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Zhang JJ, Sánchez Vidaña DI, Chan JNM, Hui ESK, Lau KK, Wang X, Lau BWM, Fong KNK. Biomarkers for prognostic functional recovery poststroke: A narrative review. Front Cell Dev Biol 2023; 10:1062807. [PMID: 36699006 PMCID: PMC9868572 DOI: 10.3389/fcell.2022.1062807] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.
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Affiliation(s)
- Jack Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | | | - Jackie Ngai-Man Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Edward S. K. Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kui Kai Lau
- Division of Neurology, Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xin Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Benson W. M. Lau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kenneth N. K. Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Häkkinen S, Chu SA, Lee SE. Neuroimaging in genetic frontotemporal dementia and amyotrophic lateral sclerosis. Neurobiol Dis 2020; 145:105063. [PMID: 32890771 DOI: 10.1016/j.nbd.2020.105063] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/30/2020] [Accepted: 08/26/2020] [Indexed: 02/06/2023] Open
Abstract
Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) have a strong clinical, genetic and pathological overlap. This review focuses on the current understanding of structural, functional and molecular neuroimaging signatures of genetic FTD and ALS. We overview quantitative neuroimaging studies on the most common genes associated with FTD (MAPT, GRN), ALS (SOD1), and both (C9orf72), and summarize visual observations of images reported in the rarer genes (CHMP2B, TARDBP, FUS, OPTN, VCP, UBQLN2, SQSTM1, TREM2, CHCHD10, TBK1).
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Affiliation(s)
- Suvi Häkkinen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Stephanie A Chu
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Suzee E Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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