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

The brain, rapid eye movement sleep, and major depressive disorder: A multimodal neuroimaging study

BACKGROUND

Evidence has established the prominent involvement of rapid eye movement (REM) sleep disturbance in major depressive disorder (MDD). However, the neural correlates of REM sleep in MDD and their clinical significance are less clear.

METHODS

Cross-sectional and longitudinal polysomnography and resting-state functional MRI data were collected from 131 MDD patients and 71 healthy controls to measure REM sleep and voxel-mirrored homotopic connectivity (VMHC). Correlation and mediation analyses were performed to examine the associations between REM sleep, VMHC, and clinical variables. Moreover, we conducted spatial correlations between the neural correlates of REM sleep and a multimodal collection of reference brain maps to facilitate genetic, structural and functional annotations.

RESULTS

MDD patients exhibited REM sleep abnormalities manifesting as higher REM sleep latency and lower REM sleep duration, which were correlated with decreased VMHC of the precentral gyrus and inferior parietal lobe and mediated their associations with more severe anxiety symptoms. Longitudinal data showed that VMHC increase of the inferior parietal lobe was related to improvement of depression symptoms in MDD patients. Spatial correlation analyses revealed that the neural correlates of REM sleep in MDD were linked to gene categories primarily involving cellular metabolic process, signal pathway, and ion channel activity as well as linked to cortical microstructure, metabolism, electrophysiology, and cannabinoid receptor.

CONCLUSION

These findings may add important context to the growing literature on the complex interplay between sleep and MDD, and more broadly may inform future treatment for depression via regulating sleep.

Molecular mechanisms and behavioral relevance underlying neural correlates of childhood neglect

BACKGROUND

Childhood neglect is associated with brain changes, yet the molecular mechanisms and behavioral relevance underlying such associations remain elusive.

METHODS

We calculated fractional amplitude of low-frequency fluctuations (fALFF) using resting-state functional MRI and tested their correlation with childhood neglect across a large sample of 510 healthy young adults. Then, we investigated the spatial relationships of the identified neural correlates of childhood neglect with gene expression, neurotransmitter, and behavioral domain atlases.

RESULTS

We found that more severe childhood neglect was correlated with higher fALFF in the bilateral anterior cingulate cortex. Remarkably, the identified neural correlates of childhood neglect were spatially correlated with expression of gene categories primarily involving neuron, synapse, ion channel, cognitive and perceptual processes, and physiological response and regulation. Concurrently, there were significant associations between the neural correlates and specific neurotransmitter systems including serotonin and GABA. Finally, neural correlates of childhood neglect were associated with diverse behavioral domains implicating mental disorders, emotion, cognition, and sensory perception.

LIMITATIONS

The cross-sectional study design cannot unequivocally establish causality.

CONCLUSIONS

Our findings may not only add to the current knowledge regarding the relationship between childhood neglect and mental health, but also have clinical implications for developing preventive strategies for individuals exposed to childhood neglect who are at risk for mental disorders.

Neural Correlates of Early-Life Urbanization and Their Spatial Relationships with Gene Expression, Neurotransmitter, and Behavioral Domain Atlases

Previous neuroimaging research has established associations between urban exposure during early life and alterations in brain function and structure. However, the molecular mechanisms and behavioral relevance of these associations remain largely unknown. Here, we aimed to address this question using a combined analysis of multimodal data. Initially, we calculated amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV) using resting-state functional and structural MRI to investigate their associations with early-life urbanization in a large sample of 511 healthy young adults. Then, we examined the spatial relationships of the identified neural correlates of early-life urbanization with gene expression, neurotransmitter, and behavioral domain atlases. Results showed that higher early-life urbanization scores were correlated with increased ALFF of the right fusiform gyrus and decreased GMV of the left dorsal medial prefrontal cortex and left precuneus. Remarkably, the identified neural correlates of early-life urbanization were spatially correlated with expression of gene categories primarily involving immune system process, signal transduction, and cellular metabolic process. Concurrently, there were significant associations between the neural correlates and specific neurotransmitter systems including dopamine, acetylcholine, and serotonin. Finally, we found that the ALFF correlates were associated with behavioral terms including "perception," "sensory," "cognitive control," and "reasoning." Apart from expanding existing knowledge of early-life urban environmental risk for mental disorders and health in general, our findings may contribute to an emerging framework for integrating social science, neuroscience, genetics, and public policy to respond to the major health challenge of world urbanization.

