BACKGROUND: The clinical effects of deep brain stimulation for neurological conditions manifest across multiple timescales, spanning seconds to months, and involve direct electrical modulation, neuroplasticity, and network reorganization. In epilepsy, the delayed effects of deep brain stimulation on seizures limit optimization. Single pulse electrical stimulation and the resulting pulse evoked potentials offer a measure network effective connectivity and excitability. This study leverages single pulse and high frequency thalamic stimulation during stereotactic electroencephalography to assess seizure network engagement, modulate network activity, and track changes in excitability and epileptiform abnormalities. METHODS: Ten individuals with drug resistant epilepsy undergoing clinical stereotactic electroencephalography were enrolled in this retrospective cohort study. Each underwent a trial of high frequency (145 Hz) thalamic stimulation. Pulse evoked potentials were acquired before and after high frequency stimulation. Baseline evoked potential root-mean-square amplitude assessed seizure network engagement, and modulation of amplitude (post high frequency stimulation versus baseline; Cohen's d effect size) assessed change in network excitability. Interictal epileptiform discharge rates were measured by an automated classifier at baseline and during high frequency stimulation. Statistical significance was determined using paired-sample t-tests (p<0.05 significance level). This study was approved by the Mayo Clinic Institutional Review Board, with informed consent obtained from all participants. RESULTS: Thalamic stimulation delivered for >1.5 hours significantly reduced pulse evoked potential amplitudes in connected areas compared to baseline, with the degree of modulation correlated with baseline connectivity strength. Shorter stimulation durations did not induce reliable changes. High frequency stimulation immediately suppressed interictal epileptiform discharge rates in seizure networks with strong baseline thalamocortical connectivity. Pulse evoked potentials delineated the anatomical distribution of network engagement, revealing distinct patterns across thalamic subfields. CONCLUSION: Pulse evoked potentials and thalamic stimulation during stereotactic electroencephalography provide novel network biomarkers to evaluate target engagement and modulation of large-scale networks across acute and subacute timescales. This approach demonstrates potential for efficient, data-driven neuromodulation optimization, and a new paradigm for personalized deep brain stimulation in epilepsy.

• Klíčová slova
• deep brain stimulation, effective connectivity, electrophysiology, epilepsy, neuromodulation,
• Publikační typ
• časopisecké články MeSH
• preprinty MeSH

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.

• Klíčová slova
• canine, implantable devices for sensing and stimulation, intracranial EEG, sleep classification,
• MeSH
• bdění fyziologie   MeSH
• elektroencefalografie metody   MeSH
• elektrokortikografie MeSH
• psi MeSH
• spánek REM fyziologie   MeSH
• spánek * fyziologie   MeSH
• stadia spánku * fyziologie   MeSH
• zvířata MeSH
• Check Tag
• psi MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

• Klíčová slova
• ambulatory intracranial EEG, automated sleep scoring, deep brain stimulation, electrical brain stimulation, epilepsy, implantable devices,
• MeSH
• epilepsie komplikace   MeSH
• hipokampus MeSH
• hluboká mozková stimulace * metody   MeSH
• lidé MeSH
• mozek MeSH
• nuclei anteriores thalami * MeSH
• poruchy spánku a bdění * komplikace   diagnóza   terapie   MeSH
• retrospektivní studie MeSH
• thalamus MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

• MeSH
• cirkadiánní rytmus MeSH
• elektroencefalografie MeSH
• epilepsie prevence a kontrola   MeSH
• hluboká mozková stimulace metody   MeSH
• lidé MeSH
• psi MeSH
• riziko MeSH
• thalamus fyziologie   MeSH
• záchvaty prevence a kontrola   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• psi MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH