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

Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder

BACKGROUND

Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.

METHODS

We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.

RESULTS

Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.

LIMITATIONS

These include measuring anhedonia using interest-activity score and employing a cross-sectional design.

CONCLUSIONS

This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.

The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms

BACKGROUND

Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underlying biological mechanisms responsible for cortical changes in the AD cortex remain unclear.

METHODS

Fifty-eight participants with AD and 67 normal controls underwent high-resolution 3 T structural brain MRI. Using surface-based morphometry (SBM), we created vertex-wise maps for group comparisons in terms of five measures: cortical thickness, fractal dimension, gyrification index, Toro's gyrification index and sulcal depth respectively. Five machine learning (ML) models combining SBM parameters were established to predict AD. In addition, transcription-neuroimaging association analyses, as well as Mendelian randomization of AD and cortical thickness data, were conducted to investigate the genetic mechanisms and biological functions of AD.

RESULTS

AD patients exhibited topological changes in cortical complexity, with increased complexity in the frontal and temporal cortex and decreased complexity in the insula cortex, alongside extensive cortical atrophy. Combining different SBM measures could aid disease diagnosis. The genes involved in cell structure support and the immune response were the strongest contributors to cortical anatomical features in AD patients. The identified genes associated with AD cortical morphology were overexpressed or underexpressed in excitatory neurons, oligodendrocytes, and astrocytes.

CONCLUSION

Complexity alterations of the cerebral surface may be associated with a range of biological processes and molecular mechanisms, including immune responses. The present findings may contribute to a more comprehensive understanding of brain morphological patterns in AD patients.

A longitudinal functional connectivity study of bipolar Disorder: from the view of default mode network and its association with gene expression

OBJECTIVE

Abnormal functions involving brain regions within the default mode network (DMN) have been reported in bipolar disorder (BD). However, most previous studies were cross-sectional. Therefore, a longitudinal study was conducted to observe the change trajectory of symptoms and functional connectivity (FC) of DMN in BD patients. Imaging transcriptomics is used for finding spatially transcriptional correlation of FC changes.

METHODS

Eighty-two BD patients (43 patients finished the follow-up after 3-month medication treatment) and matched 88 healthy controls were included to perform seed-based FC analysis. The correlation between FC alteration and clinical symptoms was explored with multiple regression analysis. Utilizing imaging transcriptomics, genes from the Allen Human Brain Atlas associated with abnormal imaging phenotypes were obtained by spatial Spearman correlation analysis.

RESULTS

BD patients exhibited increased FC between the DMN and the orbitofrontal cortex (OFC), left cerebellar Crus II/VIIb, right inferior frontal gyrus, and bilateral anterior cingulate cortex. After treatment, elevated FC of DMN-OFC tended to normalize, while decreased FC within the inferior parietal gyrus (IPG) was observed. Two FC alterations of left IPG were positively associated with the Stroop Color-Word Test (p = 0.0014, 0.0019 respectively). Enrichment analysis suggested association genes were involved in ribosomal function, membrane transport, and enzymatic activity.

CONCLUSION

The findings further suggest that FC of DMN may relate to symptomatic changes and therapeutic mechanisms of BD. Imaging transcriptomics provides a new perspective for researching genetic factors of BD. However, the small sample and single center of this study may impact the representativeness of the results.

Molecular mechanisms explaining sex-specific functional connectivity changes in chronic insomnia disorder

BACKGROUND

This study investigates the hypothesis that chronic insomnia disorder (CID) is characterized by sex-specific changes in resting-state functional connectivity (rsFC), with certain molecular mechanisms potentially influencing CID's pathophysiology by altering rsFC in relevant networks.

METHODS

Utilizing a resting-state functional magnetic resonance imaging (fMRI) dataset of 395 participants, including 199 CID patients and 196 healthy controls, we examined sex-specific rsFC effects, particularly in the default mode network (DMN) and five insomnia-genetically vulnerable regions of interest (ROIs). By integrating gene expression data from the Allen Human Brain Atlas, we identified genes linked to these sex-specific rsFC alterations and conducted enrichment analysis to uncover underlying molecular mechanisms. Additionally, we simulated the impact of sex differences in rsFC with different sex compositions in our dataset and employed machine learning classifiers to distinguish CID from healthy controls based on sex-specific rsFC data.

