P1-264: Structural and Diffusion Tensor Imaging in MCI Subjects With Intermediate Risk of Alzheimer’s Disease Based on CSF Profile

2016 ◽  
Vol 12 ◽  
pp. P514-P514
Author(s):  
Michela Pievani ◽  
Moira Marizzoni ◽  
Lorenzo Pini ◽  
Jorge Jovicich ◽  
Flavio Nobili ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Intermediate Risk ◽  
And Diffusion

IC-P-122: Structural and Diffusion Tensor Imaging in MCI Subjects With Intermediate Risk of Alzheimer’s Disease Based on CSF Profile

2016 ◽  
Vol 12 ◽  
pp. P90-P91
Author(s):  
Michela Pievani ◽  
Moira Marizzoni ◽  
Lorenzo Pini ◽  
Jorge Jovicich ◽  
Flavio Nobili ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Intermediate Risk ◽  
And Diffusion

O1-07-02: LONGITUDINAL VOLUMETRIC AND DIFFUSION TENSOR IMAGING IN FAMILIAL ALZHEIMER'S DISEASE

2014 ◽  
Vol 10 ◽  
pp. P141-P142
Author(s):  
Natalie Sarah Ryan ◽  
Ivor Simpson ◽  
Jennifer M. Nicholas ◽  
Kelvin K. Leung ◽  
Shona Clegg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Familial Alzheimer’S Disease ◽  
Familial Alzheimer's Disease ◽  
And Diffusion

scholarly journals Diffusion tensor imaging for Alzheimer's disease: A review of concepts and potential clinical applicability

2009 ◽  
Vol 3 (4) ◽  
pp. 268-274 ◽  
Author(s):  
Luciano de Gois Vasconcelos ◽  
Sonia Maria Dozzi Brucki ◽  
Andrea Parolin Jackowiski ◽  
Orlando Francisco Amodeo Bueno
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Cognitive Assessments ◽  
Clinical Role ◽  
Pubmed Database ◽  
Apparent Diffusion ◽  
And Diffusion ◽  
Diffusion Tensor Imaging Dti

Abstract In view of the urgent need to identify an early and specific biomarker for Alzheimer's disease (AD), a PubMed database search was performed using the terms "Alzheimer disease" and "Diffusion Magnetic Resonance Imaging" to enable review of Diffusion tensor imaging (DTI) concepts and its potential clinical role in AD evaluation. Detailed analysis of selected abstracts showed that the main DTI measures, fractional anisotropy and apparent diffusion coefficient, indicators of fiber tract integrity, provide a direct assessment of WM fibers and may be used as a new biomarker for AD. These findings were found to correlate with cognitive assessments, rates of AD progression and were also able to differentiate among groups including mild cognitive impairment, AD, and other dementias. Despite several consistent DTI findings in AD patients, there is still a lack of knowledge and studies on the DTI field. DTI is not yet ready for clinical use, and requires extensive further research in order to achieve this goal.

Relationship between apathy and diffusion tensor imaging metrics of the brain in Alzheimer's disease

2011 ◽  
Vol 27 (7) ◽  
pp. 722-726 ◽  
Author(s):  
Miho Ota ◽  
Noriko Sato ◽  
Yasuhiro Nakata ◽  
Kunimasa Arima ◽  
Masatake Uno
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
The Brain ◽  
And Diffusion

scholarly journals Combined Structural MR and Diffusion Tensor Imaging Classify the Presence of Alzheimer’s Disease With the Same Performance as MR Combined With Amyloid Positron Emission Tomography: A Data Integration Approach

2022 ◽  
Vol 15 ◽  
Author(s):  
Daniel Agostinho ◽  
Francisco Caramelo ◽  
Ana Paula Moreira ◽  
Isabel Santana ◽  
Antero Abrunhosa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Imaging Modality ◽  
Imaging Modalities ◽  
Support Vector ◽  
Clinical Approach ◽  
Amyloid Pet ◽  
And Diffusion

Background: In recent years, classification frameworks using imaging data have shown that multimodal classification methods perform favorably over the use of a single imaging modality for the diagnosis of Alzheimer’s Disease. The currently used clinical approach often emphasizes the use of qualitative MRI and/or PET data for clinical diagnosis. Based on the hypothesis that classification of isolated imaging modalities is not predictive of their respective value in combined approaches, we investigate whether the combination of T1 Weighted MRI and diffusion tensor imaging (DTI) can yield an equivalent performance as the combination of quantitative structural MRI (sMRI) with amyloid-PET.Methods: We parcellated the brain into regions of interest (ROI) following different anatomical labeling atlases. For each region of interest different metrics were extracted from the different imaging modalities (sMRI, PiB-PET, and DTI) to be used as features. Thereafter, the feature sets were reduced using an embedded-based feature selection method. The final reduced sets were then used as input in support vector machine (SVM) classifiers. Three different base classifiers were created, one for each imaging modality, and validated using internal (n = 41) and external data from the ADNI initiative (n = 330 for sMRI, n = 148 for DTI and n = 55 for PiB-PET) sources. Finally, the classifiers were ensembled using a weighted method in order to evaluate the performance of different combinations.Results: For the base classifiers the following performance levels were found: sMRI-based classifier (accuracy, 92%; specificity, 97% and sensitivity, 87%), PiB-PET (accuracy, 91%; specificity, 89%; and sensitivity, 92%) and the lowest performance was attained with DTI (accuracy, 80%; specificity, 76%; and sensitivity, 82%). From the multimodal approaches, when integrating two modalities, the following results were observed: sMRI+PiB-PET (accuracy, 98%; specificity, 98%; and sensitivity, 99%), sMRI+DTI (accuracy, 97%; specificity, 99%; and sensitivity, 94%) and PiB-PET+DTI (accuracy, 91%; specificity, 90%; and sensitivity, 93%). Finally, the combination of all imaging modalities yielded an accuracy of 98%, specificity of 97% and sensitivity of 99%.Conclusion: Although DTI in isolation shows relatively poor performance, when combined with structural MR, it showed a surprising classification performance which was comparable to MR combined with amyloid PET. These results are consistent with the notion that white matter changes are also important in Alzheimer’s Disease.

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