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Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
[article]
2021
arXiv
pre-print
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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
2015
Procedia Computer Science
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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
2012
IEEE Reviews in Biomedical Engineering
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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
2014
Frontiers in Neuroinformatics
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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
2003
NeuroImage
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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
2019
Neurophotonics
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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
2016
Frontiers in Neuroscience
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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]
2020
bioRxiv
pre-print
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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
2017
Frontiers in Neuroscience
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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]
2017
bioRxiv
pre-print
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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
2022
Frontiers in Human Neuroscience
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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]
2020
bioRxiv
pre-print
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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
2014
Journal of Neuroscience
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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
2018
Frontiers in Neuroscience
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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
2017
NeuroImage
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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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