A Neuromarker for Classification and Prediction in Unmedicated Depression

Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective – WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 brain regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (HAMD scores) in discovery sample ( r = 0.34, p = 0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a neuromarker for the classification and prediction of MDD patients. Funding Statement: This work was supported by the National Key Project of Research and Development (2018AAA0100705), National Natural Science Foundation of China (61871077, 61533006, U1808204, and 61673089), and Sichuan Science and Technology Program (2018TJPT0016). Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: This study was approved by the Ethics Committee of Southwest University and First Affiliated Hospital of Chongqing Medical University. Written informed consent was obtained from all subjects

Altered white matter structural networks in drug-naïve patients with obsessive-compulsive disorder.

White matter (WM) alteration is considered to be a vital neurological mechanism of obsessive-compulsive disorder (OCD). However, little is known regarding the changes in topological organization of WM structural network in OCD. We acquired diffusion tensor imaging (DTI) datasets from 28 drug-naïve OCD patients and 28 well-matched healthy controls (HC). A deterministic fiber tracking approach was used to construct the whole-brain structural connectome. Group differences in global and nodal topological properties as well as rich-club organizations were compared by using graph theory analysis. The relationship between the altered network metrics and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was calculated. Compared with controls, OCD patients exhibited a significantly decreased small-worldness (σ), normalized clustering coefficient (γ) and shortest path length (Lp), as well as an increased global efficiency (Eglob). The nodal efficiency (Enodal) was found to be reduced in the left middle frontal gyrus, and increased in the right parahippocampal gyrus and bilateral putamen in OCD patients. Besides, OCD patients showed increased rich-club, feeder and local connection strength, and the connection strength of the rich-club was positively correlated with the total Y-BOCS score. Our findings emphasized a central role for the complicatedly changed topological architecture of brain structural networks in the pathological mechanism underlying OCD.

Impaired Topological Properties of Gray Matter Structural Covariance Network in Epilepsy Children With Generalized Tonic-Clonic Seizures: A Graph Theoretical Analysis.

Modern network science has provided exciting new opportunities for understanding the human brain as a complex network of interacting regions. The improved knowledge of human brain network architecture has made it possible for clinicians to detect the network changes in neurological diseases. Generalized tonic–clonic seizure (GTCS) is a subtype of epilepsy characterized by generalized spike-wave discharge involving the bilateral hemispheres during seizure. Network researches in adults with GTCS exhibited that GTCS can be conceptualized as a network disorder. However, the overall organization of the brain structural covariance network in children with GTCS remains largely unclear. Here, we used a graph theory method to assess the gray matter structural covariance network organization of 14 pediatric patients diagnosed with GTCS and 29 healthy control children. The group differences in regional and global topological properties were investigated. Results revealed significant changes in nodal betweenness locating in brain regions known to be abnormal in GTCS (the right thalamus, bilateral temporal pole, and some regions of default mode network). The network hub analysis results were in accordance with the regional betweenness, which presented a disrupted regional topology of structural covariance network in children with GTCS. To our knowledge, the present study is the first work reporting the changes of structural topological properties in children with GTCS. The findings contribute new insights into the understanding of the neural mechanisms underlying GTCS and highlight critical regions for future neuroimaging research in children with GTCS.

Aberrancies of Brain Network Structures in Patients with Anosmia

Patients with anosmia exhibit structural and functional brain abnormalities. The present study explored changes in brain white matter (WM) in non-neurodegenerative anosmia using diffusion-tensor-based network analysis. Twenty patients with anosmia and sixteen healthy controls were recruited in the cross-sectional, case-control study. Participants underwent olfactory tests (orthonasal and retronasal), neuropsychological assessment (cognitive function and depressive symptoms) and diffusion tensor imaging measurement. Tract-Based Spatial Statistics, graph theoretical analysis and Network-Based Statistics were used to explore the white matter. There was no significant difference in fractional anisotropy (FA) between patients and controls. In global network topological properties comparisons, patients exhibited higher γ and λ levels than controls, and both groups satisfied the criteria of small-world (σ > 1). In local network topological properties, patients had reduced betweenness, degree and efficiency (global and local), as well as increased shortest path length and cluster coefficient in olfactory-related brain areas (anterior cingulum, lenticular nucleus, putamen, hippocampus, amygdala, caudate nucleus, orbito-frontal gyrus). Olfactory threshold scores and the retronasal score were negatively correlated with γ and λ, and the retronasal score was positively correlated with FA values in certain WM tracts, i.e. middle cerebellar peduncle, right inferior cerebellar peduncle, left inferior cerebellar peduncle, right cerebral peduncle, left cerebral peduncle, left cingulum (cingulate gyrus), right cingulum (hippocampus), superior fronto-occipital fasciculus, and, left tapetum. Patients with anosmia demonstrated relevant WM network dysfunction though their structural integrity remained intact. Their retronasal olfaction deficits revealed to be more strongly associated with WM alterations compared with orthonasal olfactory scores.

