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Статті в журналах з теми "Neuroimaging biomarkers"

Автор: Grafiati

Опубліковано: 15 січня 2025

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1

Hager, Brandon M., and Matcheri S. Keshavan. "Neuroimaging Biomarkers for Psychosis." Current Behavioral Neuroscience Reports 2, no. 2 (March 6, 2015): 102–11. http://dx.doi.org/10.1007/s40473-015-0035-4.

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2

Mishra, Asht Mangal, Harrison Bai, Alexandra Gribizis, and Hal Blumenfeld. "Neuroimaging biomarkers of epileptogenesis." Neuroscience Letters 497, no. 3 (June 2011): 194–204. http://dx.doi.org/10.1016/j.neulet.2011.01.076.

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3

Mackey, Sean, Henry T. Greely, and Katherine T. Martucci. "Neuroimaging-based pain biomarkers." PAIN Reports 4, no. 4 (2019): e762. http://dx.doi.org/10.1097/pr9.0000000000000762.

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4

Risacher, Shannon L. "Neuroimaging in Dementia." CONTINUUM: Lifelong Learning in Neurology 30, no. 6 (December 2024): 1761–89. https://doi.org/10.1212/con.0000000000001509.

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ABSTRACT OBJECTIVE This article captures the current literature regarding the use of neuroimaging measures to study neurodegenerative diseases, including early- and late-onset Alzheimer disease, vascular cognitive impairment, frontotemporal lobar degeneration disorders, dementia with Lewy bodies, and Parkinson disease dementia. In particular, the article highlights significant recent changes in novel therapeutics now available for the treatment of Alzheimer disease and in defining neurodegenerative disease using biological frameworks. Studies summarized include those using structural and functional MRI (fMRI) techniques, as well as metabolic and molecular emission tomography imaging (ie, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]). LATEST DEVELOPMENTS Neuroimaging measures are considered essential biomarkers for the detection and diagnosis of most neurodegenerative diseases. The recent approval of anti-amyloid antibody therapies has highlighted the importance of MRI and PET techniques in treatment eligibility and monitoring for associated side effects. Given the success of the initial biomarker-based classification system for Alzheimer disease (the amyloid, tau, neurodegeneration [A/T/N] framework), researchers in vascular cognitive impairment have created similar techniques for biomarker-based diagnosis. Further, the A/T/N framework for Alzheimer disease has been updated to include several pathologic targets for biomarker detection. ESSENTIAL POINTS Neurodegenerative diseases have a major health impact on millions of patients around the world. Neuroimaging biomarkers are rapidly becoming major diagnostic tools for the detection, monitoring, and treatment of neurodegenerative diseases. This article educates readers about the current literature surrounding the use of neuroimaging tools in neurodegenerative diseases along with recent important developments in the field.

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5

Russo, Antonio, Marcello Silvestro, Alessandro Tessitore, and Gioacchino Tedeschi. "Functional Neuroimaging Biomarkers in Migraine: Diagnostic, Prognostic and Therapeutic Implications." Current Medicinal Chemistry 26, no. 34 (December 12, 2019): 6236–52. http://dx.doi.org/10.2174/0929867325666180406115427.

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Background: In current migraine clinical practice, conventional neuroimaging examinations are often sought to exclude possible causes of secondary headaches or migraineassociated disorders. Contrariwise, although advanced Magnetic Resonance Imaging (MRI) has improved tremendously our understanding of human brain processes in migraine patients, to the state of the art they have not superseded the conventional neuroimaging techniques in the migraine clinical setting. Methods: A comprehensive review was conducted of PubMed citations by entering the keyword “marker” and/or “biomarker” combined with “migraine” and/or “headache”. Other keywords included “imaging” or “neuroimaging”, “structural” or “functional”. The only restriction was English-language publication. The abstracts of all articles meeting these criteria were reviewed, and the full text was retrieved and examined for relevant references. Results: Several authors tried to identify imaging biomarkers able to identify different migraine phenotypes or, even better, to follow-up the same migraine patients during the course of the disease, to predict the evolution into more severe phenotypes and, finally, the response to specific treatment. Conclusion: The identification of diagnostic, prognostic and therapeutic advanced neuroimaging biomarkers in the migraine clinical setting, in order to approach to patients in a more and more rational and “tailored” way, is extremely intriguing and futuristic. Unfortunately, reliable and robust neuroimaging biomarkers are still lacking for migraine, probably due to both not completely understood pathogenesis and clinical and neuroimaging heterogeneity. Although further longitudinal advanced neuroimaging studies, aimed to identify effective neuroimaging biomarkers, are needed, this review aims to collect the main and most recent works on this topic.

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6

Lai, Chien-Han. "Promising Neuroimaging Biomarkers in Depression." Psychiatry Investigation 16, no. 9 (September 25, 2019): 662–70. http://dx.doi.org/10.30773/pi.2019.07.25.2.

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7

Houenou, Josselin. "Neuroimaging biomarkers in bipolar disorder." Frontiers in Bioscience E4, no. 2 (2012): 593–606. http://dx.doi.org/10.2741/e402.

