Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum.
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ABSTRACT:
SUBMITTER: Qing Z
PROVIDER: S-EPMC8288084 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1-42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T-), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.
| S-EPMC6484365 | biostudies-literature
Project description:Background and purposeAccording to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress.Materials and methodsStructural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01).ResultsOur findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas.ConclusionOur results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.
| S-EPMC9336220 | biostudies-literature
Project description:Elucidating distinct morphological atrophy patterns of Alzheimer's disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD.
| S-EPMC9505506 | biostudies-literature
Project description:In mesial temporal lobe epilepsy (mTLE), the causal relationship of morphometric alterations between hippocampus and the other regions, that is, how the hippocampal atrophy leads to progressive morphometric alterations in the epileptic network regions remains largely unclear. In this study, a causal network of structural covariance (CaSCN) was proposed to map the causal effects of hippocampal atrophy on the network-based morphometric alterations in mTLE. It was hypothesized that if cross-sectional morphometric MRI data could be attributed temporal information, for example, by sequencing the data according to disease progression information, GCA would be a feasible approach for constructing a CaSCN. Based on a large cohort of mTLE patients (n = 108), the hippocampus-associated CaSCN revealed that the hippocampus and the thalamus were prominent nodes exerting causal effects (i.e., GM reduction) on other regions and that the prefrontal cortex and cerebellum were prominent nodes being subject to causal effects. Intriguingly, compensatory increased gray matter volume in the contralateral temporal region and post cingulate cortex were also detected. The method unraveled richer information for mapping network atrophy in mTLE relative to the traditional methods of stage-specific comparisons and structured covariance network. This study provided new evidence on the network spread mechanism in terms of the causal influence of hippocampal atrophy on progressive brain structural alterations in mTLE. Hum Brain Mapp 38:753-766, 2017. © 2016 Wiley Periodicals, Inc.
| S-EPMC6866709 | biostudies-literature
Project description:Background:We aimed to investigate how altered intrinsic connectivity networks (ICNs) affect pathologic changes of Alzheimer's disease (AD) at a network-based level. Methods:Thirty normal controls (NCs), 23 patients with AD-mild cognitive impairment (MCI), and 20 patients with AD-dementia were enrolled. We compared the organization of grey matter structural covariance and functional connectivity in ICNs between NCs and all AD patients who were amyloid ? (A?)-positive. We further used seed-based interregional covariance analysis to compare structural and A? plaque covariance in default mode network (DMN) between AD-MCI and AD-dementia groups. Results:The patients with AD had increased functional interregional covariance among the regions of the ICN anchored to dorsal caudate (DC) seeds compared to the NCs. The increased connectivity was associated with extended patterns of reduced A? plaque covariance in the AD-dementia group compared to the AD-MCI group within the striatal network anchored to DC seeds. Patterns of lower A? plaque covariance in the AD-dementia group compared to the AD-MCI group were more extended within the network anchored to DC seeds than within the DMN, which was undergoing functional failure in the patients with AD. Significant decreased structural covariance in the AD-dementia group compared to the AD-MCI group was more extended in the DMN during functional failure. Conclusions:Functional connectivity in ICNs affects the topographic spread of molecular pathologies. The temporal trajectory of pathologic alterations can be well demonstrated by pathologic covariance comparisons between different clinical stages. Pathologic covariance can provide critical support to pathologic interactions at network and molecular levels.
| S-EPMC6304529 | biostudies-other
Project description:Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by both motor and non-motor symptoms. A convergent pathophysiological hallmark of PD is an early selective vulnerability within the basal ganglia circuit. However, the causal interactions between basal ganglia atrophy and progressive structural network alterations in PD remain unaddressed. Here, we adopted voxel-based morphometry method to measure gray matter (GM) volume for each participant (n = 84 PD patients and n = 70 matched healthy controls). Patients were first divided into three stages according to the Hoehn and Yahr (H&Y) and the Part III of Unified Parkinson's Disease Rating Scale scores respectively to analyze the stage-specific GM atrophy patterns. Then, the modulation of early caudate atrophy over other brain structures was evaluated using the whole-brain voxel-wise and region-of-interest-wise causal structural covariance network approaches. We found that GM atrophy progressively expands from the basal ganglia to the angular gyrus, temporal areas, and eventually spreads through the subcortical-cortical networks as PD progresses. Notably, we identified a shared caudate-associated degeneration network including the basal ganglia, thalamus, cerebellum, sensorimotor cortex, and cortical association areas with the PD progressive factors. These findings suggest that the early structural vulnerability of basal ganglia in PD may play a pivotal role in the modulation of motor and non-motor circuits at the structural level. Our work provides evidence for a novel mechanism of network degeneration that underlies the pathology of PD and may have potential clinical applications in the development of early predictors of PD onset and progress.
