OBJECTIVE: To determine whether individuals with subjective cognitive decline (SCD) have changes in whole-brain network characteristics and intracerebral node characteristics in the structural network, and whether there is a difference between SCD with and without Apolipoprotein E4 (APOEε4).
METHODS: This cross-sectional study included 36 individuals without SCD without APOEε4 (healthy control, HC group), 21 individuals with SCD with APOEε4 (APOEε4+ group), and 33 individuals with SCD without APOEε4 (APOEε4- group). The white matter structural network was constructed using the fractional anisotropy (FA) based deterministic fiber tracking method. Graph theory was used to analyze the whole-brain network characteristics and intracerebral node characteristics of the three groups.
RESULTS: Regarding the whole-brain network characteristics, all three groups exhibited small-worldness in their structural networks. The clustering coefficient (Cp) and local efficiency (Eloc) in the APOEε4+ and APOEε4- groups were significantly lower than in the HC group (p < 0.05), but no significant difference in Cp or Eloc was observed between the APOEε4+ and APOEε4- groups. Regarding intracerebral node characteristics, there were significant differences in some brain regions, mainly the default mode network (DMN), the occipital lobe, the temporal lobe, and subcortical regions. The change in intracerebral node characteristics was different between the APOEε4+ group and the APOEε4- group.
CONCLUSIONS: Individuals with SCD demonstrate changes in whole-brain network characteristics and intracerebral node characteristics in the structural network. Moreover, differences exist between APOEε4+ and APOEε4- individuals.

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as \"connectotypes\". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.

Generalized fractional anisotropy (GFA) can eliminate the crossing fiber effect, which may be more reflective of brain tissue changes in patients with cerebral small vessel disease (CSVD). This study aimed to explore the alterations of structural networks based on GFA and its relationship with cognitive performance in CSVD patients. We recruited 50 CSVD patients which were divided into two groups: cognitive impairment (CSVD-CI) and normal cognition (CSVD-NC), and 22 healthy controls (HCs). All participants underwent the Montreal Cognitive Assessment (MoCA) and MRI examinations. The structural topological properties were compared among the three groups. The correlation between these structural alterations and MoCA was analyzed. Compared with HCs, significantly decreased nodal efficiency and connectivity were detected in the corticothalamic pathways in both patient groups, of which some were significantly decreased in CSVD-CIs compared with CSVD-NCs. Moreover, both patient groups exhibited global network disruption including decreased global efficiency and increased characteristic path length compared with HCs. Furthermore, the nodal efficiency in the right pallidum positively correlated with MoCA in CSVD-NCs controlling for nuisance variables (r = 0.471, p = 0.031). The alterations in corticothalamic pathways indicated that the brain structural network underwent extensive disruption, providing evidence for the consideration of CSVD as a global brain disease.

The Postural Instability/Gait Difficulty (PIGD) subtype of Parkinson\'s disease (PD) has a faster disease progression, a higher risk of cognitive and motor decline, yet the alterations of structural topological organization remain unknown. Diffusion Tensor Imaging (DTI) and 3D-TI scanning were conducted on 31 PD patients with PIGD (PD-PIGD), 30 PD patients without PIGD (PD-non-PIGD) and 35 Healthy Controls (HCs). Structural networks were constructed using DTI brain white matter fiber tractography. A graph theory approach was applied to characterize the topological properties of complex structural networks, and the relationships between significantly different network metrics and motor deficits were analyzed within the PD-PIGD group. PD-PIGD patients exhibited increased shortest path length compared with PD-non-PIGD and HCs (P < 0.05, respectively). Additionally, PD-PIGD patients exhibited decreased nodal properties, mainly in the cerebellar vermis, prefrontal cortex, paracentral lobule, and visual regions. Notably, the degree centrality of the cerebellar vermis was negatively correlated with the PIGD score (r = -0.390; P = 0.030) and Unified Parkinson\'s Disease Rating Scale Part III score (r = -0.436; P = 0.014) in PD-PIGD patients. Furthermore, network-based statistical analysis revealed decreased structural connectivity between the prefrontal lobe, putamen, supplementary motor area, insula, and cingulate gyrus in PD-PIGD patients. Our findings demonstrated that PD-PIGD patients existed abnormal structural connectomes in the cerebellar vermis, frontal-parietal cortex and visual regions. These topological differences can provide a topological perspective for understanding the potential pathophysiological mechanisms of PIGD in PD.

