Adaptive modulation of brain hemodynamics across stereotyped running episodes

During locomotion, theta and gamma rhythms are essential to ensure timely communication between brain structures. However, their metabolic cost and contribution to neuroimaging signals remain elusive. To finely characterize neurovascular interactions during locomotion, we simultaneously recorded mesoscale brain hemodynamics using functional ultrasound (fUS) and local field potentials (LFP) in numerous brain structures of freely-running overtrained rats. Locomotion events were reliably followed by a surge in blood flow in a sequence involving the retrosplenial cortex, dorsal thalamus, dentate gyrus and CA regions successively, with delays ranging from 0.8 to 1.6 seconds after peak speed. Conversely, primary motor cortex was suppressed and subsequently recruited during reward uptake. Surprisingly, brain hemodynamics were strongly modulated across trials within the same recording session; cortical blood flow sharply decreased after 10–20 runs, while hippocampal responses strongly and linearly increased, particularly in the CA regions. This effect occurred while running speed and theta activity remained constant and was accompanied by an increase in the power of hippocampal, but not cortical, high-frequency oscillations (100–150 Hz). Our findings reveal distinct vascular subnetworks modulated across fast and slow timescales and suggest strong hemodynamic adaptation, despite the repetition of a stereotyped behavior.


Statistics
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Software and code
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Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability BERGEL Oct 20, 2020 fUS data was collected via custom code. For the 1st set of recordings, LFP and video were collected using custom code developed in Labview 2016 (National Instruments, Austin, TX, USA). For the 2nd set of recordings, LFP and video were collected via the Cereplex Direct Software Suite (version 7.0.6.0, Blackrock Mircrosystems, Salt Lake City, UT, USA). All non-commercial custom source codes used in this study are protected by INSERM and can only be shared upon request, with the written agreement of INSERM.
All analysis were performed using custom code under MATLAB (version R2017b). NPMK package (version 4.5.3.0) was used to import the raw LFP Data into MATLAB. Simplex method was implemented via custom code in LabView 2016. All custom codes used for the analysis of functional ultrasound/EEG/video data used in this study are protected by INSERM and can only be shared upon request, with the written agreement of INSERM.
All data and software supporting the findings of this study are available from the corresponding authors upon reasonable request.