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8

van der Miesen, Maite M., Martin A. Lindquist, and Tor D. Wager. "Neuroimaging-based biomarkers for pain." PAIN Reports 4, no. 4 (2019): e751. http://dx.doi.org/10.1097/pr9.0000000000000751.

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9

Mok, Vincent. "Neuroimaging biomarkers in vascular dementia." Journal of the Neurological Sciences 455 (December 2023): 120937. http://dx.doi.org/10.1016/j.jns.2023.120937.

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10

Nestor, Peter. "Neuroimaging biomarkers in Alzheimer's disease." Journal of the Neurological Sciences 455 (December 2023): 120938. http://dx.doi.org/10.1016/j.jns.2023.120938.

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11

Safitri, Dian Oktaria, and AAAA. Kusumawardhani. "Aspek Neurobiologi dan Neuroimaging Bunuh Diri." Cermin Dunia Kedokteran 48, no. 8 (August 2, 2021): 289–95. http://dx.doi.org/10.55175/cdk.v48i8.110.

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Sejumlah 50% pelaku bunuh diri pernah melakukan percobaan bunuh diri sebelumnya. Sulitnya prediktor klinis dan tidak terdapatnya biomarker spesifik, menyulitkan prediksi perilaku bunuh diri. Perkembangan neurobiologi dan neuroimaging dapat memprediksi terjadinya upaya bunuh diri. Fifty percent individuals who committed suicide have previously conducted suicide attempts. Rare clinical predictors and the absence of specific biomarkers, lead to difficulties in predicting suicidal behavior. Neurobiology and neuroimaging may predict the occurrence of suicide.

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12

Eimeren, Thilo, Angelo Antonini, Daniela Berg, Nico Bohnen, Roberto Ceravolo, Alexander Drzezga, Günter U. Höglinger, et al. "Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System." Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11, no. 1 (April 2, 2019): 301–9. http://dx.doi.org/10.1016/j.dadm.2019.01.011.

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13

Oktaria Safitri, Dian, and AAAA Kusumawardhani. "Aspek Neurobiologi dan Neuroimaging Bunuh Diri." Cermin Dunia Kedokteran 48, no. 8 (August 12, 2021): 289. http://dx.doi.org/10.55175/cdk.v48i8.1445.

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<p>Sejumlah 50 % pelaku bunuh diri pernah melakukan percobaan bunuh diri sebelumnya. Sulitnya prediktor klinis dan tidak terdapatnya biomarker spesifik, menyulitkan prediksi perilaku bunuh diri. Perkembangan neurobiologi dan neuroimaging dapat memprediksi terjadinya upaya bunuh diri.</p><p>Fifty percent individuals who committed suicide have previously conducted suicide attempts. Rare clinical predictors and the absence of specific biomarkers, lead to difficulties in predicting suicidal behavior. Neurobiology and neuroimaging may predict the occurrence of suicide.</p>

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14

Polyakova, T. A., and A. V. Arablinsky. "Neuroimaging and molecular biomarkers of dementia." Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 117, no. 6 (2017): 16. http://dx.doi.org/10.17116/jnevro20171176216-22.

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15

Rees, Elin M., Rachael I. Scahill, and Nicola Z. Hobbs. "Longitudinal Neuroimaging Biomarkers in Huntington's Disease." Journal of Huntington's Disease 2, no. 1 (2013): 21–39. http://dx.doi.org/10.3233/jhd-120030.

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16

Mascalchi, Mario, and Alessandra Vella. "Neuroimaging Biomarkers in SCA2 Gene Carriers." International Journal of Molecular Sciences 21, no. 3 (February 4, 2020): 1020. http://dx.doi.org/10.3390/ijms21031020.

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A variety of Magnetic Resonance (MR) and nuclear medicine (NM) techniques have been used in symptomatic and presymptomatic SCA2 gene carriers to explore, in vivo, the physiopathological biomarkers of the neurological dysfunctions characterizing the associated progressive disease that presents with a cerebellar syndrome, or less frequently, with a levodopa-responsive parkinsonian syndrome. Morphometry performed on T1-weighted images and diffusion MR imaging enable structural and microstructural evaluation of the brain in presymptomatic and symptomatic SCA2 gene carriers, in whom they show the typical pattern of olivopontocerebellar atrophy observed at neuropathological examination. Proton MR spectroscopy reveals, in the pons and cerebellum of SCA2 gene carriers, a more pronounced degree of abnormal neurochemical profile compared to other spinocerebellar ataxias with decreased NAA/Cr and Cho/Cr, increased mi/Cr ratios, and decreased NAA and increased mI concentrations. These neurochemical abnormalities are detectable also in presymtomatic gene carriers. Resting state functional MRI (rsfMRI) demonstrates decreased functional connectivity within the cerebellum and of the cerebellum with fronto-parietal cortices and basal ganglia in symptomatic SCA2 subjects. 18F-fluorodeoxyglucose Positron Emission Tomography (PET) shows a symmetric decrease of the glucose uptake in the cerebellar cortex, the dentate nucleus, the brainstem and the parahippocampal cortex. Single photon emission tomography and PET using several radiotracers have revealed almost symmetric nigrostriatal dopaminergic dysfunction irrespective of clinical signs of parkinsonism which are already present in presymtomatic gene carriers. Longitudinal small size studies have proven that morphometry and diffusion MR imaging can track neurodegeneration in SCA2, and hence serve as progression biomarkers. So far, such a capability has not been reported for proton MR spectroscopy, rsfMRI and NM techniques. A search for the best surrogate marker for future clinical trials represents the current challenge for the neuroimaging community.

