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Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies

Rajeev D. S. Raizada, Yune-Sang Lee, Gaolang Gong
2013 PLoS ONE  
Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies.  ...  We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in "searchlight" pattern-based analyses.  ...  Contributed reagents/materials/analysis tools: RDSR YSL. Wrote the paper: RDSR YSL.  ... 
doi:10.1371/journal.pone.0069566 pmid:23922740 pmcid:PMC3724912 fatcat:skcnikrfmndtbnwxw47lhldy4i

Mesh Learning for Classifying Cognitive Processes [article]

Mete Ozay, Ilke Öztekin, Uygar Öztekin, Fatos T. Yarman Vural
2015 arXiv   pre-print
In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model.  ...  Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.  ...  Then, we employed a Gaussian Naïve Bayes (GNB) classifier to measure the voxel scores considering the classification accuracies.  ... 
arXiv:1205.2382v3 fatcat:iqwyj3lk6fg6fhnk6qjft7quze

Audiovisual Representations of Valence: a Cross-study Perspective

Svetlana V. Shinkareva, Chuanji Gao, Douglas Wedell
2020 Affective Science  
In a searchlight analysis, the representation of valence was localized to the right postcentral and supramarginal gyri, left superior frontal and middle frontal cortices, and right pregenual anterior cingulate  ...  In a leave-one-study-out cross-validation procedure, we trained the classifiers on fMRI data from five studies and predicted valence, positive or negative, for each of the participants in the left-out  ...  Gaussian Naïve Bayes was implemented using a Fast Gaussian Naïve Bayes toolbox (Ontivero-Ortega et al., 2017) .  ... 
doi:10.1007/s42761-020-00023-9 fatcat:drbmuza2inglfc4j7iipciqtsy

The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs

Marc N. Coutanche, Sharon L. Thompson-Schill
2012 NeuroImage  
For investigations that use multi-voxel pattern analysis (MVPA), however, employing many short runs might improve a classifier's ability to generalize across irrelevant pattern variations and detect condition-related  ...  Performance improvements also extended to an information brain mapping 'searchlight' procedure.  ...  Acknowledgments We thank Avi Chanales and Carol Gianessi for assistance with stimulus preparation, and members of the Thompson-Schill lab for helpful discussions.  ... 
doi:10.1016/j.neuroimage.2012.03.076 pmid:22498658 pmcid:PMC3587765 fatcat:ezgaqbh4trbohmnbcqnstq4j6q

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

Matthias S. Treder
2020 Frontiers in Neuroscience  
High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis.  ...  MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms.  ...  Gaussian Naive Bayes: features are conditionally independent, yielding diagonal covariance matrices.  ... 
doi:10.3389/fnins.2020.00289 pmid:32581662 pmcid:PMC7287158 fatcat:7mxsgmesynayjk5q34jehmm5j4

Can a Single Brain Region Predict a Disorder?

Jean Honorio, Dardo Tomasi, Rita Z. Goldstein, Hoi-Chung Leung, Dimitris Samaras
2012 IEEE Transactions on Medical Imaging  
folds; while for group classification (e.g. cocaine addicted vs. control subjects), voxels are scattered and less stable.  ...  Schizophrenia and 81.5% for Alzheimer's disease).  ...  ACKNOWLEDGMENTS We thank Philippe Pinel and Bertrand Thirion for providing us with the Fast Acquisition dataset. This work was supported in part by NIDA Grants 1 R01 DA020949, R21 DA02062,  ... 
doi:10.1109/tmi.2012.2206047 pmid:22752119 fatcat:fyukb36xefhmpgy5ihew5xqwne

Information mapping with pattern classifiers: A comparative study

Francisco Pereira, Matthew Botvinick
2011 NeuroImage  
Finally, we introduce a publically available software toolbox designed specifically for information mapping.  ...  on searchlights unless otherwise mentioned): • voxelGNB -single voxel gaussian naive bayes • voxelGNB smooth -single voxel gaussian naive bayes over spatially smoothed data (using a 3×3×3 uniform smoothing  ...  We then trained/tested Gaussian Naive Bayes searchlight classifiers to produce analytical p-values, and ran a permutation test using them as well.  ... 
doi:10.1016/j.neuroimage.2010.05.026 pmid:20488249 pmcid:PMC2975047 fatcat:7f2wfpag7zcctm6skexv5d7iwy

Simple fully automated group classification on brain fMRI

Jean Honorio, Dimitris Samaras, Dardo Tomasi, Rita Goldstein
2010 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI datasets that have high dimensionality, small number of subjects, high noise level, high  ...  accuracy compared to commonly used feature selection and classification techniques.  ...  using a Gaussian Naïve Bayes classifier on the 3×3×3 voxel neighborhood as feature set.  ... 
doi:10.1109/isbi.2010.5490196 dblp:conf/isbi/HonorioSTG10 fatcat:hpvbbx2a6vd4djmgj7xw4tg22a

