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Recent developments in multivariate pattern analysis for functional MRI

Zhi Yang, Fang Fang, Xuchu Weng
2012 Neuroscience Bulletin  
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

Michael Hanke
2010 Frontiers in Neuroscience  
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

Marco Zorzi, Maria Grazia Di Bono, Wim Fias
2011 NeuroImage  
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

Ian A. Clark, Katherine E. Niehaus, Eugene P. Duff, Martina C. Di Simplicio, Gari D. Clifford, Stephen M. Smith, Clare E. Mackay, Mark W. Woolrich, Emily A. Holmes
2014 Behaviour Research and Therapy  
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

Eleni Zarogianni, Thomas W.J. Moorhead, Stephen M. Lawrie
2013 NeuroImage: Clinical  
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

S LACONTE, S STROTHER, V CHERKASSKY, J ANDERSON, X HU
2005 NeuroImage  
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

Meenal J. Patel, Alexander Khalaf, Howard J. Aizenstein
2016 NeuroImage: Clinical  
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

Xiaoyun Liang, Chia-Lin Koh, Chun-Hung Yeh, Peter Goodin, Gemma Lamp, Alan Connelly, Leeanne M. Carey
2021 Brain Sciences  
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

Hans-Otto Karnath, Christoph Sperber, Christopher Rorden
2018 NeuroImage  
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

Roser Sala-Llonch, Stephen M. Smith, Mark Woolrich, Eugene P. Duff
2018 Human Brain Mapping  
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

Pegah Kassraian-Fard, Caroline Matthis, Joshua H. Balsters, Marloes H. Maathuis, Nicole Wenderoth
2016 Frontiers in Psychiatry  
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

Somayeh Shahsavarani, Ivan T. Abraham, Benjamin J. Zimmerman, Yuliy M. Baryshnikov, Fatima T. Husain
2020 Frontiers in Computational Neuroscience  
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

R.J. Dolan
2008 Neuron  
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

Jérôme-Alexis Chevalier, Tuan-Binh Nguyen, Joseph Salmon, Gaël Varoquaux, Bertrand Thirion
2021 NeuroImage  
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

James R. Williamson, Doug Sturim, Trina Vian, Joseph Lacirignola, Trey E. Shenk, Sophia Yuditskaya, Hrishikesh M. Rao, Thomas M. Talavage, Kristin J. Heaton, Thomas F. Quatieri
2021 Frontiers in Neurology  
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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