RESULTS

We identified both shared and sex-specific rsFC changes in the DMN and the five genetically vulnerable ROIs, with gene expression variations associated with these sex-specific connectivity differences. Enrichment analysis highlighted genes involved in synaptic signaling, ion channels, and immune function as potential contributors to CID pathophysiology through their influence on connectivity. Furthermore, our findings demonstrate that different sex compositions significantly affect study outcomes and higher diagnostic performance in sex-specific rsFC data than combined sex.

CONCLUSIONS

This study uncovered both shared and sex-specific connectivity alterations in CID, providing molecular insights into its pathophysiology and suggesting considering sex differences in future fMRI-based diagnostic and treatment strategies.

The expression of insulin signaling and N-methyl-D-aspartate receptor genes in areas of gray matter atrophy is associated with cognitive function in type 2 diabetes

Type 2 diabetes (T2DM) is associated with brain abnormalities and cognitive dysfunction, including increased risk for Alzheimer's disease. However, the mechanisms of T2DM-related dementia remain poorly understood. We obtained retrospective data from the Mayo Clinic Study of Aging for 271 individuals with T2DM and 542 demographically matched non-diabetic controls (age 51-89, 62% male). We identified regions of significant gray matter atrophy in the T2DM group and then determined which genes were significantly expressed in these brain regions using imaging transcriptomics. We selected 15 candidate genes involved in insulin signaling, lipid metabolism, amyloid processing, N-methyl-D-aspartate-mediated neurotransmission, and calcium signaling. The T2DM group demonstrated significant gray matter atrophy in regions of the default mode, frontal-parietal, and sensorimotor networks (p < 0.05 cluster threshold corrected for false discovery rate, FDR). IRS1, AKT1, PPARG, PRKAG2, and GRIN2B genes were significantly expressed in these same regions (R2 > 0.10, p < 0.03, FDR corrected). Bayesian network analysis indicated significant directional paths among all 5 genes as well as the Clinical Dementia Rating score. Directional paths among genes were significantly altered in the T2DM group (Structural Hamming Distance = 12, p = 0.004), with PPARG expression becoming more important in the context of T2DM-related pathophysiology. Alterations of brain transcriptome patterns occurred in the absence of significant cognitive deficit or amyloid accumulation, potentially representing an early biomarker of T2DM-related dementia.

Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [18F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77-0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

Hippocampal gray matter volume alterations in patients with first-episode and recurrent major depressive disorder and their associations with gene profiles

BACKGROUND

Recent studies indicate that patients with first-episode drug-naïve (FEDN) and recurrent major depressive disorder (R-MDD) exhibit distinct atrophy patterns in the hippocampal subregions along the proximal-distal axis. However, it remains unclear whether such differences occur along the long axis and how they may relate to specific genes.

METHODS

In the present study, we analyzed T1-weighted images from 421 patients (FEDN: n = 232; R-MDD: n = 189) and 544 normal controls (NC) as part of the REST-meta-MDD consortium. Additionally, transcriptome maps and structural Magnetic Resonance Imaging (MRI) data of six donated brains were obtained from the Allen Human Brain Atlas (AHBA). We first identified changes in gray matter volume (GMV) within the hippocampus of both FEDN and R-MDD patients and then integrated these findings with AHBA transcriptome data to investigate the genes associated with hippocampal GMV changes.

RESULTS

Compared to NC, FEDN patients displayed reduced GMV in the left hippocampal tail, whereas R-MDD patients exhibited decreased GMV in the bilateral hippocampal body and increased GMV in the bilateral hippocampal tail. Further analysis revealed that expression levels of SYTL2 positively correlated with GMV changes in the hippocampus of FEDN patients, while SORCS3 and SLIT2 positively correlated with those in R-MDD.

