Catherine Duclos

Background: Individuals with Long COVID (LC) often report fatigue, brain fog, and post-exertional malaise (PEM), which may reflect disrupted brain function. Using High-density EEG (hd-EEG) we compared whole-brain functional network properties between LC and healthy controls at rest, examined task-related network reorganization, and assessed associations with symptom severity.Methods: Twenty LC individuals (15 females, mean age 42.7 ± 9.8, all right-handed) reporting fatigue, brain fog, and PEM and 20 age- and sex-matched controls completed self-reported symptom questionnaires and cognitive testing during an online session. Resting-state 64-channel EEG was recorded with eyes closed at baseline (pre-) and post-a motor learning (ML) task. Functional connectivity, graph-theory, complexity, and spectral features were extracted from pre- and post-ML recordings. Between-group differences and group-by-time changes from pre- to post-task were assessed, as well as associations between EEG features and clinical scores.Results: LC participants exhibited a narrower alpha bandwidth (p= 0.035) than controls. From pre- to post-task, the LC group showed a smaller increase in global efficiency (p= 0.014) and a greater decrease in participation coefficient (p= 0.003) than controls. Changes in the participation coefficient and global efficiency correlated with fatigue and cognitive failure scores in the combined sample, but not in the LC group alone.Conclusions: Narrower alpha bandwidth may indicate reduced neural flexibility in LC. Altered post-task network integration and efficiency further suggest a reduced capacity to adapt network properties to cognitive demands. These preliminary findings identify EEG-based network properties as potential targets for intervention.

BackgroundLong COVID (LC) consists of fatigue and brain fog are among the most reported symptoms in LC, yet their underlying neural mechanisms remain unclear. In this context, we aimed to identify neural correlates associated with LC symptoms.MethodsElectroencephalography (EEG) was used to quantify oscillatory activity during rest and sustained handgrips in the somatomotor cortex (SMC). Twenty LC participants reporting fatigue, brain fog, and post-exertional malaise (PEM) were recruited, along with 20 age- and sex-matched controls. The main LC symptoms were assessed using the Fatigue Severity Scale (FSS), the Cognitive Failures Questionnaire (CFQ), and the DePaul Symptom Questionnaire for PEM (DSQ-PEM).ResultsLC participants showed a smaller post-movement beta rebound (PMBR) during handgrips in the SMC (electrode C3, p=0.02) compared to controls, which negatively correlated with cognitive symptoms (r = -0.35, p=0.02). They also showed a slower alpha central frequency (CF) in the SMC at rest (electrode C5, p=0.003), which correlated with fatigue (r = -.264, p=0.019).ConclusionsBoth the lower PMBR and slower alpha CF suggest impaired inhibitory control and reduced excitability in the SMC of LC individuals. Together, these features, which were associated with fatigue and brain fog, highlight altered dynamics in the SMC of LC individuals. These findings offer mechanistic insight into LC symptoms and support the use of EEG-based biomarkers for diagnosis, stratification, and monitoring of targeted interventions.

Objective. Closed-loop auditory stimulation (CLAS) can enhance slow oscillations during non-rapid eye movement sleep; however, its broader use has been limited by technical and temporal constraints. We introduce and validate CLAS-hdEEG , a high-density electroencephalographic (hd-EEG) software platform integrated within the Magstim EGI environment, capable of real-time delta wave detection and phase-targeted auditory stimulation with low detection-to-stimulation latency, achieved through event-based detection of delta wave extrema. Approach. The CLAS-hdEEG software continuously streams 128-channel EEG data, applies an online moving average filter to Fz for real-time delta wave detection, and triggers brief pink noise bursts at the detected peak or trough of ongoing oscillations. Detection thresholds were based on established amplitude crossings, a minimum peak-to-peak difference of 75 μ V , and duration limits of 160–1700 ms. Synchronization between neural detection and stimulus onset was verified using digital input hardware (DIN) markers recorded by the EGI acquisition system. The system was validated in 14 healthy participants during N3 sleep under peak, trough, and sham stimulation conditions. Phase-targeting success was defined as auditory bursts occurring within predefined 90 ^∘ phase windows of the oscillation (0 ^∘ –90 ^∘ for in-phase blocks and 180 ^∘ –270 ^∘ for anti-phase blocks). Main results. Across all sessions, auditory stimuli were delivered with a mean detection-to-stimulation latency of 20.03 ± 0.5 ms and an overall phase-targeting success rate of 0.913, indicating that 91.3% of stimuli occurred within the predefined 90 ^∘ target phase windows. Online and offline detections showed strong agreement ( F 1 ≈ 0.80 ), confirming that real-time processing preserved the fidelity of offline algorithms. Significance. This study establishes the technical and temporal validity of CLAS-hdEEG, the first hd-EEG software platform for CLAS within the EGI environment. By combining low detection-to-stimulation latency with high-density spatial sampling, this framework provides a robust and extensible platform for investigating how auditory stimulation interacts with large-scale neural dynamics across diverse states of consciousness.

