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Journal articles on the topic 'FMRI Data'

Author: Grafiati

Published: 6 September 2023

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1

Ihalainen, Toni, Linda Kuusela, Sampsa Turunen, Sami Heikkinen, Sauli Savolainen, and Outi Sipilä. "Data quality in fMRI and simultaneous EEG–fMRI." Magnetic Resonance Materials in Physics, Biology and Medicine 28, no. 1 (April 26, 2014): 23–31. http://dx.doi.org/10.1007/s10334-014-0443-6.

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2

Kim, Jaehee. "Statistical analysis issues for fMRI data." Journal of the Korean Data And Information Science Sociaty 29, no. 6 (November 30, 2018): 1353–63. http://dx.doi.org/10.7465/jkdi.2018.29.6.1353.

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3

Zhang, Chuncheng, and Zhiying Long. "Euler’s Elastica Regularization for Voxel Selection of fMRI Data." International Journal of Signal Processing Systems 8, no. 2 (June 2020): 32–41. http://dx.doi.org/10.18178/ijsps.8.2.32-41.

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Multivariate analysis methods have been widely applied to functional Magnetic Resonance Imaging (fMRI) data to reveal brain activity patterns and decode brain states. Among the various multivariate analysis methods, the multivariate regression models that take high-dimensional fMRI data as inputs while using relevant regularization were proposed for voxel selection or decoding. Although some previous studies added the sparse regularization to the multivariate regression model to select relevant voxels, the selected sparse voxels cannot be used to map brain activity of each task. Compared to the sparse regularization, the Euler’s Elastica (EE) regularization that considers the spatial information of data can identify the clustered voxels of fMRI data. Our previous study added EE Regularization to Logical Regression (EELR) and demonstrated its advantages over the other regularizations in fMRI-based decoding. In this study, we further developed a multivariate regression model using EE in 3D space as constraint for voxel selection. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of EE regression model. The performance of EE regression was compared with the Generalized Linear Model (GLM) and Total Variation (TV) regression in brain activity detection, and was compared with GLM, Laplacian Smoothed L0 norm (LSL0) and TV regression methods in feature selection for brain state decoding. The results indicated that EE regression possessed better sensitivity to detect brain regions specific to a task than did GLM and better spatial detection power than TV regression. Moreover, EE regression outperformed GLM, LSL0 and TV in feature selection.

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4

Mumford, Jeanette A., and Russell A. Poldrack. "Modeling group fMRI data." Social Cognitive and Affective Neuroscience 2, no. 3 (September 1, 2007): 251–57. http://dx.doi.org/10.1093/scan/nsm019.

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5

Heinzle, J., S. Anders, S. Bode, C. Bogler, Y. Chen, R. M. Cichy, K. Hackmack, et al. "Multivariate decoding of fMRI data." e-Neuroforum 18, no. 1 (January 1, 2012): 1–16. http://dx.doi.org/10.1007/s13295-012-0026-9.

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AbstractThe advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-invasive measurement of brain function that is now widely used in cognitive neuroscience. Traditionally, fMRI data has been analyzed looking for overall activity changes in brain regions in response to a stimulus or a cognitive task. Now, recent developments have introduced more elaborate, content-based analysis techniques. When multivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of information these encode about specific task-variables. Here we provide an overview of several developments, spanning from applications in cognitive neuroscience (perception, attention, reward, decision making, emotional communication) to methodology (information flow, surface-based searchlight decoding) and medical diagnostics.

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6

Parida, Shantipriya, and Satchidananda Dehuri. "Review of fMRI Data Analysis." International Journal of E-Health and Medical Communications 5, no. 2 (April 2014): 1–26. http://dx.doi.org/10.4018/ijehmc.2014040101.