CONCLUSIONS

Our results suggest that GMV alterations in hippocampal subfields along the long axis differ between FEDN and R-MDD, reflecting progressive hippocampal deterioration with prolonged depression, potentially supported by the expression of specific genes. These findings offer valuable insights into the distinct neural and genetic mechanisms underlying FEDN and R-MDD, which may aid in the development of more targeted and effective treatment strategies for MDD subtypes.

Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

BACKGROUND

The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear.

METHODS

Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers.

RESULTS

We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D1, D2, dopamine transporter), acetylcholine (VAChT, M1), gamma-aminobutyric acid A (GABAA), and glutamate (NMDA) receptors as key predictors for SSD (R2adjusted = 0.58, false discovery rate [FDR]-corrected p < .05) and CHR-P (R2adjusted = 0.6, pFDR < .05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis.

CONCLUSIONS

Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.

Functional gradient of the fusiform gyrus and its underlying molecular basis

Evidence has evinced the functional complexity, anatomical heterogeneity, connectivity diversity, and clinical relevance of the fusiform gyrus. We aimed to investigate the hierarchical organization of the fusiform gyrus and its underlying molecular basis. Resting-state functional MRI data of 793 healthy subjects were collected from a discovery dataset and two independent cross-scanner, cross-race validation datasets. Functional gradients of the fusiform gyrus were calculated based on the voxel-wise fusiform gyrus-to-cerebrum functional connectivity to reflect its functional organization. Transcription-neuroimaging spatial correlation analysis was performed to determine genes with expression levels tracking the fusiform gyrus functional gradient. The dominant functional gradient that explained the greatest connectivity variance showed an anterior-posterior axis across the fusiform gyrus. More important, there was a strong association between the fusiform gyrus-dominant gradient and gene expression profiles, with two gene sets contributing significantly to this association yet differing in their specific expression and functional annotation. In addition, the fusiform gyrus-dominant gradient was linked closely to intrinsic geometry, slightly to cortical morphology, and gradually to behavioral domains from high-level cognitive processes to low-level sensory functions. Our findings add to the extant knowledge regarding the topographic organization of the fusiform gyrus by informing a novel conceptualization of how functional heterogeneity and multiplicity co-occur within the fusiform gyrus.

Molecular architecture of the altered cortical complexity in autism

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II. Using fractal dimension as an index for quantifying cortical complexity, we identified significant regional alterations in ASD, within the left temporoparietal, left peripheral visual, right central visual, left somatomotor (including the insula), and left ventral attention networks. Partial least squares regression analysis revealed gene sets associated with these cortical complexity changes, enriched for biological functions related to synaptic transmission, synaptic plasticity, mitochondrial dysfunction, and chromatin organization. Cell-specific analyses, protein-protein interaction network analysis and gene temporal expression profiling further elucidated the dynamic molecular landscape associated with these alterations. These findings indicate that ASD-related alterations in cortical complexity are closely linked to specific genetic pathways. The combined analysis of neuroimaging and transcriptomic data enhances our understanding of how genetic factors contribute to brain structural changes in ASD.

Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety

Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

The promise of molecular science in brain health. What breakthroughs are anticipated in the next 20 years?

Brain health means optimal physiological brain function across the normal life-course. It encompasses not only healthy brain aging but also brain diseases, their diagnosis and treatment. In all these areas, molecular science has advanced our understanding. This multi-disciplinary review combines viewpoints from laboratory science, clinical medicine and the bioscience industry. First, we review the advances that molecular science has brought to brain health in the past twenty years. These include therapeutic antibodies for CNS diseases (multiple sclerosis, Alzheimer disease) and the dramatic introduction of RNA-targeted therapeutics. Second, we highlight areas where greater molecular understanding is needed. Salient examples are the relation of brain structure to cognitive symptoms, and molecular biomarkers for diagnosis, target discovery and testing of interventions. Finally, we speculate on aspects of molecular science that are likely to advance brain health in the next twenty years. These include: cell senescence and chronobiology; gene editing (notably, CRISPR) and RNA targeting (RNA interference, miRNA manipulation); brain-immune interactions; novel drug targets (AQP4, HIF1, Toll-like receptors); and novel chemistry to make new drugs (molecular machines, quantum molecular modelling and "click" chemistry). Early testing of the relationships between molecular pathways and clinical manifestations will drive much-needed breakthroughs in neurology and psychiatry.

Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study

The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.

The myelin water imaging transcriptome: myelin water fraction regionally varies with oligodendrocyte-specific gene expression

Identifying sensitive and specific measures that can quantify myelin are instrumental in characterizing microstructural changes in neurological conditions. Neuroimaging transcriptomics is emerging as a valuable technique in this regard, offering insights into the molecular basis of promising candidates for myelin quantification, such as myelin water fraction (MWF). We aimed to demonstrate the utility of neuroimaging transcriptomics by validating MWF as a myelin measure. We utilized data from a normative MWF brain atlas, comprised of 50 healthy subjects (mean age = 25 years, range = 17-42 years) scanned at 3 Tesla. Magnetic resonance imaging data included myelin water imaging to extract MWF and T1 anatomical scans for image registration and segmentation. We investigated the inter-regional distributions of gene expression data from the Allen Human Brain Atlas in conjunction with inter-regional MWF distribution patterns. Pearson correlations were used to identify genes with expression profiles mirroring MWF. The Single Cell Type Atlas from the Human Protein Atlas was leveraged to classify genes into gene sets with high cell type specificity, and a control gene set with low cell type specificity. Then, we compared the Pearson correlation coefficients for each gene set to determine if cell type-specific gene expression signatures correlate with MWF. Pearson correlation coefficients between MWF and gene expression for oligodendrocytes and adipocytes were significantly higher than for the control gene set, whereas correlations between MWF and inhibitory/excitatory neurons were significantly lower. Our approach in integrating transcriptomics with neuroimaging measures supports an emerging technique for understanding and validating MRI-derived markers such as MWF.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain

PURPOSE

The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach.

METHODS

51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions.

RESULTS

Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning.

CONCLUSIONS

Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Association of Gene Expression and Tremor Network Structure

BACKGROUND

Transcriptomic changes in the essential tremor (ET)-associated cerebello-thalamo-cortical "tremor network" and their association to brain structure have not been investigated.

OBJECTIVE

The aim was to characterize molecular changes associated with network-level imaging-derived phenotypes (IDP) found in ET.

METHODS

We performed an imaging-transcriptomic study in British adults using imaging-genome-wide association study summary statistics (UK Biobank "BIG40" cohort; n = 33,224, aged 40-69 years). We imputed imaging-transcriptomic associations for 184 IDPs and analyzed functional enrichment of gene modules and aggregate network-level phenotypes. Validation was performed in cerebellar-tissue RNA-sequencing data from ET patients and controls (n = 55).

RESULTS

Among 237,896 individual predicted gene expression levels for 6063 unique genes/transcripts, we detected 2269 genome-wide significant associations (Bonferroni P < 2.102e-7, 0.95%). These were concentrated in intracellular volume fraction measures of white matter pathways and in genes with putative links to tremor (MAPT, ARL17A, KANSL1, SPPL2C, LRRC37A4P, PLEKHM1, and FMNL1). Whole-tremor-network cortical thickness was associated with a gene module linked to mitochondrial organization and protein quality control (r = 0.91, P = 2e-70), whereas white-gray T1-weighted magnetic resonance imaging (MRI) contrast in the tremor network was associated with a gene module linked to sphingolipid synthesis and ethanolamine metabolism (r = -0.90, P = 2e-68). Imputed association effect sizes and RNA-sequencing log-fold change in the validation dataset were significantly correlated for cerebellar peduncular diffusion MRI phenotypes, and there was a close overlap of significant associations between both datasets for gray matter phenotypes (χ2 = 6.40, P = 0.006).

