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Cited by 85Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity StudiesAlzheimer's disease research and treatments

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease

Martí-Juan, Gerard, Sanroma-Guell, Gerard, Piella, Gemma·2020·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2020.105348
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Abstract

Background and objectives Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. Methods We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. Results After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. Conclusions Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.

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  • One widely used hypothesis-driven analysis assesses statistical differences of tracts of interest between groups using statistical group-wise comparison methods such as Student's t-test, ANOVA, or more advanced methods.

    “Usually, given a quantitative measure of interest, a selected summary statistic (e.g. mean of the chosen microstructural measure) is extracted from the tract(s)-of-interest and is used to compute the level of group differences (e.g., the p-value) using statistical group-wise comparison methods such as Student’s t-test, ANOVA, or other more advanced statistical analysis methods (Habeck and Stern, 2”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Martí-Juan et al., 2020

    Section 4.5 (p. 16) discusses statistical methods for detecting longitudinal MRI group differences and names ANOVA as a comparator. It does not describe hypothesis-driven comparisons of tracts of interest, extraction of a summary microstructural measure, or use of Student’s t-test or p-values. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 30

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Authors
Martí-Juan, G., Sanroma-Guell, G., & Piella, G.
Affiliations
Universitat Pompeu Fabra (ES)
ORCID
Piella, Gemma
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2020-06
Volume
189
Pages
105348
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2020.105348
OpenAlex
W3002097015
Cited by
85
References
12
Citations by year
2026: 6 · 2025: 8 · 2024: 22 · 2023: 19 · 2022: 19 · 2021: 6 · 2020: 5
FWCI
2.00
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Computer Methods and Programs in Biomedicine (publishedVersion)
  • PubMed (publishedVersion)
  • DZNE Pub (submittedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
Funders
  • Ministerio de Economía y Competitividad
  • Fundació la Marató de TV3
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, longitudinal neuroimaging, machine learning, statistical learning, disease progression modeling, computer-aided diagnosis, missing data, longitudinal analysis, neuroimaging data
MeSH terms
Machine Learning, Alzheimer Disease, Humans, Longitudinal Studies, Disease Progression, Neuroimaging

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