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Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies
2013
PLoS ONE
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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]
2015
arXiv
pre-print
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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
2020
Affective Science
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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
2012
NeuroImage
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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
2020
Frontiers in Neuroscience
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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?
2012
IEEE Transactions on Medical Imaging
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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
2011
NeuroImage
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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
2010
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
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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]
2021
bioRxiv
pre-print
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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
2015
Frontiers in Human Neuroscience
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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
2015
Frontiers in Neuroinformatics
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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
2015
Frontiers in Human Neuroscience
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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
2014
Cerebral Cortex
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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
2019
Journal of Vision
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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
2009
NeuroImage
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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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