CONCLUSIONS

The identified genes and processes are potential treatment targets for ET, and our results help characterize molecular changes that could in future be used for patient treatment selection or prognosis prediction. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia

Human brain organization involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. In this study, optimized processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell types and cytoarchitectonics, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas and BrainSpan), we found that C1-C3 represent generalizable transcriptional programs that are coordinated within cells and differentially phased during fetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional program for adolescent brain development, which can lead to atypical supragranular cortical connectivity in people at high genetic risk for schizophrenia.

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

Researchers' sex/gender identity influences how sex/gender question is investigated in neuroscience: an example from an OHBM meeting

Gender inequality and diversity in STEM is a challenging field of research. Although the relation between the sex/gender of the researcher and the scientific research practices has been previously examined, less interest has been demonstrated towards the relation between sex/gender of the researcher and the way sex/gender as a variable is explored. Here, we examine, from a neurofeminist perspective, both questions: whether sex/gender identity is related to the examination of sex/gender as a variable and whether different approaches towards examining sex/gender are being used in different topics of study within neuroscience. Using the database of submitted posters to the Organization of Human Brain Mapping 2022 annual conference, we identified abstracts examining a sex/gender-related research question. Among these target abstracts, we identified four analytical categories, varying in their degree of content-related complexity: (1) sex/gender as a covariate, (2) sex/gender as a binary variable for the study of sex/gender differences, (3) sex/gender with additional biological information, and (4) sex/gender with additional social information. Statistical comparisons between sex/gender of researcher and the target abstract showed that the proportion of abstracts from Non-binary or Other first authors compared to both Women and Men was lower for all submitted abstracts than for the target abstracts; that more researchers with sex/gender-identity other than man implemented analytical category of sex/gender with additional social information; and, for instance, that research involving cognitive, affective, and behavioural neuroscience more frequently fit into the sex/gender with additional social information-category. Word cloud analysis confirmed the validity of the four exploratorily identified analytical categories. We conclude by discussing how raising awareness about contemporary neurofeminist approaches, including perspectives from the global south, is critical to neuroscientific and societal progress.

Global-brain functional connectivity related with trait anxiety and its association with neurotransmitters and gene expression profiles

BACKGROUND

Numerous studies have explored the neural correlates of trait anxiety, a predisposing factor for several stress-related disorders. However, the findings from previous studies are inconsistent, which might be due to the limited regions of interest (ROI). A recent approach, named global-brain functional connectivity (GBC), has been demonstrated to address the shortcomings of ROI-based analysis. Furthermore, research on the transcriptome-connectome association has provided an approach to link the microlevel transcriptome profile with the macroscale brain network. In this paper, we aim to explore the neurobiology of trait anxiety with an imaging transcriptomic approach using GBC, biological neurotransmitters, and transcriptome profiles.

METHODS

Using a sample of resting-state fMRI data, we investigated trait anxiety-related alteration in GBC. We further used behavioral analysis, spatial correlation analysis, and postmortem gene expression to separately assess the cognitive functions, neurotransmitters, and transcriptional profiles related to alteration in GBC in individuals with trait anxiety.

RESULTS

GBC values in the ventromedial prefrontal cortex and the precuneus were negatively correlated with levels of trait anxiety. This alteration was correlated with behavioral terms including social cognition, emotion, and memory. A strong association was revealed between trait anxiety-related alteration in GBC and neurotransmitters, including dopaminergic, serotonergic, GABAergic, and glutamatergic systems in the ventromedial prefrontal cortex and the precuneus. The transcriptional profiles explained the functional connectivity, with correlated genes enriched in transmembrane signaling.

LIMITATIONS

Several limitations should be taken into account in this research. For example, future research should consider using some different approaches based on dynamic or task-based functional connectivity analysis, include more neurotransmitter receptors, additional gene expression data from different samples or more genes related to other stress-related disorders. Meanwhile, it is of great significance to include a larger sample size of individuals with a diagnosis of major depression disorder or other disorders for analysis and comparison and apply stricter multiple-comparison correction and threshold settings in future research.

