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47/100

✓ Peer-reviewed alzhub usefulness score 47 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Casanova, R., Whitlow, C. T., Wagner, B., Williamson, J., Shumaker, S. A., Maldjian, J. A., et al. (2011). High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization. Frontiers in neuroinformatics. https://doi.org/10.3389/fninf.2011.00022

Vancouver

Casanova R, Whitlow CT, Wagner B, Williamson J, Shumaker SA, Maldjian JA, et al. High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization. Frontiers in neuroinformatics. 2011. doi:10.3389/fninf.2011.00022. PMID: 22016732.

BibTeX

@article{casanova2011-22016732,
  author  = {Casanova, R. and Whitlow, C. T. and Wagner, B. and Williamson, J. and Shumaker, S. A. and Maldjian, J. A. and Espeland, M. A.},
  title   = {{High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization}},
  journal = {Frontiers in neuroinformatics},
  year    = {2011},
  doi     = {10.3389/fninf.2011.00022},
  pmid    = {22016732},
  url     = {https://doi.org/10.3389/fninf.2011.00022}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

Key findings

Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively).
We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data.
Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.

Why it matters

This study used penalized logistic regression to classify structural MRI images of 49 cognitively normal and 49 Alzheimer disease subjects, achieving higher accuracy with gray matter volume maps than white matter maps. It is a machine learning classification method for diagnosing Alzheimer disease, not a treatment, cure or prevention.

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Study details

Participants98
Effect size82.5%
Evidence strengthLow

How this study scored 47/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in neuroinformatics)
Journal's citation record	1 / 4	Frontiers in Neuroinformatics: journal h-index 111 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	1 / 5	2011 — within 20 year(s) of the newest study here
Sample size	1 / 4	98 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	4 / 7	Cited 92 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	5.4 citations a year over 17 year(s) since 2011
Impact for its field and year	3 / 6	Cited 3.2× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 7 other studies in this library
Still being cited	1 / 4	Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Mark Andrew Espeland: h-index 90 (OpenAlex, whole career)
Institution's track record	1 / 3	Wake Forest University: institution h-index 835 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 22016732)
Free to read	2 / 2	Free to read
Total	47 / 100	—

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