Spatiotemporal multiscale ICA could invariantly extract task (motor) modes from wavelet subbands of fMRI data

Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm

This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.

Brain functional connectivity (FC) invariance and variability under timeseries editing (timeset operation)

Functional connectivity (FC) is defined by temporal correlations between pairwise timeseries signals, thus inheriting the correlation invariance property. In this report, we look into FC properties under versatile timeseries manipulations, as classified into cardinality-preserved or -reduced timeset operations. We show the effect of timeset operations on brain FC mapping by task-evoked and resting-state fMRI experiments through two data analysis methods: seed-based correlation analysis (SCA) and independent component analysis (ICA). The FC invariance and variability were numerically assessed by a spatial correlation (scorr) of a newly generated FC map after timeset operation against a reference of FC map with the original time setting. In the fingertapping task fMRI experiment, the FC invariance under cardinality-preserved timeset operation was verified with a fingertapping motor function (MOT) extracted by SCA (scorr = 1) and by ICA (scorr >0.98). Under timeset deletion editing, ICA yielded more FC variability (scorr <1) than SCA. Similar FC variability behavior was observed with resting-state fMRI experiments. In conclusion, brain FC mapping (networking) is theoretically invariant to arbitrary timepoint reordering during timeseries data preprocessing, and it is generally variant to timepoint reduction editing except for legitimate downsizing as governed by Nyquist sampling theorem and compressive sensing theory.