Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain mapping approaches to multivariate predictive models …
using traditional univariate brain mapping approaches to multivariate predictive models …
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …
Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm
J Albright… - Alzheimer's & Dementia …, 2019 - Elsevier
Abstract Introduction There is a 99.6% failure rate of clinical trials for drugs to treat
Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily …
Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily …
Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals
Energy consumption is an important issue in continuous wireless telemonitoring of
physiological signals. Compressed sensing (CS) is a promising framework to address it, due …
physiological signals. Compressed sensing (CS) is a promising framework to address it, due …
Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data
Neuroimaging techniques have greatly enhanced the understanding of neurodiversity
(human brain variation across individuals) in both health and disease. The ultimate goal of …
(human brain variation across individuals) in both health and disease. The ultimate goal of …
Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer's disease
Sparse learning has been widely investigated for analysis of brain images to assist the
diagnosis of Alzheimer's disease and its prodromal stage, ie, mild cognitive impairment …
diagnosis of Alzheimer's disease and its prodromal stage, ie, mild cognitive impairment …
EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features
V Bairagi - International Journal of Information Technology, 2018 - Springer
Alzheimer disease is one of the most common and fastest growing neurodegenerative
diseases in the western countries. Development of different biomarkers tools are key issues …
diseases in the western countries. Development of different biomarkers tools are key issues …
Multi-frequency electromagnetic tomography for acute stroke detection using frequency-constrained sparse Bayesian learning
Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute
stroke. This paper presents a compact and non-radiative tomographic modality, ie multi …
stroke. This paper presents a compact and non-radiative tomographic modality, ie multi …
Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer's disease
Abstract Alzheimer's disease (AD), the most common form of dementia, not only causes
progressive impairment of memory and other cognitive functions of patients, but also …
progressive impairment of memory and other cognitive functions of patients, but also …
Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care
system but also an emotional burden to patients and their families. Making accurate …
system but also an emotional burden to patients and their families. Making accurate …