Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Christos Davatzikos; Yong Fan; Xiaoying Wu; Dinggang Shen; Susan M. Resnick

doi:10.1016/j.neurobiolaging.2006.11.010

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Christos Davatzikos, Yong Fan, Xiaoying Wu, Dinggang Shen, Susan M. Resnick

Research output: Contribution to journal › Article › peer-review

346 Citations (Scopus)

Abstract

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Original language	English
Pages (from-to)	514-523
Number of pages	10
Journal	Neurobiology of Aging
Volume	29
Issue number	4
DOIs	https://doi.org/10.1016/j.neurobiolaging.2006.11.010
Publication status	Published - 2008 Apr

Bibliographical note

Funding Information:
We gratefully acknowledge the assistance of Yang An, M.Sc., National Institute on Aging, in statistical analysis, the BLSA participants and staff, the staff of the MRI facility at Johns Hopkins Hospital, and Dr. Juan Troncoso, Department of Pathology, Johns Hopkins University, for neuropathologic assessments. This study was supported in part by NIH funding sources N01-AG-3-2124 and R01-AG14971 and by the Intramural Research Program of the NIH, National Institute on Aging.

Keywords

MCI
MRI
Pattern recognition
Prodromal Alzheimer's disease

ASJC Scopus subject areas

General Neuroscience
Ageing
Clinical Neurology
Developmental Biology
Geriatrics and Gerontology

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10.1016/j.neurobiolaging.2006.11.010

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title = "Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging",

abstract = "We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.",

keywords = "MCI, MRI, Pattern recognition, Prodromal Alzheimer's disease",

author = "Christos Davatzikos and Yong Fan and Xiaoying Wu and Dinggang Shen and Resnick, {Susan M.}",

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doi = "10.1016/j.neurobiolaging.2006.11.010",

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AU - Shen, Dinggang

AU - Resnick, Susan M.

N1 - Funding Information: We gratefully acknowledge the assistance of Yang An, M.Sc., National Institute on Aging, in statistical analysis, the BLSA participants and staff, the staff of the MRI facility at Johns Hopkins Hospital, and Dr. Juan Troncoso, Department of Pathology, Johns Hopkins University, for neuropathologic assessments. This study was supported in part by NIH funding sources N01-AG-3-2124 and R01-AG14971 and by the Intramural Research Program of the NIH, National Institute on Aging.

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N2 - We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

AB - We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

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KW - Pattern recognition

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Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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