scholarly journals Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes

2018 ◽  
Author(s):  
Yun Wang ◽  
Chenxiao Xu ◽  
Ji-Hwan Park ◽  
Seonjoo Lee ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Mri ◽  
Structural Mri ◽  
List Type ◽  
Csf Biomarker ◽  
Multimodal Mri ◽  
And Diffusion

ABSTRACTAccurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.HighlightsWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.In predicting the progression from MCI to AD, the morphometry model showed the best performance.Two independent clinical datasets were used in this study: one for model building, the other for generalizability testing.

scholarly journals Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

2018 ◽  
Author(s):  
Yun Wang ◽  
Chenxiao Xu ◽  
Ji-Hwan Park ◽  
Seonjoo Lee ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
High Throughput ◽  
Classification Accuracy ◽  
Diffusion Mri ◽  
Single Site ◽  
Csf Biomarker ◽  
Optimal Classification ◽  
Multimodal Mri ◽  
And Diffusion

ABSTRACTAccurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multi-site dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD.

scholarly journals Joint Assessment of Structural, Perfusion, and Diffusion MRI in Alzheimer's Disease and Frontotemporal Dementia

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zhang ◽  
Norbert Schuff ◽  
Christopher Ching ◽  
Duygu Tosun ◽  
Wang Zhan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Diffusion Tensor ◽  
Healthy Control ◽  
Multimodal Mri ◽  
Imaging Markers ◽  
And Diffusion ◽  
Joint Assessment ◽  
Diffusion Tensor Imaging Dti

Most MRI studies of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases. Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4 Tesla MRI. GM loss and GM hypoperfusion were measured using high-resolution T1 and arterial spin labeling MRI (ASL-MRI). WM degradation was measured with diffusion tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD, structural and functional damages were similar. Joint assessments of multimodal MRI have potential value to provide new imaging markers for improved differential diagnoses between FTD and AD.

Histochemical visualization and diffusion MRI at 7 Tesla in the TgCRND8 transgenic model of Alzheimer’s disease

2010 ◽  
Vol 215 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Jonathan D. Thiessen ◽  
Kathryn A. C. Glazner ◽  
Solmaz Nafez ◽  
Angela E. Schellenberg ◽  
Richard Buist ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Mri ◽  
7 Tesla ◽  
Transgenic Model ◽  
Model Of Alzheimer’S Disease ◽  
And Diffusion

scholarly journals The Temporal Relationships between White Matter Hyperintensities, Neurodegeneration, Amyloid β, and Cognition

2020 ◽  
Author(s):  
Mahsa Dadar ◽  
Richard Camicioli ◽  
Simon Duchesne ◽  
D. Louis Collins ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Cognitive Decline ◽  
White Matter Hyperintensities ◽  
Amyloid Β ◽  
Disease Assessment ◽  
White Matter Hyperintensity ◽  
List Type ◽  
Temporal Relationships

ABSTRACTINTRODUCTIONCognitive decline in Alzheimer’s disease is associated with amyloid-β accumulation, neurodegeneration and cerebral small vessel disease, but the temporal relationships between these factors is not well established.METHODSData included white matter hyperintensity (WMH) load, grey matter (GM) atrophy and Alzheimer’s Disease Assessment Scale-Cognitive-Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (Aβ1-42) for 461 participants from the Alzheimer’s Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships between baseline WMH, GM, and Aβ1-42 to changes in WMH, GM, Aβ1-42, and cognition at one-year follow-up.RESULTSBaseline WMHs and Aβ1-42 predicted WMH increase and GM atrophy. Baseline WMHs, GM, and Aβ1-42 predicted worsening cognition. Only baseline Aβ1-42 predicted change in Aβ1-42.DISCUSSIONBaseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline Aβ1-42 predicted WMH increase, suggesting a potential role of amyloid in WM damage.Research in ContextSystematic Review: Both amyloid β and neurodegeneration are primary pathologies in Alzheimer’s disease. White matter hyperintensities (indicative of presence of cerebrovascular disease) might also be part of the pathological changes in Alzheimer’s. However, the temporal relationship between white matter hyperintensities, amyloid β, neurodegeneration, and cognitive decline is still unclear.Interpretation: Our results establish a potential temporal order between white matter hyperintensities, amyloid β, neurodegeneration, and cognitive decline, showing that white matter hyperintensities precede neurodegeneration and cognitive decline. The results provide some evidence that amyloid β deposition, in turn, precedes accumulation of white matter hyperintensities.Future Directions: The current findings reinforce the need for future longitudinal investigations of the mechanisms through which white matter hyperintensities impact the aging population in general and Alzheimer’s disease patients, in particular.

scholarly journals White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI

Brain ◽  
2009 ◽  
Vol 132 (9) ◽  
pp. 2579-2592 ◽  
Author(s):  
Y. Zhang ◽  
N. Schuff ◽  
A.-T. Du ◽  
H. J. Rosen ◽  
J. H. Kramer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Frontotemporal Dementia ◽  
Diffusion Mri ◽  
White Matter Damage

Neuroimage Classification for Early Diagnosis of Alzheimer’s Disease

2012 ◽  
pp. 336-350
Author(s):  
Yong Fan ◽  
Christos Davatzikos
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Psychiatric Disorders ◽  
General Framework ◽  
Disease Onset ◽  
Structural Mri ◽  
Early Prediction ◽  
Psychometric Assessment ◽  
Combining Information

Diagnostic criteria for neurological and psychiatric disorders are typically based on clinical and psychometric assessment, which might not be effective for early detection of the disease onset. For brain disorders such as Alzheimer’s Disease (AD), neuroimaging can potentially play an important role in the development of imaging-based biomarkers. Following voxel-wise univariate neuroimage analysis methods, machine learning and pattern recognition based neuroimage analysis techniques have been increasingly adopted in neuroimaging studies of neurological and psychiatric disorders, aiming to provide tools that classify individuals, based on their neuroimaging scans, rather than detect statistical group difference. The machine learning based methods, optimally combining information of multiple measures derived from images, have demonstrated promising performance in diagnosis of AD and early prediction of conversion of Mild Cognitive Impairment (MCI) individuals. This chapter introduces the general framework of such techniques with a focus on structural MRI analyses and their applications to studies of AD.

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