| S-EPMC8764481 | biostudies-literature
Project description:The progression of cognitive deficits in Alzheimer's disease and semantic dementia is accompanied by grey matter atrophy and white matter deterioration. The impact of neuronal loss on the structural network connectivity in these dementia subtypes is, however, not well understood. In order to gain a more refined knowledge of the topological organization of white matter alterations in dementia, we used a network-based approach to analyze the brain's structural connectivity network. Diffusion-weighted and anatomical MRI images of groups with eighteen Alzheimer's disease and six semantic dementia patients, as well as twenty-one healthy controls were recorded to reconstruct individual connectivity networks. Additionally, voxel-based morphometry, using grey and white matter volume, served to relate atrophy to altered structural connectivity. The analyses showed that Alzheimer's disease is characterized by decreased connectivity strength in various cortical regions. An overlap with grey matter loss was found only in the inferior frontal and superior temporal areas. In semantic dementia, significantly reduced network strength was found in the temporal lobes, which converged with grey and white matter atrophy. Therefore, this study demonstrated that the structural disconnection in early Alzheimer's disease goes beyond grey matter atrophy and is independent of white matter volume loss, an observation that was not found in semantic dementia.
| S-EPMC5147505 | biostudies-other
Project description:BACKGROUND:Alzheimer's disease (AD) is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration. In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain. The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations. We hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores. METHODS:Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted magnetic resonance imaging with seed-based correlation analysis. The patients were stratified into two genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28). Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor. RESULTS:For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores. In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the interconnected peak clusters were correlated with the clinical scores. Despite a small number of cases, the A homozygotes showed greater covariance strength than the G carriers among all four SCNs. CONCLUSIONS:Our results suggest that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes. The greater covariance strength in the four SCNs shown in the A homozygotes suggests that different Bcl-2 polymorphisms play different modulatory roles.
| S-EPMC5806294 | biostudies-literature
Project description:BackgroundCushing disease (CD) is a rare clinical neuroendocrine disease. CD is characterized by abnormal hypercortisolism induced by a pituitary adenoma with the secretion of adrenocorticotropic hormone. Individuals with CD usually exhibit atrophy of gray matter volume. However, little is known about the alterations in topographical organization of individuals with CD. This study aimed to investigate the structural covariance networks of individuals with CD based on the gray matter volume using graph theory analysis.MethodsHigh-resolution T1-weighted images of 61 individuals with CD and 53 healthy controls were obtained. Gray matter volume was estimated and the structural covariance network was analyzed using graph theory. Network properties such as hubs of all participants were calculated based on degree centrality.ResultsNo significant differences were observed between individuals with CD and healthy controls in terms of age, gender, and education level. The small-world features were conserved in individuals with CD but were higher than those in healthy controls. The individuals with CD showed higher global efficiency and modularity, suggesting higher integration and segregation as compared to healthy controls. The hub nodes of the individuals with CD were Short insular gyri (G_insular_short_L), Anterior part of the cingulate gyrus and sulcus (G_and_S_cingul-Ant_R), and Superior frontal gyrus (G_front_sup_R).ConclusionsSignificant differences in the structural covariance network of patients with CD were found based on graph theory. These findings might help understanding the pathogenesis of individuals with CD and provide insight into the pathogenesis of this CD.
| S-EPMC11004566 | biostudies-literature
Project description:Alzheimer's disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain's connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model's rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer's disease assessment.
| S-EPMC5747552 | biostudies-literature