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Kafadar, E.

Publications and source records attributed to Kafadar, E..

3 recordsLinked to original sources
bioRxiv2026

Persisting perceptual abnormalities in psychedelic users are associated with conditioned hallucinations and impaired sensory processing

Serotonergic psychedelics (SP) are increasingly used in clinical research and naturalistic settings, but their psychotic-like side effects, including persisting perceptual abnormalities (PPAs), are poorly understood. Psychosis-associated hallucinations are associated with susceptibility to conditioned hallucinations and computationally-estimated overweighting of perceptual expectations, or priors. However, SPs are widely argued to reduce prior weighting. We surveyed 186 naturalistic SP users on prior SP use, SP-associated PPA history, and current PPAs. Participants completed the visual conditioned hallucinations (VCH) task, in which conditioning induces perception of absent stimuli. Behavioral data were used to fit parameters of a computational model to estimate latent states driving percepts and responses. Past and current PPAs were associated with younger age at first use and higher SP doses, lower visual thresholds, higher VCH rate and confidence, and reduced sensory discrimination. Among model parameters, however, only reduced decision precision tracked both measures and mediated the dose-PPA relationship; relative prior weighting rose equivocally, as expected when priors and sensory evidence gain precision together. SP-related PPAs may therefore arise from a noisy visual system biased toward detection, in which priors act as templates that convert sensory noise into expected percepts. These findings may point to a tractable model for how psychotic-like perception emerges.

neuroscience↗
bioRxiv2025

Conditional-longitudinal brain growth charts detect MRI changes with birth weight and psychopathology

ImportanceBrain maturation varies between individuals, particularly during dynamic developmental periods like adolescence. Directly assessing differences in longitudinal trajectories can reveal deviations from normative patterns. ObjectiveWe present novel conditional-longitudinal normative models that characterize variability in brain maturation. We utilize these models to examine whether differences in longitudinal trajectories are associated with birth weight (BW), gestational age (GA), and longitudinal psychopathology derived from behavioral assessments. DesignCross-sectional and conditional-longitudinal normative models were developed for brain volumes derived from the first two neuroimaging timepoints from the Adolescent Brain Cognitive Development (ABCD) Study. Conditional-longitudinal models index an individuals expected brain volume at follow-up conditioned on their baseline measurement. Models were fit with split-half cross-validation on demographically matched samples. SettingThe ABCD Study is a multi-site, population-based study ParticipantsParticipants were excluded based on imaging quality flags and missing data, leaving 10,830 at baseline and 7,262 at follow-up. ExposuresBW and GA were derived from parent-report questionnaires. General psychopathology scores were calculated using a bifactor model. Main Outcomes and MeasuresWe calculated cross-sectional and conditional-longitudinal centiles, respectively quantifying individual deviations in size and change between timepoints. Sensitivity analyses included covariates for parental income and education as well as current weight and height. ResultsThe sample was 10,830 at baseline (48.2% F,age 9-10y) and 7,262 at follow-up (46.6% F,age 11-13y). Conditional-longitudinal centiles were sensitive to individual differences in brain change between timepoints. Lower BW was associated with lower conditional-longitudinal centiles, suggesting larger decreases in brain volumes over time (27 regions pfdr<0.05, {beta}max=0.08). Lower conditional-longitudinal centiles were associated with greater increases in psychopathology scores, suggesting with increased psychopathology brain volumes show greater decrease (37 regions pfdr<0.05, {beta}max=0.06). Notably, changes in psychopathology were not related to brain size at either timepoint, indexed by cross-sectional centiles. Conclusions and RelevanceModels that capture individual-level deviations from expected growth trajectories, rather than static positions on a growth curve, are particularly informative for assessing developmental change. Novel conditional-longitudinal models address this gap in lifespan brain imaging. Using this framework, we demonstrate robust associations between individual trajectory deviations, perinatal adversity, and longitudinally assessed mental health symptoms. Condition-longitudinal models hold promise for applications across psychiatric neuroscience, from development to aging. Key Points QuestionHow do differences in brain maturation trajectories, quantified by novel conditional-longitudinal models, relate to perinatal factors and mental health in adolescence? FindingsIn this longitudinal analysis of neuroimaging data from the Adolescent Brain Cognitive Development (ABCD) Study, conditional-longitudinal normative models revealed that trajectories of brain maturation in adolescence are associated with birth weight, and with longitudinal changes in mental health. MeaningConditional longitudinal models detect inter-individual variability in brain maturation, which is related to both perinatal factors and concurrent changes in psychopathology.

neuroscience↗
bioRxiv2025

Calibration of MRI-based reference intervals to new samples

Reference intervals, defined as intervals containing a new observation with a specified probability relative to reference data, would be clinically useful in assessing brain magnetic resonance imaging (MRI). Brain charts, which are estimates of MRI phenotypes across covariates such as age and sex, can be used to construct reference intervals. However, the reference data used to fit intervals often differs from a new sample in terms of study design, MRI acquisition, and image preprocessing. Application of MRI reference intervals to new samples remains a challenging problem. Here, we propose a new method called Reference interval calibration via conFormal prediction (ReForm) that adjusts reference intervals for a new sample. Our method builds on recent work in conformal prediction, which yields intervals with guaranteed coverage for new observations. Through resampling experiments in Lifespan Brain Chart Consortium cortical thickness data, we compare ReFormed reference intervals to refitting intervals, statistical harmonization methods, and model-based adjustment of intervals. Notably for patient privacy concerns, ReForm does not require sharing of reference data. Yet, our empirical results demonstrate that ReForm controls FPR similarly or better than alternative methods which require sharing reference data. Finally, we provide recommendations for practical applications of ReForm and an R package (https://github.com/andy1764/ReForm) for calibrating reference intervals using ReForm.

neuroscience↗