Sparse logistic regression for whole-brain classification of fMRI data. - sprookjes - yvandenbroek - TriplyDB

Sparse logistic regression for whole-brain classification of fMRI data.

Sparse logistic regression for whole-brain classification of fMRI data.

http://www.wikidata.org/entity/Q30386116

about

Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate fMRI decoding models Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective Tonotopic and Field-Specific Representation of Long-Lasting Sustained Activity in Rat Auditory Cortex.Connectomics and new approaches for analyzing human brain functional connectivity A review of feature reduction techniques in neuroimaging Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotyping Predicting the knowledge-recklessness distinction in the human brain.A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns.Spectral organization of the human lateral superior temporal gyrus revealed by intracranial recordings.Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas Model-based feature construction for multivariate decoding.The support feature machine: classification with the least number of features and application to neuroimaging data.Measuring neural representations with fMRI: practices and pitfalls.Sparse Methods for Biomedical Data SGPP: spatial Gaussian predictive process models for neuroimaging data Neuroimaging Evidence for 2 Types of Plasticity in Association with Visual Perceptual Learning Tensor Regression with Applications in Neuroimaging Data Analysis.Graph-based inter-subject pattern analysis of FMRI data.Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.Granular computing with multiple granular layers for brain big data processing.Neuroanatomical correlates of developmental dyscalculia: combined evidence from morphometry and tractography.Penalized least squares regression methods and applications to neuroimaging Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine.Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns.Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia.High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization.Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems.Nonpolitical images evoke neural predictors of political ideology.Alzheimer's disease risk assessment using large-scale machine learning methods Neural Activity Reveals Preferences Without Choices MANIA-a pattern classification toolbox for neuroimaging data.MRI-based intelligence quotient (IQ) estimation with sparse learning.How does a child solve 7 + 8? Decoding brain activity patterns associated with counting and retrieval strategies.Combining graph and machine learning methods to analyze differences in functional connectivity across sex.Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities.Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder.Will big data yield new mathematics? An evolving synergy with neuroscience.Diagnostic neuroimaging across diseases.

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P2860

Sparse logistic regression for whole-brain classification of fMRI data.

http://www.wikidata.org/entity/Q30386116

description

2010 nî lūn-bûn

@nan

2010 թուականի Փետրուարին հրատարակուած գիտական յօդուած

@hyw

2010 թվականի փետրվարին հրատարակված գիտական հոդված

@hy

2010年の論文

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2010年論文

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2010年論文

@zh-hant

2010年論文

@zh-hk

2010年論文

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2010年論文

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2010年论文

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name

Sparse logistic regression for whole-brain classification of fMRI data.

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Sparse logistic regression for whole-brain classification of fMRI data.

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type

label

Sparse logistic regression for whole-brain classification of fMRI data.

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Sparse logistic regression for whole-brain classification of fMRI data.

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prefLabel

Sparse logistic regression for whole-brain classification of fMRI data.

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Sparse logistic regression for whole-brain classification of fMRI data.

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J.NEUROIMAGE.2010.02.040

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Sparse logistic regression for whole-brain classification of fMRI data.

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P2093

Daniel A Abrams

http://www.w3.org/2001/XMLSchema#string

Kaustubh Supekar

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Srikanth Ryali

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P2860

Predicting the orientation of invisible stimuli from activity in human primary visual cortex

Machine learning classifiers and fMRI: a tutorial overview

Bach speaks: a cortical "language-network" serves the processing of music.

Musical structure is processed in "language" areas of the brain: a possible role for Brodmann Area 47 in temporal coherence.

From air oscillations to music and speech: functional magnetic resonance imaging evidence for fine-tuned neural networks in audition.

Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns.

Selective attention to semantic and syntactic features modulates sentence processing networks in anterior temporal cortex.

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data.

Interpreting single trial data using groupwise regularisation.

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logistic regression model

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2856747

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