Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia

G Mirzaei, H Adeli - Biomedical Signal Processing and Control, 2022 - Elsevier
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …

The complexity of Alzheimer's disease: an evolving puzzle

C Ferrari, S Sorbi - Physiological reviews, 2021 - journals.physiology.org
The history of Alzheimer's disease (AD) started in 1907, but we needed to wait until the end
of the century to identify the components of pathological hallmarks and genetic subtypes and …

Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

R La Joie, AV Visani, SL Baker, JA Brown… - Science translational …, 2020 - science.org
β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological
hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a …

Machine learning for precision psychiatry: opportunities and challenges

D Bzdok, A Meyer-Lindenberg - Biological Psychiatry: Cognitive …, 2018 - Elsevier
The nature of mental illness remains a conundrum. Traditional disease categories are
increasingly suspected to misrepresent the causes underlying mental disturbance. Yet …

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

DP Veitch, MW Weiner, PS Aisen, LA Beckett… - Alzheimer's & …, 2019 - Elsevier
Introduction The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to
validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite …

Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion

R Kong, J Li, C Orban, MR Sabuncu, H Liu… - Cerebral …, 2019 - academic.oup.com
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to
delineate individual-specific brain networks. A major question is whether individual-specific …

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

AL Young, RV Marinescu, NP Oxtoby… - Nature …, 2018 - nature.com
The heterogeneity of neurodegenerative diseases is a key confound to disease
understanding and treatment development, as study cohorts typically include multiple …

Brain aging patterns in a large and diverse cohort of 49,482 individuals

Z Yang, J Wen, G Erus, ST Govindarajan, R Melhem… - Nature medicine, 2024 - nature.com
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as
well as by age-related and often coexisting pathologies. Magnetic resonance imaging and …

Biological subtypes of Alzheimer disease: a systematic review and meta-analysis

D Ferreira, A Nordberg, E Westman - Neurology, 2020 - AAN Enterprises
Objective To test the hypothesis that distinct subtypes of Alzheimer disease (AD) exist and
underlie the heterogeneity within AD, we conducted a systematic review and meta-analysis …

[HTML][HTML] Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification

M Böhle, F Eitel, M Weygandt, K Ritter - Frontiers in aging …, 2019 - frontiersin.org
Deep neural networks have led to state-of-the-art results in many medical imaging tasks
including Alzheimer's disease (AD) detection based on structural magnetic resonance …