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17

Polyakova, T. A., and A. V. Arablinsky. "Neuroimaging and Molecular Biomarkers for Dementia." Neuroscience and Behavioral Physiology 49, no. 4 (April 26, 2019): 406–12. http://dx.doi.org/10.1007/s11055-019-00747-7.

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18

Argyelan, Miklos, Todd Lencz, Styliani Kaliora, Deepak Sarpal, Noah Weissman, Peter Kingsley, Anil Malhotra, and Georgios Petrides. "349. Neuroimaging Biomarkers of ECT Response." Biological Psychiatry 81, no. 10 (May 2017): S143. http://dx.doi.org/10.1016/j.biopsych.2017.02.366.

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19

Martucci, Katherine T., and Sean C. Mackey. "Neuroimaging of Pain." Anesthesiology 128, no. 6 (June 1, 2018): 1241–54. http://dx.doi.org/10.1097/aln.0000000000002137.

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Abstract Neuroimaging research has demonstrated definitive involvement of the central nervous system in the development, maintenance, and experience of chronic pain. Structural and functional neuroimaging has helped elucidate central nervous system contributors to chronic pain in humans. Neuroimaging of pain has provided a tool for increasing our understanding of how pharmacologic and psychologic therapies improve chronic pain. To date, findings from neuroimaging pain research have benefitted clinical practice by providing clinicians with an educational framework to discuss the biopsychosocial nature of pain with patients. Future advances in neuroimaging-based therapeutics (e.g., transcranial magnetic stimulation, real-time functional magnetic resonance imaging neurofeedback) may provide additional benefits for clinical practice. In the future, with standardization and validation, brain imaging could provide objective biomarkers of chronic pain, and guide treatment for personalized pain management. Similarly, brain-based biomarkers may provide an additional predictor of perioperative prognoses.

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20

Mo, Ni. "Huntington’s Disease: A General Overview." Transactions on Social Science, Education and Humanities Research 5 (April 1, 2024): 495–500. http://dx.doi.org/10.62051/gjkq6086.

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This article overviews Huntington’s Disease (HD), emphasizing the development of neuroimaging biomarkers, pathogenesis discovery, and clinical treatment. The fundamental knowledge of HD includes its definition, diagnosis as an etiological subtype of neurodegenerative diseases in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), clinical neuropsychiatric symptoms, and social impact. The development of neuroimaging biomarkers is discussed with different developmental stages of HD. Mental health issues in the HD population including depression, anxiety, and psychosis were reviewed. The suicidal rate in this population is alarming, indicating the necessity to make both medical and mental health services available. Clinical treatments, including pharmacological and non-pharmacological, are overviewed with the timeline from now till the future. Recommendations are given for refining current research and future research design regarding neuroimaging biomarker exploration, early diagnosis, and potential treatment investigation. This review can provide some guidance to the development of more advanced diagnostic tools and effective treatments for this population.

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21

Shuster, Linda I. "Considerations for the Use of Neuroimaging Technologies for Predicting Recovery of Speech and Language in Aphasia." American Journal of Speech-Language Pathology 27, no. 1S (March 2018): 291–305. http://dx.doi.org/10.1044/2018_ajslp-16-0180.

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Purpose The number of research articles aimed at identifying neuroimaging biomarkers for predicting recovery from aphasia continues to grow. Although the clinical use of these biomarkers to determine prognosis has been proposed, there has been little discussion of how this would be accomplished. This is an important issue because the best translational science occurs when translation is considered early in the research process. The purpose of this clinical focus article is to present a framework to guide the discussion of how neuroimaging biomarkers for recovery from aphasia could be implemented clinically. Method The genomics literature reveals that implementing genetic testing in the real-world poses both opportunities and challenges. There is much similarity between these opportunities and challenges and those related to implementing neuroimaging testing to predict recovery in aphasia. Therefore, the Center for Disease Control's model list of questions aimed at guiding the review of genetic testing has been adapted to guide the discussion of using neuroimaging biomarkers as predictors of recovery in aphasia. Conclusion The adapted model list presented here is a first and useful step toward initiating a discussion of how neuroimaging biomarkers of recovery could be employed clinically to provide improved quality of care for individuals with aphasia.

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22

Ganesh, Chilukuri, Gandikota Harshavardhan, Naishadham Radha Sri Keerthi, Raj Veer Yabaji, and Rajveer Yabaji. "Advancements in Alzheimer's Disease Classification: Integrating Machine Learning, Neuroimaging, and Biomarkers." SCT Proceedings in Interdisciplinary Insights and Innovations 3 (January 6, 2025): 499. https://doi.org/10.56294/piii2025499.

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Alzheimer's disease (AD), a progressive neurodegenerative disorder, leads to cognitive decline, memory loss, and impaired daily functioning. Early detection and precise classification are critical for timely intervention and personalized care. These abstract reviews recent advancements in brain disease classification, particularly for AD, highlighting the use of machine learning algorithms, neuroimaging methods, and biomarker analysis. Machine learning models trained on neuroimaging data, such as MRI and PET scans, have demonstrated efficacy in distinguishing Alzheimer's disease, mild cognitive impairment (MCI), and healthy individuals. Biomarker studies involving cerebrospinal fluid (CSF) and blood samples provide critical insights into AD pathology, supporting disease classification efforts. Integrating diverse data types, including imaging, genetic, and clinical information, can significantly enhance the accuracy and reliability of classification models. Emerging deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enable the extraction of complex patterns from heterogeneous data sources, improving classification outcomes. Nonetheless, challenges persist, such as the requirement for large-scale, multi-centre datasets, uniform imaging protocols, and greater interpretability of machine learning models.Keywords: Alzheimer's disease; Machine Learning; Neuroimaging; Biomarkers

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23

P. Wylie, Korey, Jason Smucny, Kristina T. Legget, and Jason R. Tregellas. "Targeting Functional Biomarkers in Schizophrenia with Neuroimaging." Current Pharmaceutical Design 22, no. 14 (April 27, 2016): 2117–23. http://dx.doi.org/10.2174/1381612822666160127113912.

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24

Risacher, Shannon, and Andrew Saykin. "Neuroimaging Biomarkers of Neurodegenerative Diseases and Dementia." Seminars in Neurology 33, no. 04 (November 14, 2013): 386–416. http://dx.doi.org/10.1055/s-0033-1359312.

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25

Phillips, Mary L. "Coming of Age?: Neuroimaging Biomarkers in Youth." American Journal of Psychiatry 167, no. 1 (January 2010): 4–7. http://dx.doi.org/10.1176/appi.ajp.2009.09101546.

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26

Fu, Cynthia H. Y. "Linking Neuroimaging-Based Predictive Biomarkers and Mechanisms." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3, no. 3 (March 2018): 203–4. http://dx.doi.org/10.1016/j.bpsc.2018.01.005.

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27

de Natale, Edoardo Rosario, Heather Wilson, and Marios Politis. "Predictors of RBD progression and conversion to synucleinopathies." Current Neurology and Neuroscience Reports 22, no. 2 (February 2022): 93–104. http://dx.doi.org/10.1007/s11910-022-01171-0.

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Abstract Purpose of review Rapid eye movement (REM) sleep behaviour disorder (RBD) is considered the expression of the initial neurodegenerative process underlying synucleinopathies and constitutes the most important marker of their prodromal phase. This article reviews recent research from longitudinal research studies in isolated RBD (iRBD) aiming to describe the most promising progression biomarkers of iRBD and to delineate the current knowledge on the level of prediction of future outcome in iRBD patients at diagnosis. Recent findings Longitudinal studies revealed the potential value of a variety of biomarkers, including clinical markers of motor, autonomic, cognitive, and olfactory symptoms, neurophysiological markers such as REM sleep without atonia and electroencephalography, genetic and epigenetic markers, cerebrospinal fluid and serum markers, and neuroimaging markers to track the progression and predict phenoconversion. To-date the most promising neuroimaging biomarker in iRBD to aid the prediction of phenoconversion is striatal presynaptic striatal dopaminergic dysfunction. Summary There is a variety of potential biomarkers for monitoring disease progression and predicting iRBD conversion into synucleinopathies. A combined multimodal biomarker model could offer a more sensitive and specific tool. Further longitudinal studies are warranted to iRBD as a high-risk population for early neuroprotective interventions and disease-modifying therapies.

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28

Tabor, Jason B., Benjamin L. Brett, Lindsay Nelson, Timothy Meier, Linden C. Penner, Andrew R. Mayer, Ruben J. Echemendia, et al. "Role of biomarkers and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion: a systematic review." British Journal of Sports Medicine 57, no. 12 (June 2023): 789–97. http://dx.doi.org/10.1136/bjsports-2022-106680.