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Classification of brain states obtained through functional magnetic resonance imaging (fMRI) poses a serious challenges for neuroimaging community to uncover discriminating patterns of brain state activity that define independent thought processes. This challenge came into existence because of the large number of voxels in a typical fMRI scan, the classifier is presented with a massive feature set coupled with a relatively small training samples. One of the most popular research topics in last few years is the application of machine learning algorithms for mental states classification, decoding brain activation, and finding the variable of interest from fMRI data. In classification scenario, different algorithms have different biases, in the sequel performances differs across datasets, and for a particular dataset the accuracy varies from classifier to classifier. To overcome the limitations of individual techniques, hybridization or fusion of these machine learning techniques emerged in recent years which have shown promising result and open up new direction of research. This paper reviews the machine learning techniques ranging from individual classifiers, ensemble, and hybrid techniques used in cognitive classification with a well balance treatment of their applications, performance, and limitations. It also discusses many open research challenges for further research.

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7

Worsley, K. J. "Detecting activation in fMRI data." Statistical Methods in Medical Research 12, no. 5 (October 2003): 401–18. http://dx.doi.org/10.1191/0962280203sm340ra.

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8

Simmons, W. Kyle, Patrick S. F. Bellgowan, and Alex Martin. "Measuring selectivity in fMRI data." Nature Neuroscience 10, no. 1 (January 2007): 4–5. http://dx.doi.org/10.1038/nn0107-4.

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9

Friman, Ola, Magnus Borga, Peter Lundberg, and Hans Knutsson. "Adaptive analysis of fMRI data." NeuroImage 19, no. 3 (July 2003): 837–45. http://dx.doi.org/10.1016/s1053-8119(03)00077-6.

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10

Rydell, Joakim, Hans Knutsson, and Magnus Borga. "Bilateral Filtering of fMRI Data." IEEE Journal of Selected Topics in Signal Processing 2, no. 6 (December 2008): 891–96. http://dx.doi.org/10.1109/jstsp.2008.2007826.

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11

Knutson, Brian, Kiefer Katovich, and Gaurav Suri. "Inferring affect from fMRI data." Trends in Cognitive Sciences 18, no. 8 (August 2014): 422–28. http://dx.doi.org/10.1016/j.tics.2014.04.006.

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12

Iyengar, Satish. "Case for fMRI data repositories." Proceedings of the National Academy of Sciences 113, no. 28 (June 29, 2016): 7699–700. http://dx.doi.org/10.1073/pnas.1608146113.

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13

Rugg, Michael D., and Sharon L. Thompson-Schill. "Moving Forward With fMRI Data." Perspectives on Psychological Science 8, no. 1 (January 2013): 84–87. http://dx.doi.org/10.1177/1745691612469030.

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14

Zhang, Jiang, Huafu Chen, Fang Fang, Hualin Liu, and Wei Liao. "A FREQUENCY SIGNAL METHOD FOR fMRI DATA ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 377–83. http://dx.doi.org/10.4015/s1016237210002134.

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Currently, all the data processing strategies for functional magnetic resonance imaging (fMRI) utilize temporal informationpaying little attention to or totally ignoring frequency information. In this paper, a new method is proposed to detect the functional activation regions in the brain by using the frequency information of fMRI time series. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. The validity of the proposed FEI approach is confirmed by analyzing the result of the simulated synthesized data. Additionally, the comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the Support Vector Machine (SVM) methods of fMRI data analysis indicate an obvious superiority of the former. In vivo fMRI studies of subjects with event-related experiment reveal that FEI method can enable the effective detection of brain functional activation.

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15

Li, Weida, Mingxia Liu, Fang Chen, and Daoqiang Zhang. "Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2653–60. http://dx.doi.org/10.1609/aaai.v34i03.5650.

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Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects. However, the existing functional alignment methods cannot handle well various kinds of fMRI datasets today, especially when they are not temporally-aligned, i.e., some of the subjects probably lack the responses to some stimuli, or different subjects might follow different sequences of stimuli. In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. However, the high dimension of fMRI data and the use of multiple subjects makes the crude framework time-consuming or unpractical. To address this issue, we further regularize the framework, so that a novel feasible kernel-based optimization, which permits non-linear feature extraction, could be theoretically developed. Specifically, a low-dimension assumption is imposed on each new feature space to avoid overfitting caused by the high-spatial-low-temporal resolution of fMRI data. Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned fMRI data, but also suitable for dealing with temporally-unaligned fMRI data.

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Choi, Uk-Su, Yul-Wan Sung, and Seiji Ogawa. "Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics." Brain Sciences 13, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/brainsci13010008.

