David Raphael Schreier

Brainstem white matter (WM) bundles are essential conduits for neural signals that modulate homeostasis and consciousness. Their architecture forms the anatomic basis for brainstem connectomics, subcortical circuit models, and deep brain navigation tools. However, their small size and complex morphology, compared to cerebral WM, makes mapping and segmentation challenging in neuroimaging. As a result, fundamental questions about brainstem modulation of human homeostasis and consciousness remain unanswered. We leverage diffusion MRI tractography to create BrainStem Bundle Tool (BSBT), which automatically segments eight WM bundles in the rostral brainstem. BSBT performs segmentation on a custom probabilistic fiber map using a convolutional neural network architecture tailored to detect small anatomic structures. We demonstrate BSBT’s robustness across diffusion MRI acquisition protocols with in vivo scans of healthy subjects and ex vivo scans of human brain specimens with corresponding histology. BSBT also detected distinct brainstem bundle alterations in patients with Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and traumatic brain injury through tract-based analysis and classification tasks. Finally, we provide proof-of-principle evidence for the prognostic utility of BSBT in a longitudinal analysis of traumatic coma recovery. BSBT creates opportunities for scalable mapping of brainstem WM bundles and investigation of their role in a broad spectrum of neurological disorders.

Purpose: Dexmedetomidine (DEX) is a well-tolerated sedative drug that induces a sleep-like state. DEX sedation offers a model to study transitions between different states of consciousness (indicated by, eg, behavior, the electroencephalogram (EEG), or transcranial magnetic stimulation (TMS) evoked EEG responses). However, the effects of repeated DEX exposure on recovery are poorly understood and will be investigated in this pilot study. Participants and Methods: We aim to enroll 12 healthy volunteers (6 females, 6 males). Although we do not expect TMS-EEG to interfere with DEX sedation, due to the paucity of evidence this study uses a parallel arm design (TMS-EEG, non-TMS-EEG). Participants will be sedated twice, one week apart, and responsiveness monitored by a click-task to auditory beeps. A 64-channel EEG and additional physiological signals will be recorded. Cognition and vigilance tests will be performed before sedation (baseline), after return of responsiveness (ROR), and before discharge. TMS-EEG will be performed at baseline, during sedation, and during recovery. Using a smartwatch and questionnaires, we will assess sleep quality, sleepiness, and experiences during sedation and TMS-EEG. Results: We will report the difference of time to ROR between the first and second study visit, and explore potential differences across sex and study arms. We will evaluate state transitions by comparing responsiveness, traditional EEG signatures, TMS-evoked EEG responses (ie, perturbational complexity index), and report on cognition and vigilance test performance. Conclusion: This pilot trial will report on the effect of repeated DEX exposure on the recovery period, and the investigation of state transitions will advance our scientific understanding of altered states of consciousness. REDEX will provide valuable insights and data for designing future DEX sedation studies. Moreover, we will report on the potential of biological sex as a confounding factor and the feasibility of TMS-EEG under DEX.

Early detection of consciousness in critically ill patients with severe brain injuries has a profound impact on prognostication and decisions about whether to continue or limit life-sustaining therapy. The current state-of-the-art protocol to detect signs of consciousness includes standardized behavioral assessments, task-based functional magnetic resonance imaging (fMRI), and task-based electroencephalography (EEG). However, this approach has limited diagnostic sensitivity because auditory pathways may be disrupted, and conscious patients may lack attention, language, or other cognitive functions required to perform a task. Transcranial magnetic stimulation EEG (TMS-EEG) has the potential to overcome these limitations by directly perturbing the cerebral cortex to compute the perturbational complexity index, a neural correlate of consciousness. To date, TMS-EEG studies have focused on patients in the subacute or chronic stage of recovery from severe brain injury. Here, we report the proof-of-concept application of TMS-EEG for a critically ill patient in the acute stage of brain injury recovery. We show that TMS-EEG is feasible to perform as part of a multimodal protocol in the intensive care unit and that TMS-EEG may detect signs of consciousness that elude current state-of-the-art assessments.