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ObjectiveDetermine the role of fluid-based biomarkers, advanced neuroimaging, genetic testing and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion (SRC).DesignSystematic review.Data sourcesSearches of seven databases from 1 January 2001 through 24 March 2022 using keywords and index terms relevant to concussion, sports and neurobiological recovery. Separate reviews were conducted for studies involving neuroimaging, fluid biomarkers, genetic testing and emerging technologies. A standardised method and data extraction tool was used to document the study design, population, methodology and results. Reviewers also rated the risk of bias and quality of each study.Eligibility criteria for selecting studiesStudies were included if they: (1) were published in English; (2) represented original research; (3) involved human research; (4) pertained only to SRC; (5) included data involving neuroimaging (including electrophysiological testing), fluid biomarkers or genetic testing or other advanced technologies used to assess neurobiological recovery after SRC; (6) had a minimum of one data collection point within 6 months post-SRC; and (7) contained a minimum sample size of 10 participants.ResultsA total of 205 studies met inclusion criteria, including 81 neuroimaging, 50 fluid biomarkers, 5 genetic testing, 73 advanced technologies studies (4 studies overlapped two separate domains). Numerous studies have demonstrated the ability of neuroimaging and fluid-based biomarkers to detect the acute effects of concussion and to track neurobiological recovery after injury. Recent studies have also reported on the diagnostic and prognostic performance of emerging technologies in the assessment of SRC. In sum, the available evidence reinforces the theory that physiological recovery may persist beyond clinical recovery after SRC. The potential role of genetic testing remains unclear based on limited research.ConclusionsAdvanced neuroimaging, fluid-based biomarkers, genetic testing and emerging technologies are valuable research tools for the study of SRC, but there is not sufficient evidence to recommend their use in clinical practice.PROSPERO registration numberCRD42020164558.

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Rittman, Timothy. "Neurological update: neuroimaging in dementia." Journal of Neurology 267, no. 11 (July 7, 2020): 3429–35. http://dx.doi.org/10.1007/s00415-020-10040-0.

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Abstract Neuroimaging for dementia has made remarkable progress in recent years, shedding light on diagnostic subtypes of dementia, predicting prognosis and monitoring pathology. This review covers some updates in the understanding of dementia using structural imaging, positron emission tomography (PET), structural and functional connectivity, and using big data and artificial intelligence. Progress with neuroimaging methods allows neuropathology to be examined in vivo, providing a suite of biomarkers for understanding neurodegeneration and for application in clinical trials. In addition, we highlight quantitative susceptibility imaging as an exciting new technique that may prove to be a sensitive biomarker for a range of neurodegenerative diseases. There are challenges in translating novel imaging techniques to clinical practice, particularly in developing standard methodologies and overcoming regulatory issues. It is likely that clinicians will need to lead the way if these obstacles are to be overcome. Continued efforts applying neuroimaging to understand mechanisms of neurodegeneration and translating them to clinical practice will complete a revolution in neuroimaging.

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McCreary, Cheryl R., Marina Salluzzi, Linda B. Andersen, David Gobbi, Louis Lauzon, Feryal Saad, Eric E. Smith, and Richard Frayne. "Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan." BMJ Open 10, no. 8 (August 2020): e038120. http://dx.doi.org/10.1136/bmjopen-2020-038120.

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IntroductionA number of MRI methods have been proposed to be useful, quantitative biomarkers of neurodegeneration in ageing. The Calgary Normative Study (CNS) is an ongoing single-centre, prospective, longitudinal study that seeks to develop, test and assess quantitative magnetic resonance (MR) methods as potential biomarkers of neurodegeneration. The CNS has three objectives: first and foremost, to evaluate and characterise the dependence of the selected quantitative neuroimaging biomarkers on age over the adult lifespan; second, to evaluate the precision, variability and repeatability of quantitative neuroimaging biomarkers as part of biomarker validation providing proof-of-concept and proof-of-principle; and third, provide a shared repository of normative data for comparison to various disease cohorts.Methods and analysisQuantitative MR mapping of the brain including longitudinal relaxation time (T1), transverse relaxation time (T2), T2*, magnetic susceptibility (QSM), diffusion and perfusion measurements, as well as morphological assessments are performed. The Montreal Cognitive Assessment (MoCA) and a brief, self-report medical history will be collected. Mixed regression models will be used to characterise changes in quantitative MR biomarker measures over the adult lifespan. In this report, we describe the study design, strategies to recruit and perform changes to the acquisition protocol from inception to 31 December 2018, planned statistical approach and data sharing procedures for the study.Ethics and disseminationParticipants provide signed informed consent. Changes in quantitative MR biomarkers measured over the adult lifespan as well as estimates of measurement variance and repeatability will be disseminated through peer-reviewed scientific publication.

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31

Palta, Priya, Kevin Sullivan, and Natascha Merten. "AD BLOOD BIOMARKERS IN DIVERSE COMMUNITY SETTINGS: A LONGITUDINAL PERSPECTIVE FROM THE ARIC STUDY." Innovation in Aging 8, Supplement_1 (December 2024): 1. https://doi.org/10.1093/geroni/igae098.0001.