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Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

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17

Baumgartner, Richard, Ray Somorjai, and Lawrence Ryner. "Are global methods appropriate for fMRI data analysis? An in vivo fMRI study of the spatio-temporal heterogeneity of fMRI data." NeuroImage 13, no. 6 (June 2001): 74. http://dx.doi.org/10.1016/s1053-8119(01)91417-x.

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18

Power, Jonathan D., Mark Plitt, Stephen J. Gotts, Prantik Kundu, Valerie Voon, Peter A. Bandettini, and Alex Martin. "Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data." Proceedings of the National Academy of Sciences 115, no. 9 (February 12, 2018): E2105—E2114. http://dx.doi.org/10.1073/pnas.1720985115.

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“Functional connectivity” techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

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19

Salch, Andrew, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, and Vaibhav A. Diwadkar. "From mathematics to medicine: A practical primer on topological data analysis (TDA) and the development of related analytic tools for the functional discovery of latent structure in fMRI data." PLOS ONE 16, no. 8 (August 12, 2021): e0255859. http://dx.doi.org/10.1371/journal.pone.0255859.

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fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. “Structure” within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA (“persistent homology”) to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.

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20

Yang, Biao, Jinmeng Cao, Tiantong Zhou, Li Dong, Ling Zou, and Jianbo Xiang. "Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis." Computational and Mathematical Methods in Medicine 2018 (July 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/3018356.

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Background. Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method. An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results. The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions. The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.

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21

SRIKANTH, R., and A. G. RAMAKRISHNAN. "WAVELET-BASED ESTIMATION OF HEMODYNAMIC RESPONSE FUNCTION FROM fMRI DATA." International Journal of Neural Systems 16, no. 02 (April 2006): 125–38. http://dx.doi.org/10.1142/s012906570600055x.

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We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,1 where correlated fMRI noise is modeled by a second order polynomial functions.

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22

Gordon, Nathan J., Feroze B. Mohamed, Steven M. Platek, Harris Ahmad, J. Michael Williams, and Scott H. Faro. "The Effectiveness of fMRI Data when Combined with Polygraph Data." European Polygraph 12, no. 1 (March 1, 2018): 19–25. http://dx.doi.org/10.2478/ep-2018-0002.

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Abstract The Integrated Zone Comparison Technique (IZCT) was utilized with computerized polygraph instrumentation and the Academy for Scientific Investigative Training’s Horizontal Scoring System ASIT PolySuite algorithm, as part of a blind study in the detection of deception. This paper represents a synergy analysis of combining fMRI only deception data with each of the three individual physiological parameters that are used in polygraph. They include the electro-dermal response (EDR), pneumo, and cardio measurements. In addition, we compared the detection accuracy analysis using each single parameter by itself. The fMRI score and each individual polygraph parameter score on individual subjects were averaged to establish an overall score.

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23

Huang, Xiaojie, Jun Xiao, and Chao Wu. "Design of Deep Learning Model for Task-Evoked fMRI Data Classification." Computational Intelligence and Neuroscience 2021 (August 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/6660866.

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Machine learning methods have been successfully applied to neuroimaging signals, one of which is to decode specific task states from functional magnetic resonance imaging (fMRI) data. In this paper, we propose a model that simultaneously utilizes characteristics of both spatial and temporal sequential information of fMRI data with deep neural networks to classify the fMRI task states. We designed a convolution network module and a recurrent network module to extract the spatial and temporal features of fMRI data, respectively. In particular, we also add the attention mechanism to the recurrent network module, which more effectively highlights the brain activation state at the moment of reaction. We evaluated the model using task-evoked fMRI data from the Human Connectome Project (HCP) dataset, the classification accuracy got 94.31%, and the experimental results have shown that the model can effectively distinguish the brain states under different task stimuli.

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24

Ellis, Cameron T., Michael Lesnick, Gregory Henselman-Petrusek, Bryn Keller, and Jonathan D. Cohen. "Feasibility of topological data analysis for event-related fMRI." Network Neuroscience 3, no. 3 (January 2019): 695–706. http://dx.doi.org/10.1162/netn_a_00095.

