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Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
TLDR
High-dimensional non-linear pattern classification methods applied to functional magnetic resonance images were used to discriminate between the spatial patterns of brain activity associated with lie and truth, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
TLDR
High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI),
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study
TLDR
This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone.
Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities.
TLDR
This study confirms previous findings of reduced frontotemporal volumes and suggests new hypotheses, especially involving occipital association and speech production areas, and suggests the potential utility of magnetic resonance imaging as a diagnostic aid.
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements
TLDR
This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods, which demonstrates not only high classification accuracy but also good stability.
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI
TLDR
These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.
Development Trends of White Matter Connectivity in the First Years of Life
TLDR
The results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development, and found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks.
High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables
TLDR
Experimental results demonstrate that this regression scheme achieves higher estimation accuracy and better generalizing ability than Support Vector Regression (SVR).
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
TLDR
A novel brain tumor segmentation method developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency could segment brain images slice‐by‐slice, much faster than those based on image patches.
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