1
|
Milner T, Brown MRG, Jones C, Leung AWS, Brémault-Phillips S. Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring. Front Digit Health 2024; 6:1265846. [PMID: 38510280 PMCID: PMC10952843 DOI: 10.3389/fdgth.2024.1265846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitoring MCI with traditional biomarkers is noteworthy. Digital biomarkers hold new promise in understanding MCI. Identifying digital biomarkers specifically for MCI, however, is complex. The biomarker profile for MCI is expected to be multidimensional with multiple phenotypes based on different etiologies. Advanced methodological approaches, such as high-dimensional statistics and deep machine learning, will be needed to build these multidimensional digital biomarker profiles for MCI. Comparing patients to these MCI phenotypes in clinical practice can assist clinicians in better determining etiologies, some of which may be reversible, and developing more precise care plans. Key considerations in developing reliable multidimensional digital biomarker profiles specific to an MCI population are also explored.
Collapse
Affiliation(s)
- Tracy Milner
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Matthew R. G. Brown
- Department of ComputingScience, University of Alberta, Edmonton, AB, Canada
- Heroes in Mind, Advocacy and Research Consortium (HiMARC), University of Alberta, Edmonton, AB, Canada
| | - Chelsea Jones
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
- Heroes in Mind, Advocacy and Research Consortium (HiMARC), University of Alberta, Edmonton, AB, Canada
| | - Ada W. S. Leung
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Suzette Brémault-Phillips
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
- Heroes in Mind, Advocacy and Research Consortium (HiMARC), University of Alberta, Edmonton, AB, Canada
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| |
Collapse
|
2
|
Mousa D, Zayed N, Yassine IA. Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data. Sci Rep 2023; 13:21559. [PMID: 38057476 PMCID: PMC10700324 DOI: 10.1038/s41598-023-48693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.
Collapse
Affiliation(s)
- Doaa Mousa
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.
| | - Nourhan Zayed
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt
- Mechanical Engineering Department, The British University in Egypt, Cairo, Egypt
| | - Inas A Yassine
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
| |
Collapse
|