Introduction: Delta waves (0.1–4 Hz) are a hallmark of unconsciousness in both sleep and general anesthesia (GA). Closed-loop auditory stimulation (CLAS) delivers brief sounds phase-locked to ongoing slow oscillations and can modulate slow-wave activity (SWA) during sleep, but its efficacy and translational relevance under propofol anesthesia, particularly in the presence of nociceptive input, remain unknown. Methods: We describe a prospective, within-subject protocol in 30 neurologically healthy adults undergoing elective surgery under propofol GA. Intraoperative high-density electroencephalography (hd-EEG; 128 channels) is recorded while a real-time CLAS algorithm detects δ -waves at the Fz electrode, and triggers pink-noise bursts in three conditions: in-phase (peak-locked), anti-phase (trough-locked), and sham . An extended in-phase block is then combined with a standardized tetanic nociceptive stimulus (100 Hz, 70 mA, 30 s) to test robustness under an arousal challenge. Depth of hypnosis is monitored with the Bispectral Index (BIS), and nociceptive/autonomic reactivity with the Nociception Level (NOL) index. Primary outcomes quantify δ -wave morphology and SWA (amplitude, slopes, duration/frequency, transition frequency, density, power spectral density), along with topography, source estimation, and propagation. Secondary outcomes include depth of anesthesia (BIS), nociceptive balance (NOL), functional connectivity (weighted and directed phase-lag indices), graph-theoretical organization (global efficiency, clustering, modularity, small-worldness), and EEG microstates (duration, coverage, transitions). Anticipated results: We hypothesize that in-phase CLAS will increase amplitude, slope, duration, and density of δ -waves, and decrease frequency and transition frequency relative to sham , whereas anti-phase CLAS will cause a disruption these parameters relative to sham . Under nociception, in-phase CLAS is expected to these parameters preserve δ -wave integrity, with effects attenuated relative to non-nociceptive conditions. We anticipate BIS values to remain within the surgical range but trend lower during in-phase CLAS, alongside reduced nociceptive/autonomic responses. At the network level, we expect CLAS-induced δ -wave reinforcement to be associated with more locally coherent δ-band activity and reduced frontoparietal integration, consistent with deeper unconsciousness. Discussion: If confirmed, these findings would position CLAS as a candidate neuromodulation strategy to reinforce anesthetic δ -wave dynamics and help stabilize anesthesia without necessarily increasing pharmacological dose, while also providing a systems-level test of the role of slow oscillations in sustaining unconsciousness.

Background: Withdrawal of life-sustaining treatments (WLST) is a leading cause of death in patients with severe acquired brain injuries (ABI). These decisions often occur under conditions of prognostic uncertainty and time-critical therapeutic windows and may be shaped by a complex interplay of factors. Elucidating these influences is essential to ensure that WLST decisions are made in an informed, unbiased, and transparent manner, and in alignment with wishes of the patients as well as their surrogate decision makers. Objective: Conduct a scoping review of literature to identify, elaborate and analyze the various factors that influence decisions to WLST in adult patients with ABI. This review aims to provide a comprehensive understanding of current practices. Methods: This scoping review, conducted according to PRISMA-ScR guidelines, examined literature on WLST in adult ABI, in whom brain death had not been declared. The search was conducted in PubMed and Web of Science, up to August 2024. Studies were screened by title/abstract and full text, with data systematically extracted. Only original, peer-reviewed articles focusing on WLST in adult severe ABI patients were included. N = 2,963 independent papers were initially found, of which N = 2,881 were excluded. A final count of N = 81 independent papers were included. Results: Demographic factors (age, sex, race, socioeconomic status, etc.; n = 50), prognosis and clinical factors ( n = 59), family preferences ( n = 28), physician-related factors and institutional context ( n = 31), formal medical directive ( n = 13), ethical/legal frameworks ( n = 13), geographical differences ( n = 9) and religious beliefs ( n = 5) all played pivotal roles in WLST decisions. Older age consistently emerged as a determinant for WLST, as well as poor prognosis and white race. Conclusion: WLST decisions are most often made for older adults, with age consistently identified as a key predictor, independent of the clinical severity of ABI. Additional factors such as race, socioeconomic status, advance directives, and variations in healthcare provider attitudes and institutional policies further contribute to disparities in WLST practices. Understanding these intersecting influences is essential to recognizing potential biases and promoting more equitable, patient-centered end-of-life decision-making.