Brainstem white matter bundles are essential conduits for neural signaling involved in modulation of vital functions ranging from homeostasis to human consciousness. Their architecture forms the anatomic basis for brainstem connectomics, subcortical mesoscale circuit models, and deep brain navigation tools. However, their small size and complex morphology compared to cerebral white matter structures makes mapping and segmentation challenging in neuroimaging. This results in a near absence of automated brainstem white matter tracing methods. We leverage diffusion MRI tractography to create BrainStem Bundle Tool (BSBT), which segments eight key white matter bundles in the rostral brainstem. BSBT performs automated segmentation on a custom probabilistic fiber map generated from tractography with a convolutional neural network architecture tailored for detection of small structures. We demonstrate BSBTs robustness across diffusion MRI acquisition protocols through validation on healthy subject in vivo scans and ex vivo scans of brain specimens with corresponding histology. Using BSBT, we reveal distinct brainstem white matter bundle alterations in Alzheimer’s disease, Parkinson’s disease, and acute traumatic brain injury cohorts through tract-based analysis and classification tasks. Finally, we provide proof-of-principle evidence supporting the prognostic utility of BSBT in a longitudinal analysis of coma recovery. BSBT creates opportunities to automatically map brainstem white matter in large imaging cohorts and investigate its role in a broad spectrum of neurological disorders.

David R Schreier, Veronique E C Vael, Gert Jan Lammers; Breaking the borders of wakefulness and sleep – the search for novel biomarkers to quantify sleepin

Background Deep brain stimulation of central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger spike-wave discharges (SWDs). Objectives To report the probability of inducing SWDs during CT-DBS in awake mice. Methods Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording EEG. We titrated stimulation current by gradually increasing it at each frequency until an SWD appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused an SWD during titration. Results In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused SWDs at currents lower than the titrated current, at currents as low as 20 μA. Conclusion Our study found a small but significant probability of inducing SWDs even after titration and at relatively low currents. EEG should be closely monitored for SWDs when performing CT-DBS in both research and clinical settings.

Background and Objectives This study aimed to evaluate and predict the effects of interictal epileptiform discharges (IEDs) on driving ability using simple reaction tests and a driving simulator. Methods Patients with various epilepsies were evaluated with simultaneous EEGs during their response to visual stimuli in a single-flash test, a car-driving video game, and a realistic driving simulator. Reaction times (RTs) and missed reactions or crashes (miss/crash) during normal EEG and IEDs were measured. IEDs, as considered in this study, were a series of epileptiform potentials (>1 potential) and were classified as generalized typical, generalized atypical, or focal. RT and miss/crash in relation to IED type, duration, and test type were analyzed. RT prolongation, miss/crash probability, and odds ratio (OR) of miss/crash due to IEDs were calculated. Results Generalized typical IEDs prolonged RT by 164 ms, compared with generalized atypical IEDs (77.0 ms) and focal IEDs (48.0 ms) (p < 0.01). Generalized typical IEDs had a session miss/crash probability of 14.7% compared with a zero median for focal and generalized atypical IEDs (p < 0.01). Long repetitive bursts of focal IEDs lasting >2 seconds had a 2.6% miss/crash probability_IED. Cumulated miss/crash probability could be predicted from RT prolongation: 90.3 ms yielded a 20% miss/crash probability. All tests were nonsuperior to each other in detecting miss/crash probabilities_IED (zero median for all 3 tests) or RT prolongations (flash test: 56.4 ms, car-driving video game: 75.5 ms, simulator 86.6 ms). IEDs increased the OR of miss/crash in the simulator by 4.9-fold compared with normal EEG. A table of expected RT prolongations and miss/crash probabilities for IEDs of a given type and duration was created. Discussion IED-associated miss/crash probability and RT prolongation were comparably well detected by all tests. Long focal IED bursts carry a low risk, while generalized typical IEDs are the primary cause of miss/crash. We propose a cumulative 20% miss/crash risk at an RT prolongation of 90.3 ms as a clinically relevant IED effect. The IED-associated OR in the simulator approximates the effects of sleepiness or low blood alcohol level while driving on real roads. A decision aid for fitness-to-drive evaluation was created by providing the expected RT prolongations and misses/crashes when IEDs of a certain type and duration are detected in routine EEG.