Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Towards a brain‐based predictome of mental illness
Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …
recent years and have deepened our understanding of both cognitively healthy and …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge
Algorithms for computer-aided diagnosis of dementia based on structural MRI have
demonstrated high performance in the literature, but are difficult to compare as different data …
demonstrated high performance in the literature, but are difficult to compare as different data …
[HTML][HTML] From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics
Psychiatric disorders are increasingly being recognised as having a biological basis, but
their diagnosis is made exclusively behaviourally. A promising approach for …
their diagnosis is made exclusively behaviourally. A promising approach for …
[HTML][HTML] PRoNTo: pattern recognition for neuroimaging toolbox
In the past years, mass univariate statistical analyses of neuroimaging data have been
complemented by the use of multivariate pattern analyses, especially based on machine …
complemented by the use of multivariate pattern analyses, especially based on machine …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
[HTML][HTML] The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience
ADHD-200 consortium - Frontiers in systems neuroscience, 2012 - frontiersin.org
OPINION ARTICLE published: 05 September 2012 doi: 10.3389/fnsys. 2012.00062 tarballs,
and via NITRC Image Repository (NITRC-IR3) which supports searches by phenotypic …
and via NITRC Image Repository (NITRC-IR3) which supports searches by phenotypic …
[HTML][HTML] Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database
Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging
of the human brain. We employed a recently validated method for robust cross-sectional and …
of the human brain. We employed a recently validated method for robust cross-sectional and …
[HTML][HTML] Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels,
including symptoms, disease course, and biological underpinnings. These form a …
including symptoms, disease course, and biological underpinnings. These form a …