Biology subjects

Turkheimer, F. E.

Publications and source records attributed to Turkheimer, F. E..

26 records · Page 2Linked to original sources

bioRxiv2021

Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain

The expansion of neuroimaging techniques over the last decades has opened a wide range of new possibilities to characterize brain dysfunction in several neurological and psychiatric disorders. However, the lack of specificity of most of these techniques, such as magnetic resonance imaging (MRI)-derived measures, to the underlying molecular and cellular properties of the brain tissue poses limitations to the amount of information one can extract to inform precise models of brain disease. The integration of transcriptomic and neuroimaging data, known as imaging transcriptomics, has recently emerged as an indirect way forward to test and/or generate hypotheses about potential cellular and transcriptomic pathways that might underly specific changes in neuroimaging MRI biomarkers. However, the validity of this approach is yet to be examined in-depth. Here, we sought to bridge this gap by performing imaging transcriptomic analyses of the regional distribution of well-known molecular markers, assessed by positron emission tomography (PET), in the healthy human brain. We focused on tracers spanning different elements of the biology of the brain, including neuroreceptors, synaptic proteins, metabolism, and glia. Using transcriptome-wide data from the Allen Brain Atlas, we applied partial least square regression to rank genes according to their level of spatial alignment with the regional distribution of these neuroimaging markers in the brain. Then, we performed gene set enrichment analyses to explore the enrichment for specific biological and cell-type pathways among the genes most strongly associated with each neuroimaging marker. Overall, our findings show that imaging transcriptomics can recover plausible transcriptomic and cellular correlates of the regional distribution of benchmark molecular imaging markers, independently of the type of parcellation used to map gene expression and neuroimaging data. Our data support the plausibility and robustness of imaging transcriptomics as an indirect approach for bridging gene expression, cells and macroscopical neuroimaging and improving our understanding of the biological pathways underlying regional variability in neuroimaging features.

neuroscience↗

bioRxiv2021

Cellular and molecular signatures of in vivo GABAergic neurotransmission in the human brain

Diverse GABAergic interneuron microcircuits orchestrate information processing in the brain. Understanding the cellular and molecular composition of these microcircuits, and whether these can be imaged by available non-invasive in vivo methods is crucial for the study of GABAergic neurotransmission in health and disease. Here, we use human gene expression data and state-of-the-art imaging transcriptomics to uncover co-expression patterns between GABAA receptor subunits and interneuron subtype-specific markers, and to decode the cellular and molecular signatures of gold-standard GABA PET radiotracers, [11C]Ro15-4513 and [11C]flumazenil. We find that the interneuron marker somatostatin is co-expressed with GABAA receptor-subunit genes GABRA5 and GABRA2, and their distribution maps onto [11C]Ro15-4513 binding in vivo. In contrast, the interneuron marker parvalbumin co-expressed with more predominant GABAA receptor subunits (GABRA1, GABRB2 and GABRG2), and their distribution tracks [11C]flumazenil binding in vivo. These results have important implications for the non-invasive study of GABAergic microcircuit dysfunction in psychiatric conditions.

neuroscience↗

bioRxiv2021

Differences in social brain function in autism spectrum disorder are linked to the serotonin transporter

Alterations in the serotonergic control of brain pathways responsible for facial-emotion processing in people with autism spectrum disorder (ASD) may be a target for intervention. However, the molecular underpinnings of autistic-neurotypical serotonergic differences are challenging to access in vivo. Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) has helped define molecular-enriched fMRI brain networks based on a priori information about the spatial distribution of neurochemical systems from available PET templates. Here, we used REACT to estimate the dominant fMRI signal related to the serotonin transporter (5-HTT) distribution during processing of aversive facial expressions of emotion processing in adults with and without ASD. We first predicted a group difference in baseline (placebo) functioning of this system. We next used a single 20 mg oral dose of citalopram, i.e. a serotonin reuptake inhibitor, to test the hypothesis that network activity in people with and without ASD would respond differently to inhibition of 5-HTT. To confirm the specificity of our findings, we also repeated the analysis with 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 receptor maps. We found a baseline group difference in the 5-HTT-enriched response to faces in the ventromedial prefrontal cortex. A single oral dose of citalopram shifted the response in the ASD group towards the neurotypical baseline but did not alter response in the control group. Our findings suggest that the 5HTT-enriched functional network is dynamically different in ASD during processing of socially relevant stimuli. Whether this acute neurobiological response to citalopram in ASD translates to a clinical target will be an important next step.

