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SUMMARY:Poster Flash Talks Group A: Generative modelling predicts mechanis
 ms of thalamocortical synaptic dysfunction in young people at risk of psyc
 hosis - Lioba Berndt (University of Exeter)
DTSTART:20251202T145000Z
DTEND:20251202T145500Z
UID:TALK241084@talks.cam.ac.uk
DESCRIPTION:&nbsp\;\nLioba C S Berndt1\,2\,5 Rosina M Diebel1 Nicholas A D
 onnelly2\,4 Jeremy Hall3 Marianne B M van den Bree3 Rick A Adams5\,6 Alexa
 nder D Shaw1 Matthew W Jones2\n1 Department of Psychology\, Faculty of Hea
 lth & Life Sciences\, University of Exeter\, UK. 2 School of Physiology\, 
 Pharmacology and Neuroscience\, University of Bristol\, UK. 3 Neuroscience
  and Mental Health Innovation Institute and Centre for Neuropsychiatric Ge
 netics and Genomics\, Cardiff University\, Cardiff\, UK. 4 Avon and Wiltsh
 ire Mental Health Partnership NHS Trust\, Bristol\, UK. 5 Hawkes Institute
 \, University College London\, UK. 6 Institute of Cognitive Neuroscience\,
  University College London\, UK.\nBackground: 22q11.2 deletion syndrome (2
 2q11.2DS) is associated with a spectrum of psychiatric outcomes and is the
  strongest known genetic risk factor for schizophrenia. Sleep disturbances
  and sleep EEG alterations (including in thalamocortical slow-wave and spi
 ndle oscillations) feature early in 22q11.2DS\, correlating with psychopat
 hology and potentially reflecting the neurodevelopmental circuit dysfuncti
 on mediating psychiatric risk. However\, scalp EEG analyses alone lack mec
 hanistic resolution\, limiting the understanding required for precision tr
 eatment development. Computational modelling offers a framework to overcom
 e this by inferring underlying synaptic mechanisms from EEG\, bridging gen
 etic risk with circuit-level signatures and predicting therapeutic targets
 .\nMethods: We applied conductance-based thalamocortical Dynamic Causal Mo
 delling (DCM) to sleep and wake (resting state) EEG from young people with
  22q11.2DS (n=28\, M=14.6\, SD=3.4) and sibling controls (n=17\, M=13.7\, 
 SD=3.4). We aimed to: (a) identify key synaptic parameters distinguishing 
 22q11.2DS neurophysiology across vigilance states\, utilising hierarchical
  Bayesian inference (Parametric Empirical Bayes\, PEB) for robust group co
 mparisons (parameters exceeding 99% posterior probability considered signi
 ficant)\; (b) evaluate state-dependent associations between these synaptic
  parameters and a range of clinical/cognitive measures using LASSO regress
 ion followed by appropriate statistical models with FDR correction\; and (
 c) predict which synaptic adjustments may shift 22q11.2DS EEG signatures t
 owards those of controls by simulating both parameter-specific perturbatio
 ns and system-wide scaling of AMPA\, NMDA\, GABA-A\, and GABA-B receptor p
 roperties.\nResults: Bayesian model comparison across 15 architectures con
 firmed that optimally capturing state-dependent EEG spectral dynamics requ
 ired incorporation of all four major receptor types (AMPA\, NMDA\, GABA-A\
 , GABA-B). This model achieved the highest posterior probability (0.76) an
 d the lowest Bayesian Information Criterion (iBIC = 24.3). It provided sub
 stantially stronger support compared to alternatives\, particularly those 
 relying on single receptor systems (e.g.\, NMDA-only\, iBIC = 679.7\; GABA
 B-only\, iBIC = 573.8)\, highlighting the necessity of modeling multiple i
 nteracting receptor dynamics. Synaptic dysfunction in 22q11.2DS was more p
 ronounced during sleep\, with alterations (identified via PEB\, Pp > 0.99)
  becoming progressively more extensive from wakefulness through NREM to RE
 M sleep. We identified distinct\, state-dependent clinical correlates (e.g
 .\, stronger N3 thalamocortical connectivity linked to reported sleep prob
 lems\, r = 0.57\, pFDR < 0.05). Parameter-specific perturbation analyses h
 ighlighted that adjustments in specific connections\, particularly increas
 ing gain in NMDA-mediated superficial pyramidal connections\, were critica
 l for aligning simulated EEG spectra with control patterns across multiple
  sleep stages. System-wide perturbation analysis consistently revealed tha
 t increasing overall NMDA system gain produced the most effective alignmen
 t of simulated EEG spectra with control patterns across all vigilance stat
 es\, with the strongest effect observed during N3 sleep (ES=0.45\, p=0.001
 ).\nConclusion: Computational modelling across all arousal states predicts
  state-dependent synaptic pathophysiology in 22q11.2DS. This highlights sl
 eep EEG as a sensitive window into underlying circuit dysfunctions\, under
 scoring the importance of sleep neurophysiology in potential biomarkers of
  complex neurodevelopmental disorders. Our findings provide circuit-level 
 evidence that putative cortical NMDA hypofunction contributes to this synd
 rome's sleep EEG alterations and clinical symptoms\, predicting therapeuti
 c avenues that warrant preclinical and clinical experimentation.
LOCATION:Seminar Room 1\, Newton Institute
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