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A Surgical Protocol for a Large, Resealable Cranial Window Enabling Longitudinal, Multi-Modal Electrophysiological Recordings of the Mouse Default Mode Network

Kiiso, T.; Partanen, P.; Jacobs, K.; Varga, B. A.; Palva, S.; Palva, M.; Castren, E.; Balin, R.

2025-11-14 neuroscience
10.1101/2025.11.13.688186 bioRxiv
Show abstract

The Default Mode Network (DMN) is a central large-scale brain network implicated in a range of cognitive functions and neuropsychiatric disorders, such as major depressive disorder (MDD). Studying the DMNs complex dynamics in animal models provides invaluable insights into its function in both healthy and pathological states. However, performing a stable, long-term, and large-scale electrophysiological recordings from the multiple, deep, and distributed nodes of the DMN in awake, behaving mice has been a significant challenge. Here, we present a novel, two-phase surgical protocol developed to create a large (4x7.6 mm), durable, and resealable cranial window in mice. The procedure is designed to preserve the integrity of the dura mater, which is paramount for long-term brain health and recording stability. This window facilitates repeated, longitudinal recordings from over 1,000 electrodes simultaneously by combining surface-level micro-electrocorticography ({micro}ECoG) with two high-density intracranial electrode probes, allowing unprecedented access to the DMN. This technique provides a robust platform for multi-modal, multi-scale interrogation of network-wide electrophysiological dynamics over several weeks, opening new avenues for investigating the neuroplastic changes underlying the pathophysiology of brain disorders and for evaluating the chronic effects of novel therapeutics. SUMMARYThis protocol describes a two-phase surgical method to create a large, resealable, dura-sparing cranial window in mice. This technique enables chronic, multi-modal electrophysiological recordings from distributed, deep brain networks, such as the Default Mode Network, over several weeks.

Published in Journal of Visualized Experiments (predicted rank #1) · training set

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