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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

368
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
368
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
568

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Longitudinal analysis of brain structure using existence probability.

Norihide Maikusa1, Tadanori Fukami2, Hiroshi Matsuda3

  • 1Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.

Brain and Behavior
|October 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) by quantitatively evaluating brain atrophy. The approach shows high accuracy in classifying participants, aiding in early diagnosis.

Keywords:
Alzheimer's diseaseMRIlongitudinal

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Area of Science:

  • Neuroimaging
  • Medical Diagnostics
  • Biostatistics

Background:

  • Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by progressive brain atrophy.
  • Early and accurate detection is crucial for effective management and treatment.

Purpose of the Study:

  • To develop and validate a quantitative method for assessing longitudinal brain structural changes.
  • To enable early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

Main Methods:

  • Utilized existence probabilities from segmented magnetic resonance (MR) images (T1-weighted) of gray matter, white matter, and cerebrospinal fluid.
  • Applied the method to data from 110 normal cognition (NL), 165 MCI, and 82 AD participants from the Japanese Alzheimer's Disease Neuroimaging Initiative database.
  • Calculated coefficients of probability change (CPC) and developed a machine-learning algorithm for classification.

Main Results:

  • Achieved high area under the receiver operating characteristic curve (ROC) values (up to 0.908) for atrophy indicators using CPC.
  • Demonstrated maximum classification accuracies of 92.1% for NL-AD and 81.2% for NL-MCI using CPC values over a six-month period.

Conclusions:

  • The proposed quantitative method effectively detects longitudinal brain structural changes indicative of AD and MCI.
  • The findings support the method's utility for early diagnosis and intervention in neurodegenerative diseases.