Oscillations occurring simultaneously in a given area represent a physiological unit of brain states. They allow for temporal segmentation of spikes and support distinct behaviors. To establish how multiple oscillatory components co-varies simultaneously and influence neuronal firing during sleep and wakefulness in mice, we describe a multi-variate analytical framework for constructing the state space of hippocampal oscillations. Examining the co-occurrence patterns of oscillations on the state space, across species, uncovered the presence of network constraints and distinct set of cross-frequency interactions during wakefulness as compared to sleep. We demonstrated how the state space can be used as a canvas to map the neural firing and found that distinct neurons during navigation were tuned to different sets of simultaneously occurring oscillations during sleep. This multivariate analytical framework provides a window to move beyond classical bivariate pipelines, for investigating oscillations and neuronal firing, thereby allowing to factor-in the complexity of oscillation-population interactions.
Datasets used in this study are available at Crcns.org (HC11 dataset) and Donders Repository (https://data.donders.ru.nl/collections/di/dcn/DSC_62002873_05_861)All codes are available made at https://github.com/brijeshmodi12/network_state_space
Recordings from hippocampal area CA1, PRE, during and POST novel spatial learning.crcns.org (id hc11).
- Francesco P Battaglia
- Federico Stella
- Francesco P Battaglia
- Enrico Cherubini
- Enrico Cherubini
- Brijesh Modi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: In compliance with Dutch law and institutional regulations, all animal procedures concerningrecordings from freely moving or sleeping mice were approved by the Central Commissie Dierproeven(CCD) and conducted in accordance with the Experiments on Animals Act (project number 2016-014and protocol numbers 0029).All experiments from head-restrained animals were performed in accordance with the Italian AnimalWelfare legislation (D.L. 26/2014) that implemented the European Committee Council Directive(2010/63 EEC) and were approved by local veterinary authorities, the EBRI ethical committee, andthe Italian Ministry of Health (565/PR18) All efforts were made to minimize animal suffering and toreduce the number of animals used
- Antonio Fernandez-Ruiz, Cornell University, United States
© 2023, Modi et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.
The strength of a fear memory significantly influences whether it drives adaptive or maladaptive behavior in the future. Yet, how mild and strong fear memories differ in underlying biology is not well understood. We hypothesized that this distinction may not be exclusively the result of changes within specific brain regions, but rather the outcome of collective changes in connectivity across multiple regions within the neural network. To test this, rats were fear conditioned in protocols of varying intensities to generate mild or strong memories. Neuronal activation driven by recall was measured using c-fos immunohistochemistry in 12 brain regions implicated in fear learning and memory. The interregional coordinated brain activity was computed and graph-based functional networks were generated to compare how mild and strong fear memories differ at the systems level. Our results show that mild fear recall is supported by a well-connected brain network with small-world properties in which the amygdala is well-positioned to be modulated by other regions. In contrast, this connectivity is disrupted in strong fear memories and the amygdala is isolated from other regions. These findings indicate that the neural systems underlying mild and strong fear memories differ, with implications for understanding and treating disorders of fear dysregulation.