Disorders of consciousness (DoC) are characterized by impaired arousal and/or awareness, ranging from coma to unresponsive wakefulness syndrome, minimally conscious state, and cognitive motor dissociation. Pharmacological treatment options remain limited, complicated by the heterogeneity of etiologies, such as traumatic brain injury, stroke, and infections. The lack of rigorous clinical trials has led to off-label use of treatments, often without clear mechanistic understanding, posing challenges for effective patient care. In this perspective, the authors report on key studies concerning the effectiveness of pharmacological interventions, including dopaminergic and GABAergic agents, antidepressants, statins, and anticonvulsants, in promoting recovery of consciousness in DoC. Robust longitudinal clinical trials are needed, with priority given to early subacute phase intervention. Outcomes should be better defined, considering immediate responses to medication while also increasing the emphasis on long-term quality of life. Unified functional and mechanistic frameworks are needed to guide research and foster collaboration. Furthermore, a shift toward personalized medicine would benefit this heterogeneous population. Moving forward, assessing the efficacy of more unconventional or 'paradoxical' pharmacological options in treatment plans will be essential. The authors also expect an increased use of AI tools to identify factors that best predict treatment responses.

Introduction Therapeutic interventions for disorders of consciousness lack consistency; evidence supports non-invasive brain stimulation, but few studies assess neuromodulation in acute-to-subacute brain-injured patients. This study aims to validate the feasibility and assess the effect of a multi-session transcranial alternating current stimulation (tACS) intervention in subacute brain-injured patients on recovery of consciousness, related brain oscillations and brain network dynamics. Methods and analyses The study is comprised of two phases: a validation phase (n=12) and a randomised controlled trial (n=138). Both phases will be conducted in medically stable brain-injured adult patients (traumatic brain injury and hypoxic-ischaemic encephalopathy), with a Glasgow Coma Scale score ≤12 after continuous sedation withdrawal. Recruitment will occur at the intensive care unit of a Level 1 Trauma Centre in Montreal, Quebec, Canada. The intervention includes a 20 min 10 Hz tACS at 1 mA intensity or a sham session over parieto-occipital cortical sites, repeated over five consecutive days. The current’s frequency targets alpha brain oscillations (8–13 Hz), known to be associated with consciousness. Resting-state electroencephalogram (EEG) will be recorded four times daily for five consecutive days: pre and post-intervention, at 60 and 120 min post-tACS. Two additional recordings will be included: 24 hours and 1-week post-protocol. Multimodal measures (blood samples, pupillometry, behavioural consciousness assessments (Coma Recovery Scale-revised), actigraphy measures) will be acquired from baseline up to 1 week after the stimulation. EEG signal analysis will focus on the alpha bandwidth (8–13 Hz) using spectral and functional network analyses. Phone assessments at 3, 6 and 12 months post-tACS, will measure long-term functional recovery, quality of life and caregivers’ burden. Ethics and dissemination Ethical approval for this study has been granted by the Research Ethics Board of the CIUSSS du Nord-de-l’Île-de-Montréal (Project ID 2021–2279). The findings of this two-phase study will be submitted for publication in a peer-reviewed academic journal and submitted for presentation at conferences. The trial’s results will be published on a public trial registry database (ClinicalTrials.gov). Trial registration number NCT05833568.