CONCLUSIONS

Our research employed multimodal data to investigate GBC in the context of trait anxiety and to establish its associations with neurotransmitters and transcriptome profiles. This approach may improve understanding of the neural mechanism, together with the biological and molecular genetic foundations of GBC in trait anxiety.

Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease

PURPOSE

Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort.

METHODS

We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging.

RESULTS

TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003).

CONCLUSION

By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction.

Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder

Major depressive disorder (MDD) has been shown to involve widespread changes in low-level sensorimotor and higher-level cognitive functions. Recent research found that a primary-to-transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type-specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex- and age-matched healthy controls derived from the REST-meta-MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary-to-transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as  their paralleled subcortical impairments. The correlations between MDD-related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary-to-transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case-control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co-occurrence of high-level cognitive impairments with low-level sensorimotor abnormalities in MDD.

Phenomic Imaging

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Towards a neuroimaging biomarker for predicting cognitive behavioural therapy outcomes in treatment-naive depression: Preliminary findings

Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.

Association of protein distribution and gene expression revealed by positron emission tomography and postmortem gene expression in the dopaminergic system of the human brain

PURPOSE

The topological distribution of dopamine-related proteins is determined by gene transcription and subsequent regulations. Recent research strategies integrating positron emission tomography with a transcriptome atlas have opened new opportunities to understand the influence of regulation after transcription on protein distribution. Previous studies have reported that messenger (m)-RNA expression levels spatially correlate with the density maps of serotonin receptors but not with those of transporters. This discrepancy may be due to differences in regulation after transcription between presynaptic and postsynaptic proteins, which have not been studied in the dopaminergic system. Here, we focused on dopamine D1 and D2/D3 receptors and dopamine transporters and investigated their region-wise relationship between mRNA expression and protein distribution.

METHODS

We examined the region-wise correlation between regional binding potentials of the target region relative to that of non-displaceable tissue (BPND) values of 11C-SCH-23390 and mRNA expression levels of dopamine D1 receptors (D1R); regional BPND values of 11C-FLB-457 and mRNA expression levels of dopamine D2/D3 receptors (D2/D3R); and regional total distribution volume (VT) values of 18F-FE-PE2I and mRNA expression levels of dopamine transporters (DAT) using Spearman's rank correlation.

RESULTS

We found significant positive correlations between regional BPND values of 11C-SCH-23390 and the mRNA expression levels of D1R (r = 0.769, p = 0.0021). Similar to D1R, regional BPND values of 11C-FLB-457 positively correlated with the mRNA expression levels of D2R (r = 0.809, p = 0.0151) but not with those of D3R (r = 0.413, p = 0.3095). In contrast to D1R and D2R, no significant correlation between VT values of 18F-FE-PE2I and mRNA expression levels of DAT was observed (r = -0.5934, p = 0.140).

CONCLUSION

We found a region-wise correlation between the mRNA expression levels of dopamine D1 and D2 receptors and their respective protein distributions. However, we found no region-wise correlation between the mRNA expression levels of dopamine transporters and their protein distributions, indicating different regulatory mechanisms for the localization of pre- and postsynaptic proteins. These results provide a broader understanding of the application of the transcriptome atlas to neuroimaging studies of the dopaminergic nervous system.

Indirect evidence for altered dopaminergic neurotransmission in very premature-born adults

