AIMC Topic: Brain Mapping
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Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.
International journal of geriatric psychiatry
Feb 17, 2015
OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate ...
Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.
Human brain mapping
Feb 9, 2015
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying ...
Physics instruction induces changes in neural knowledge representation during successive stages of learning.
NeuroImage
Feb 7, 2015
Incremental instruction on the workings of a set of mechanical systems induced a progression of changes in the neural representations of the systems. The neural representations of four mechanical systems were assessed before, during, and after three ...
Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.
International journal of neural systems
Jan 19, 2015
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...
Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.
NeuroImage
Jan 5, 2015
Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional s...
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.
NeuroImage
Jan 3, 2015
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 month...
LinkRbrain: multi-scale data integrator of the brain.
Journal of neuroscience methods
Dec 18, 2014
BACKGROUND: LinkRbrain is an open-access web platform for multi-scale data integration and visualization of human brain data. This platform integrates anatomical, functional, and genetic knowledge produced by the scientific community.
Latching chains in K-nearest-neighbor and modular small-world networks.
Network (Bristol, England)
Nov 11, 2014
Latching dynamics retrieve pattern sequences successively by neural adaption and pattern correlation. We have previously proposed a modular latching chain model in Song et al. (2014) to better accommodate the structured transitions in the brain. Diff...
Manifold regularized multitask feature learning for multimodality disease classification.
Human brain mapping
Oct 3, 2014
Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint select...
Diagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach.
Journal of neural transmission (Vienna, Austria : 1996)
Jul 19, 2014
While neuroimaging research has advanced our knowledge about fear circuitry dysfunctions in anxiety disorders, findings based on diagnostic groups do not translate into diagnostic value for the individual patient. Machine-learning generates predictiv...
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