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Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometric features in patterns of data. Several recent studies have successfully applied TDA to study various forms of neural data; however, to our knowledge, TDA has not been successfully applied to data from event-related fMRI designs. Event-related fMRI is very common but limited in terms of the number of events that can be run within a practical time frame and the effect size that can be expected. Here, we investigate whether persistent homology—a popular TDA tool that identifies topological features in data and quantifies their robustness—can identify known signals given these constraints. We use fmrisim, a Python-based simulator of realistic fMRI data, to assess the plausibility of recovering a simple topological representation under a variety of conditions. Our results suggest that persistent homology can be used under certain circumstances to recover topological structure embedded in realistic fMRI data simulations.

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25

Ashby, F. Gregory, and Jennifer G. Waldschmidt. "Fitting computational models to fMRI data." Behavior Research Methods 40, no. 3 (August 2008): 713–21. http://dx.doi.org/10.3758/brm.40.3.713.

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26

Waldvogel, Daniel, Peter van Gelderen, Ilka Immisch, Christopher Pfeiffer, and Mark Hallett. "The variability of serial fMRI data." NeuroReport 11, no. 17 (November 2000): 3843–47. http://dx.doi.org/10.1097/00001756-200011270-00048.

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27

Grethe, Jeffrey S., John D. Van Horn, Jeffrey B. Woodward, Souheil Inati, Peter J. Kostelec, Javed A. Aslam, Daniel Rockmore, Daniela Rus, and Michael S. Gazzaniga. "The fMRI data center: An introduction." NeuroImage 13, no. 6 (June 2001): 135. http://dx.doi.org/10.1016/s1053-8119(01)91478-8.

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28

Hahn, C., H. Handels, M. F. Nitschke, U. H. Melchert, and S. J. Pöppl. "Comparison of FMRI Data Analysis Techniques." NeuroImage 7, no. 4 (May 1998): S598. http://dx.doi.org/10.1016/s1053-8119(18)31431-9.

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29

Pandey, Pankaj, Byom Kesh Jha, and Neelam Sinha. "Analyzing Cognitive States Using fMRI Data." Procedia Computer Science 90 (2016): 35–41. http://dx.doi.org/10.1016/j.procs.2016.07.007.

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30

Heller, Ruth, Damian Stanley, Daniel Yekutieli, Nava Rubin, and Yoav Benjamini. "Cluster-based analysis of FMRI data." NeuroImage 33, no. 2 (November 2006): 599–608. http://dx.doi.org/10.1016/j.neuroimage.2006.04.233.

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31

Quirós, Alicia, Raquel Montes Diez, and Dani Gamerman. "Bayesian spatiotemporal model of fMRI data." NeuroImage 49, no. 1 (January 2010): 442–56. http://dx.doi.org/10.1016/j.neuroimage.2009.07.047.

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32

Janoos, Firdaus, Raghu Machiraju, and Istvan A. Morocz. "Decoding brain states from fMRI data." International Journal of Psychophysiology 77, no. 3 (September 2010): 322–23. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.244.

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33

Choi, Kyungmee. "Spatial Correlations of Brain fMRI data." Communications for Statistical Applications and Methods 12, no. 1 (April 1, 2005): 241–52. http://dx.doi.org/10.5351/ckss.2005.12.1.241.

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34

Lindquist, Martin A. "The Statistical Analysis of fMRI Data." Statistical Science 23, no. 4 (November 2008): 439–64. http://dx.doi.org/10.1214/09-sts282.

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35

Pyka, M., A. Balz, A. Jansen, A. Krug, and E. Hüllermeier. "A WEKA Interface for fMRI Data." Neuroinformatics 10, no. 4 (March 18, 2012): 409–13. http://dx.doi.org/10.1007/s12021-012-9144-3.

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36

Roels, Sanne P., Beatrijs Moerkerke, and Tom Loeys. "Bootstrapping fMRI Data: Dealing with Misspecification." Neuroinformatics 13, no. 3 (February 12, 2015): 337–52. http://dx.doi.org/10.1007/s12021-015-9261-x.

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37

Maydeu-Olivares, Alberto, and Gregory Brown. "Modeling fMRI Data: Challenges and Opportunities." Psychometrika 78, no. 2 (March 8, 2013): 240–42. http://dx.doi.org/10.1007/s11336-013-9332-6.

