Summary: The brain remains highly active during sleep, and detailed mapping of that activity can reveal early signs of neurological and sleep disorders. Using rare intracranial EEG data, researchers refined computational techniques to distinguish functional regions of the cerebral cortex by their characteristic electrical signatures, even when the brain is at rest.
These results suggest that more precise cortical mapping could support earlier detection of conditions such as Alzheimer’s disease, epilepsy and various sleep disorders, and could inform more targeted, personalized neurological therapies.
Key facts:
- Cortical mapping breakthroughs: Distinct electrical activity patterns were identified across cortical regions during sleep and wakefulness.
- Clinical potential: The approach may aid early diagnosis and monitoring of neurodegenerative, epileptic and sleep-related conditions.
- Novel computational tools: New methods refine how intracranial signals are analyzed to parcel the cortex by function.
Source: KTU
“Complex processes are going on in the brain when we sleep,” says Dr Karolina Armonaitė, a neuroscientist at Kaunas University of Technology (KTU) in Lithuania.
Dr Armonaitė explains that a finer understanding of how different cortical areas behave across sleep stages could improve diagnosis and monitoring of both sleep disorders and neurological diseases. Early functional changes can be subtle and localized: for example, Alzheimer’s disease may begin with small, localized cortical alterations long before clinical symptoms appear, while disorders such as schizophrenia involve disrupted synchrony between regions.
During her PhD research at Università Telematica Internazionale UNINETTUNO, Armonaitė focused on functional cortex parcellation—seeking to determine whether cortical regions can be identified solely from their intrinsic electrical activity, without external stimulation, and whether such identification holds across different sleep states.
The cerebral cortex is functionally heterogeneous: different areas show distinct dynamics depending on whether a person is awake, asleep, or transitioning between states. Armonaitė’s study tested whether these differences are robust enough to identify cortical parcels from intracranial recordings alone.
Accurate parcellation connects structure to function—linking specific cortical zones to vision, language, movement or memory—and this mapping has practical clinical consequences. For instance, identifying the epileptic focus precisely helps surgeons plan resections with better assessment of risks to critical functions. Similarly, knowing which deep structures are involved in Parkinson’s disease informs targeted neurostimulation strategies that alleviate motor symptoms.
Precise region identification also underpins brain-computer interfaces and computational models of brain activity, as well as studies that examine how psychiatric and neurodegenerative diseases affect distinct neural circuits.
Unique computational methods and rare data
Armonaitė’s analysis used intracranial stereotactic electroencephalography (sEEG) recorded from 55 patients. These recordings are rare because electrodes are implanted directly in brain tissue during neurosurgical evaluation—typically for patients with drug-resistant epilepsy—so the signals reflect local cortical activity with much greater fidelity than scalp EEG.
Focusing first on well-characterized regions—the precentral (motor), postcentral (sensory) and superior temporal (auditory) gyri—the study examined power spectral density and searched for scale-free, power-law behavior across frequency bands during wakefulness and sleep. Rather than treating resting activity as random noise, the work treats those signals as structured information encoded by neural networks.
The findings show consistent power-law dynamics in the examined areas and indicate that the power-law exponent in higher frequency ranges can reliably distinguish cortical parcels in both sleep and wake states. In other words, each region exhibits a characteristic scale-free pattern that remains relatively stable across behavioral states.
From a methodological perspective, the study introduces computational tools that improve cortex parcellation based on local neurodynamics. These tools make it possible to classify cortical areas by their intrinsic electrical fingerprints and to explore how these fingerprints change in disease.
Clinical implications and future directions
While not yet a direct clinical test, this research opens multiple avenues for practical application. One promising direction is the development of digital models or digital twins of cortical regions that capture cell structure, interactions and electrical parameters. Such models could predict how a specific cortical area would respond to stimulation and support the design of personalized neuromodulation therapies.
More immediately, comparing the electrical markers of healthy cortical areas during sleep with data from patients who have sleep disorders could reveal localized dysfunctions that standard sleep assessments miss. Sleep is an active, dynamic process involving memory consolidation, metabolic clearance and synaptic reorganization; subtle deviations in sleep-related neural patterns may signal early neurodegenerative changes.
If we can define how a healthy cortex behaves across states and tasks, clinicians and researchers can search for departures from those norms—potentially detecting disease earlier and improving our understanding of its mechanisms. This work therefore contributes both to early diagnosis and to the design of targeted interventions informed by precise functional mapping.
About this sleep and brain mapping research news
Author: Aldona Tuur
Source: KTU
Contact: Aldona Tuur – KTU
Image: The image is credited to Neuroscience News
Original Research: Open access. “Analysis of power law behavior of local cortical neurodynamics” by Karolina Armonaitė et al., Physica D: Nonlinear Phenomena. DOI: 10.1016/j.physd.2025.134733
Abstract
Analysis of power law behavior of local cortical neurodynamics
Increasing evidence indicates that neuronal electrical activity—neurodynamics—carries signatures specific to cortical parcels, potentially enabling functional classification even during resting states. Existing feature-extraction algorithms can succeed in narrow, well-defined cases but often struggle to identify stable markers across broader populations.
This study analyzes intracranial sEEG recordings from 55 subjects to assess power-law behavior in power spectral density across wakefulness and sleep within three gyri: precentral, postcentral and superior temporal. Results show scale-free dynamics characterized by a power-law exponent in the high-frequency range that distinguishes cortical parcels in both wakefulness and sleep, suggesting stable local patterns that may be largely state-independent.
These insights provide useful guidance for evaluating physiological aspects of local neurodynamics and support population-level approaches to functional cortex parcellation, with implications for early detection of neurological and sleep disorders and the development of targeted neuromodulation strategies.