Whole-brain model replicates sleep-like slow-wave dynamics generated by stroke lesions.

Focal brain injuries, such as stroke, cause local structural damage as well as alteration of neuronal activity in distant brain regions. Experimental evidence suggests that one of these changes is the appearance of sleep-like slow waves in the otherwise awake individual. This pattern is prominent in areas surrounding the damaged region and can extend to connected brain regions in a way consistent with the individual's specific long-range connectivity patterns. In this paper we present a generative whole-brain model based on (f)MRI data that, in combination with the disconnection mask associated with a given patient, explains the effects of the sleep-like slow waves originated in the vicinity of the lesion area on the distant brain activity. Our model reveals new aspects of their interaction, being able to reproduce functional connectivity patterns of stroke patients and offering a detailed, causal understanding of how stroke-related effects, in particular slow waves, spread throughout the brain. The presented findings demonstrate that the model effectively captures the links between stroke occurrences, sleep-like slow waves, and their subsequent spread across the human brain.

Sleep-like cortical dynamics during wakefulness and their network effects following brain injury.

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery.

Design and validation of a diagnostic suspicion checklist to differentiate epileptic from psychogenic nonepileptic seizures (PNES-DSC).

OBJECTIVE: Psychogenic non-epileptic seizures (PNES) are complex clinical manifestations and misdiagnosis as status epilepticus remains high, entailing deleterious consequences for patients. Video-electroencephalography (vEEG) remains the gold-standard method for diagnosing PNES. However, time and economic constraints limit access to vEEG, and clinicians lack fast and reliable screening tools to assist in the differential diagnosis with epileptic seizures (ES). This study aimed to design and validate the PNES-DSC, a clinically based PNES diagnostic suspicion checklist with adequate sensitivity (Se) and specificity (Sp) to discriminate PNES from ES. METHODS: A cross-sectional study with 125 patients (n = 104 drug-resistant epilepsy; n = 21 PNES) admitted for a vEEG protocolised study of seizures. A preliminary PNES-DSC (16-item) was designed and used by expert raters blinded to the definitive diagnosis to evaluate the seizure video recordings for each patient. Cohen's kappa coefficient, leave-one-out cross-validation (LOOCV) and balance accuracy (BAC) comprised the main validation analysis. RESULTS: The final PNES-DSC is a 6-item checklist that requires only two to be present to confirm the suspicion of PNES. The LOOCV showed 71.4% BAC (Se = 45.2%; Sp = 97.6%) when the expert rater watched one seizure video recording and 83.4% BAC (Se = 69.6%; Sp = 97.2%) when the expert rater watched two seizure video recordings. CONCLUSION: The PNES-DSC is a straightforward checklist with adequate psychometric properties. With an integrative approach and appropriate patient history, the PNES-DSC can assist clinicians in expediting the final diagnosis of PNES when vEEG is limited. The PNES-DSC can also be used in the absence of patients, allowing clinicians to assess seizure recordings from smartphones.

Recording physiological and pathological cortical activity and exogenous electric fields using graphene microtransistor arrays in vitro.

Graphene-based solution-gated field-effect transistors (gSGFETs) allow the quantification of the brain's full-band signal. Extracellular alternating current (AC) signals include local field potentials (LFP, population activity within a reach of hundreds of micrometers), multiunit activity (MUA), and ultimately single units. Direct current (DC) potentials are slow brain signals with a frequency under 0.1 Hz, and commonly filtered out by conventional AC amplifiers. This component conveys information about what has been referred to as "infraslow" activity. We used gSGFET arrays to record full-band patterns from both physiological and pathological activity generated by the cerebral cortex. To this end, we used an in vitro preparation of cerebral cortex that generates spontaneous rhythmic activity, such as that occurring in slow wave sleep. This examination extended to experimentally induced pathological activities, including epileptiform discharges and cortical spreading depression. Validation of recordings obtained via gSGFETs, including both AC and DC components, was accomplished by cross-referencing with well-established technologies, thereby quantifying these components across different activity patterns. We then explored an additional gSGFET potential application, which is the measure of externally induced electric fields such as those used in therapeutic neuromodulation in humans. Finally, we tested the gSGFETs in human cortical slices obtained intrasurgically. In conclusion, this study offers a comprehensive characterization of gSGFETs for brain recordings, with a focus on potential clinical applications of this emerging technology.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets.

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.