neuroscience↗

bioRxiv2021

MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention

MRI data can be used as input to machine learning models to accurately predict brain age in healthy human subjects. A large difference between predicted and chronological brain age (the so-called BrainAGE score) has been associated with disease and neurodegeneration, indicating the potential utility of neuroimaging-based ageing biomarkers. So far, most brain age prediction studies have been carried out on humans. However, it is important for such a biomarker to be validated on laboratory animals too, in order to better account for specific environmental or genetic factors within a more controlled laboratory framework. In this work, we developed a new algorithm for rat brain age prediction based on the combination of Gaussian process regression and a logistic regression classifier. The algorithm was trained on a cohort of 31 normal rats. High prediction accuracy was achieved using leave-one-out cross-validation (mean absolute error = 4.87 weeks, correlation between predicted and chronological age r = 0.92), supporting the validity and potential of the method. Furthermore, the trained model was tested on two independent groups of 24 rats each: a new normal control group and a "healthy lifestyle" group that underwent long-term environmental enrichment and dietary restriction (EEDR) between 3 and 17 months of age. After fitting a linear mixed-effects model, the BrainAGE values were found to increase more slowly with chronological age in the EEDR group than in the controls (slope = 0.52 vs. 0.61; p = 0.015 for the interaction term). When survival analysis was performed with a Cox regression model, the BrainAGE score at 5 months of age had a significant prediction power (p = 0.03). Our results demonstrate that BrainAGE, as computed by the proposed approach, is significantly modulated by EEDR intervention, hence it is a sensitive marker of biological ageing. These findings also support the potential of lifestyle-related prevention approaches to slow down the brain ageing process. Moreover, the results of the survival analysis further demonstrate that BrainAGE is indeed a predictor of ageing outcome.

neuroscience↗

bioRxiv2021

Transcriptional and cellular signatures of cortical morphometric similarity remodelling in chronic pain

Chronic pain is a highly debilitating and poorly understood condition. Here, we attempt to advance our understanding of the brain mechanisms driving chronic pain by investigating alterations in morphometric similarity (MS) and corresponding transcriptomic and cellular signatures, in three cohorts of patients with distinct chronic pain syndromes (knee osteoarthritis, low back pain and fibromyalgia). We uncover a novel pattern of cortical MS remodelling involving mostly MS increases in the insula and limbic cortex, which cuts across the boundaries of specific pain syndromes. We show that cortical MS remodelling in chronic pain spatially correlates with the brain-wide expression of genes involved in the glial immune response and neuronal plasticity. Cortical remodelling in chronic pain might involve a disruption of multiple elements of the cellular architecture of the brain. Therefore, multi-target therapeutic approaches tackling both glial activation and neuronal hyperexcitability might better encompass the full neurobiology of chronic pain.

neuroscience↗

bioRxiv2021

A candidate neuroimaging biomarker for detection of neurotransmission-related functional alterations and prediction of pharmacological analgesic response in chronic pain

BackgroundChronic pain is a world-wide clinical challenge. Response to analgesic treatment is limited and difficult to predict. Functional MRI (fMRI) has been suggested as a potential solution. However, while most analgesics target specific neurotransmission pathways, fMRI-based biomarkers are not specific for any neurotransmitter system, limiting our understanding of how they might contribute to predict treatment response. MethodsHere, we sought to bridge this gap by applying Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT) to investigate whether neurotransmission-enriched functional connectivity (FC) mapping can provide insights into the brain mechanisms underlying chronic pain and inter-individual differences in analgesic response after a placebo or duloxetine. Chronic knee osteoarthritis (OA) pain patients (n=56) underwent pre-treatment brain scans in two clinical trials. Study 1 (n=17) was a 2-week single-blinded placebo pill trial. Study 2 (n=39) was a 3-month double-blinded randomized trial comparing placebo to duloxetine, a dual serotonin-noradrenaline reuptake inhibitor. ResultsAcross two independent studies, we found that chronic pain OA patients present FC alterations in the FC related to the serotonin (SERT) and noradrenaline (NET) transporters, when compared to age-matched healthy controls. Placebo responders presented with higher pre-treatment dopamine transporter (DAT)-enriched FC than non-responders. Duloxetine responders presented with higher pre-treatment SERT and NET-enriched FC than non-responders. Pre-treatment SERT and NET-enriched FC achieved predictive positive values of duloxetine response up to 85.71%. ConclusionNeurotransmission-enriched FC mapping might hold promise as a new mechanistic-informed biomarker for functional brain alterations and prediction of response to pharmacological analgesia in chronic pain.

