Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

AbstractAn organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

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Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Neuroinformatics ◽

10.1007/s12021-021-09511-0 ◽

2021 ◽

Author(s):

Nestor Timonidis ◽

Alberto Llera ◽

Paul H. E. Tiesinga

Keyword(s):

Gene Expression ◽

Mouse Brain ◽

Structural Connectivity ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

Synaptic Function ◽

Reticular Nucleus ◽

Sources Of Information ◽

Independent Components ◽

Brain Connectome

AbstractFinding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

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Regional heterogeneity in gene expression, regulation and coherence in hippocampus and dorsolateral prefrontal cortex across development and in schizophrenia

10.1101/426213 ◽

2018 ◽

Cited By ~ 3

Author(s):

L Collado-Torres ◽

EE Burke ◽

A Peterson ◽

JH Shin ◽

RE Straub ◽

...

Keyword(s):

Gene Expression ◽

Prefrontal Cortex ◽

Brain Region ◽

Dorsolateral Prefrontal Cortex ◽

Large Scale ◽

Brain Regions ◽

Second Phase ◽

Regional Heterogeneity ◽

Surrogate Variable Analysis ◽

Dorsolateral Prefrontal

AbstractRecent large-scale genomics efforts have better characterized the molecular correlates of schizophrenia in postmortem human neocortex, but not hippocampus which is a brain region prominently implicated in its pathogenesis. Here in the second phase of the BrainSeq Consortium (Phase II), we have generated RiboZero RNA-seq data for 900 samples across both the dorsolateral prefrontal cortex (DLPFC) and the hippocampus (HIPPO) for 551 individuals (286 affected by schizophrenia disorder: SCZD). We identify substantial regional differences in gene expression, in both pre- and post-natal life, and find widespread differences in how genes are regulated across development. By extending quality surrogate variable analysis (qSVA) to multiple brain regions, we identified 48 and 245 differentially expressed genes (DEG) by SCZD diagnosis (FDR<5%) in HIPPO and DLPFC, respectively, with surprisingly minimal overlap in DEG between the two brain regions. We further identified 205,618 brain region-dependent eQTLs (FDR<1%) and found that 124 GWAS risk loci contain eQTLs in at least one of the regions. We also identify potential molecular correlates of in vivo evidence of altered prefrontal-hippocampal functional coherence in schizophrenia. These results underscore the complexity and regional heterogeneity of the transcriptional correlates of schizophrenia, and suggest future schizophrenia therapeutics may need to target molecular pathologies localized to specific brain regions.

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An architectonic type principle in the development of laminar patterns of cortico-cortical connections

Brain Structure and Function ◽

10.1007/s00429-021-02219-6 ◽

2021 ◽

Author(s):

Sarah F. Beul ◽

Alexandros Goulas ◽

Claus C. Hilgetag

Keyword(s):

Structural Connectivity ◽

Macaque Monkey ◽

Brain Regions ◽

Adult Brain ◽

Mammalian Brain ◽

Cortical Connections ◽

Cortical Projections ◽

Structural Connections ◽

High Degree ◽

The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

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Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

Communications Biology ◽

10.1038/s42003-021-01700-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Arian Ashourvan ◽

Preya Shah ◽

Adam Pines ◽

Shi Gu ◽

Christopher W. Lynn ◽

...

Keyword(s):

White Matter ◽

Maximum Entropy ◽

Large Scale ◽

Structural Connectivity ◽

Quantitative Relationship ◽

Brain Regions ◽

Maximum Entropy Model ◽

Entropy Model ◽

Activation Patterns ◽

Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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On the structural connectivity of large-scale models of brain networks at cellular level

Scientific Reports ◽

10.1038/s41598-021-83759-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giuseppe Giacopelli ◽

Domenico Tegolo ◽

Emiliano Spera ◽

Michele Migliore

Keyword(s):

Large Scale ◽

Brain Networks ◽

Structural Connectivity ◽

Network Models ◽

Brain Regions ◽

Cellular Level ◽

Scale Model ◽

Mathematical Framework ◽

Underlying Mechanisms ◽

Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

Nature Communications ◽

10.1038/s41467-021-22399-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Angela M. Crist ◽

Kelly M. Hinkle ◽

Xue Wang ◽

Christina M. Moloney ◽

Billie J. Matchett ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Digital Pathology ◽

Selective Vulnerability ◽

Brain Regions ◽

Biochemical Analyses ◽

The Brain ◽

Relevant Gene ◽

Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

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Physical positioning markedly enhances brain transduction after intrathecal AAV9 infusion

Science Advances ◽

10.1126/sciadv.aau9859 ◽

2018 ◽

Vol 4 (11) ◽

pp. eaau9859 ◽

Cited By ~ 7

Author(s):

Michael J. Castle ◽

Yuhsiang Cheng ◽

Aravind Asokan ◽

Mark H. Tuszynski

Keyword(s):

Gene Expression ◽

Gene Delivery ◽

Neurological Disorders ◽

Fold Increase ◽

Brain Regions ◽

Intrathecal Administration ◽

Simple Method ◽

Virus Serotype ◽

Cortical Regions ◽

The Brain

Several neurological disorders may benefit from gene therapy. However, even when using the lead vector candidate for intrathecal administration, adeno-associated virus serotype 9 (AAV9), the strength and distribution of gene transfer to the brain are inconsistent. On the basis of preliminary observations that standard intrathecal AAV9 infusions predominantly drive reporter gene expression in brain regions where gravity might cause cerebrospinal fluid to settle, we tested the hypothesis that counteracting vector “settling” through animal positioning would enhance vector delivery to the brain. When rats are either inverted in the Trendelenburg position or continuously rotated after intrathecal AAV9 infusion, we find (i) a significant 15-fold increase in the number of transduced neurons, (ii) a marked increase in gene delivery to cortical regions, and (iii) superior animal-to-animal consistency of gene expression. Entorhinal, prefrontal, frontal, parietal, hippocampal, limbic, and basal forebrain neurons are extensively transduced: 95% of transduced cells are neurons, and greater than 70% are excitatory. These findings provide a novel and simple method for broad gene delivery to the cortex and are of substantial relevance to translational programs for neurological disorders, including Alzheimer’s disease and related dementias, stroke, and traumatic brain injury.

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Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

10.1101/2020.10.28.359943 ◽

2020 ◽

Cited By ~ 1

Author(s):

Gustavo Deco ◽

Kevin Aquino ◽

Aurina Arnatkevičiūtė ◽

Stuart Oldham ◽

Kristina Sabaroedin ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Global Gene Expression ◽

Brain Regions ◽

Biophysical Model ◽

Neuronal Dynamics ◽

Regional Heterogeneity ◽

Magnetic Resonance Imaging Mri

AbstractBrain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions

10.1101/2021.10.06.21264635 ◽

2021 ◽

Author(s):

Rahat Hasan ◽

Jack Humphrey ◽

Conceicao Bettencourt ◽

Tammaryn Lashley ◽

Pietro Fratta ◽

...

Keyword(s):

Gene Expression ◽

Frontotemporal Lobar Degeneration ◽

Molecular Mechanisms ◽

Rna Binding ◽

Temporal Cortex ◽

Neuronal Loss ◽

Underlying Disease ◽

Brain Regions ◽

Neuronal Markers ◽

The Brain

Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared post-mortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.

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