NDLI: A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory

Content Provider	IEEE Xplore Digital Library
Author	Ruijuan Yun Chung-Chih Lin Shuicai Wu Chu-Chung Huang Ching-Po Lin Yi-Ping Chao
Copyright Year	2013
Description	Author affiliation: Coll. of Life Sci. & Bio-Eng., Beijing Univ. of Technol., Beijing, China (Ruijuan Yun; Shuicai Wu) \|\| Dept. of Comput. Sci. & Inf. Eng., Chang Gung Univ., Taoyuan, Taiwan (Chung-Chih Lin; Yi-Ping Chao) \|\| Inst. of Neurosci., Nat. Yang-Ming Univ., Taipei, Taiwan (Ching-Po Lin) \|\| Inst. of Radiol. Sci., Nat. Yang-Ming Univ., Taipei, Taiwan (Chu-Chung Huang)
Abstract	In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).
Starting Page	527
Ending Page	530
File Size	1220114
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Brain modeling Diffusion tensor imaging Feature extraction Dementia Educational institutions mild cognitive impairment (MCI) Diffusion tensor imaging (DTI) graph theory cognitive abilities screening instrument (CASI) linear regression model Gaussian processes model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI)

Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

The European DTI Study on Dementia — A multicenter DTI and MRI study on Alzheimer's disease and Mild Cognitive Impairment

Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease

A Gaussian Mixture Model Based Diagnosis of Alzheimer's Using Diffusion Tensor Imaging

Screening for Mild Cognitive Impairment: Comparison of "MCI Specific" Screening Instruments.

Neuroinflammation and brain functional disconnection in Alzheimer's disease

Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI).

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory

Similar Documents

Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI)

Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

The European DTI Study on Dementia — A multicenter DTI and MRI study on Alzheimer's disease and Mild Cognitive Impairment

Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease

A Gaussian Mixture Model Based Diagnosis of Alzheimer's Using Diffusion Tensor Imaging

Screening for Mild Cognitive Impairment: Comparison of "MCI Specific" Screening Instruments.

Neuroinflammation and brain functional disconnection in Alzheimer's disease

Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI).

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory