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Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts [article]

Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
2021 arXiv   pre-print
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.  ...  We have previously shown the kinematic features vary largely across head impact types, resulting in different patterns of brain deformation.  ...  For example, a data-driven emulator was developed to simulate the kinematics of head impacts with the principal components found by principal component analysis (PCA) (e.g., 15 principal components for  ... 
arXiv:2110.14116v1 fatcat:jz67jlsrwnfsbdbhl5lgi2gfdy

Dynamic Data Driven Approach for Modeling Human Error

Wan-Lin Hu, Janette J. Meyer, Zhaosen Wang, Tahira Reid, Douglas E. Adams, Sunil Prabnakar, Alok R. Chaturvedi
2015 Procedia Computer Science  
Dynamic system analysis methods were then used to analyze the raw data using principal components analysis and the least squares complex exponential method.  ...  This paper describes early developments of a dynamic data driven platform to predict operator error and trigger appropriate intervention before the error happens.  ...  approach based on principal component analysis with singular value decomposition.  ... 
doi:10.1016/j.procs.2015.05.298 fatcat:7osyz2ryyzfxxliahd3wzj7cy4

Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery

Vince D. Calhoun, Tülay Adali
2012 IEEE Reviews in Biomedical Engineering  
Secondly, multi-subject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network.  ...  We will also show examples of some of the differences observed in the default mode and resting networks in the diseased brain.  ...  order selection, and hence fit naturally into the framework of data-driven analysis methods such as ICA.  ... 
doi:10.1109/rbme.2012.2211076 pmid:23231989 pmcid:PMC4433055 fatcat:t6d2d3hu4nabbk73waqnxqk2fu

A review of multivariate analyses in imaging genetics

Jingyu Liu, Vince D. Calhoun
2014 Frontiers in Neuroinformatics  
We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis  ...  , independent component analysis (ICA), and clustering.  ...  Other types of data-driven approaches, as reviewed in (Jombart et al., 2009) , mainly include principal component analysis (PCA), principal coordinate analysis, non-metric dimensional scaling, and correspondence  ... 
doi:10.3389/fninf.2014.00029 pmid:24723883 pmcid:PMC3972473 fatcat:h4l6khb5zbhl3ox32vymjlwbqi

Real-time independent component analysis of fMRI time-series

F Esposito
2003 NeuroImage  
Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions  ...  Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses.  ...  Independent Component Analysis (ICA) is one of the most promising approaches to the off-line multivariate and data-driven analysis of fMRI data (McKeown et al., 1998a; Brown et al., 2001) .  ... 
doi:10.1016/j.neuroimage.2003.08.012 pmid:14683723 fatcat:eq3mztt2cnagtf42f53i5d65u4

Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia

Lindsey M. Brier, Eric C. Landsness, Abraham Z. Snyder, Patrick W. Wright, Grant A. Baxter, Adam Q. Bauer, Jin-Moo Lee, Joseph P. Culver
2019 Neurophotonics  
Principal component analysis of GCaMP6 sleep/anesthesia data in the delta band revealed that the slow oscillation is largely confined to the first three components.  ...  Further, discrepancies between activity dynamics observed with hemoglobin versus calcium (GCaMP6) imaging have not been reconciled.  ...  This work was supported by the National Institute of Neurological Disorders and Stroke [Grant Nos.  ... 
doi:10.1117/1.nph.6.3.035002 pmid:31930154 pmcid:PMC6952529 fatcat:5uytf43mojfcfkleh6wmfn2zgy

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

Mustafa S. Cetin, Jon M. Houck, Barnaly Rashid, Oktay Agacoglu, Julia M. Stephen, Jing Sui, Jose Canive, Andy Mayer, Cheryl Aine, Juan R. Bustillo, Vince D. Calhoun
2016 Frontiers in Neuroscience  
network components in the resting state.  ...  Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia.  ...  The subject-specific data reduction principal component analysis was performed and 100 principal components retained by using a standard economy-size decomposition .  ... 
doi:10.3389/fnins.2016.00466 pmid:27807403 pmcid:PMC5070283 fatcat:m23fkub4sjed3b2t2bd7tvjv34

Altered functional brain dynamics in chromosome 22q11.2 deletion syndrome during facial affect processing [article]