The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called \"rich-club\" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects\' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

OBJECTIVE: Growing evidences have documented various abnormalities of the white matter bundles in people with narcolepsy. We sought to evaluate topological properties of brain structural networks, and their association with symptoms and neuropathophysiological features in people with narcolepsy.
METHODS: Diffusion tensor imaging was conducted for people with narcolepsy (n = 30) and matched healthy controls as well as symptoms assessment. Structural connectivity for each participant was generated to analyze global and regional topological properties and their correlations with narcoleptic features. Further human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data.
RESULTS: A wide and dramatic decrease in structural connectivities was observed in people with narcolepsy, with descending network degree and global efficiency. These metrics were not only correlated with sleep latency and awakening features, but also reflected alterations of sleep macrostructure in people with narcolepsy. Network-based statistics identified a small hyperenhanced subnetwork of cingulate gyrus that was closely related to rapid eye movement sleep behavior disorder (RBD) in narcolepsy. Further imaging genetics analysis suggested glutamatergic signatures were responsible for the preferential vulnerability of connectivity alterations in people with narcolepsy, while additional PET/SPECT data verified that structural alteration was significantly correlated with metabotropic glutamate receptor 5 (mGlutR5) and N-methyl-D-aspartate receptor (NMDA).
CONCLUSIONS: People with narcolepsy endured a remarkable decrease in the structural architecture, which was not only closely related to narcolepsy symptoms but also glutamatergic signatures.

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Diffusion tensor imaging (DTI) studies have demonstrated white matter (WM) abnormalities in patients with temporal lobe epilepsy (TLE). However, alterations in the topological properties of the WM structural network in patients with TLE remain unclear. Graph theoretical analysis provides a new perspective for evaluating the connectivity of WM structural networks.
DTI was used to map the structural networks of 18 patients with TLE (10 males and 8 females) and 29 (17 males and 12 females) age- and gender-matched normal controls (NC). Graph theory was used to analyze the whole-brain networks and their topological properties between the two groups. Finally, partial correlation analyses were performed on the weighted network properties and clinical characteristics, namely, duration of epilepsy, verbal intelligence quotient (IQ), and performance IQ.
Patients with TLE exhibited reduced global efficiency and increased characteristic path length. A total of 31 regions with nodal efficiency alterations were detected in the fractional anisotropy_ weighted network of the patients. Communication hubs, such as the middle temporal gyrus, right inferior temporal gyrus, left calcarine, and right superior parietal gyrus, were also differently distributed in the patients compared with the NC. Several node regions showed close relationships with duration of epilepsy, verbal IQ, and performance IQ.
Our results demonstrate the disruption of the WM structural network in TLE patients. This study may contribute to the further understanding of the pathological mechanism of TLE.

To reveal the network-level structural disruptions associated with cognitive dysfunctions in different cerebral small vessel disease (CSVD) burdens, we used probabilistic diffusion tractography and graph theory to investigate the brain network topology in 67 patients with a severe CSVD burden (CSVD-s), 133 patients with a mild CSVD burden (CSVD-m) and 89 healthy controls. We used one-way analysis of covariance to assess the altered topological measures between groups, and then evaluated their Pearson correlation with cognitive parameters. Both the CSVD and control groups showed efficient small-world organization in white matter (WM) networks. However, compared with CSVD-m patients and controls, CSVD-s patients exhibited significantly decreased local efficiency, with partially reorganized hub distributions. For regional topology, CSVD-s patients showed significantly decreased nodal efficiency in the bilateral anterior cingulate gyrus, caudate nucleus, right opercular inferior frontal gyrus (IFGoperc), supplementary motor area (SMA), insula and left orbital superior frontal gyrus and angular gyrus. Intriguingly, global/local efficiency and nodal efficiency of the bilateral caudate nucleus, right IFGoperc, SMA and left angular gyrus showed significant correlations with cognitive parameters in the CSVD-s group, while only the left pallidum showed significant correlations with cognitive metrics in the CSVD-m group. In conclusion, the decreased local specialization of brain structural networks in patients with different CSVD burdens provides novel insights into understanding the brain structural alterations in relation to CSVD severity. Cognitive correlations with brain structural network efficiency suggest their potential use as neuroimaging biomarkers to assess the severity of CSVD.