Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective

Topic: Risk, prevention and care in population studies

Summary There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.

Key findings

• Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia.
• We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level.
• There is some evidence that, in particular, support vector machines seem to be promising.
• However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation.
• Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.

Why it matters

This review of machine learning applied to functional MRI data for brain and psychiatric disorders concluded that most studies are explorative proof-of-concept efforts and the field is still far from real clinical application. It does not test any treatment, cure or prevention for Alzheimer's disease.

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Study details

• Study designmeta-analysis
• Evidence strengthHigh

Read the original

• DOI: 10.3174/ajnr.a3713
• PubMed: 24029388
• Original source →
• Free full text: American Journal of Neuroradiology →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{sundermann2014-24029388,
  author  = {Sundermann, B. and Herr, D. and Schwindt, W. and Pfleiderer, B.},
  title   = {{Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective}},
  journal = {AJNR. American journal of neuroradiology},
  year    = {2014},
  doi     = {10.3174/ajnr.a3713},
  pmid    = {24029388},
  url     = {https://doi.org/10.3174/ajnr.a3713}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease Brain : a journal of neurology · 2010-04-21 · usefulness score 64/100 · Getting a diagnosis
• Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging Radiology · 2011-01-19 · usefulness score 47/100 · Brain scans and imaging

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• Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review Human brain mapping · 2021-05-04 · usefulness score 71/100

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