Brain : a journal of neurology
September 9, 2026 · via Europe PMC
Perfusion-dependent spreading depolarization signatures identify tissue vulnerability in stroke.
Flaherty SM, Eladly A, Hills K, +13 more, Wykes RC.
AI summary — for doctors, generated from the abstract only
In rodent stroke models, spreading depolarization waveforms tracked local perfusion and predicted harmful vascular responses, and low-dose ketamine shortened them.
What was studied
A preclinical study combining graphene micro-transistor arrays with blood flow and oxygenation imaging in two focal ischemia models.
Key findings
- CSD duration and morphology scaled with local perfusion
- A characteristic waveform localized profoundly hypoperfused tissue
- Electrophysiology alone predicted vasodilatory vs vasoconstrictive responses
- Low-dose ketamine shortened CSDs and prevented inverse hemodynamic responses
Clinical relevance
Proposes CSD signatures as tissue-state biomarkers; animal findings not yet translated.
Who should care
Limitations
Animal models; human applicability untested.
CSD waveforms encode tissue vulnerability in stroke models.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Cortical spreading depolarizations (CSDs) are large-scale disruptions of neuronal homeostasis that contribute to secondary injury after ischaemic stroke and occur across a range of acute and chronic neurological disorders. In healthy cortex, CSDs are typically coupled to compensatory vasodilation, whereas in ischaemic tissue they can evoke inverse haemodynamic responses that exacerbate metabolic stress and promote lesion progression, highlighting the need for approaches that can resolve localised electrophysiological and vascular dynamics as a function of evolving tissue metabolic state. Here, we combine high-density graphene micro-transistor arrays with optical cerebral blood flow and oxygenation imaging to map perfusion-dependent electrophysiological and haemodynamic dynamics of CSDs during focal cerebral ischaemia. Using two complementary stroke models, we show that CSD waveform duration and morphology scale systematically with local perfusion gradients, revealing distinct electrophysiological signatures that reflect tissue metabolic state. These signatures resolve a continuum from relatively preserved cortex to markedly ischaemic regions within a single preparation. We identify a characteristic CSD waveform that precisely localises profoundly hypoperfused tissue and demonstrate that specific electrophysiological features predict whether a given CSD will elicit vasodilatory, biphasic, or vasoconstrictive haemodynamic responses. Using neural network modelling, we further show that DC-coupled electrophysiology alone can accurately predict local vascular responses to CSDs, indicating that critical information about tissue state and neurovascular risk is encoded within the CSD waveform itself in these models. Concurrent oxygenation imaging reveals a focal pre-onset decline in tissue oxygenation at sites of CSD initiation and shows that prolonged CSDs impose sustained metabolic burden, linking electrophysiological dynamics to tissue oxygen recovery. Finally, we demonstrate that low-dose ketamine shortens CSD duration, thereby preventing the inverse haemodynamic response in metabolically compromised tissue, providing a physiological mechanism for its reported neuroprotective effects. Together, these findings establish perfusion-dependent CSD signatures as high-fidelity functional biomarkers of tissue state and neurovascular risk, enabling real-time delineation of severely compromised, at-risk, and relatively preserved cortex. While demonstrated here in ischaemic stroke, this framework has broad relevance for monitoring injury evolution and therapeutic response in neurological disorders where spreading depolarizations play a pathogenic role.
DOI
10.1093/brain/awag305
PMID
42714064
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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