Craven, A. | bioRxiv Science

bioRxiv2019

Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

In a stable environment the brain can minimize processing required for sensory input by forming a predictive model of the surrounding world and suppressing neural response to predicted stimuli. Unpredicted stimuli lead to a prediction error signal propagation through the perceptual network, and resulting adjustment to the predictive model. The inter-regional plasticity which enables the model-building and model-adjustment is hypothesized to be mediated via glutamatergic receptors. While pharmacological challenge studies with glutamate receptor ligands have demonstrated impact on prediction-error indices, it is not clear how inter-individual differences in the glutamate system affect the prediction-error processing in non-medicated state. In the present study we examined 20 healthy young subjects with resting-state proton MRS spectroscopy to characterize glutamate+glutamine (rs-Glx) levels in their Heschls gyrus (HG), and related this to HG functional connectivity during a roving auditory oddball protocol. No rs-Glx effects were found within the frontotemporal prediction-error network. Larger rs-Glx signal was related to stronger connectivity between HG and bilateral inferior parietal lobule during unpredictable auditory stimulation. We also found effects of rs-Glx on the coherence of default mode network (DMN) and frontoparietal network (FPN) during unpredictable auditory stimulation. Our results demonstrate the importance of Glx in modulating long-range connections and wider networks in the brain during perceptual inference.

neuroscience↗

bioRxiv2019

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts. MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics. ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

neuroscience↗