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38

de Zwart, Jacco A., Peter van Gelderen, Masaki Fukunaga, and Jeff H. Duyn. "Reducing correlated noise in fMRI data." Magnetic Resonance in Medicine 59, no. 4 (2008): 939–45. http://dx.doi.org/10.1002/mrm.21507.

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39

Hartvig, Niels V�ver, and Jens Ledet Jensen. "Spatial mixture modeling of fMRI data." Human Brain Mapping 11, no. 4 (2000): 233–48. http://dx.doi.org/10.1002/1097-0193(200012)11:4<233::aid-hbm10>3.0.co;2-f.

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40

Kherif, F. "Multivariate Model Specification for fMRI Data." NeuroImage 16, no. 4 (August 2002): 1068–83. http://dx.doi.org/10.1006/nimg.2002.1094.

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41

Svensén, Markus, Frithjof Kruggel, and Habib Benali. "ICA of fMRI Group Study Data." NeuroImage 16, no. 3 (July 2002): 551–63. http://dx.doi.org/10.1006/nimg.2002.1122.

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42

Bednařík, Petr, Ivan Tkáč, Federico Giove, Mauro DiNuzzo, Dinesh K. Deelchand, Uzay E. Emir, Lynn E. Eberly, and Silvia Mangia. "Neurochemical and BOLD Responses during Neuronal Activation Measured in the Human Visual Cortex at 7 Tesla." Journal of Cerebral Blood Flow & Metabolism 35, no. 4 (January 7, 2015): 601–10. http://dx.doi.org/10.1038/jcbfm.2014.233.

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Several laboratories have consistently reported small concentration changes in lactate, glutamate, aspartate, and glucose in the human cortex during prolonged stimuli. However, whether such changes correlate with blood oxygenation level—dependent functional magnetic resonance imaging (BOLD-fMRI) signals have not been determined. The present study aimed at characterizing the relationship between metabolite concentrations and BOLD-fMRI signals during a block-designed paradigm of visual stimulation. Functional magnetic resonance spectroscopy (fMRS) and fMRI data were acquired from 12 volunteers. A short echo-time semi-LASER localization sequence optimized for 7 Tesla was used to achieve full signal-intensity MRS data. The group analysis confirmed that during stimulation lactate and glutamate increased by 0.26±0.06 μmol/g (∼30%) and 0.28±0.03 μmol/g (∼3%), respectively, while aspartate and glucose decreased by 0.20±0.04 μmol/g (∼5%) and 0.19±0.03 μmol/g (∼16%), respectively. The single-subject analysis revealed that BOLD-fMRI signals were positively correlated with glutamate and lactate concentration changes. The results show a linear relationship between metabolic and BOLD responses in the presence of strong excitatory sensory inputs, and support the notion that increased functional energy demands are sustained by oxidative metabolism. In addition, BOLD signals were inversely correlated with baseline γ-aminobutyric acid concentration. Finally, we discussed the critical importance of taking into account linewidth effects on metabolite quantification in fMRS paradigms.

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43

Rajeswari, R., and R. Rajesh. "On the Efficient Compression of fMRI Data Series of Brain." Neuroradiology Journal 21, no. 6 (December 2008): 737–43. http://dx.doi.org/10.1177/197140090802100601.

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Four dimensional medical images, namely fMRI, require a large volume of memory to store the data. Compression techniques are therefore used for the storage and transmission of these medical images. This paper proposes a coding scheme for volumetric images which in the first stage recognizes significant images and in the second stage compresses those images using a JPEG-LS coding scheme. An example implementation for 4D fMRI data series of brain stored in ANALYZE file format is illustrated in this paper. The proposed scheme provides efficient compression for 4D fMRI medical images.

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Saeidi, Maham, Waldemar Karwowski, Farzad V. Farahani, Krzysztof Fiok, P. A. Hancock, Ben D. Sawyer, Leonardo Christov-Moore, and Pamela K. Douglas. "Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences." Brain Sciences 12, no. 8 (August 17, 2022): 1094. http://dx.doi.org/10.3390/brainsci12081094.

