Evidence map›Paper›PMID 29085292›Full record

ArticleFrontiers in aging neuroscience2017

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers.

Hao Guan, Tao Liu, Jiyang Jiang, Dacheng Tao, Jicong Zhang, Haijun Niu, Wanlin Zhu, Yilong Wang, Jian Cheng, Nicole A Kochan and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in aging neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Heterogeneity in Alzheimer's Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022
    Review
  8. Article
  9. Mild Cognitive Impairment and Mild Dementia: The Role ofPharmaceuticals (Basel, Switzerland) · 2021
    Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hao GuanSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Tao LiuSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Jiyang JiangCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Dacheng TaoUBTech Sydney Artificial Intelligence Institute, Faculty of Engineering and Information Technologies, University of Sydney, Darlington, NSW, Australia.
Jicong ZhangSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Haijun NiuSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Wanlin ZhuCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Yilong WangBeijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jian ChengNIBIB, NICHD, National Institutes of Health, Bethesda, MD, United States.
Nicole A KochanCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Henry BrodatyCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Perminder SachdevCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Wei WenCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.

Indexed as

biomarkerearly diagnosisfeature selectionlongitudinal datamachine learningmild cognitive impairmentMRI

Identifiers

PMID29085292
PMCPMC5649145

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.