Network analysis of neuroimaging in mice

Scharwächter, Leon; Schmitt, Felix Johannes; Pallast, Niklas; Fink, Gereon R.; Aswendt, Markus

doi:10.1016/j.neuroimage.2022.119110

Cited by 12 publications

(15 citation statements)

References 134 publications

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“…Network dynamics and task-related changes before and after training can further be defined based on metrics of functional connectivity (Biswal et al, 1995; Ma et al, 2016; Scharwächter et al, 2022). These measures relate to the temporal dependence between spatially remote neurophysiological events, with positive correlations reflecting integration or temporally coordinated activity across brain regions.…”

Section: Resultsmentioning

confidence: 99%

“…for target-attending mice at baseline/ post training = 0.024 ± 4.9E-5/ 0.055 ± 7.3E-5). A graph theory approach was then applied to calculate regional connectivity in a specific subnetwork (Scharwächter et al, 2022). Here, we focused on a subnetwork of relevant sensory, prefrontal, and memory-related brain areas (nodes) that also showed activation during the se-fMRI (Fig.…”

Section: Functional Connectivity Patterns At Rest Across Mice With Di...mentioning

confidence: 99%

“…Connectivity analysis using graph theory was conducted with AIDAconnect 1.1. (Scharwächter et al, 2022), which is based on FSLNets v0.6, Niftyreg (Modat et al, 2010(Modat et al, , 2014Ourselin et al, 2001;Rueckert et al, 1999), the BCT toolbox (Rubinov & Sporns, 2010) and MATLAB 2023a (MathWorks, USA). Mice were separated again into the same two groups at baseline/ after training (n = 7/ 6 for attentive and n = 7/ 7 for inattentive mice) defined by behavioural outcomes.…”

Section: Resting-state Fmri Analysismentioning

confidence: 99%

“…Stimulus-evoked (se-fMRI) and resting-state (rs-fMRI) functional MRI are useful techniques to explore these brain-wide circuits and have previously been used to investigate perceptual expectation (Choi et al, 2017; Livesey et al, 2007) and to predict the timing, location, or form of prospective sensory stimuli in humans (Schubotz, 2007). Recently, functional MRI, as a whole-brain approach, has become a powerful method to explore the enhancement of learning performance (Alwashmi et al, 2024) and plasticity in neural networks using graph theory analysis (Ripp et al, 2018; Scharwächter et al, 2022). Here, we apply MRI techniques under multisensory stimulation before and after mice are trained in a paradigm using auditory-cue and visual-target sensory stimuli presented at expected or unexpected temporal delays.…”

Section: Introductionmentioning

confidence: 99%

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Neural networks representing temporal expectation in mice

Wendlandt,

Wenk,

Henschke

et al. 2024

Preprint

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The ability to attend to specific moments in time is crucial for survival across species facilitating perception and motor performance by leveraging prior temporal knowledge for predictive processing. Despite its importance, the neural mechanisms underlying the utilization of macro-scale and meso-scale neural resources during temporal processing and their relationship to behavioural strategies and motor responses remain largely unexplored. To investigate the capacity for predictive temporal structure of multisensory stimuli to optimize motor behaviour, we established a behavioural paradigm, in which mice were trained to an auditory-cue and visual-target presented at expected or unexpected temporal delays. Using a combination of stimulus-evoked and resting-state functional magnetic resonance imaging, we examined task-related evoked activity in brain-wide networks and found that that the formation of temporal expectations relying on accumulated sensory information and combined multisensory input involves plasticity across large macro-scale cortical networks comprised of primary sensory systems, sensory association areas including posterior parietal cortex, retrosplenial cortex, prefrontal top-down executive control centres of the brain, as well as hippocampal networks. Additionally, employing in vivo two-photon calcium imaging, we explored local single-cell dynamics within the posterior parietal cortex during this task and found that temporal expectation could be decoded directly from neuronal activity within this brain region. Overall, our study provides insights into the neural correlates underlying the formation of multisensory temporal expectations in the mouse brain and highlights the recruitment of neural resources across temporally-driven statistical learning processes.

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Section: Resultsmentioning

confidence: 99%

Section: Functional Connectivity Patterns At Rest Across Mice With Di...mentioning

confidence: 99%

Section: Resting-state Fmri Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural networks representing temporal expectation in mice

Wendlandt,

Wenk,

Henschke

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Network modeling based on graph theory is a great model for studying dynamic relationship of functional related units. Astrocyte volumetric images could be converted into a graph by different correlation metrics among functional subdomains [ 38 ] . Spatio-Temporal Tensor Analysis and Spatio-Temporal Representation Factorization and other DNN-based techniques (deep neural network) provide a computational framework for feature extraction [ 39 – 40 ] .…”

Section: Discussionmentioning

confidence: 99%

A visualization pipeline for <i>in vivo</i> two-photon volumetric astrocytic calcium imaging

Sun¹,

Hu²,

Deng³

et al. 2022

J Biomed Res

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Astrocytes, the multi-functional glial cells with the most abundant population in the brain, integrate information across their territories to regulate neuronal synaptic and cerebrovascular activities. Astrocytic calcium (Ca 2+ ) signaling is the major readout of cellular functional state of astrocytes. The conventional two-photon in vivo imaging usually focuses on a single horizontal focal plane to capture the astrocytic Ca 2+ signals, which leaves >80% spatial information undetected. To fully probe the Ca 2+ activity across the whole astrocytic territory, we developed a pipeline for imaging and visualizing volumetric astrocytic Ca 2+ time-lapse images. With the pipeline, we discovered a new signal distribution pattern from three-dimensional (3D) astrocytic Ca 2+ imaging data of mice under isoflurane anesthetic states. The tools developed in this study enable a better understanding of the spatiotemporal patterns of astrocytic activity in 3D space.

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