A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

S Rathore, M Habes, MA Iftikhar, A Shacklett… - NeuroImage, 2017 - Elsevier
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …

Parkinson's disease dementia: a neural networks perspective

J Gratwicke, M Jahanshahi, T Foltynie - Brain, 2015 - academic.oup.com
In the long-term, with progression of the illness, Parkinson's disease dementia affects up to
90% of patients with Parkinson's disease. With increasing life expectancy in western …

Deep learning approach for early detection of Alzheimer's disease

HA Helaly, M Badawy, AY Haikal - Cognitive computation, 2022 - Springer
Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till
now. However, available medicines can delay its progress. Therefore, the early detection of …

An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings

PN Alves, C Foulon, V Karolis, D Bzdok… - Communications …, 2019 - nature.com
The brain is constituted of multiple networks of functionally correlated brain areas, out of
which the default-mode network (DMN) is the largest. Most existing research into the DMN …

Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis

Y Zhang, H Zhang, X Chen, SW Lee, D Shen - Scientific reports, 2017 - nature.com
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …

DeepAD: Alzheimer's disease classification via deep convolutional neural networks using MRI and fMRI

S Sarraf, DD DeSouza, J Anderson, G Tofighi… - BioRxiv, 2016 - biorxiv.org
To extract patterns from neuroimaging data, various techniques, including statistical
methods and machine learning algorithms, have been explored to ultimately aid in …

REST: a toolkit for resting-state functional magnetic resonance imaging data processing

XW Song, ZY Dong, XY Long, SF Li, XN Zuo, CZ Zhu… - PloS one, 2011 - journals.plos.org
Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years.
However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI …

DPARSF: a MATLAB toolbox for" pipeline" data analysis of resting-state fMRI

C Yan, Y Zang - Frontiers in systems neuroscience, 2010 - frontiersin.org
Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more
attention because of its effectiveness, simplicity and non-invasiveness in exploration of the …

The cerebellum in Alzheimer's disease: evaluating its role in cognitive decline

HIL Jacobs, DA Hopkins, HC Mayrhofer, E Bruner… - Brain, 2018 - academic.oup.com
The cerebellum has long been regarded as essential only for the coordination of voluntary
motor activity and motor learning. Anatomical, clinical and neuroimaging studies have led to …

[HTML][HTML] Brain hyperconnectivity in children with autism and its links to social deficits

K Supekar, LQ Uddin, A Khouzam, J Phillips… - Cell reports, 2013 - cell.com
Autism spectrum disorder (ASD), a neurodevelopmental disorder affecting nearly 1 in 88
children, is thought to result from aberrant brain connectivity. Remarkably, there have been …