Abstract
Introduction: In recent years, resting-state functional magnetic resonance imaging (rsfMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer's disease.
Materials and Methods: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features, we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to a healthy category, mild stage of the disease or Alzheimer's stage.
Results: The obtained classification accuracy for the proposed method is more than 97.5%.
Conclusion: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer’s patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.
Keywords: Functional magnetic resonance imaging (fMRI), alzheimer disease, resting-state network, latent low rank representation, SVM.
Current Signal Transduction Therapy
Title:Alzheimer Disease Diagnosis from fMRI Images Based on Latent Low Rank Features and Support Vector Machine (SVM)
Volume: 16 Issue: 2
Author(s): Nastaran Shahparian, Mehran Yazdi*Mohammad Reza Khosravi
Affiliation:
- School of Electrical and Computer Engineering, Shiraz University,Iran
Keywords: Functional magnetic resonance imaging (fMRI), alzheimer disease, resting-state network, latent low rank representation, SVM.
Abstract:
Introduction: In recent years, resting-state functional magnetic resonance imaging (rsfMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer's disease.
Materials and Methods: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features, we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to a healthy category, mild stage of the disease or Alzheimer's stage.
Results: The obtained classification accuracy for the proposed method is more than 97.5%.
Conclusion: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer’s patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.
Export Options
About this article
Cite this article as:
Shahparian Nastaran, Yazdi Mehran *, Khosravi Reza Mohammad , Alzheimer Disease Diagnosis from fMRI Images Based on Latent Low Rank Features and Support Vector Machine (SVM), Current Signal Transduction Therapy 2021; 16 (2) . https://dx.doi.org/10.2174/1574362414666191202144116
DOI https://dx.doi.org/10.2174/1574362414666191202144116 |
Print ISSN 1574-3624 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-389X |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Essential Oil Chemical Composition of Vitex agnus-castus L. from Southern-West Algeria and Its Antimicrobial Activity
Current Bioactive Compounds One-Pot Four-Component Synthesis of 3-(1,3,4-Thiadiazol-2-ylamino)-3-arylpropanoates
Letters in Organic Chemistry MicroRNAs: The New Challenge for Traumatic Brain Injury Diagnosis
Current Neuropharmacology The Role of Annexin A1 and Formyl Peptide Receptor 2/3 Signaling in Chronic Corticosterone-Induced Depression-Like behaviors and Impairment in Hippocampal-Dependent Memory
CNS & Neurological Disorders - Drug Targets Electrochemical Biosensor for the Detection of Glycated Albumin
Current Alzheimer Research A Friend in Need May Not be a Friend Indeed: Role of Microglia in Neurodegenerative Diseases
CNS & Neurological Disorders - Drug Targets Synthesis of Novel N-Substituted Tetrazolonaphthalenes: Agomelatin Analogs
Letters in Organic Chemistry Nanoparticle Based Gene Therapy Approach: A Pioneering Rebellion in the Management of Psychiatric Disorders
Current Gene Therapy Editorial [Hot Topic: Technological Addictions: Are These the New Addictions?]
Current Psychiatry Reviews Protein Interaction Domains: Structural Features and Drug Discovery Applications (Part 2)
Current Medicinal Chemistry Molecular Rationales for Signal Transduction Therapy and Chemoprevention of BRCA1-Related Breast and Ovarian Tumours
Current Signal Transduction Therapy Role of Ghrelin in Drug Abuse and Reward-Relevant Behaviors: A Burgeoning Field and Gaps in the Literature
Current Drug Abuse Reviews Current Diagnostic Modalities for Vulnerable Plaque Detection
Current Pharmaceutical Design Brain SPECT with Perfusion Radiopharmaceuticals and Dopaminergic System Radiocompounds in Dementia Disorders
Current Alzheimer Research Lipophilic Analogs of Thioflavin S as Novel Amyloid-Imaging Agents
Current Alzheimer Research Psychological Stress and the Development of Heart Disease
Current Psychiatry Reviews Dexmedetomidine: A Novel Anesthetic Agent for Middle Ear Surgery
Recent Patents on CNS Drug Discovery (Discontinued) Telomere Maintenance as Therapeutic Target in Embryonal Tumours
Anti-Cancer Agents in Medicinal Chemistry Impact of Salt Concentration and pH on Surface Charged Residues: Controlling Protein Association Pathways in Carboxylesterase EstGtA2
Protein & Peptide Letters The Autism Candidate Gene Neurobeachin Encodes a Scaffolding Protein Implicated in Membrane Trafficking and Signaling
Current Molecular Medicine