NDLI: Multimodal neuroimaging in Alzheimer's disease: Contributions of multi-voxel pattern analysis to the analysis of DTI and resting-state MRI

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2015 International Workshop on Pattern Recognition in NeuroImaging

2014 International Workshop on Pattern Recognition in Neuroimaging

Title page

Message from conference chairs

Organizing committee

Tutorials

Reduction of confounding effects with voxel-wise Gaussian process regression in structural MRI

Permutation distributions of fMRI classification do not behave in accord with central limit theorem

Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution

Multi-subject Bayesian Joint Detection and Estimation in fMRI

Improved marked point process priors for single neurite tracing

Decoding perceptual thresholds from MEG/EEG

Data-driven multisubject neuroimaging analyses for naturalistic stimuli

A perceptual-to-conceptual gradient of word coding along the ventral path

A validation of a multi-spatialscale method for multivariate pattern analysis

Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

Joint laplacian diagonalization for multi-modal brain community detection

Benchmarking solvers for TV-ℓ1 least-squares and logistic regression in brain imaging

Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI

Discriminative subnetwork mining for multiple thresholded connectivity-networks-based classification of mild cognitive impairment

Improved method for automatic cerebrovascular labelling using stochastic tunnelling

A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia

EEG source reconstruction using sparse basis function representations

Parameter interpretation, regularization and source localization in multivariate linear models

Mean shrinkage improves the classification of ERP signals by exploiting additional label information

Dynamic connectivity factorization: Interpretable decompositions of non-stationarity

Functional hyperalignment of resting state FMRI sessions driven by autonomic activity

Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation

In search of biomarkers for schizophrenia using electroencephalography

Single-trials ERPs predict correct answers to intelligence test questions

Correlation bundle statistics in fMRI data

Hierarchical topographic factor analysis

Intensity normalisation for large-scale fMRI brain decoding

Unsupervised metrics of brain region significance for event-related fMRI intersession experiments

Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data

Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients

Semi-supervised learning in MCI-to-ad conversion prediction — When is unlabeled data useful?

MVPA to enhance the study of rare cognitive events: An investigation of experimental PTSD

Sensor-level maps with the kernel two-sample test

MEG decoding across subjects

Multimodal neuroimaging in Alzheimer's disease: Contributions of multi-voxel pattern analysis to the analysis of DTI and resting-state MRI

SVM aided detection of cognitive impairment in MS

Gaussian mixture models improve fMRI-based image reconstruction

Decoding memory processing from electro-corticography in human posteromedial cortex

PET imaging analysis using a parcelation approach and multiple kernel classification

Improved MEG/EEG source localization with reweighted mixed-norms

Spatial discriminant ICA for RS-fMRI characterisation

Hierarchical processing of temporal asymmetry in human auditory cortex

Multiple subject learning for inter-subject prediction

Classification of inter-subject fMRI data based on graph kernels

A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors

Causal and anti-causal learning in pattern recognition for neuroimaging

Combining neuroanatomical and clinical data to improve individualized early diagnosis of schizophrenia in subjects at high familial risk

Fast voxel selection of fMRI data based on Smoothed 10 norm

2013 International Workshop on Pattern Recognition in Neuroimaging

2012 Second International Workshop on Pattern Recognition in NeuroImaging

2011 International Workshop on Pattern Recognition in NeuroImaging

Multimodal neuroimaging in Alzheimer's disease: Contributions of multi-voxel pattern analysis to the analysis of DTI and resting-state MRI

Content Provider	IEEE Xplore Digital Library
Author	Rondinoni, C. Garrido Salmon, C.E. Rolo, J. dos Santos, A.C.
Copyright Year	2014
Description	Author affiliation: Univ. of Sao Paulo, Ribeirao Preto, Brazil (Rondinoni, C.; Garrido Salmon, C.E.; Rolo, J.; dos Santos, A.C.)
Abstract	Previous findings suggest that temporal coherence between Blood-Oxygen-Level Dependent (BOLD) activation in certain areas is specifically related to the micro-structural organization of fascicles, i.e., the more organized the fibers, the more intense is the communication between areas. This assumption was considered in the analysis of functional and effective connectivity in patients with AD. Support Vector Machines for pattern classification (PRoNTo Toolbox-UCL) were applied to verify the usefulness of Granger-causality effective connectivity maps in correctly classifying patients and controls. Nineteen patients and eighteen healthy controls were recruited for the study and were scanned using DTI and resting state functional connectivity MRI (rs fc-MRI). Analysis of covariance with age as a confounding factor was applied to DTI data to identify areas related to disease progression. Granger mapping was used to identify brain areas related to differences of effective connectivity between groups. Maps were then input to feature extraction procedures. Models were specified with second-level masks and, after training, classifiers were validated by a leave-one-subject-out schedule. The main difference area between groups was found in the white matter below BA6, in the right hemisphere. Weight vector maps showed differences in areas related to attentional processing and auditory stimulus integration. Results point to an association between normal ageing and differences in effective connectivity related to AD. Our results show that degeneration of fibers is complementary to the degeneration of cortical cells, in accordance with the notion that AD is a network disease.
Starting Page	1
Ending Page	4
File Size	549297
Page Count	4
File Format	PDF
e-ISBN	9781479941490
DOI	10.1109/PRNI.2014.6858540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-04
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Barium Pattern classification Support vector machine Granger Causality Mapping Alzheimer's disease Resting state MRI BOLD signal Diffusion tensor imaging
Content Type	Text
Resource Type	Article

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Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine

Multimodal neuroimaging analysis reveals age‐associated common and discrete cognitive control constructs

Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality

Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis.

Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases—A Resting State fMRI Study

Editorial: Current advances in multimodal human brain imaging and analysis across the lifespan: From mapping to state prediction

Exploring the effective connectivity of resting state networks in Mild Cognitive Impairment: An fMRI study combining ICA and multivariate Granger causality analysis

Multimodal neuroimaging in Alzheimer's disease: Contributions of multi-voxel pattern analysis to the analysis of DTI and resting-state MRI

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