While animal models indicate altered brain dopaminergic neurotransmission after premature birth, corresponding evidence in humans is scarce due to missing molecular imaging studies. To overcome this limitation, we studied dopaminergic neurotransmission changes in human prematurity indirectly by evaluating the spatial co-localization of regional alterations in blood oxygenation fluctuations with the distribution of adult dopaminergic neurotransmission. The study cohort comprised 99 very premature-born (<32 weeks of gestation and/or birth weight below 1500 g) and 107 full-term born young adults, being assessed by resting-state functional MRI (rs-fMRI) and IQ testing. Normative molecular imaging dopamine neurotransmission maps were derived from independent healthy control groups. We computed the co-localization of local (rs-fMRI) activity alterations in premature-born adults with respect to term-born individuals to different measures of dopaminergic neurotransmission. We performed selectivity analyses regarding other neuromodulatory systems and MRI measures. In addition, we tested if the strength of the co-localization is related to perinatal measures and IQ. We found selectively altered co-localization of rs-fMRI activity in the premature-born cohort with dopamine-2/3-receptor availability in premature-born adults. Alterations were specific for the dopaminergic system but not for the used MRI measure. The strength of the co-localization was negatively correlated with IQ. In line with animal studies, our findings support the notion of altered dopaminergic neurotransmission in prematurity which is associated with cognitive performance.

The altered hippocampal functional connectivity and serum brain-derived neurotrophic factor level predict cognitive decline in patients with knee osteoarthritis

Patients with knee osteoarthritis (KOA) often suffer from cognitive decline and increased dementia risk, but the neurobiological mechanisms remain unclear. In this study, we evaluated cognitive performance and collected brain magnetic resonance imaging (MRI) data and blood samples from cognitively normal KOA patients at baseline sessions and reevaluated their cognition after 5 years. We also collected MRI data from matched healthy controls. Results showed that KOA patients exhibited dysregulated functional connectivities between the hippocampus and thalamus/superior frontal gyrus compared with healthy controls. The altered hippocampal functional connectivities were associated with serum brain-derived neurotrophic factor (BDNF) levels and spatial expression of genes enriched in synaptic plasticity. The hippocampus-thalamus functional connectivity was significantly correlated with patients' memory scores. Moreover, the baseline hippocampus-thalamus functional connectivity and BDNF levels significantly predicted the development of cognitive decline in KOA patients in the follow-up session. Our findings provide insight into the neurobiological underpinnings of KOA and cognitive decline.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain

Neurotransmitter receptors modulate signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression measuring mRNA transcripts is often used as a proxy for receptor densities. In the present report, we comprehensively test the spatial correlation between gene expression and protein density for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography radioligand-based imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT_1A, CB₁, D₂, and MOR). These expression-density associations are related to gene differential stability and can vary between cortical and subcortical structures. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

The development of cortical functional hierarchy is associated with the molecular organization of prenatal/postnatal periods

The human cerebral cortex conforms to specific functional hierarchies facilitating information processing and higher-order cognition. Prior studies in adults have unveiled a dominant functional hierarchy spanning from sensorimotor regions to transmodal regions, which is also present in younger cohorts. However, how the functional hierarchy develops and the underlying molecular mechanisms remain to be investigated. Here, we set out to investigate the developmental patterns of the functional hierarchy for preschool children (#scans = 141, age = 2.41-6.90 years) using a parsimonious general linear model and the underlying biological mechanisms by combining the neuroimaging developmental pattern with two separate transcriptomic datasets (i.e. Allen Human Brain Atlas and BrainSpan Atlas). Our results indicated that transmodal regions were further segregated from sensorimotor regions and that such changes were potentially driven by two gene clusters with distinct enrichment profiles, namely prenatal gene cluster and postnatal gene cluster. Additionally, we found similar developmental profiles manifested in subsequent developmental periods by conducting identical analyses on the Human Connectome Projects in Development (#scans = 638, age = 5.58-21.92 years) and Philadelphia Neurodevelopment Cohort datasets (#scans = 795, age = 8-21 years), driven by concordant two gene clusters. Together, these findings illuminate a comprehensive developmental principle of the functional hierarchy and the underpinning molecular factors, and thus may shed light on the potential pathobiology of neurodevelopmental disorders.