Transcriptomic decoding of regional cortical vulnerability to major depressive disorder

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Reversibility of Cognitive Deficits and Functional Connectivity With Transfusion in Children With Sickle Cell Disease

BACKGROUND AND OBJECTIVES

People with sickle cell disease (SCD) are at risk of cognitive dysfunction independent of stroke. Diminished functional connectivity in select large-scale networks and white matter integrity reflect the neurologic consequences of SCD. Because chronic transfusion therapy is neuroprotective in preventing stroke and strengthening executive function abilities in people with SCD, we hypothesized that red blood cell (RBC) transfusion facilitates the acute reversal of disruptions in functional connectivity while white matter integrity remains unaffected.

METHODS

Children with SCD receiving chronic transfusion therapy underwent a brain MRI measuring white matter integrity with diffusion tensor imaging and resting-state functional connectivity within 3 days before and after transfusion of RBCs. Cognitive assessments with the NIH Toolbox were acquired after transfusion and then immediately before the following transfusion cycle.

RESULTS

Sixteen children with a median age of 12.5 years were included. Global assessments of functional connectivity using homotopy (p = 0.234) or modularity (p = 0.796) did not differ with transfusion. Functional connectivity within the frontoparietal network significantly strengthened after transfusion (median intranetwork Z-score 0.21 [0.17-0.30] before transfusion, 0.29 [0.20-0.36] after transfusion, p < 0.001), while there was not a significant change seen within the sensory motor, visual, auditory, default mode, dorsal attention, or cingulo-opercular networks. Corresponding to the change within the frontoparietal network, there was a significant improvement in executive function abilities after transfusion (median executive function composite score 87.7 [81.3-90.7] before transfusion, 90.3 [84.3-93.7] after transfusion, p = 0.021). Participants with stronger connectivity in the frontoparietal network before transfusion had a significantly greater improvement in the executive function composite score with transfusion (r = 0.565, 95% CI 0.020-0.851, p = 0.044). While functional connectivity and executive abilities strengthened with transfusion, there was not a significant change in white matter integrity as assessed by fractional anisotropy and mean diffusivity within 16 white matter tracts or globally with tract-based spatial statistics.

DISCUSSION

Strengthening of functional connectivity with concomitant improvement in executive function abilities with transfusion suggests that functional connectivity MRI could be used as a biomarker for acutely reversible neurocognitive injury as novel therapeutics are developed for people with SCD.

Molecular mechanisms underlying resting-state brain functional correlates of behavioral inhibition

Previous literature has established the presence of sex differences in behavioral inhibition as well as its neural substrates and related disease risk. However, there is limited evidence that speaks directly to the question of whether or not there are sex-dependent associations between behavioral inhibition and resting-state brain function and, if so, how they are modulated by the underlying molecular mechanisms. We computed functional connectivity density (FCD) using resting-state functional MRI data to examine their associations with behavioral inhibition ability measured using a Go/No-Go task across a large cohort of 510 healthy young adults. Then, we examined the spatial relationships of the FCD correlates of behavioral inhibition with gene expression and neurotransmitter atlases to explore their potential genetic architecture and neurochemical basis. A significant negative correlation between behavioral inhibition and FCD in the left superior parietal lobule was found in females but not males. Further spatial correlation analyses demonstrated that the identified neural correlates of behavioral inhibition were associated with expression of gene categories predominantly implicating essential components of the cerebral cortex (glial cell, neuron, axon, dendrite, and synapse) and ion channel activity, as well as were linked to the serotonergic system. Our findings may not only yield important insights into the molecular mechanisms underlying the female-specific neural substrates of behavioral inhibition, but also provide a critical context for understanding how biological sex might contribute to variation in behavioral inhibition and its related disease risk.

