Brice Ozenne

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.

Background: Patients with chronic cluster headache (CCH) suffer from poor sleep, which may impact their brain microstructure and parenchymal clearance of waste products. Psilocybin has shown promise for the treatment of CCH and has been linked to increased neuroplasticity with possible influences on brain microstructure. Aims: To investigate the effects of psilocybin on sleep, brain water diffusivity, and microstructure in CCH. Methods: Eleven CCH patients underwent diffusion-weighted MRI and subjective sleep quality assessment with the Pittsburgh Sleep Quality Index (PSQI) before and 1 week after three psilocybin administrations (0.14 mg/kg) spaced 1 week apart. Measures taken prior to intervention were also compared to 24 healthy controls, and subjective sleep quality was related to brain microstructure and diffusivity across groups. Results: We found that sleep was poor in CCH patients, but improved after psilocybin treatment (CCH mean PSQI change (SD) = –2.50 (2.1), p _FWER = 0.015). When analyzing brain microstructure and water diffusivity in conjunction, we found differences between CCH patients and controls, which were primarily driven by differences in grey matter. On average, psilocybin intervention in CCH patients was not associated with statistically significant changes in brain microstructure or water diffusivity. However, most patients exhibited lower white matter diffusivity and neurite volume after intervention. Subjective sleep quality showed borderline significant correlations of moderate effect size with brain microstructure and water diffusivity. Conclusion: Subjective sleep quality improved in CCH patients after psilocybin and showed some evidence of an association with measures of brain microstructure and water diffusivity. ClinicalTrials.gov identifiers: Prophylactic Effects of Psilocybin on Chronic Cluster Headache (EPOCH; NCT04280055) and The Neurobiological Effect of 5-HT2AR Modulation (NeuroPharm2; NCT03289949).

Psilocybin and other serotonergic drugs acutely alter human brain function and large-scale connectivity as measured with BOLD fMRI, but whether these effects are frequency-specific remains unknown. We applied multitaper spectral and cross-spectral analyses to resting-state fMRI data from 28 healthy volunteers scanned multiple times acutely following oral psilocybin administration (0.2 – 0.3 mg/kg), together with plasma psilocin measurements, to estimate psilocin associations with temporal frequency-specific activity and connectivity. Psilocybin produced a selective reduction in low-frequency spectral power (0.01 – 0.06 Hz ) and an increase in spectral entropy, with the strongest effects in transmodal networks. We also observed a reduction in low-frequency connectivity energy explained by the unimodal/transmodal axis. These findings demonstrate that psilocin induces spatially distributed, frequency-dependent alterations, suggesting that broadband fMRI analyses may obscure low-frequency dynamics. Frequency-resolved approaches may offer greater sensitivity for characterizing psychedelic effects on brain activity.

L’Accident Vasculaire Cérébral (AVC) est une maladie grave pour laquelle des critères très stricts encadrent l’administration du traitement curatif en phase aigüe. Ces critères limitent drastiquement l’accès à ce traitement : on estime que seuls 10% des patients atteints d’un AVC en bénéficient. L’objectif de ce travail est de proposer un modèle prédictif de l’évolution de l’AVC qui permette d’identifier le volume de tissu à risque de chaque patient. Ce volume, qui correspond au bénéfice potentiel du traitement, permettra de mieux orienter le médecin dans sa décision de traiter. Pour répondre à cet objectif nous nous intéressons aux problématiques d’évaluation de modèles prédictifs dans un contexte de faible prévalence, de modélisation prédictive sur données spatiales, de prédiction volumique en fonction de l’option de traitement et de segmentation d’images en présence d’artefacts. Les outils développés ont été rassemblés au sein d’une librairie de fonctions du logiciel R nommée MRIaggr

Classical psychedelics have regained interest for their potential to treat psychiatric and neurological disorders, and explore neural mechanisms supporting perception, cognition, and mood. Acute psychedelic effects have been linked to increases in brain complexity and entropy, but it is unclear if these changes are specific to psychedelics or reflect more general psychoactive effects. Here, we examined whether brain complexity and entropy metrics can identify features specific to the psychedelic state. Using resting-state fMRI from three placebo-controlled crossover trials (N=79; 255 sessions), we compared LSD, psilocybin, and mescaline with psychostimulants MDMA and d-Amphetamine. Compared to stimulants, psychedelics produced significant increases in meta-state complexity, short-timescale multiscale entropy, and dynamic conditional correlation entropy. Both drug classes increased Lempel-Ziv and spatial complexity, and decreased absolute modularity. Our findings highlight psychedelic-specific effects on brain signals that distinguish the acute psychedelic state from other psychoactive drug effects and may be relevant for understanding their therapeutic potential.

