Mesoscale Mapping of Mouse Cortex Reveals Frequency-Dependent Cycling between Distinct Macroscale Functional Modules.

Connectivity mapping based on resting-state activity in mice has revealed functional motifs of correlated activity. However, the rules by which motifs organize into larger functional modules that lead to hemisphere wide spatial-temporal activity sequences is not clear. We explore cortical activity parcellation in head-fixed, quiet awake GCaMP6 mice from both sexes by using mesoscopic calcium imaging. Spectral decomposition of spontaneous cortical activity revealed the presence of two dominant frequency modes (<1 and ∼3 Hz), each of them associated with a unique spatial signature of cortical macro-parcellation not predicted by classical cytoarchitectonic definitions of cortical areas. Based on assessment of 0.1-1 Hz activity, we define two macro-organizing principles: the first being a rotating polymodal-association pinwheel structure around which activity flows sequentially from visual to barrel then to hindlimb somatosensory; the second principle is correlated activity symmetry planes that exist on many levels within a single domain such as intrahemispheric reflections of sensory and motor cortices. In contrast, higher frequency activity >1 Hz yielded two larger clusters of coactivated areas with an enlarged default mode network-like posterior region. We suggest that the apparent constrained structure for intra-areal cortical activity flow could be exploited in future efforts to normalize activity in diseases of the nervous system. SIGNIFICANCE STATEMENT Increasingly, functional connectivity mapping of spontaneous activity is being used to reveal the organization of the brain. However, because the brain operates across multiple space and time domains a more detailed understanding of this organization is necessary. We used in vivo wide-field calcium imaging of the indicator GCaMP6 in head-fixed, awake mice to characterize the organization of spontaneous cortical activity at different spatiotemporal scales. Correlation analysis defines the presence of two to three superclusters of activity that span traditionally defined functional territories and were frequency dependent. This work helps define the rules for how different cortical areas interact in time and space. We provide a framework necessary for future studies that explore functional reorganization of brain circuits in disease models.
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