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Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Maps

Santosh B. Katwal, John C. Gore, Rene Marois, Baxter P. Rogers
2013 IEEE Transactions on Biomedical Engineering  
We present novel graph-based visualizations of selforganizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis.  ...  We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the  ...  Tombu for assistance with fMRI data preparation.  ... 
doi:10.1109/tbme.2013.2258344 pmid:23613020 pmcid:PMC3919688 fatcat:l5nkuvsbcrgz5dr3gnjtdp3spy

Unsupervised spatiotemporal fMRI data analysis using support vector machines

Xiaomu Song, Alice M. Wyrwicz
2009 NeuroImage  
We examine the unsupervised mapping of activated brain regions using SVM.  ...  In this work we present a new support vector machine (SVM)-based method for fMRI data analysis.  ...  Michael Miller for providing the trace eyeblink conditioning data. This research is supported by National Institute of Health (NIH) RO1 NS44617 and S10 RR15685 grants.  ... 
doi:10.1016/j.neuroimage.2009.03.054 pmid:19344772 pmcid:PMC2807732 fatcat:nkn4ob6xybcojpdzzi22hupfcm

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Source Separation on resting-state fMRI and Its Use for Early MCI Identification 488 Construction of a spatiotemporal statistical shape model of pediatric liver from cross-sectional data 491 An Open Framework  ...  FMRI Signals for Mental Disorder Diagnosis 758 The dynamic measurements of regional brain activity for resting-state fMRI: d-ALFF, d-fALFF and d-ReHo 765 Enhancing clinical MRI Perfusion maps with data-driven  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Measuring relative timings of brain activities using fMRI

Santosh B. Katwal, John C. Gore, J. Christopher Gatenby, Baxter P. Rogers
2013 NeuroImage  
To maximize sensitivity, we used high spatial and temporal resolution fMRI at ultrahigh field (7 Tesla), in conjunction with a novel datadriven technique for voxel selection using graph-based visualizations  ...  of self-organizing maps and Granger causality to measure relative timing.  ...  Acknowledgments This research was supported by National Institutes of Health, NIH 5R01EB000461 (Principal Investigator JCG).  ... 
doi:10.1016/j.neuroimage.2012.10.052 pmid:23110880 pmcid:PMC3593774 fatcat:ujrjmzhx25didhn5awz4ofbipy

fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey

Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning
2022 Brain Sciences  
With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic  ...  resonance imaging (fMRI).  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci12020228 pmid:35203991 pmcid:PMC8869956 fatcat:t664eccq6nh5plnvhac2r2gcpa

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Maryam Gholami Doborjeh, Nikola Kasabov, Zohreh Gholami Doborjeh
2017 Evolving Systems  
EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it.  ...  The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task.  ...  Bruce Russell from the University of Auckland and Dr. Grace Wang from AUT for providing us with the EEG data. We also acknowledge the assistance of Joyce D'Mello, Dr. Enmei Tu, Dr.  ... 
doi:10.1007/s12530-017-9178-8 fatcat:nme6ja6qxnfercfqxpi47bnhlq

Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data [article]

Enmei Tu, Nikola Kasabov, Jie Yang
2016 arXiv   pre-print
This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern  ...  In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning  ...  Error rate of random mapping and graph mapping on Challenge2012 Cube models any spatiotemporal data.  ... 
arXiv:1603.05594v1 fatcat:oqvkww5hfnfh7fun6lzauoevai

Resting state-fMRI approach towards understanding impairments in mTLE [article]

Nishad Singhi, Hritik Bansal
2020 arXiv   pre-print
Finally, we describe how Machine Learning can be applied to rs-fMRI data to extract resting-state networks specific to mTLE and for automated diagnosis of this disease.  ...  Mesial temporal lobe epilepsy (mTLE) is the most common form of epilepsy.  ...  A number of methods are available to analyze the rs-fMRI data like seed-based analysis, Independent component analysis (ICA) and graph based approach.  ... 
arXiv:2009.11928v1 fatcat:bd4x76n6g5h6va7lavohblrabe

