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Spatial patterns and functional profiles for discovering structure in fMRI data
2008
2008 42nd Asilomar Conference on Signals, Systems and Computers
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In both applications, our methods confirm previously known results in brain mapping and point to new research directions for exploratory analysis of fMRI data. ...
We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. ...
Spatial co-Activation Patterns in Diverse fMRI Data This study of functional connectivity included 7 subjects. ...
doi:10.1109/acssc.2008.5074650
pmid:26082607
pmcid:PMC4465961
fatcat:2sstrrvadzgqphxz6pd7erw7fi
Discovering Structure in the Space of Activation Profiles in fMRI
[chapter]
2008
Lecture Notes in Computer Science
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We present a method for discovering patterns of activation observed through fMRI in experiments with multiple stimuli/tasks. ...
Working in the space of activation profiles, we design a mixture model that finds the major activation patterns along with their localization maps and derive an algorithm for fitting the model to the fMRI ...
This research was supported in part by NIH grants NIBIB NAMIC U54-EB005149, and NCRR NAC P41-RR13218, and by the NSF CA-REER grant 0642971. ...
doi:10.1007/978-3-540-85988-8_121
fatcat:smspyms5qrewzpwdxmcejetmsm
Nonparametric hierarchical Bayesian model for functional brain parcellation
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
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We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. ...
Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. ...
Acknowledgments This research was supported in part by the NSF IIS/CRCNS 0904625 grant, the NSF CAREER grant 0642971, the MIT McGovern Institute Neurotechnology Program, and the NIH NIBIB NAMIC U54-EB005149 ...
doi:10.1109/cvprw.2010.5543434
pmid:21841977
pmcid:PMC3153957
dblp:conf/cvpr/LashkariSVHKG10
fatcat:qcjant7hqjfxpjfxhncutg2fmm
Discovering structure in the space of fMRI selectivity profiles
2010
NeuroImage
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We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. ...
We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of ...
the pattern of discovered profiles and their maps. ...
doi:10.1016/j.neuroimage.2009.12.106
pmid:20053382
pmcid:PMC2976625
fatcat:ddaj3jtsyzepzkfr3gs3fwjeye
Adaptive integration of local region information to detect fine-scale brain activity patterns
2008
Science in China Series E: Technological Sciences
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With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. ...
To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are combined to discover the fine-grained activity patterns. ...
Two GK with FWHM of 6 mm and 9 mm were used separately to smooth the data. There are two widely used GK in fMRI data analysis. ...
doi:10.1007/s11431-008-0124-7
fatcat:sn57ygvbxrcvdp7yzfuhhv63xe
Machine learning in resting-state fMRI analysis
[article]
2018
arXiv
pre-print
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Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. ...
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. ...
Acknowledgements This work was supported by NIH R01 grants (R01LM012719 and R01AG053949), the NSF NeuroNex grant 1707312, and NSF CAREER grant (1748377). ...
arXiv:1812.11477v1
fatcat:nd6j5jbspzh2rmxyyufdyesxom
Exploratory fMRI analysis without spatial normalization
2009
Information processing in medical imaging : proceedings of the ... conference
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The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both intersubject and intra-subject forms of variability in fMRI response ...
We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. ...
Acknowledgments We thank Ed Vul and Nancy Kanwisher for providing us with the fMRI data. ...
pmid:19694280
pmcid:PMC2836541
fatcat:zaxet3edxzhhtoe6t3fh6tz22e
Exploratory fMRI Analysis without Spatial Normalization
[chapter]
2009
Lecture Notes in Computer Science
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The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response ...
We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. ...
Acknowledgments We thank Ed Vul and Nancy Kanwisher for providing us with the fMRI data. ...
doi:10.1007/978-3-642-02498-6_33
fatcat:iz2t2gveq5batgfozozil6kphm
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships
[article]
2022
arXiv
pre-print
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Then, using the novel and theoretically justified weight interpretation methodology we recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical ...
The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive ...
Acknowledgment This work is supported by the Center for Bioelectric Interfaces NRU HSE, RF Government grant, AG. No. 075-15-2021-624 ...
arXiv:2211.02024v2
fatcat:hp4cesfehratxewd34sfbfc6ym
Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks
2016
Philosophical Transactions of the Royal Society of London. Biological Sciences
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Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. ...
Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific ...
We thank Gita Prabu, Roger Tait, Cinly Ooi, John Suckling and Becky Inkster for fMRI data collection and storage. ...
doi:10.1098/rstb.2015.0362
pmid:27574314
pmcid:PMC5003862
fatcat:bwyqsyiibzdmtlwxi4eqflyr5e
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
[article]
2020
arXiv
pre-print
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We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational ...
those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data. ...
Modeling structured residuals -Defining the problem: modeling spatiotemporal residuals in fMRI data fMRI data has structure in both the spatial and temporal dimension, and this spatiotemporal consistency ...
arXiv:2005.04879v1
fatcat:qsddwj7qi5gutk3vks3uf3rn2q
Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data
2012
NeuroImage
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In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. ...
The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. ...
Automatic detection of these profiles demonstrates the potential of our approach to discover novel patterns of specificity in the data. ...
doi:10.1016/j.neuroimage.2011.08.031
pmid:21884803
pmcid:PMC3972261
fatcat:dlkxqn4fizfobjgi56khpuja2u
Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia
2008
IEEE Journal on Selected Topics in Signal Processing
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Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. ...
As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. ...
The associate editor coordinating the review of this manuscript and approving it for publication was A. Cichocki. Dr. ...
doi:10.1109/jstsp.2008.2008265
pmid:19834573
pmcid:PMC2761661
fatcat:oenf7lffbvcv7ppvkflnwpa5bm
Representation Learning of Resting State fMRI with Variational Autoencoder
[article]
2020
bioRxiv
pre-print
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Resting state functional magnetic resonance imaging (rs-fMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rs-fMRI data. ...
After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. ...
Clustering in the latent space We encoded the rs-fMRI spatial pattern at every time point for 500 testing . ...
doi:10.1101/2020.06.16.155937
fatcat:bnuq7hcj3bdwtphu4vcthg4l6i
Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder
[article]
2021
bioRxiv
pre-print
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These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients. ...
patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. ...
Aside from the spatial profiles, the functional connectivity of each latent variable's spatiotemporal pattern was calculated. ...
doi:10.1101/2021.01.25.427841
fatcat:kvrc5o2uwvebrc3na4bofp2c6m
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