Anumba, N. | Search bioRxiv

bioRxiv2023

QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab, an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4-6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics. Metadata O_TBL View this table: org.highwire.dtl.DTLVardef@1f4f44dorg.highwire.dtl.DTLVardef@d31544org.highwire.dtl.DTLVardef@19384corg.highwire.dtl.DTLVardef@1bf2360org.highwire.dtl.DTLVardef@1ab9b80_HPS_FORMAT_FIGEXP M_TBL C_TBL

bioinformatics↗

bioRxiv2022

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI

In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, and has long been used as a regressor to reduce noise during data preprocessing. However, coherent neural activity in spatially-extended functional networks can also contribute to the global signal. The relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the blood oxygenation level dependent (BOLD) global signal to an adjacent non-brain tissue signal from the same scan in rs-fMRI obtained from anesthetized rats. In this dataset motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. In addition to contrasting the spatial and spectral components of these two signals, we also observed these differences across the use of three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. Here, we report differences in the spectral composition of the two signals as evaluated by a power spectral density (PSD) estimate and a fractional amplitude of low-frequency fluctuations (fALFF) calculation. Additionally, we show spatial selectivity for specific brain structures that show an increased correlation to the global signal both statically and dynamically, through Pearsons correlation and co-activation pattern analysis, respectively. All of the observed differences between the BOLD global signal and the adjacent non-brain tissue signal were maintained across all three anesthetic conditions, showing that the global signal is distinct from the noise contained in the tissue signal. This study provides a unique perspective to the contents of the global signal and their origins.

bioengineering↗