Dynamika aperiodycznego EEG zależna od stanu odzwierciedla reorganizację sieci korowej w przewlekłej epilepsji
State-dependent aperiodic EEG dynamics track cortical network reorganization in chronic epilepsy
W skrócie
[Preprint - wstępne wyniki] Badacze śledzili specjalny rodzaj aktywności elektrycznej mózgu (aperiodyczną) u myszy z epilepsją, obserwując jak zmienia się ona w różnych stanach — podczas czuwania, snu i napadów. Wyniki pokazują, że ta miara rzeczywiście oddaje zmiany w mózgu chorym na epilepsję, ale badacze odkryli też, że na wyniki wpływają zarówno rzeczywiste zmiany w sieci neuronowej, jak i zakłócenia wywoływane przez nieprawidłowe fale aktywności. Stanowi to ważne potwierdzenie, że ta metoda jest przydatna do badania epilepsji, choć trzeba być świadomym jej ograniczeń.
Oryginalny abstract (angielski)
Aperiodic (1/f-like) EEG activity has rapidly become a popular noninvasive marker of cortical network state, proposed to index excitation-inhibition (E/I) balance and increasingly applied across neurological and psychiatric disorders. However, whether this approach remains reliable in the pathological brain, where disease progressively reorganizes neural networks, alters signal morphology, and drives continuous transitions between cortical states has yet to be systematically established.Using a medication-free genetic model of chronic epilepsy (Lafora disease; Epm2a-- mice), we tracked the aperiodic component of the cortical EEG across resting wakefulness, isoflurane anesthesia, and PTZ-induced seizures of graded severity, asking how a single spectral marker behaves as the brain moves between states. Epileptic mice exhibited systematically steeper aperiodic exponents than controls, an effect that persisted after removal of interictal epileptiform discharges and was replicated using independent time-resolved spectral parameterization. Slopes steepened predictably under GABAergic anesthesia, supporting the interpretation that aperiodic activity captures biologically meaningful state transitions beyond simple contamination by pathological waveforms. Across seizure phases, the aperiodic exponent varied systematically, however, the exponent flattened during ictal activity in step with the dominant discharge morphology, revealing that pathological waveform shape itself is a substantial contributor to seizure-state exponent changes. Together, these findings indicate that aperiodic EEG dynamics reflect a combination of chronic network-state reorganization and waveform-shape-driven spectral distortion, with their relative contributions varying across brain states. These results support spectral parameterization as a sensitive approach for tracking pathological neural activity in chronic epilepsy while delineating its interpretive boundaries in the presence of pathological waveforms.