Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Abstract

Understanding how the brain's macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial striatum (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, the strongest changes in local dynamics were observed in unimodal regions, i.e., regions that process information from a single sensory modality, while changes in the local dynamics weakened along a putative cortical hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results provide a comprehensive understanding of how targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, including the role of a circuit's structural characteristics and hierarchical cortical level in shaping those dynamics. These findings contribute to ongoing attempts to understand the influence of structure-function relationships in shaping inter regional communication at subcortical and cortical levels.

Neurophysiological signatures of cortical micro-architecture

“…Finally, we find that the dominant dynamic signature of neural activity covaries with the granular cortical layer IV, consistent with the idea that layer IV receives prominent subcortical (including thalamic) feedforward projections [106,107]. Collectively, our findings build on the emerging literature on how heterogeneous micro-architectural properties along with macroscale network embedding (e.g., cortico-cortical connectivity and subcortical projections) jointly shape regional neural dynamics [38][39][40][108][109][110][111].…”

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

“…Examining dynamics within individual brain regions enables spatial interpretability through visualizing brain-wide maps of classification performance, providing a clear region-by-region picture of activity disruptions. The ability to characterize changes in BOLD dynamics at the level of individual regions has been key to addressing questions about regional differences in the response to spatially targeted brain stimulation [26,90]. It also provides a clearer way to test molecular hypotheses about regional disruption in disorders, which can be compared with rich multimodal region-level atlases spanning morphometry, cortical hierarchy, and multi-omics [34,[91][92][93] to more deeply characterize the physiological underpinnings of disease-relevant changes in future work.…”

Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI

“…For studies that handselect a single feature of interest (like a metric of autocorrelation timescale) [4,5], the large range of alternative features are left untested, leaving open the possibility that alternative statistics could be more informative and interpretable. Applications that have systematically compared the performance across a large feature library, such as hctsa [3,8,17,6,18,19], have involved substantial computational expense and statistical care in dealing with high-dimensional feature spaces (often requiring correction across thousands of features, limiting the power to detect signals in smaller samples). fMRI time series are short, noisy, and-particularly in the restingstate regime where dynamics may be approximated as close to a steady-state-are generally well-suited to being characterized by linear time-series analysis methods [20,21].…”