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Abstract Alzheimer’s disease and related dementias (ADRD) feature a prolonged preclinical stage spanning decades, with the transition from mid- to late-life marking the critical period for onset and accumulation of pathological brain changes that may lead to physical and cognitive disability. Identifying individuals at risk for cognitive and physical decline during preclinical stages when interventions or disease modifying treatments are more likely to be effective is needed. Blood biomarkers of ADRD pathology and neurodegeneration are promising cost-effective and non-invasive options to fill this gap. To date, however, there are limited data on temporal changes in blood biomarkers and their associations with cognitive, mobility, and neuroimaging outcomes in diverse community-based cohorts. This symposium will feature 23 years of data from the well-established community-based Atherosclerosis Risk in Communities (ARIC) Study cohort which assayed blood biomarkers of amyloid-β (Aβ)42/40, phosphorylated tau at threonine 181 (p-Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) using Quanterix Simoa assays on stored specimens at up to 3 timepoints in midlife and late-life in ~1,800 participants. This session will present findings on temporal blood biomarker changes from mid- to late-life and associated risk and protective factors of biomarker changes; mid- to late-life changes in blood biomarkers and associations with late-life neuroimaging measures of neurodegeneration, cerebrovascular disease, and amyloid deposition; and the associations of late-life blood biomarkers with prevalent and incident mobility and cognitive impairment.

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Arya, Rakesh, A. K. M. Ariful Haque, Hemlata Shakya, Md Masum Billah, Anzana Parvin, Md-Mafizur Rahman, Khan Mohammad Sakib, Hossain Md Faruquee, Vijay Kumar, and Jong-Joo Kim. "Parkinson’s Disease: Biomarkers for Diagnosis and Disease Progression." International Journal of Molecular Sciences 25, no. 22 (November 18, 2024): 12379. http://dx.doi.org/10.3390/ijms252212379.

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Parkinson’s disease (PD) is a progressive neurological disease that causes both motor and nonmotor symptoms. While our understanding of putative mechanisms has advanced significantly, it remains challenging to verify biomarkers with sufficient evidence for regular clinical use. Clinical symptoms are the primary basis for diagnosing the disease, which can be mild in the early stages and overlap with other neurological disorders. As a result, clinical testing and medical records are mostly relied upon for diagnosis, posing substantial challenges during both the initial diagnosis and the continuous disease monitoring. Recent biochemical, neuroimaging, and genetic biomarkers have helped us understand the pathophysiology of Parkinson’s disease. This comprehensive study focuses on these biomarkers, which were chosen based on their relevance, methodological excellence, and contribution to the field. Biochemical biomarkers, including α-synuclein and glial fibrillary acidic protein (GFAP), can predict disease severity and progression. The dopaminergic system is widely used as a neuroimaging biomarker to diagnose PD. Numerous genes and genome wide association study (GWAS) sites have been related to the development of PD. Recent research on the SNCA gene and leucine-rich repeat protein kinase 2 (LRRK2) has shown promising results. By evaluating current studies, this review intends to uncover gaps in biomarker validation and use, while also highlighting promising improvements. It emphasizes the need for dependable and reproducible indicators in improving PD diagnosis and prognosis. These biomarkers may open up new avenues for early diagnosis, disease progression tracking, and the development of personalized treatment programs.

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Hanphanitphan, Serene. "Longitudinal Assessment of Biomarkers for Predicting Alzheimer's Disease Progression: A Prospective Cohort Study in Thailand." Sriwijaya Journal of Neurology 2, no. 1 (August 21, 2024): 72–83. http://dx.doi.org/10.59345/sjn.v2i1.154.

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Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory impairment. Early identification and prediction of disease progression are critical for timely intervention and management. This prospective cohort study aimed to investigate the longitudinal trajectories of various biomarkers and their predictive value for AD progression in a Thai population. Methods: A cohort of participants, including individuals with mild cognitive impairment (MCI) and cognitively normal older adults, were recruited from memory clinics and community settings in Thailand. Baseline assessments included clinical evaluations, neuropsychological tests, and biomarker measurements (cerebrospinal fluid (CSF) biomarkers, neuroimaging, and blood-based markers). Participants underwent follow-up assessments at regular intervals over several years to track disease progression. Results: The study identified longitudinal changes in various biomarkers associated with AD progression. CSF biomarkers, such as amyloid-beta (Aβ) and tau, showed significant alterations over time, with decreasing Aβ and increasing tau levels observed in individuals transitioning from MCI to AD. Neuroimaging markers, including hippocampal volume and cortical thickness, demonstrated progressive atrophy in AD patients. Blood-based markers, such as neurofilament light chain (NfL), showed promising potential as predictors of disease progression. Conclusion: This study provides valuable insights into the longitudinal trajectories of biomarkers and their predictive value for AD progression in the Thai population. The findings support the use of a multi-modal biomarker approach for early identification and monitoring of AD, paving the way for personalized interventions and improved patient management.

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Park, Jung Eun, Tamil Iniyan Gunasekaran, Yeong Hee Cho, Seong-Min Choi, Min-Kyung Song, Soo Hyun Cho, Jahae Kim, et al. "Diagnostic Blood Biomarkers in Alzheimer’s Disease." Biomedicines 10, no. 1 (January 13, 2022): 169. http://dx.doi.org/10.3390/biomedicines10010169.

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Potential biomarkers for Alzheimer’s disease (AD) include amyloid β1–42 (Aβ1–42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1–42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1–42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson’s correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1–42 concentrations decreased. The plasma and CSF NFL/Aβ1–42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1–42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1–42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1–42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1–42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression.