Eli J Cornblath, Arun S Mahadevan, Xiaosong He, Kosha Ruparel, David M Lydon-Staley, Tyler M Moore, Ruben C Gur, Elaine H Zackai, Beverly Emanuel, Donna M McDonald-McGinn, Daniel H Wolf, Theodore D Satterthwaite (+3 others)
2020 bioRxiv   pre-print
We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with  ...  Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks, but time-varying interactions between brain  ...  Constrained principal component analysis of emotion identification task data In order to perform constrained principal component analysis (CPCA) 13,20,22 , we began with (N * T ) × P matrix X, containing  ... 
doi:10.1101/2020.12.17.423342 fatcat:no2gia63pjgypdqlo4pu74d7bq

Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Anees Abrol, Barnaly Rashid, Srinivas Rachakonda, Eswar Damaraju, Vince D. Calhoun
2017 Frontiers in Neuroscience  
In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.  ...  Accordingly, the entire dataset was transformed into 130 principal components using standard principal component analysis (PCA) at the subject level in the first data reduction step of the group ICA analysis  ...  In the first phase of the joint framework, the mCCA algorithm commences with dimensionality reduction of the feature spaces of each of the modalities using principal component analysis.  ... 
doi:10.3389/fnins.2017.00624 pmid:29163021 pmcid:PMC5682010 fatcat:d67ynabu2ndotkezdelu7x2ibm

Enter the matrix: factorization uncovers knowledge from omics [article]

Genevieve L. Stein-O'Brien, Raman Arora, Aedin C. Culhane, Alexander Favorov, Lana X. Garmire, Casey Greene, Loyal A. Goff, Yifeng Li, Alioune Ngom, Michael F. Ochs, Yanxun Xu, Elana J. Fertig
2017 bioRxiv   pre-print
These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis.  ...  The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge-answering questions from high-dimensional  ...  Acknowledgements We would like to thank J Brian Byrd, Irene Gallego Romero, Lillian Fritz-Laylin, Luciane Kagohara, Louise Klein, Daniela Witten, and members of New PI Slack for their insightful feedback  ... 
doi:10.1101/196915 fatcat:booyynhuazekvhox2ey6x2s4oy

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Xinlong Wang, Hashini Wanniarachchi, Anqi Wu, Hanli Liu
2022 Frontiers in Human Neuroscience  
In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on  ...  The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space.  ...  Next, robust principal component analysis (rPCA) (Candès et al., 2011) was applied for effective removal of common artefacts, such as head motions, saccades, and jaw clenches, from our EEG data (Turnip  ... 
doi:10.3389/fnhum.2022.853909 pmid:35620152 pmcid:PMC9127055 fatcat:4kolsatr4rhnbhvx7dzh4vbii4

Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments [article]

Marius Klug, Klaus Gramann
2020 bioRxiv   pre-print
Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA proved to be important and functional even with 16 channels.  ...  Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective  ...  ., 2019) and in the real world, which increases our understanding of human brain dynamics accompanying embodied cognitive processes as well as the impact of real world environments (e.g.  ... 
doi:10.1101/2020.06.02.129213 fatcat:mcv5pa6cqrekhaeandj6y6cxy4

Fluctuations in Oscillation Frequency Control Spike Timing and Coordinate Neural Networks

M. X. Cohen
2014 Journal of Neuroscience  
Neuroscience research spans multiple spatiotemporal scales, from subsecond dynamics of individual neurons to the slow coordination of billions of neurons during resting state and sleep.  ...  Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task.  ...  components analysis via eigenvalue decomposition of that covariance matrix.  ... 
doi:10.1523/jneurosci.0261-14.2014 pmid:24990919 pmcid:PMC6608248 fatcat:pvuh5cklzned7owa2p67h5jtm4

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Yuhui Du, Zening Fu, Vince D. Calhoun
2018 Frontiers in Neuroscience  
Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles.  ...  Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain.  ...  Science Foundation of China (Grant No. 61703253, to YD) and Natural Science Foundation of Shanxi Province (Grant No. 2016021077, to YD).  ... 
doi:10.3389/fnins.2018.00525 pmid:30127711 pmcid:PMC6088208 fatcat:b7hdtmrz4vehlhjw4fo2bewhwq

Methods for cleaning the BOLD fMRI signal

César Caballero-Gaudes, Richard C. Reynolds
2017 NeuroImage  
Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent  ...  component analysis.  ...  in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS.  ... 
doi:10.1016/j.neuroimage.2016.12.018 pmid:27956209 pmcid:PMC5466511 fatcat:nexqgsdfmvd73grkzcfnrkhesm
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