Biomarkers in Neurodegenerative Diseases
Abstract
The past decade has seen tremendous efforts in biomarker discovery and validation for neurodegenerative diseases. The source and type of biomarkers has continued to grow for central nervous system diseases, from biofluid-based biomarkers (blood or cerebrospinal fluid (CSF)), to nucleic acids, tissue, and imaging. While DNA remains a predominant biomarker used to identify familial forms of neurodegenerative diseases, various types of RNA have more recently been linked to familial and sporadic forms of neurodegenerative diseases during the past few years. Imaging approaches continue to evolve and are making major contributions to target engagement and early diagnostic biomarkers. Incorporation of biomarkers into drug development and clinical trials for neurodegenerative diseases promises to aid in the development and demonstration of target engagement and drug efficacy for neurologic disorders. This review will focus on recent advancements in developing biomarkers for clinical utility in Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).
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Nonverbal speech analysis differentiated patients with severe schizophrenia receiving cognitive remediation therapy from control participants with less severe schizophrenia.
“In this study, patients with severe schizophrenia, receiving Cognitive Remediation Therapy (CRT), were differentiated from control participants with less severe schizophrenia (no CRT recommended) by non-verbal speech analysis.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [39]
Nonverbal speech cues such as interruptions, interjections, natural turns, and response times can indicate irregular speech patterns.
“Additionally, nonverbal speech cues, e.g. counting the number of interruptions, interjections, natural turns, and response times can also indicate identifying features of irregular speech patterns [39].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [39]
Automated acoustic metrics can be extracted from speech samples that are not labeled.
“Automated acoustic metrics can be extracted from non-labeled speech samples [35], [39], [38], [102].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [39]
Nonverbal cues classified control participants from those with more severe schizophrenia with approximately 90% accuracy in a preliminary sample of 8 severe and 7 less-severe participants.
“Preliminary results from this study with participants with severe schizophrenia (n = 8) and less-severe forms of the disease (n = 7) indicate that these nonverbal cues show approximately 90% accuracy in classifying control participants from those with more severe forms of schizophrenia.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [39]
Changes in the outward flow of speech may occur before measurable language-based changes.
“Most interestingly, changes in the outward flow of speech may precede measurable language-based changes [39], [42].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [39]
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- Authors
- Jeromin, A., & Bowser, R.
- Affiliations
- Barrow Neurological Institute (US)
- Corresponding author
- Bowser, Robert
- ORCID
- Bowser, Robert
- Venue
- Advances in Neurobiology
- Publisher
- Springer International Publishing
- Published
- 2017
- Volume
- 15
- Pages
- 491-528
- ISSN
- 2190-5215 (ISSN-L)
- 2190-5223
- OpenAlex
- W2733688271
- Cited by
- 92
- References
- 260
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 11 · 2023: 20 · 2022: 16 · 2021: 13 · 2020: 5 · 2019: 7 · 2018: 8
- FWCI
- 17.51
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (RC1NS068179, NS068179, NS061867)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
- Keywords
- biomarkers, neurodegenerative diseases, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, cerebrospinal fluid biomarkers, blood biomarkers, RNA biomarkers, DNA biomarkers, neuroimaging biomarkers, familial neurodegenerative disease, drug development, clinical trials, target engagement
- MeSH terms
- Alzheimer Disease, Amyotrophic Lateral Sclerosis, Brain, DNA, Humans, Parkinson Disease, Prognosis, RNA, Biomarkers, Neurodegenerative Diseases
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