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Galińska-Skok, Beata, and Napoleon Waszkiewicz. "Markers of Schizophrenia—A Critical Narrative Update." Journal of Clinical Medicine 11, no. 14 (July 7, 2022): 3964. http://dx.doi.org/10.3390/jcm11143964.

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Schizophrenia is a long-term mental disease, associated with functional impairment. Therefore, it is important to make an accurate diagnosis and implement the proper treatment. Biomarkers may be a potential tool for these purposes. Regarding advances in biomarker studies in psychosis, the current symptom-based criteria seem to be no longer sufficient in clinical settings. This narrative review describes biomarkers of psychosis focusing on the biochemical (peripheral and central), neurophysiological, neuropsychological and neuroimaging findings as well as the multimodal approach related with them. Endophenotype markers (especially neuropsychological and occulomotor disturbances) can be currently used in a clinical settings, whereas neuroimaging glutamate/glutamine and D2/D3 receptor density changes, as well as immunological Th2 and PRL levels, seem to be potential biomarkers that need further accuracy tests. When searching for biochemical/immunological markers in the diagnosis of psychosis, the appropriate time of body fluid collection needs to be considered to minimize the influence of the stress axis on their concentrations. In schizophrenia diagnostics, a multimodal approach seems to be highly recommended.

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Reddy, Sandesh, Iyan Younus, Vidya Sridhar, and Doodipala Reddy. "Neuroimaging Biomarkers of Experimental Epileptogenesis and Refractory Epilepsy." International Journal of Molecular Sciences 20, no. 1 (January 8, 2019): 220. http://dx.doi.org/10.3390/ijms20010220.

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This article provides an overview of neuroimaging biomarkers in experimental epileptogenesis and refractory epilepsy. Neuroimaging represents a gold standard and clinically translatable technique to identify neuropathological changes in epileptogenesis and longitudinally monitor its progression after a precipitating injury. Neuroimaging studies, along with molecular studies from animal models, have greatly improved our understanding of the neuropathology of epilepsy, such as the hallmark hippocampus sclerosis. Animal models are effective for differentiating the different stages of epileptogenesis. Neuroimaging in experimental epilepsy provides unique information about anatomic, functional, and metabolic alterations linked to epileptogenesis. Recently, several in vivo biomarkers for epileptogenesis have been investigated for characterizing neuronal loss, inflammation, blood-brain barrier alterations, changes in neurotransmitter density, neurovascular coupling, cerebral blood flow and volume, network connectivity, and metabolic activity in the brain. Magnetic resonance imaging (MRI) is a sensitive method for detecting structural and functional changes in the brain, especially to identify region-specific neuronal damage patterns in epilepsy. Positron emission tomography (PET) and single-photon emission computerized tomography are helpful to elucidate key functional alterations, especially in areas of brain metabolism and molecular patterns, and can help monitor pathology of epileptic disorders. Multimodal procedures such as PET-MRI integrated systems are desired for refractory epilepsy. Validated biomarkers are warranted for early identification of people at risk for epilepsy and monitoring of the progression of medical interventions.

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Theodorou, Aikaterini, Athanasia Athanasaki, Konstantinos Melanis, Ioanna Pachi, Angeliki Sterpi, Eleftheria Koropouli, Eleni Bakola, et al. "Cognitive Impairment in Cerebral Amyloid Angiopathy: A Single-Center Prospective Cohort Study." Journal of Clinical Medicine 13, no. 23 (December 6, 2024): 7427. https://doi.org/10.3390/jcm13237427.

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Background/Objectives: Cognitive impairment represents a core and prodromal clinical feature of cerebral amyloid angiopathy (CAA). We sought to assess specific cognitive domains which are mainly affected among patients with CAA and to investigate probable associations with neuroimaging markers and Cerebrospinal Fluid (CSF) biomarkers. Methods: Thirty-five patients fulfilling the Boston Criteria v1.5 or v2.0 for the diagnosis of probable/possible CAA were enrolled in this prospective cohort study. Brain Magnetic Resonance Imaging and CSF biomarker data were collected. Every eligible participant underwent a comprehensive neurocognitive assessment. Spearman’s rank correlation tests were used to identify possible relationships between the Addenbrooke’s Cognitive Examination—Revised (ACE-R) sub-scores and other neurocognitive test scores and the CSF biomarker and neuroimaging parameters among CAA patients. Moreover, linear regression analyses were used to investigate the effects of CSF biomarkers on the ACE-R total score and Mini-Mental State Examination (MMSE) score, based on the outcomes of univariate analyses. Results: Cognitive impairment was detected in 80% of patients, and 60% had a coexistent Alzheimer’s disease (AD) pathology based on CSF biomarker profiles. Notable correlations were identified between increased levels of total tau (t-tau) and phosphorylated tau (p-tau) and diminished performance in terms of overall cognitive function, especially memory. In contrast, neuroimaging indicators, including lobar cerebral microbleeds and superficial siderosis, had no significant associations with cognitive scores. Among the CAA patients, those without AD had superior neurocognitive test performance, with significant differences observed in their ACE-R total scores and memory sub-scores. Conclusions: The significance of tauopathy in cognitive impairment associated with CAA may be greater than previously imagined, underscoring the necessity for additional exploration of the non-hemorrhagic facets of the disease and new neuroimaging markers.