Genetic mechanisms underlying brain functional homotopy: a combined transcriptome and resting-state functional MRI study

Functional homotopy, the high degree of spontaneous activity synchrony and functional coactivation between geometrically corresponding interhemispheric regions, is a fundamental characteristic of the intrinsic functional architecture of the brain. However, little is known about the genetic mechanisms underlying functional homotopy. Resting-state functional magnetic resonance imaging data from a discovery dataset (656 healthy subjects) and 2 independent cross-race, cross-scanner validation datasets (103 and 329 healthy subjects) were used to calculate voxel-mirrored homotopic connectivity (VMHC) indexing brain functional homotopy. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial correlation analysis was conducted to identify genes linked to VMHC. We found 1,001 genes whose expression measures were spatially associated with VMHC. Functional enrichment analyses demonstrated that these VMHC-related genes were enriched for biological functions including protein kinase activity, ion channel regulation, and synaptic function as well as many neuropsychiatric disorders. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons and immune cells, and during nearly all developmental periods. In addition, the VMHC-associated genes were linked to multiple behavioral domains, including vision, execution, and attention. Our findings suggest that interhemispheric communication and coordination involve a complex interaction of polygenes with a rich range of functional features.

Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox

The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods. For complete details on the use and execution of this protocol, please refer to Martins et al. (2021).

Transcriptomic and cellular decoding of functional brain connectivity changes reveal regional brain vulnerability to pro- and anti-inflammatory therapies

BACKGROUND

Systemic inflammation induces acute changes in mood, motivation and cognition that closely resemble those observed in depressed individuals. However, the mechanistic pathways linking peripheral inflammation to depression-like psychopathology via intermediate effects on brain function remain incompletely understood.

METHODS

We combined data from 30 patients initiating interferon-α treatment for Hepatitis-C and 20 anti-tumour necrosis factor (TNF) therapy for inflammatory arthritis and used resting-state functional magnetic resonance imaging to investigate acute effects of each treatment on regional global brain connectivity (GBC). We leveraged transcriptomic data from the Allen Human Brain Atlas to uncover potential biological and cellular pathways underpinning regional vulnerability to GBC changes induced by each treatment.

RESULTS

Interferon-α and anti-TNF therapies both produced differential small-to-medium sized decreases in regional GBC. However, these were observed within distinct brain regions and the regional patterns of GBC changes induced by each treatment did not correlate suggesting independent underlying processes. Further, the spatial distribution of these differential GBC decreases could be captured by multivariate patterns of constitutive regional expression of genes respectively related to: i) neuroinflammation and glial cells; and ii) glutamatergic neurotransmission and neurons. The extent to which each participant expressed patterns of GBC changes aligning with these patterns of transcriptomic vulnerability also correlated with both acute treatment-induced changes in interleukin-6 (IL-6) and, for Interferon-α, longer-term treatment-associated changes in depressive symptoms.

CONCLUSIONS

Together, we present two transcriptomic models separately linking regional vulnerability to the acute effects of interferon-α and anti-TNF treatments on brain function to glial neuroinflammation and glutamatergic neurotransmission. These findings generate hypotheses about two potential brain mechanisms through which bidirectional changes in peripheral inflammation may contribute to the development/resolution of psychopathology.

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

Diverse GABAergic interneuron networks orchestrate information processing in the brain. Understanding the principles underlying the organisation of this system in the human brain, and whether these principles are reflected by available non-invasive in vivo neuroimaging methods, is crucial for the study of GABAergic neurotransmission. Here, we use human gene expression data and state-of-the-art imaging transcriptomics to uncover co-expression patterns between genes encoding GABAA receptor subunits and inhibitory interneuron subtype-specific markers, and their association with binding patterns of the gold-standard GABA PET radiotracers [11C]Ro15-4513 and [11C]flumazenil. We found that the inhibitory interneuron marker somatostatin covaries with GABAA receptor-subunit genes GABRA5 and GABRA2, and that their distribution followed [11C]Ro15-4513 binding. In contrast, the inhibitory interneuron marker parvalbumin covaried with GABAA receptor-subunit genes GABRA1, GABRB2 and GABRG2, and their distribution tracked [11C]flumazenil binding. Our findings indicate that existing PET radiotracers may provide complementary information about key components of the GABAergic system.

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.