Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Chen, Yu; Wang, Yifei; Song, Zeyu; Yang, Fan; Gao, Tianxin; Tang, Xiaoying

doi:10.1016/j.arr.2023.101911

Cited by 42 publications

(31 citation statements)

References 164 publications

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“…This study identified 8 fibre bundle features that are most related to the progression of MCI, and these findings are consistent with the white matter microstructure changes related to brain regions during the progression from MCI to AD described in a meta-analysis [12]. Both the left and right hippocampal cingulate gyri are involved in the progression of MCI; previous studies have shown that the hippocampus is a key brain structure related to memory, and the connection between its cingulate gyrus and other brain regions is believed to play an important role in memory function [13]. Therefore, our research results suggest that abnormalities in the striatum, cingulate gyrus, and hippocampus may be related to decreased memory function during the progression of MCI to AD, and this further confirms the conclusion of Fellgiebel and Yakushev [14], who found that the diffusion coefficient of the hippocampus derived from DTI is a better predictor of the extent of situational memory impairment in early AD patients than the conventional volume index.…”

Section: Discussionsupporting

confidence: 86%

Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram

Cheng,

Li,

Peng

et al. 2023

Eur Neurol

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Objective: To construct and validate a nomogram that combines diffusion tensor imaging (DTI) parameters and clinically relevant features for predicting the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Method: A retrospective analysis was conducted on the MRI and clinical data of 121 MCI patients, of whom 32 progressed to AD during a four-year follow-up period. The MCI patients were divided into training and validation sets at a ratio of 7:3. DTI features were extracted from MCI patient data in the training set, and their dimensionality was reduced to construct a radiomics signature (RS). Then, combining the RS with independent predictors of MCI disease progression, a joint model was constructed, and a nomogram was generated. Finally, the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the diagnostic and clinical efficacy of the nomogram based on the data from the validation set. Result: The AUCs of the RS in the training and validation sets were 0.81 and 0.84, with sensitivities of 0.87 and 0.78 and specificities of 0.71 and 0.81, respectively. Multiple logistic regression analysis showed that the RS, clinical dementia rating scale score, and Alzheimer's disease assessment scale score were the independent predictors of progression and thus used to construct the nomogram. The AUCs of the nomogram in the training and validation sets were 0.89 and 0.91, respectively, with sensitivities of 0.78 and 0.89 and specificities of 0.90 and 0.88, respectively. DCA showed that the nomogram was the most valuable model for predicting the progression of MCI to AD and that it provided greater net benefits than other analysed models. Conclusion: Changes in white matter fibre bundles can serve as predictive imaging markers for MCI disease progression, and the combination of white matter DTI features and relevant clinical features can be used to construct a nomogram with important predictive value for MCI disease progression.

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Section: Discussionsupporting

confidence: 86%

Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram

Cheng,

Li,

Peng

et al. 2023

Eur Neurol

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“…quently 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.…”

supporting

confidence: 74%

“…With the development of brain imaging and network modeling techniques, human brain connectomics has provided valuable insights into understanding dynamic neuronal network changes at the system level in various brain disorders (Bassett & Sporns, 2017). As a disconnection syndrome, AD is characterized by both structural and functional network changes throughout its continuum (Chen et al, 2023;Yu et al, 2021). With diffusion MRI, our previous studies have demonstrated decreased topological efficiency of the white matter (WM) structural network in amnestic mild cognitive impairment (MCI) and subjective cognitive decline (SCD) patients (Shu et al, 2018), especially involving key regions of the default mode network and striatum network (Rasero et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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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“…Our study demonstrates that FA alone exhibited weak, nonsignificant strength of association with nearly all the study's outcome measurements. Recently, a study conducted on the ADNI3 cohort by Chen et al (2023) [41] demonstrated that FA within specific MTL regions, particularly the hippocampal cingulum, exhibited correlations with tau burden. Our findings provide a more comprehensive examination of the compartmental complexity of WM, revealing that a stronger and more detailed association can be derived with NODDI than from the FA metric measurement alone.…”

Section: Discussionmentioning

confidence: 99%

“…However, FA can be a somewhat ambiguous metric, with the decline potentially attributable to various structural factors, including demyelination or a decrease in axon density [42]. While the development of alternative diffusion analysis pipelines has helped mitigate some of these limitations associated with DTI, the application of NODDI has emerged as a valuable method for assessing microstructural changes [41]. Our study demonstrates that FA alone exhibited weak, non-significant strength of association with nearly all the study’s outcome measurements.…”

Section: Discussionmentioning

confidence: 99%

NODDI-derived measures of microstructural integrity in medial temporal lobe white matter pathways are associated with Alzheimer’s disease pathology and cognitive outcomes

Parker,

Adams,

Kim

et al. 2023

Preprint

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INTRODUCTION: Diffusion tensor imaging has been used to assess white matter (WM) changes in the early stages of Alzheimer's disease (AD). However, the tensor model is necessarily limited by its assumptions. Neurite Orientation Dispersion and Density Imaging (NODDI) can offer insights into microstructural features of WM change. We assessed whether NODDI more sensitively detects AD-related changes in medial temporal lobe WM than traditional tensor metrics. METHODS: Standard diffusion and NODDI metrics were calculated for medial temporal WM tracts from 199 older adults drawn from ADNI3 who also received PET to measure pathology and neuropsychological testing. RESULTS: NODDI measures in medial temporal tracts were more strongly correlated to cognitive performance and pathology than standard measures. The combination of NODDI and standard metrics exhibited the strongest prediction of cognitive performance in random forest analyses. CONCLUSIONS: NODDI metrics offer additional insights into contributions of WM degeneration to cognitive outcomes in the aging brain.

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Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Cited by 42 publications

References 164 publications

Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram

Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram

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

NODDI-derived measures of microstructural integrity in medial temporal lobe white matter pathways are associated with Alzheimer’s disease pathology and cognitive outcomes

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