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Abstract
Determining cell types is critical for understanding neural circuits but remains elusive
in the living human brain. Current approaches discriminate units into putative cell
classes using features of the extracellular action potential (EAP); in absence of
ground truth data, this remains a problematic procedure. We find that EAPs in deep
structures of the brain exhibit robust and systematic variability during the cardiac
cycle. These cardiac-related features refine neural classification. We use these features
to link bio-realistic models generated from in vitro human whole-cell recordings
of morphologically classified neurons to in vivo recordings. We differentiate aspiny
inhibitory and spiny excitatory human hippocampal neurons and, in a second stage,
demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct
intrinsic electrophysiological properties and phase-locking characteristics to endogenous
oscillations. This multi-modal approach markedly improves cell classification in humans,
offers interpretable cell classes, and is applicable to other brain areas and species.
During the heartbeat, the brain pulsates and recording electrodes move. Mosher et
al. show that, in the living human brain, such movement affects the spike waveform
leading to enhanced separation between cell types. Single-cell models of human neurons
reveal distinct properties of the cell types identified in vivo .