White Matter Changes in Alzheimer’s  Disease Revealed by Diffusion  Tensor Imaging with TBSS

Chen, Hongyan; Wang, Kai; Yao, Jingfan; Dai, Jianping; Ma, Jun; Li, Shaowu; Lin, Aiming; Chen, Qian; Chen, Xuzhu; Zhang, Yumei

doi:10.4236/wjns.2015.51007

Cited by 10 publications

(11 citation statements)

References 33 publications

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“…Corticospinal tract was the disrupted projection fiber along with disruptions in forceps minor and forceps Disruptions in intrahemispheric and projection fiber tracts and white matter changes related to aging Decreased FA was spatially extensive in AD subjects; however, significant effects of decreased FA in comparison with MCI cohort were observed in voxels of specific fiber pathways; in particular, the inferior longitudinal fasciculus, forceps minor, forceps major, SLF, anterior thalamic radiation, IFOF, and corticospinal tract. These findings were consistent with existing literature (Chen et al, 2015;Mayo et al, 2019;Serra et al, 2010). Lower FA reflects extensive demyelination and light axonal packing as it would leave more intercellular water than dense packing (Feldman et al, 2010).…”

Section: Discussionsupporting

confidence: 92%

White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease

et al. 2021

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Background: Alzheimer's disease (AD) is associated with impairment of large-scale brain networks, disruption in structural connections, and functional disconnection between distant brain regions. Although decreased functional connectivity has been thoroughly investigated and reported by existing functional neuroimaging literature, this study investigated network-based differences due to the structural changes in white matter pathways in AD patients. We hypothesize that diffusion metrics of disrupted tracts that go through cognitive networks related with intrinsic awareness, motor movement, and executive control can be utilized as biomarkers to distinguish prodromal stage from AD stage. Methods: Diffusion MRI data of a total 154 subjects, including patients with clinical AD (n = 47) and patients with mild cognitive impairment (MCI) (n = 107) was used. To study structural changes associated with white matter fiber pathways voxel-averaged diffusion metrics and fiber density metrics were calculated. Results: Study revealed that AD patients exhibit disruptions in intrahemispheric tracts and projection fiber tracts as suggested by diffusion indices. Our whole brain analysis revealed that network differences within default mode network (DMN), sensory motor network, and frontoparietal networks are associated with disruption in inferior fronto-occipital fasciculus (IFOF), corticospinal tract, and superior longitudinal fasciculus. Global function revealed by Mini Mental State Examination correlate with those fiber pathways that form reciprocal connections within networks associated with motor movement and executive control. Conclusion: Diffusion metrics appear to be more sensitive than fiber density metrics in differentiating the structural changes in the white matter. Decreased fractional anisotropy along with increased mean diffusivity and radial diffusivity in forceps minor, corticospinal tract, and IFOF as an imaging biomarker would be ideal to distinguish AD patients from MCI patients. Difference of DMN, sensory motor network, and frontal parietal network in our study reveals that AD patients may suffer from poor motor movement and degraded executive control.

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Section: Discussionsupporting

confidence: 92%

White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease

et al. 2021

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“…The literature on topological disruptions along the AD continuum (Daianu et al, 2015; Lo et al, 2010; Rasero et al, 2017; Yan et al, 2018) supports the outcomes of our investigation, highlighting the potential of network efficiency as a cost‐effective, effective, and noninvasive biomarker for AD imaging. Moreover, we note that the distribution of ADPRs overlaps with the pathological progression frequently observed in the frontal, cingulate, precuneus, striatum, parietal, and lateral temporal cortices (Chen et al, 2023; Villemagne et al, 2018). The cerebral regions identified in our study closely align with confirmed sites of AD pathology deposition, strongly indicating that pathological deposition may contribute to deviations in local efficiency, ultimately resulting in cognitive decline.…”

Section: Discussionsupporting

confidence: 55%

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

Zhang,

Chen,

Huang

et al. 2024

Human Brain Mapping

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Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0‐T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β‐amyloid (Aβ), phosphorylated tau181 (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10−24; quadratic trend: t = 7.708, p = 2.25 × 10−13; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10−10; quadratic trend: t = 3.896, p = 1.22 × 10−4; adjusted R2 = 0.330), p‐tau181 (linear trend: t = 8.452, p = 1.61 × 10−15; quadratic trend: t = 6.316, p = 1.05 × 10−9; adjusted R2 = 0.346) and Aβ42/Aβ40 (linear trend: t = −3.257, p = 1.27 × 10−3; quadratic trend: t = −1.662, p = 9.76 × 10−2; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = −0.61, p = 6.3 × 10−7), NfL (R = −0.57, p = 6.4 × 10−6), and p‐tau181 (R = −0.48, p = 2.0 × 10−4). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = −0.224, 95% confidence interval [CI] = [−0.417 to −0.029], p = .0196 for MMSE; ab = −0.198, 95% CI = [−0.42 to −0.003], p = .0438 for MOCA) or NfL (ab = −0.224, 95% CI = [−0.417 to −0.029], p = .0196 for MMSE; ab = −0.198, 95% CI = [−0.42 to −0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network‐plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

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“…Recent studies [16][17][18][19] suggest a direct role of white matter (WM) degeneration in AD/MCI pathogenesis. Similarly, diffusion tensor imaging (DTI)-based studies [17] show lower fractional anisotropy (FA) and higher mean diffusivity (MD) in MCI and AD patients compared to controls.…”

Section: Introductionmentioning

confidence: 99%

White Matter Network Alterations in Alzheimer’s Disease Patients

Lama

Lee

2020

Applied Sciences

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Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

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White Matter Changes in Alzheimer’s Disease Revealed by Diffusion Tensor Imaging with TBSS

Cited by 10 publications

References 33 publications

White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease

White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

White Matter Network Alterations in Alzheimer’s Disease Patients

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