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

Zitong Lu, Yixuan Ku
2020 Frontiers in Neuroinformatics  
Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated  ...  NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results.  ...  Schematic diagram for representational analysis for fMRI data using NeuroRA. (A) The calculating process for ROI-based analysis.  ... 
doi:10.3389/fninf.2020.563669 pmid:33424573 pmcid:PMC7787009 fatcat:4f4z23fllval7ayyzdip5vraqe

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
We first categorize the types of spatio-temporal data and briefly introduce the popular deep learning models that are used in STDM.  ...  transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.  ...  In the area of fMRI data analysis, fMRI time series data are usually used to study the functional brain network and diagnose disease.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

Brain Connectivity Analysis: A Short Survey

E. W. Lang, A. M. Tomé, I. R. Keck, J. M. Górriz-Sáez, C. G. Puntonet
2012 Computational Intelligence and Neuroscience  
It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective.  ...  The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments.  ...  Simulations based on synthetic fMRI data show good correspondence of the resulting effective connectivity structures to Granger causality mapping [119] .  ... 
doi:10.1155/2012/412512 pmid:23097663 pmcid:PMC3477528 fatcat:zlwqwx6n7feefdrfx5pnhhbmhe

2021 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 13

2021 IEEE Transactions on Cognitive and Developmental Systems  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Cheng, L., +, TCDS March 2021 151-161 Performance Comparison of Gesture Recognition System Based on Differ-Data visualization An Unsupervised Approach for Knowledge Construction Applied to Per-sonal Robots  ...  ., +, TCDS Sept. 2021 546-554 Spatiotemporal Dynamical Analysis of Brain Activity During Mental Fatigue Process.  ... 
doi:10.1109/tcds.2021.3137068 fatcat:r2zbw6js65fpnenn4kybim3kw4

Deciphering Neural Codes of Memory during Sleep

Zhe Chen, Matthew A. Wilson
2017 Trends in Neurosciences  
We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework ("memory first, meaning later") for unbiased assessment of SANC.  ...  Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex.  ...  This work is supported by an NSF/NIH CRCNS award IIS-1307645 (to Z.C. and M.A.W.) from the US National Science Foundation, an NSF/NIH CRCNS award R01-NS100065 (to Z.C.) from the NINDS, the Office of Naval  ... 
doi:10.1016/j.tins.2017.03.005 pmid:28390699 pmcid:PMC5434457 fatcat:3fwjiuci4zcfnpvgb74d6tgcym

A self-organizing neural network architecture for learning human-object interactions [article]

Luiza Mici, German I. Parisi, Stefan Wermter
2018 arXiv   pre-print
In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos.  ...  Our model consists of a hierarchy of Grow-When-Required (GWR) networks that learn prototypical representations of body motion patterns and objects, accounting for the development of action-object mappings  ...  Acknowledgments The authors gratefully acknowledge partial support by the EU-and City of Hamburg-funded program Pro-Exzellenzia 4.0, the German Research Foundation DFG under project CML (TRR 169), and  ... 
arXiv:1710.01916v2 fatcat:eu7c7wn3anfx5hjzabufbrrdvq

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

Nikola Kasabov, Nathan Matthew Scott, Enmei Tu, Stefan Marks, Neelava Sengupta, Elisa Capecci, Muhaini Othman, Maryam Gholami Doborjeh, Norhanifah Murli, Reggio Hartono, Josafath Israel Espinosa-Ramos, Lei Zhou (+8 others)
2016 Neural Networks  
Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data.  ...  The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM).  ...  We were helped with the organization of this paper by Joyce D'Mello.  ... 
doi:10.1016/j.neunet.2015.09.011 pmid:26576468 fatcat:hytvg4eekjfazd4244z3o6cmdu
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