Background: We previously showed that day‐to‐day sleep variability was associated with biomarkers of Alzheimer’s disease (AD) pathology. Given that brain‐derived neurotrophic factor (BDNF) activity has been involved in both AD and cognitive impacts of lifestyle risk factors, we sought to evaluate day‐to‐day sleep variability in those carrying the Met allele of the Val66Met BDNF polymorphism, which affects BDNF release. Additionally, we explored whether BDNF polymorphism modified the association between CSF AD pathology and day‐to‐day sleep variability.Method: The PREVENT‐AD cohort enrolls dementia‐free persons with a parental history of sporadic AD. We characterized 203 dementia‐free participants from this cohort at the polymorphic BDNF locus, identifying 136 persons with genotype Val/Val (age 68.2±5.3y, 75%women), 67 Met heterozygotes/homozygotes (68.4±5.6y, 73% women, including six Met/Met homozygotes). After a week of actigraphy, we calculated these participants’ standard deviation of day‐to‐day sleep characteristics. We used t‐tests to compare day‐to‐day sleep variability in sleep characteristics between Val homozygotes and Met carriers. In a subsample of 101 participants, we assayed CSF Aβ₄₂, t‐tau and p‐tau181, and used age‐ and sex‐adjusted linear regressions with an interaction term to test for moderation by BDNF genotype (36 Met carriers vs. 65 Val homozygotes) between sleep variability and CSF biomarker results.Result: As compared to Val homozygotes, sleep was disturbed and unstable in BDNF Met carriers (heterozygotes/homozygotes). They had higher day‐to‐day variability in sleep fragmentation (activity counts, wake after sleep onset, fragmentation index), sleep onset latency, and sleep duration, resulting in weekly sleep patterns that were more fragmented and less efficient. Further, BDNF genotype moderated the association between CSF t‐tau and p‐tau181 with day‐to‐day sleep duration variability, such that only Met carriers showed an association between elevated tau concentration and sleep duration variability.Conclusion: BDNF Met carriers had disturbed and unstable daily sleep patterns. Because the Met allele is known to decrease plasticity of neural mechanisms, the ability to achieve healthy and stable sleep may depend on such mechanisms. Thus, poorer sleep in Met carriers might be a biological mechanism by which altered BDNF activity affects tau pathology or, alternatively, accumulated AD pathology in Met carriers may disrupt sleep‐wake patterns.

Background: Both short and long sleep duration have been associated with increased risk of developing Alzheimer’s Disease (AD). While short sleep has been hypothesized to be a risk factor promoting neurodegenerative processes. Long sleep has been viewed as a marker of ongoing neurodegeneration, potentially as a result of neuroinflammation. The objective of the study is to evaluate sleep patterns measured with actigraphy close to expected symptom onset in association with neuroinflammatory biomarkers. We leveraged the PREVENT‐AD cohort in which participants with a parental history of sporadic AD are studied before clinical onset.Method: 203 participants (age: 68.5±5.4 years, 151 women) free of dementia were tested with a week of actigraphy. Nighttime sleep duration, time in bed, sleep latency, sleep efficiency, and number of sleep bouts were calculated using the Actiware scoring algorithm (medium threshold). The standard deviation of these characteristics was calculated across actigraphy days to assess day‐to‐day variability. Years to expected onset was calculated by the number of years remaining before the age of clinical onset of a parent, or the earliest age of clinical onset if both parents developed AD. A subset of 100 participants underwent a lumbar puncture, and IL‐6 and MCP‐1 levels were assessed using OLINK technology in cerebrospinal fluid (CSF). The association between sleep characteristics, years to expected onset and inflammatory markers was assessed using linear regressions adjusted for age, sex, and time interval between measurements.Result: Being closer to expected onset was associated with longer nighttime sleep duration (b = ‐0.19, p = 0.015) and lower sleep bouts day‐to‐day variability (b = 0.17, p = 0.027). Higher CSF IL‐6 was associated with longer sleep duration and time in bed (b = 0.30, p = 0.006; b = 0.24, p = 0.028), whereas higher MCP‐1 was associated with longer nighttime sleep duration (b = 0.25, p = 0.019). Higher CSF MCP‐1 levels were associated with lower sleep bouts day‐to‐day variability and shorter sleep onset latency only in those within 10 years of expected onset (interaction p = 0.012 and 0.061; b = ‐0.31, p = 0.043; b = ‐0.31, p = 0.047).Conclusion: Proximity to parental onset of sporadic AD onset was associated with a longer sleep profile that is more consolidated and stable, which may be due to proinflammatory processes.