Conserved Laminar-Cellular Architecture Predicts the Spectrolaminar Motif
Aidan G Mulvey, Maxwell J. Lichtenfeld, Hamed NejatiD, Yihan Sophy Xiong, Brock M. CarlsoniD, Brian MitchelliD, Diego Mendoza-HallidayiD, Jacob Alan WesterbergiD, Alex Maier, Alvaro DuqueiD, André M. BastosiD
Abstract
We recently identified the spectrolaminar motif, a layer-resolved pattern of local field potential (LFP) power, which is preserved across many areas of the primate cortex. It remains unclear which specific cytoarchitectonic features can explain the spectrolaminar motif. To investigate this, we performed histological and neurophysiological analyses of 11 brain areas spanning the macaque cortical hierarchy to examine the relationship between the laminar patterns of LFP power and the laminar distributions of cellular density for parvalbumin, calbindin, and calretinin interneurons, and pyramidal neurons. We tested whether cellular distributions are conserved and if they predict the corresponding variation in LFP power comprising the spectrolaminar motif. We found that the cytoarchitectonic and spectrolaminar data were intrinsically low-dimensional, with two-component patterns conserved across areas, and that the spectrolaminar motif could be predicted from these laminar cellular gradients. Critically, the regression coefficients mapping structure (cytoarchitecture) to function (spectrolaminar motif) were themselves highly conserved across areas, suggesting that a common cellular-to-spectral rule operates throughout cortex rather than a distinct rule per region. To evaluate this structure-function model, we tested it against post-mortem data from schizophrenia patients. We tested whether empirically-observed cytoarchitectonic changes from postmortem studies on inhibitory interneuron dysfunction in schizophrenia could be used to predict spectrolaminar alterations using our structure-function model. We found that they did, and that the functional changes match what is known from non-invasive neurophysiology in schizophrenia. This work moves beyond simple structure-function correlation to establish a predictive mathematical framework that informs healthy and aberrant brain circuitry.
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