Biology subjects

Anumba, N.

Publications and source records attributed to Anumba, N..

4 recordsLinked to original sources

bioRxiv2024

The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats

Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.

neuroscience↗

bioRxiv2024

Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

neuroscience↗

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↗