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Recent developments in multivariate pattern analysis for functional MRI
2012
Neuroscience Bulletin
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Compared with the traditional univariate methods, MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data. ...
Multivariate pattern analysis (MVPA) is a recently-developed approach for functional magnetic resonance imaging (fMRI) data analyses. ...
This review was supported by grants from the National Natural Science Foundation of China (30900366, 31070905) ...
doi:10.1007/s12264-012-1253-3
pmid:22833038
pmcid:PMC5561894
fatcat:b7w5olxdbbdyhap26whh6lj6p4
Statistical learning analysis in neuroscience: aiming for transparency
2010
Frontiers in Neuroscience
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for the analysis of neural data. ...
Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. ...
Halchenko was supported by the National Science Foundation (grant: SBE 0751008) and the James McDonnell Foundation (grant: 220020127). ...
doi:10.3389/neuro.01.007.2010
pmid:20582270
pmcid:PMC2891484
fatcat:6tncphwbtnbyjcewxj6nlkuv2q
Distinct representations of numerical and non-numerical order in the human intraparietal sulcus revealed by multivariate pattern recognition
2011
NeuroImage
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Based on the hypothesis that the fine-grained distinction between representations of numerical vs. letter order in hIPS might simply be invisible to conventional fMRI data analysis, we used support vector ...
machines (SVM) to reanalyse the data of Fias et al. (2007) . ...
W.F. acknowledges the support of Ghent University (Multidisciplinary Research Partnership "The integrative neuroscience of behavioral control") and of Interuniversitary Attraction Poles program of the ...
doi:10.1016/j.neuroimage.2010.06.035
pmid:20600989
fatcat:mse4a2kjebgszmnny27zrjyqsy
First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage
2014
Behaviour Research and Therapy
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To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma ...
We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peritraumatic brain activation was able to predict later intrusive memories ...
We compared both linear discriminant analysis and support vector machines as classifiers. ...
doi:10.1016/j.brat.2014.07.010
pmid:25151915
pmcid:PMC4222599
fatcat:rnbz47pmufdmponu6verziw73u
Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level
2013
NeuroImage: Clinical
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data analysis. ...
Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging ...
patients; SCID-I, Structural Clinical Interview; SMLR, sparse multinomial logistic regression; SVM, Support Vector Machine; SVR, Support Vector Regression; SVM-RFE, Support Vector Machine with Recursive ...
doi:10.1016/j.nicl.2013.09.003
pmid:24273713
pmcid:PMC3814947
fatcat:rcwcwtcxx5hcrgoqwdb33gbysa
Support vector machines for temporal classification of block design fMRI data
2005
NeuroImage
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This paper treats support vector machine (SVM) classification applied to block design fMRI, extending our previous work with linear discriminant analysis [LaConte, S.The quantitative evaluation of functional ...
As the SVM has many unique properties, we examine the interpretation of support vector models with respect to neuroimaging data. ...
Acknowledgments Many people have helped with various aspects of this project. We especially wish to acknowledge Dr. Jihong Chen, Dr. Yasser Kadah, Dr. Scott Peltier, Dr. Shing-Chung Ngan, Mr. ...
doi:10.1016/j.neuroimage.2005.01.048
pmid:15907293
fatcat:vnuozidvx5bs7ldgkoa7aabhse
Studying depression using imaging and machine learning methods
2016
NeuroImage: Clinical
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This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression ...
These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. ...
Acknowledgments This research was supported by NIH grant R01MH076079, University of Pittsburgh Clinical Scientist Training Program (UL1 TL1TR000005), and NIMH Medical Student Research Fellowship (R25 MH054318 ...
doi:10.1016/j.nicl.2015.11.003
pmid:26759786
pmcid:PMC4683422
fatcat:o2i47nzpozcyjmstvwjxmnn37e
Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
2021
Brain Sciences
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Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. ...
Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. ...
Acknowledgments: We acknowledge support for conduct of the research from the National Health and Medical Research Council of Australia and thank the people with stroke who participated in the study. ...
doi:10.3390/brainsci11111388
pmid:34827387
pmcid:PMC8615819
fatcat:jeib5vx2crexdiobzakvbsgrwm
Mapping human brain lesions and their functional consequences
2018
NeuroImage
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This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate ...
analyses of structural imaging data. ...
Christoph Sperber was supported by the Friedrich Naumann Foundation. We thank Grigori Yourganov and Ged Ridgway for their helpful comments on the manuscript. ...
doi:10.1016/j.neuroimage.2017.10.028
pmid:29042216
pmcid:PMC5777219
fatcat:flxag4o5j5hwnhalpkghghgw4u
Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination
2018
Human Brain Mapping
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Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized ...
The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. ...
In addition, we evaluated band-pass filtered data in four
| Classification We assessed the discriminative capabilities of connectivity features using a multiclass linear Support Vector Machine (SVM) ...
doi:10.1002/hbm.24381
pmid:30259597
pmcid:PMC6492132
fatcat:egknkah2hvg6vdfiuyz7nxrsla
Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example
2016
Frontiers in Psychiatry
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The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). ...
We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. ...
support Vector Machines The basic idea of linear SVMs is to construct an optimal linear decision boundary that is maximally far from the data samples On the right side (B), a one-dimensional example data ...
doi:10.3389/fpsyt.2016.00177
pmid:27990125
fatcat:ghjomxkztncylde7qijntwm7pi
Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI
2020
Frontiers in Computational Neuroscience
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Further, using different machine learning techniques including support vector machines, discriminant analyses, and convolutional neural networks, our results revealed that the manifestation of the group-level ...
Our study adds to the growing body of research on developing diagnostic tools to identify neurological disorders, such as tinnitus, using resting state fMRI data. ...
Support Vector Machine In the past three decades, support vector machine (SVM) has emerged as one of the most popular classification techniques. ...
doi:10.3389/fncom.2019.00094
pmid:32038211
pmcid:PMC6984040
fatcat:diwhjsmqnjhhhiqd5ajebm5br4
Neuroimaging of Cognition: Past, Present, and Future
2008
Neuron
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s research is supported by the Wellcome Trust. ...
An example is the use of support vector machines (SVMs) to establish statistical dependence between distributed responses in a circumscribed part of the brain and some experimental variable. ...
and data analysis. ...
doi:10.1016/j.neuron.2008.10.038
pmid:18995825
pmcid:PMC2699840
fatcat:gry5cw45lvcfdfaxpeicktuqvi
Decoding with Confidence: Statistical Control on Decoder Maps
2021
NeuroImage
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Then, we present a decoding procedure that can control the δ-FWER: the Ensemble of Clustered Desparsified Lasso (EnCluDL), a procedure for multivariate statistical inference on high-dimensional structured ...
data. ...
Insights on choosing the number of clusters Here, we report the results obtained of the experiment task-fMRI data ( Section 4.5 ) studying the impact of (number of clusters) on the -FWER control and the ...
doi:10.1016/j.neuroimage.2021.117921
pmid:33722670
fatcat:adh7kzculjap7b3k5ag7x6enva
Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes
2021
Frontiers in Neurology
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This latent factor was positively correlated with four of the ImPACT composites: verbal memory, visual memory, visual motor speed and reaction speed. ...
using fMRI) to complement existing diagnostic tools, such as the Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), that are used for this purpose. ...
ACKNOWLEDGMENTS Authors would like to thank Laurel Keyes for early data collection support. ...
doi:10.3389/fneur.2021.665338
fatcat:m6ryb4aqnbgera2ssu5h43xv34
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