Regression models for partially localized fMRI connectivity analyses

Background

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.

Methods

In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches.

Results

We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information.

Discussion

While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Regression models for partially localized fMRI connectivity analyses

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is the assumption of complete localization (or spatial alignment) of brain regions across subjects. Alternative approaches completely eschew localization assumptions by treating connections as statistically exchangeable (for example, using the density of connectivity between nodes). Yet other approaches, such as hyperalignment, attempt to align subjects on function as well as structure, thereby achieving a different sort of template-based localization. In this paper, we propose the use of simple regression models to characterize connectivity. To that end, we build regression models on subject-level Fisher transformed regional connection matrices using geographic distance, homotopic distance, network labels, and region indicators as covariates to explain variation in connections. While we perform our analysis in template-space in this paper, we envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. A byproduct of this style of analysis is the ability to characterize the fraction of variation in subject-level connections explained by each type of covariate. Using Human Connectome Project data, we found that network labels and regional characteristics contribute far more than geographic or homotopic relationships (considered non-parametrically). In addition, visual regions had the highest explanatory power (i.e., largest regression coefficients). We also considered subject repeatability and found that the degree of repeatability seen in fully localized models is largely recovered using our proposed subject-level regression models. Further, even fully exchangeable models retain a sizeable amount of repeatability information, despite discarding all localization information. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Transcriptional substrates of brain structural and functional impairments in drug-naive first-episode patients with major depressive disorder

BACKGROUND

Despite remarkable success in identifying genetic risk factors for depression, there are still open questions about the exact genetic mechanisms underlying certain disease phenotypes, such as brain structural and functional impairments.

METHODS

Comprehensive multi-modal neuroimaging meta-analyses were conducted to examine changes in brain structure and function in drug-naive first-episode patients with major depressive disorder (DF-MDD). Combined with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial association analyses were performed to identify genes whose expression related to these brain structural and functional changes, followed by a range of gene functional signature analyses.

RESULTS

Meta-analyses revealed gray matter atrophy in the insula, temporal pole, cerebellum and postcentral gyrus, and a complex pattern of hyper-function in the temporal pole and hypo-function in the cuneus/precuneus, angular gyrus and lingual gyrus in DF-MDD. Moreover, these brain structural and functional changes were spatially associated with the expression of 1194 and 1733 genes, respectively. Importantly, there were commonalities and differences in the two gene sets and their functional signatures including functional enrichment, specific expression, behavioral relevance, and constructed protein-protein interaction networks.

LIMITATIONS

The results merit further verification using a large sample of DF-MDD.

CONCLUSIONS

Our findings not only corroborate the polygenic nature of depression, but also suggest common and distinct genetic modulations of brain structural and functional impairments in this disorder.

Radiomic features of gray matter in never-treated first-episode schizophrenia

Alterations of radiomic features (RFs) in gray matter are observed in schizophrenia, of which the results may be limited by small study samples and confounding effects of drug therapies. We tested for RFs alterations of gray matter in never-treated first-episode schizophrenia (NT-FES) patients and examined their associations with known gene expression profiles. RFs were examined in the first sample with 197 NT-FES and 178 healthy controls (HCs) and validated in the second independent sample (90 NT-FES and 74 HCs). One-year follow-up data were available from 87 patients to determine whether RFs were associated with treatment outcomes. Associations between identified RFs in NT-FES and gene expression profiles were evaluated. NT-FES exhibited alterations of 30 RFs, with the greatest involvement of microstructural heterogeneity followed by measures of brain region shape. The identified RFs were mainly located in the central executive network, frontal-temporal network, and limbic system. Two baseline RFs with the involvement of microstructural heterogeneity predicted treatment response with moderate accuracy (78% for the first sample, 70% for the second sample). Exploratory analyses indicated that RF alterations were spatially related to the expression of schizophrenia risk genes. In summary, the present findings link brain abnormalities in schizophrenia with molecular features and treatment response.