Jayabal, V. | bioRxiv Science

bioRxiv2025

Aperiodic neural dynamics define a novel signature of glioma-induced excitation-inhibition dysregulation

Diffuse gliomas remodel neuronal circuits with prognostic and therapeutic significance for patients. Electrophysiologic measures of cortical excitability hold promise for monitoring disease progression and evaluating therapeutic responses. The power law exponent (aperiodic slope) reflects the balance between excitatory and inhibitory activity within neuronal networks, a critical aspect of normal brain function often disrupted in neurological conditions. Despite its potential, the significance of the aperiodic slope in glioma-infiltrated tissue and its underlying cellular processes has not been fully investigated. Here, we integrate multi-modal electrophysiological analysis with transcriptomic profiling to analyze the aperiodic slope in both normal and glioma-infiltrated cortex. We determine that glioma infiltration induces a flattening of the aperiodic slope, indicating a shift toward excitation dominance that varies according to tumor subtype and correlates with impairments in semantic naming. Single-nucleus RNA sequencing revealed that cortical regions with flat aperiodic slope exhibit transcriptional programs enriched in glutamatergic signaling, membrane depolarization, and excitatory synaptic transmission. The aperiodic slope responds to pharmacologically induced changes in cortical inhibition during propofol administration, a GABAA agonist. Our results establish the aperiodic slope as a robust biomarker of glioma-associated excitation-inhibition imbalance, with potential applications in tumor classification and treatment monitoring.

cancer biology↗

bioRxiv2022

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Recording from deep neural structures such as hippocampus non-invasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with SEEG signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

neuroscience↗