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Van Bogaert, Patrick. "Physiopathology of Atypical Evolutions of Idiopathic Focal Epilepsies in Childhood." Journal of Pediatric Epilepsy 05, no. 03 (July 1, 2016): 133–38. http://dx.doi.org/10.1055/s-0036-1585066.

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This article aims to discuss if the underlying etiology is an important determinant of atypical evolution of idiopathic focal epilepsy (IFE) in childhood and if there might be biomarkers that would predict atypical evolution. It appears that the determinants of atypical evolution remain largely unknown but that both genetic and epigenetic factors are likely to be involved. The analysis of scalp electroencephalography remains the best biomarker of atypical evolution. However, functional neuroimaging methods of investigation are helpful to better understand how epileptic activity affects brain functioning at rest and during goal-directed tasks and to delineate the extent of brain networks that are impaired by epileptic activity. Moreover, neuroimaging bring new arguments favoring the idea that typical and atypical IFE should not be viewed as distinct entities but as a unique syndrome with various degrees of severity that should be treated using tailored strategies and robust biomarkers of efficacy.

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Woo, Choong-Wan, Luke J. Chang, Martin A. Lindquist, and Tor D. Wager. "Building better biomarkers: brain models in translational neuroimaging." Nature Neuroscience 20, no. 3 (February 23, 2017): 365–77. http://dx.doi.org/10.1038/nn.4478.

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Sintini, Irene, Jonathan Graff-Radford, Matthew L. Senjem, Christopher G. Schwarz, Mary M. Machulda, Peter R. Martin, David T. Jones, et al. "Longitudinal neuroimaging biomarkers differ across Alzheimer’s disease phenotypes." Brain 143, no. 7 (June 23, 2020): 2281–94. http://dx.doi.org/10.1093/brain/awaa155.

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Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.

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Vassilaki, Maria, Jeremiah A. Aakre, Michelle M. Mielke, Yonas E. Geda, Walter K. Kremers, Rabe E. Alhurani, Mary M. Machulda, et al. "Multimorbidity and neuroimaging biomarkers among cognitively normal persons." Neurology 86, no. 22 (April 13, 2016): 2077–84. http://dx.doi.org/10.1212/wnl.0000000000002624.

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Dazzan, P. "Neuroimaging biomarkers to predict treatment response in schizophrenia." European Neuropsychopharmacology 26 (October 2016): S140. http://dx.doi.org/10.1016/s0924-977x(16)30935-x.

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Habeck, Christian, Norman L. Foster, Robert Perneczky, Alexander Kurz, Panagiotis Alexopoulos, Robert A. Koeppe, Alexander Drzezga, and Yaakov Stern. "Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease." NeuroImage 40, no. 4 (May 2008): 1503–15. http://dx.doi.org/10.1016/j.neuroimage.2008.01.056.

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Cole, James H., and Katja Franke. "Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers." Trends in Neurosciences 40, no. 12 (December 2017): 681–90. http://dx.doi.org/10.1016/j.tins.2017.10.001.

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Bigler, Erin D. "Neuroimaging Biomarkers in Mild Traumatic Brain Injury (mTBI)." Neuropsychology Review 23, no. 3 (August 24, 2013): 169–209. http://dx.doi.org/10.1007/s11065-013-9237-2.

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Chetelat, Gael, Eider M. Arenaza-Urquijo, and Prashanthi Vemuri. "RELATIONSHIPS BETWEEN LIFESTYLE FACTORS AND AD NEUROIMAGING BIOMARKERS." Alzheimer's & Dementia 13, no. 7 (July 2017): P1446—P1447. http://dx.doi.org/10.1016/j.jalz.2017.07.483.

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Tregellas, Jason R. "Neuroimaging Biomarkers for Early Drug Development in Schizophrenia." Biological Psychiatry 76, no. 2 (July 2014): 111–19. http://dx.doi.org/10.1016/j.biopsych.2013.08.025.

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Villemagne, Victor L., and Gaël Chételat. "Neuroimaging biomarkers in Alzheimer’s disease and other dementias." Ageing Research Reviews 30 (September 2016): 4–16. http://dx.doi.org/10.1016/j.arr.2016.01.004.

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Perlmutter, Joel S., and Scott A. Norris. "Neuroimaging biomarkers for Parkinson disease: Facts and fantasy." Annals of Neurology 76, no. 6 (November 7, 2014): 769–83. http://dx.doi.org/10.1002/ana.24291.

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Colvee-Martin, Helena, Juan Rayo Parra, Gabriel Antonio Gonzalez, Warren Barker, and Ranjan Duara. "Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer’s Disease." Diagnostics 14, no. 7 (March 27, 2024): 704. http://dx.doi.org/10.3390/diagnostics14070704.

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An improved understanding of the pathobiology of Alzheimer’s disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer’s disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer’s disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.

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