neuroscience↗

bioRxiv2018

Dynamic 11C-PiB PET shows cerebrospinal fluid flow alterations in Alzheimer’s disease and multiple sclerosis

Cerebrospinal fluid (CSF) plays an important role in the clearance of solutes and maintenance of brain homeostasis. 11C-PiB PET was recently proposed as a tool for detection of CSF clearance alterations in Alzheimers disease. The current study seeks to investigate the magnitude of 11C-PiB PET signal in the lateral ventricles of an independent group of Alzheimers and mild cognitive impairment subjects. We have also evaluated multiple sclerosis as a model of disease with CSF clearance alterations without amyloid-beta tissue accumulation.\n\nMethodsA set of Alzheimers and mild cognitive impairment subjects and a set of multiple sclerosis subjects with matched healthy controls underwent MRI and dynamic 11C-PiB PET. Manual lateral ventricle regions of interest were generated from MRI data. PET data was analysed using a simplified reference tissue model with cerebellum or a supervised reference region, for the Alzheimers and multiple sclerosis datasets, respectively. Magnitude of 11C-PiB signal in the lateral ventricles was calculated as area under curve from 35 to 80 minutes and standard uptake value ratio (SUVR) from 50 to 70 minutes. Compartmental modelling analysis was performed on a separate dataset containing Alzheimers and matched healthy control data with an arterial input function to further understand the kinetics of the lateral ventricular 11C-PiB signal.\n\nResultsAnalysis of variance revealed significant group differences in lateral ventricular SUVR across the Alzheimers, mild cognitive impairment, and healthy control groups (p=0.004). Additional pairwise comparisons revealed significantly lower lateral ventricular SUVR in Alzheimers compared to healthy controls (p<0.001) and mild cognitive impairment (p=0.029). Lateral ventricular SUVR was also significantly lower in multiple sclerosis compared to healthy controls (p=0.008). Compartmental modelling analysis revealed significantly lower uptake rates of 11C-PiB signal from blood (p=0.005) and brain tissue (p=0.004) to the lateral ventricles in Alzheimers compared to healthy controls. This analysis also revealed significantly lower clearance of 11C-PiB signal out of the lateral ventricles in Alzheimers compared to healthy controls (p=0.002).\n\nConclusionOverall, these results indicate that dynamic 11C-PiB PET can be used to observe pathological changes in cerebrospinal fluid dynamics and that cerebrospinal fluid-mediated clearance is reduced in Alzheimers disease and multiple sclerosis compared to healthy controls.

neuroscience↗

bioRxiv2018

Increased Cerebral Blood Flow after single dose of antipsychotics in healthy volunteers depends on dopamine D2 receptor density profiles.

As a result of neuro-vascular coupling, the functional effects of antipsychotics in human brain have been investigated in both healthy and clinical populations using haemodynamic markers such as regional Cerebral Blood Flow (rCBF). However, the relationship between observed haemodynamic effects and the pharmacological action of these drugs has not been fully established. Here, we analysed MRI-based rCBF data from a placebo-controlled study in healthy volunteers, who received a single dose of three different D2 receptor antagonists and tested the association of the main effects of the drugs on rCBF against normative population maps of D2R protein density and gene-expression data. In particular, we correlated CBF changes after antipsychotic administration with non-displaceable binding potential (BPND) template maps of the high affinity D2-antagonist Positron Emission Tomography (PET) ligand [18F]Fallypride and brain post-mortem microarray mRNA expression data for the DRD2 gene. For all antipsychotics, rCBF changes were directly proportional to brain D2R densities and DRD2 mRNA expression measures, although PET BPND spatial profiles explained more variance as compared with mRNA profiles (PET R2 range= 0.20-0.60, mRNA PET R2 range 0.04-0.20, pairwise-comparisons all p<0.05). In addition, the spatial coupling between {Delta}CBF and D2R profiles varied between the different antipsychotics tested, possibly reflecting differential affinities. Overall, these results indicate that the functional effects of antipsychotics as measured with rCBF are tightly correlated with the distribution of their target receptors in striatal and extra-striatal regions. Our results further demonstrate the link between neurotransmitter targets and haemodynamic changes reinforcing rCBF as a robust in-vivo marker of drug effects. This work is important in bridging the gap between pharmacokinetic and pharmacodynamics of novel and existing compounds.

pharmacology and toxicology↗