Biology subjects

McCutcheon, R. A.

Publications and source records attributed to McCutcheon, R. A..

3 recordsLinked to original sources

bioRxiv2026

Convergent Multimodal Evidence of Cortical Excitation-Inhibition Imbalance in Psychosis

Psychosis is increasingly understood as a disorder of disrupted cortical excitation-inhibition balance, yet robust non-invasive translational biomarkers remain lacking. The resting-state fMRI Hurst exponent (HE) and EEG aperiodic spectral exponent are promising complementary biomarkers, with lower values in each proposed to reflect a shift towards cortical hyperexcitability, but they have not been jointly examined in psychosis, and the spatial and molecular architecture of HE alterations remains poorly defined. We therefore tested for convergent systems-level signatures across independent cohorts and modalities, using resting-state fMRI (107 patients, 53 controls) and EEG (547 patients, 363 controls). Whole-brain and regional HE were estimated using wavelet methods, and EEG aperiodic exponents were quantified using spectral parameterisation. Compared with healthy controls, individuals with psychosis showed reduced whole-brain HE and widespread regional reductions. Regional HE case-control differences were associated with cortical gene-expression patterns, with enrichment for potassium channel and GABA receptor pathways, and correlated with noradrenergic, muscarinic, serotonergic, glutamatergic and dopaminergic receptor density maps, but not with cortical thickness or symptom or cognitive measures. In the independent EEG cohort, psychosis was similarly associated with a reduced aperiodic spectral exponent. Together, these findings provide cross-modal evidence for altered cortical resting-state dynamics in psychosis, consistent with a shift towards cortical hyperexcitability. Integration with receptor-density and transcriptomic maps implicates biologically plausible molecular pathways and supports HE and EEG aperiodic activity as scalable translational biomarkers in psychosis.

neuroscience↗

bioRxiv2025

Cortical Iron in Schizophrenia: A Quantitative Susceptibility Mapping and Diffusion Tensor Imaging MRI Study

Background and HypothesisCognitive and negative symptoms in schizophrenia remain poorly treated. Iron dysregulation has been implicated as a potential mechanism underlying cognitive dysfunction and schizophrenia. While elevated postmortem iron in Brodmann areas 10-11 has been linked to schizophrenia, this has not been assessed in vivo. We therefore used iron-sensitive MRI to test whether cortical iron is elevated in individuals with schizophrenia compared to healthy controls. Study DesignWe acquired quantitative susceptibility mapping (QSM) MRI to measure magnetic susceptibility ({chi}), a marker of iron, in 158 participants aged 18-45 (73 with schizophrenia and 76 matched healthy controls). As {chi} is reduced by myelin, we conducted diffusion tensor imaging (DTI) to assess mean diffusivity, an iron-insensitive marker also reduced by myelin. Study ResultsPrimary analyses showed no significant case-control differences in {chi} in the whole cortex (p=0.675) or Brodmann areas 10-11 (p=0.537). Exploratory analyses examined {chi} for 362 cortical regions and a voxelwise analysis, correcting for multiple comparisons. Two left temporo-parieto-occipital (TPO) junction regions showed significantly elevated {chi} in schizophrenia: the posterior TPO junction (d=0.752, p<0.001) and the superior temporal visual area (d=0.638, p=0.033), which remained significant after adjusting for mean diffusivity and clinical covariates (p=0.001 and p=0.023, respectively). Voxelwise analysis confirmed elevated {chi} in schizophrenia in the left TPO junction (peak t=5.62). ConclusionsThis study provides the first in vivo evidence of elevated cortical iron in schizophrenia, suggesting regional iron accumulation may contribute to cortical pathology.

neuroscience↗

bioRxiv2023

Data Driven Taxonomy for Antipsychotic Medication: A New Classification System

BackgroundThere are over 25 licensed antipsychotic medications with diverse pharmacological and clinical profiles. Antipsychotics are commonly described as either typical or atypical, but this does not accurately reflect pharmacological profiles. There is thus a need for a data driven antipsychotic classification scheme suitable for clinicians and researchers which maps onto both pharmacological and clinical effects. MethodWe analysed affinities of 27 antipsychotics for 42 receptors from 3,325 receptor binding studies. We used a clustering algorithm to group antipsychotics based on their pattern of receptor affinity. Using a machine learning model, we examined the ability of this grouping to predict antipsychotic-induced side effects quantified according to an umbrella review of clinical trial and treatment guideline data. ResultsClustering resulted in four groups of antipsychotics. The predominant receptor affinity and effect/side effect fingerprints of these four groups were defined, as follows: Group 1 - Muscarinic (M3-M5) receptor antagonism; Cholinergic and metabolic side effects. Group 2 - Dopamine (D2) partial agonism and adrenergic antagonism; Globally low side effect burden. Group 3 - Serotonergic and dopaminergic antagonism; Globally moderate side effect burden. Group 4 - Dopaminergic antagonism; Extrapyramidal and motor side effects. Groups 1 and 4 were more efficacious than clusters 2 and 3. The novel classification was superior to existing approaches when predicting side effects. ConclusionsA receptor affinity-based grouping not only reflects compound pharmacology but also detects meaningful clinical differences to a greater extent than existing approaches. The approach has the potential to benefit both patients and researchers by guiding treatment and informing drug development.

pharmacology and toxicology↗