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Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies. Here, we propose an end-to-end graph convolutional network (GCN) framework with three convolutional layers to classify task fMRI data from the Human Connectome Project dataset. We compared the predictive performance of our GCN model across four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the functional graph. The empirical results indicated that our GCN framework accurately predicted individual differences (0.978 and 0.976) with the NetMF and RandNE embedding methods, respectively. Furthermore, to assess the effects of individual differences, we tested the classification performance of the model on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data.

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Roux, Franck-Emmanuel, Danielle Ibarrola, Michel Tremoulet, Yves Lazorthes, Patrice Henry, Jean-Christophe Sol, and Isabelle Berry. "Methodological and Technical Issues for Integrating Functional Magnetic Resonance Imaging Data in a Neuronavigational System." Neurosurgery 49, no. 5 (November 1, 2001): 1145–57. http://dx.doi.org/10.1097/00006123-200111000-00025.

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ABSTRACT OBJECTIVE The aim of this article was to analyze the technical and methodological issues resulting from the use of functional magnetic resonance image (fMRI) data in a frameless stereotactic device for brain tumor or pain surgery (chronic motor cortex stimulation). METHODS A total of 32 candidates, 26 for brain tumor surgery and six chronic motor cortex stimulation, were studied by fMRI scanning (61 procedures) and intraoperative cortical brain mapping under general anesthesia. The fMRI data obtained were analyzed with the Statistical Parametric Mapping 99 software, with an initial analysis threshold corresponding to P < 0.001. Subsequently, the fMRI data were registered in a frameless stereotactic neuronavigational device and correlated to brain mapping. RESULTS Correspondence between fMRI-activated areas and cortical mapping in primary motor areas was good in 28 patients (87%), although fMRI-activated areas were highly dependent on the choice of paradigms and analysis thresholds. Primary sensory- and secondary motor-activated areas were not correlated to cortical brain mapping. Functional mislocalization as a result of insufficient correction of the echo-planar distortion was identified in four patients (13%). Analysis thresholds (from P < 0.0001 to P < 10−12) more restrictive than the initial threshold (P < 0.001) had to be used in 25 of the 28 patients studied, so that fMRI motor data could be matched to cortical mapping spatial data. These analysis thresholds were not predictable preoperatively. Maximal tumor resection was accomplished in all patients with brain tumors. Chronic motor cortex electrode placement was successful in each patient (significant pain relief >50% on the visual analog pain scale). CONCLUSION In brain tumor surgery, fMRI data are helpful in surgical planning and guiding intraoperative brain mapping. The registration of fMRI data in anatomic slices or in the frameless stereotactic neuronavigational device, however, remained a potential source of functional mislocalization. Electrode placement for chronic motor cortex stimulation is a good indication to use fMRI data registered in a neuronavigational system and could replace somatosensory evoked potentials in detection of the central sulcus.

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Noh, Ju-Hyeon, Jun-Hyeok Kim, and Hee-Deok Yang. "Classification of Alzheimer’s Progression Using fMRI Data." Sensors 23, no. 14 (July 12, 2023): 6330. http://dx.doi.org/10.3390/s23146330.

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In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer’s by analyzing 4D fMRI data.

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Wang, Lijun, Yu Lei, Ying Zeng, Li Tong, and Bin Yan. "Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/645921.

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Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

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Goel, Anshika, Saurav Roy, Khushboo Punjabi, Ritwick Mishra, Manjari Tripathi, Deepika Shukla, and Pravat K. Mandal. "PRATEEK: Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research." Journal of Alzheimer's Disease 83, no. 1 (August 31, 2021): 305–17. http://dx.doi.org/10.3233/jad-210440.

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Background: In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases. Objective: To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer’s disease). Methods: A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- “PRATEEK”. Results: The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities. Conclusion: This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.

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Gui, Renzhou, Tongjie Chen, and Han Nie. "Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/7691294.

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In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.

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Mahmoudi, Abdelhak, Sylvain Takerkart, Fakhita Regragui, Driss Boussaoud, and Andrea Brovelli. "Multivoxel Pattern Analysis for fMRI Data: A Review." Computational and Mathematical Methods in Medicine 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/961257.

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Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

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