Changes of brain structural network properties in patients with rapid eye movement sleep behavior disorder

To investigate the changes in behaviors and brain structural network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD). Twenty patients with iRBD (iRBD group) and 22 healthy control subjects were evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn-Yahr staging. Diffusion tensor imaging and graph- theoretical analysis were performed for analyzing the topological characteristics of brain structural networks of the patients, and the correlation between the behavioral changes and the changes in the topological characteristics of the brain networks was analyzed. The UPDRS score was significantly higher in iRBD group than in the healthy control group (P < 0.05). No significant difference was found in small-world attributes between the patients with iRBD and the control subjects (P>0.05). The patients with iRBD exhibited significantly shortened characteristic shortest path length Lp (P < 0.05) and significantly increased global efficiency, local efficiency and assortativity (P < 0.05). Examination of regional topological properties of the brain network revealed abnormal node properties in the frontal, temporal, parietal, occipital, and striatal and limbic lobes in patients with iRBD. The patients also had significantly increased degree centrality of the left pallidum and enhanced nodal efficiency in the left thalamus, superior temporal gyrus, temporal pole and bilateral superior occipital gyrus, bilateral putamens as well as the right gyrus rectus, amygdala, supramarginal gyrus, and middle temporal gyrus. The nodal local efficiency was significantly increased in the left superior frontal gyrus, middle cingulate gyrus, superior parietal gyrus, bilateral fusiform gyrus, right superior motor area, postcentral gyrus and angular gyrus of the patients with iRBD. The nodal shortest path was significantly shortened in the left superior motor area, pallidum, thalamus, superior temporal gyrus, temporal pole, bilateral putamens, bilateral superior occipital gyrus, right rectus gyrus, amygdala, supramarginal gyrus and middle temporal gyrus, and the nodal clustering coefficient was significantly lowered in the left superior occipital gyrus of the patients. In patients with iRBD, the UPDRS I score was positively correlated with the nodal efficiency in the right supramarginal gyrus (r=0.50, P < 0.05) and local nodal efficiency in the right fusiform gyrus (r=0.53, P < 0.05), and negatively correlated with the nodal clustering coefficient in the left superior occipital gyrus (r=-0.552, P < 0.05). Patients with iRBD present with abnormal changes in mental condition, behaviors, emotions, activities of daily living and motor functions. The brain structural network of patient with iRBD still has a small-world property with abnormal global topological property and abnormal distribution of local topological property in the cortex, striatum and limbic system.

Small-world EEG network analysis of functional connectivity in developmental dyslexia after visual training intervention.

Aberrations in functional connectivity in children with developmental dyslexia have been found in electroencephalographic studies using graph analysis. How training with visual tasks can modify the functional semantic network in developmental dyslexia remains unclear. We investigate local and global topological properties of functional networks in multiple EEG frequency ranges based on a small-world propensity method in controls, pre- and post-training dyslexic children during visual word/pseudoword processing. Results indicated that the EEG network topology in dyslexics before the training was more integrated than controls, and after training - more segregated and similar to that of the controls in the theta (θ: 4-8), alpha (α: 8-13), beta (β1: 13-20; β2: 20-30), and gamma (γ1: 30-48; γ2: 52-70 Hz) bands for three graph measures. The pre-training dyslexics exhibited a reduced strength and betweenness centrality of the left anterior temporal and parietal regions in the θ, α, β1 and γ1-frequency bands, compared to the controls. The simultaneous appearance of hubs in the left hemisphere (or both hemispheres) at temporal and parietal (α-word/γ-pseudoword discrimination), temporal and middle frontal cortex (θ, α-word), parietal and middle frontal cortex (β1-word), parietal and occipitotemporal cortices (θ-pseudoword), identified in the EEG-based functional networks of normally developing children were not present in the networks of dyslexics. The hub distribution for dyslexics in the θ, α, and β1 bands became similar to that of the controls. The topological organization of functional networks and the less efficient network configuration (long characteristic path length) in dyslexics compared to the more optimal global organization in the controls was studied for the first time after remediation training.

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.