Dataset Information

White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum.

ABSTRACT: Instead of assuming a constant relationship between brain abnormalities and memory impairment, we aimed to examine the stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in the Alzheimer's disease (AD) continuum. We assessed grey matter volume, white matter (WM) microstructural measures (free-water (FW) and FW-corrected fractional anisotropy), and functional connectivity of the default mode network (DMN) in 54 amnestic mild cognitive impairment (aMCI) and 46 AD. We employed a novel sparse varying coefficient model to investigate how the associations between abnormal brain measures and memory impairment varied throughout disease continuum. We found lower functional connectivity in the DMN was related to worse memory across AD continuum. Higher widespread white matter FW and lower fractional anisotropy in the fornix showed a stronger association with memory impairment in the early aMCI stage; such WM-memory associations then decreased with increased dementia severity. Notably, the effect of the DMN atrophy occurred in early aMCI stage, while the effect of the medial temporal atrophy occurred in the AD stage. Our study provided evidence to support the hypothetical progression models underlying memory dysfunction in AD cascade and underscored the importance of FW increases and DMN degeneration in early stage of memory deficit.

SUBMITTER: Ji F

PROVIDER: S-EPMC6426923 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum.

Ji Fang F Pasternak Ofer O Ng Kwun Kei KK Chong Joanna Su Xian JSX Liu Siwei S Zhang Liwen L Shim Hee Youn HY Loke Yng Miin YM Tan Boon Yeow BY Venketasubramanian Narayanaswamy N Chen Christopher Li-Hsian CL Zhou Juan Helen JH

Scientific reports 20190318 1

Instead of assuming a constant relationship between brain abnormalities and memory impairment, we aimed to examine the stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in the Alzheimer's disease (AD) continuum. We assessed grey matter volume, white matter (WM) microstructural measures (free-water (FW) and FW-corrected fractional anisotropy), and functional connectivity of the default mode network (DMN) in 54 amnestic mild cognitive im ...[more]

PMID: 30894627

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Project description:Previous studies have shown that the cholinergic nucleus basalis of Meynert and its white matter projections are affected in Alzheimer's disease dementia and mild cognitive impairment. However, it is still unknown whether these alterations can be found in individuals with subjective cognitive decline, and whether they are more pronounced than changes found in conventional brain volumetric measurements. To address these questions, we investigated microstructural alterations of two major cholinergic pathways in individuals along the Alzheimer's disease continuum using an in vivo model of the human cholinergic system based on neuroimaging. We included 402 participants (52 Alzheimer's disease, 66 mild cognitive impairment, 172 subjective cognitive decline and 112 healthy controls) from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study. We modelled the cholinergic white matter pathways with an enhanced diffusion neuroimaging pipeline that included probabilistic fibre-tracking methods and prior anatomical knowledge. The integrity of the cholinergic white matter pathways was compared between stages of the Alzheimer's disease continuum, in the whole cohort and in a CSF amyloid-beta stratified subsample. The discriminative power of the integrity of the pathways was compared to the conventional volumetric measures of hippocampus and nucleus basalis of Meynert, using a receiver operating characteristics analysis. A multivariate model was used to investigate the role of these pathways in relation to cognitive performance. We found that the integrity of the cholinergic white matter pathways was significantly reduced in all stages of the Alzheimer's disease continuum, including individuals with subjective cognitive decline. The differences involved posterior cholinergic white matter in the subjective cognitive decline stage and extended to anterior frontal white matter in mild cognitive impairment and Alzheimer's disease dementia stages. Both cholinergic pathways and conventional volumetric measures showed higher predictive power in the more advanced stages of the disease, i.e. mild cognitive impairment and Alzheimer's disease dementia. In contrast, the integrity of cholinergic pathways was more informative in distinguishing subjective cognitive decline from healthy controls, as compared with the volumetric measures. The multivariate model revealed a moderate contribution of the cholinergic white matter pathways but not of volumetric measures towards memory tests in the subjective cognitive decline and mild cognitive impairment stages. In conclusion, we demonstrated that cholinergic white matter pathways are altered already in subjective cognitive decline individuals, preceding the more widespread alterations found in mild cognitive impairment and Alzheimer's disease. The integrity of the cholinergic pathways identified the early stages of Alzheimer's disease better than conventional volumetric measures such as hippocampal volume or volume of cholinergic nucleus basalis of Meynert.

Project description:BackgroundThis study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer's disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, "radiality", as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column.MethodsWe analyzed neuroimages from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers.ResultsThe results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward.ConclusionRadiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.

Project description:Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Dataset Information

White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum.

Publications

White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum.

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