Group sequential trials (GST) allow for early stopping of a clinical trial for efficacy or futility, without compromising its validity. Statistical methodology for GST is well established when the endpoint is observed immediately, but less so for endpoints that are measured with a delay, such as repeatedly measured outcomes for which the primary measurement of interest is taken after several months. The latter can result in pipeline subjects at an interim analysis. These subjects may have early outcome measurements available, but their final endpoint is yet to be observed. Accounting for these early measurements has been shown to increase statistical power. Most importantly, pipeline patients will contribute with additional data after a decision to stop enrollment has been taken at an interim analysis. To make the best use of all available data, these data are ideally incorporated in the final analysis in a formal way. In this tutorial paper, we provide guidance on how to plan a GST with repeated measurements and a delayed endpoint and how to analyze the data resulting from these trials. We discuss existing methods, and also expand on them, for example adding a nonbinding stopping rule for futility and working out the computational details to derive valid p‐values and confidence intervals. We also provide an R package and R code to perform the methods discussed in this paper.

Being able to aggregate results from many acceptable data analysis pipelines (multiverse analyses) is a desirable feature in almost all aspects of imaging neuroscience. This is because multiple noise sources may contaminate the acquired imaging data, and different pipelines will attenuate or remove those noise source effects differentially. Here, we used multiple preprocessing pipelines that are known to impact the final results and conclusions of Positron Emission Tomography (PET) neuroimaging studies significantly. We developed conceptual and practical tools for statistical analyses that aggregate pipeline results and a new sensitivity analysis testing for hypotheses across pipelines, such as “no effect across all pipelines” or “at least one pipeline with no effect”. The proposed framework is generic and can be applied to any multiverse scenario. Code to reproduce all analyses and figures is openly available, including a step-by-step tutorial, so other researchers can carry out their own multiverse analysis.

It has become increasingly common to probe correlations between human brain imaging measures of receptor/protein binding and function using population-level brain maps drawn from independent cohorts, estimating correlations across regions. This strategy raises issues of interpretation that we highlight here with a multimodal brain imaging dataset and simulation studies. Twenty-four healthy participants completed neuroimaging with both [11C]Cimbi-36 positron emission tomography and magnetic resonance imaging scans to estimate receptor binding potential (BP) and cerebral blood flow (CBF), respectively, in 18 cortical/subcortical regions. Correlations between BP and CBF were estimated in three ways: 1) Pearson correlation across regions of mean regional BP and CBF (ρ _l ), to mimic studies using data from independent cohorts; 2) Pearson correlation between BP and CBF across participants in each region (ρ ₂ ); or 3) the correlation between BP and CBF across participants across all regions within a single linear mixed effects model (ρ ₃ ). We observed a significant positive correlation across regions ( _l = 0.672; p = 0.0023). Region-specific correlations across participants were substantively lower and not statistically significant ( ₂ : mean = 0.140, range = -0.112 to 0.336; all p > 0.10), nor when estimated simultaneously within a linear mixed model ( ₃ = 0.138, p = 0.26). Our simulation study illustrated that regional differences in BP or CBF mean and variance can bias across regions correlations by 1000% or create a type-1 error of 100%. Our observations that both the estimated correlation and the statistical significance can differ greatly highlights that inferring across region correlation as evidence for correlation across participants is erroneous. Without validated methods that limit confounding and other biases, we discourage future studies from inferring across region correlation of population-level brain maps from independent cohorts in this manner.

Brain age estimated from structural magnetic resonance images is commonly used as a biomarker of biological aging and brain health. Ideally, as a clinically valid biomarker, brain age should indicate the current state of health and be predictive of future disease onset and detrimental changes in brain biology. In this preregistered study, we evaluated and compared the clinical validity, i.e., diagnostic and prognostic performance, of six publicly available brain age prediction packages using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Baseline brain age differed significantly between groups consisting of individuals with normal cognitive function, mild cognitive impairment, and Alzheimer’s disease for all packages, but with comparable performance to estimates of gray matter volume. Further, brain age estimates were not centered around zero for cognitively normal subjects and showed considerable variation between packages. Finally, brain age was only weakly correlated with disease onset, memory decline, and gray matter atrophy within four years from baseline in individuals without neurodegenerative disease. The systematic discrepancy between chronological age and brain age among healthy subjects, combined with the weak associations between brain age and longitudinal changes in memory performance or gray matter volume, suggests that the current brain age estimates have limited clinical validity as a biomarker for biological aging.