MVPA does not reveal neural representations of hierarchical linguistic structure in MEG [article]

Sophie Arana, Jan-Mathijs Schoffelen, Tom Mitchell, Peter Hagoort
2021 bioRxiv   pre-print
We discuss methodological limits of our analysis as well as cognitive theories of "shallow processing", i.e. in how far rich semantic information can prevent thorough syntactic analysis during processing  ...  In this study we recorded Magnetoencephalography (MEG) during reading of structurally ambiguous sentences to probe neural activity for representations of underlying phrase structure. 10 human subjects  ...  It is made Panel A: Accuracy is plotted over time for part-of-speech classification (nouns vs verbs) using Gaussian Naive Bayes (red).  ... 
doi:10.1101/2021.02.19.431945 fatcat:wkysuanknncszggkhwyrcx7zkq

Neural regions discriminating contextual information as conveyed through the learned preferences of others

Su Mei Lee, Gregory McCarthy
2015 Frontiers in Human Neuroscience  
No evidence for context discrimination was found in the pSTS.  ...  Multi-voxel pattern analysis (MVPA) was used to assess if these different contexts could be discriminated in the pSTS and elsewhere in the brain.  ...  Acknowledgments We thank Na Yeon Kim for assistance with data collection.  ... 
doi:10.3389/fnhum.2015.00492 pmid:26441592 pmcid:PMC4562242 fatcat:46ttyy4fn5emvmdggri5ov6gze

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

Martin N. Hebart, Kai Görgen, John-Dylan Haynes
2015 Frontiers in Neuroinformatics  
The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity  ...  While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification  ...  We thank the members of the Haynes lab and many volunteers from around the world for trying prepublication releases of the toolbox.  ... 
doi:10.3389/fninf.2014.00088 pmid:25610393 pmcid:PMC4285115 fatcat:naz7nsjso5eipksm3if7mqq3ay

Accurately decoding visual information from fMRI data obtained in a realistic virtual environment

Andrew Floren, Bruce Naylor, Risto Miikkulainen, David Ress
2015 Frontiers in Human Neuroscience  
Compared with maps produced by general linear models and the searchlight approach, these sensitivity maps revealed a more diverse pattern of information relevant to the classification of cognitive state  ...  To better understand the source of this information in the brain, a novel form of sensitivity analysis was developed to use NN to quantify the degree to which each voxel contributed to classification.  ...  For completeness, we also tested a Gaussian naive Bayes classifier (GNB) (Duda and Hart, 1973) , and k-nearest neighbor classifier (KNN).  ... 
doi:10.3389/fnhum.2015.00327 pmid:26106315 pmcid:PMC4460535 fatcat:grutj6alebbwtgcpcoziu3kwgi

Creating Concepts from Converging Features in Human Cortex

Marc N. Coutanche, Sharon L. Thompson-Schill
2014 Cerebral Cortex  
These results fulfill three key requirements for a neural convergence zone: a convergence result (object identity), ingredients (color and shape), and the link between them.  ...  A novel decoding-dependency analysis revealed that identity information in left ATL was specifically predicted by the temporal convergence of shape and color codes in early visual regions.  ...  Rogers, and an anonymous reviewer for their insightful comments. Conflict of Interest: None declared.  ... 
doi:10.1093/cercor/bhu057 pmid:24692512 pmcid:PMC4537422 fatcat:es5uisikfbdzxmgrwfc5b3gh3a

Subtle predictive movements reveal actions regardless of social context

Emalie G. McMahon, Charles Y. Zheng, Francisco Pereira, Ray Gonzalez, Leslie G. Ungerleider, Maryam Vaziri-Pashkam
2019 Journal of Vision  
Moreover, a linear SVM is the best classifier for our data considering that the common alternative linear classifiers (logistic regression, linear discriminate analysis, and Gaussian Naïve Bayes classifiers  ...  Using the Searchmight Toolbox (Pereira & Botvinick, 2011) and in-house MATLAB codes, a Gaussian Naïve Bayes (GNB) classifier was trained to classify the direction of movement from the x and y optical-flow  ... 
doi:10.1167/19.7.16 pmid:31355865 pmcid:PMC6662941 fatcat:qngy7azsnzgkvh4lb7k63lzfjq

Machine learning classifiers and fMRI: A tutorial overview

Francisco Pereira, Tom Mitchell, Matthew Botvinick
2009 NeuroImage  
Whereas this might seem remote from the generative model idea introduced earlier, the classification decisions of Gaussian Naive Bayes (GNB) and Fisher's Linear Discriminant Analysis (LDA) can be expressed  ...  P (x|classk) = Q m j=1 P (xj|classk), then we have a Gaussian Naive Bayes (GNB) classifier [32] .  ... 
doi:10.1016/j.neuroimage.2008.11.007 pmid:19070668 pmcid:PMC2892746 fatcat:h7tyyjyfzzcyvjd4e2syjtedza
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