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Full-Text Articles in Medical Sciences
Cerebrospinal Fluid Neurofilament Light Concentration Predicts Brain Atrophy And Cognition In Alzheimer's Disease, Kunal Dhiman, Veer Bala Gupta, Victor L. Villemagne, Dhamidhu Eratne, Petra L. Graham, Christopher Fowler, Pierrick Bourgeat, Qiao-Xin Li, Steven Collins, Ashley I. Bush, Christopher C. Rowe, Colin L. Masters, David Ames, Eugene Hone, Kaj Blennow, Henrik Zetterberg, Ralph N. Martins
Cerebrospinal Fluid Neurofilament Light Concentration Predicts Brain Atrophy And Cognition In Alzheimer's Disease, Kunal Dhiman, Veer Bala Gupta, Victor L. Villemagne, Dhamidhu Eratne, Petra L. Graham, Christopher Fowler, Pierrick Bourgeat, Qiao-Xin Li, Steven Collins, Ashley I. Bush, Christopher C. Rowe, Colin L. Masters, David Ames, Eugene Hone, Kaj Blennow, Henrik Zetterberg, Ralph N. Martins
Research outputs 2014 to 2021
Introduction
This study assessed the utility of cerebrospinal fluid (CSF) neurofilament light (NfL) in Alzheimer's disease (AD) diagnosis, its association with amyloid and tau pathology, as well as its potential to predict brain atrophy, cognition, and amyloid accumulation.
Methods
CSF NfL concentration was measured in 221 participants from the Australian Imaging, Biomarkers & Lifestyle Flagship Study of Ageing (AIBL).
Results
CSF NfL levels as well as NfL/amyloid β (Aβ42) were significantly elevated in AD compared to healthy controls (HC; P < .001), and in mild cognitive impairment (MCI) compared to HC (P = .008 NfL; P< .001 NfL/Aβ42). CSF NfL and NfL/Aβ42 differentiated AD from HC with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.84 and 0.90, respectively. CSF NfL and NfL/Aβ42 predicted cortical amyloid load, brain atrophy, and cognition.
Discussion
CSF NfL is a biomarker of neurodegeneration, correlating with cognitive impairment and brain neuropathology.
Brainwave Nets: Are Sparse Dynamic Models Susceptible To Brain Manipulation Experimentation?, Diego C. Nascimento, Marco A. Pinto-Orellana, Joao P. Leite, Dylan J. Edwards, Francisco Louzada, Taiza E.G. Santos
Brainwave Nets: Are Sparse Dynamic Models Susceptible To Brain Manipulation Experimentation?, Diego C. Nascimento, Marco A. Pinto-Orellana, Joao P. Leite, Dylan J. Edwards, Francisco Louzada, Taiza E.G. Santos
Research outputs 2014 to 2021
© Copyright © 2020 Nascimento, Pinto-Orellana, Leite, Edwards, Louzada and Santos. Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) …