Statistical Power or More Precise Insights into Neuro-Temporal Dynamics? Assessing the Benefits of Rapid Temporal Sampling in fMRI

Dowdle, Logan T.; Ghose, Geoffrey M.; Uğurbil, Kâmil; Yacoub, Essa; Vizioli, Luca

doi:10.1101/2021.06.05.447164

Cited by 2 publications

(2 citation statements)

References 196 publications

(326 reference statements)

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“…Sample sizes and statistical thresholds are known to have a major impact on the statistical power and accuracy of GLM-based ROI selection. Previous research has revealed that GLM has limited statistical power when inferring from fMRI data [50,51]. However, we used GLM-based ROI selection in the task-related fMRI datasets, which may affect the final result when we estimate functional connectivity.…”

Section: Discussionmentioning

confidence: 99%

Functional Connectome of the Human Brain with Total Correlation

Li¹,

Steeg²,

Yu³

et al. 2022

Preprint

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Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.Information theory is a valuable method to measure interactions in nonlinear systems [6], leading to its increasing use in neuroscience [7][8][9][10][11][12][13][14]. However, although functional connectivity has already become a hot research topic

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Section: Discussionmentioning

confidence: 99%

Functional Connectome of the Human Brain with Total Correlation

Li¹,

Steeg²,

Yu³

et al. 2022

Preprint

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“…These methods, popularized by the Human Connectome Project (Smith et al, 2013b; Ugurbil et al, 2013), also push gradient echo (GE)-based fMRI data towards the thermal noise-dominated regime as repetition times and, consequently, flip angles and the signal magnitude detected in each image decreases (Smith et al, 2013b; Ugurbil et al, 2013). While rapid sampling has led to a greater understanding of neural functioning and the BOLD response (Dowdle et al, 2021a; Polimeni and Lewis, 2021), it is not without consequences. The spatially non-uniform noise amplification introduced by parallel imaging reconstructions (i.e., the g-factor noise (Pruessmann et al, 1999)) further exacerbates the thermal noise penalty (Pruessmann et al, 1999; Todd et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

NORDIC Increases the Sensitivity and Preserves the Spatiotemporal Precision of fMRI Responses

Dowdle

Vizioli

Moeller

et al. 2021

Preprint

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As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs. Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, the consistency of unbiased estimations of task responses at the single-voxel, single run level, using a finite impulse response model were evaluated. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was examined. Finally, to determine if NORDIC alters or removes important temporal information, an exhaustive K-fold cross validation approach was employed, using unbiased task responses to predict held out timeseries, quantified using R2. After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved without significant compromises in spatial and temporal resolution. Cross-validated R2s based on the unbiased models show that NORDIC is not distorting the temporal structure of the data and is the best predictor of non-denoised time courses. The results demonstrate that 1 run of NORDIC data is equivalent to using 2 to 3 original runs, and performs equally well across a diverse array of functional imaging protocols.

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Statistical Power or More Precise Insights into Neuro-Temporal Dynamics? Assessing the Benefits of Rapid Temporal Sampling in fMRI

Cited by 2 publications

References 196 publications

Functional Connectome of the Human Brain with Total Correlation

Functional Connectome of the Human Brain with Total Correlation

NORDIC Increases the Sensitivity and Preserves the Spatiotemporal Precision of fMRI Responses

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