Biology subjects

Yee, Y.

Publications and source records attributed to Yee, Y..

18 recordsLinked to original sources

bioRxiv2026

Algorithmic Geometry Shapes the Biological Meaning of Thalamocortical Structural Covariance

Neuroimaging studies often derive and discuss structural covariance (SC) as a representation of underlying brain circuitry that reflects coordinated maturation, anatomical connectivity, transcriptional similarity, and shared genetic influences. These associations motivate its use to study brain organization across development, aging, and disease. However, the algorithms used to derive SC networks impose constraints that may shape their biological interpretation. To understand the impact of algorithm choice, we used the thalamocortical system as an example and compared five common representations of voxelwise SC, namely, principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), orthogonal projective NMF (OPNMF), and \textit{k}-means clustering, in 755 participants aged 8--22 years from the Philadelphia Neurodevelopmental Cohort (PNC). SC networks derived from deformation-based morphometry (DBM) were evaluated against three complementary biological benchmarks: age-related individual differences, fractional-anisotropy (FA)-based microstructural connectivity of regionally-specific thalamocortical connections, and Allen Human Brain Atlas gene expression. Each algorithm demonstrated underlying sensitivity to differing aspects of neurobiology: PCA and OPNMF showed the strongest associations between participant-level component weights and age. NMF, OPNMF, and \textit{k}-means showed greater spatial overlap between positive cortical SC and tractography-derived projections. ICA and PCA showed stronger correspondence between thalamic and cortical gene-expression profiles across the transcriptome, whereas NMF, OPNMF, and \textit{k}-means more often identified thalamic components with relatively high core-matrix gene expression. This study emphasizes that algorithmic constraints effectively act as biological priors and decomposition methods do not provide neutral compression of the data. We conclude that it is critical to consider whether the algorithmic objective matches the representational geometry of the neurobiology being studied. Overall, our findings suggest that SC studies should select and justify algorithms according to the biological question, explicitly considering whether their constraints favour distributed gradients, overlapping regional parts, or discrete clusters. Meanwhile, interpretations of SC findings could also be informed by the specific algorithmic constraints used to derive them.

neuroscience↗

bioRxiv2026

Experimental Quality Control Induces Changes in Allen Mouse Brain Connectomes

The Allen Mouse Brain Connectivity Atlas (AMBCA) is widely used to represent structural connectivity in the mouse brain. The AMBCA consists of tracer injection experiments where neuronal projections axonally connected to the initial injection site are labelled. The resulting whole-brain structural connectomes, derived from a subset of these experiments in C57BL/6 mice, have been used in several studies of connectomic architectures. However, through close inspection of n=437 distinct experiments used in a publicly-available connectome (Knox et al., 2018), we observed experiments with off-target injections, diffuse projections, unrealistically small injections and projections, and anatomical misalignments, affecting the accuracy and applicability of these connectivity experiments. We applied a combined automated and manual quality control (QC) and identified n=56 ([~]13% of the original n=437) experiments representing a wide variety of injection and projection failures across the brain. Automated QC was used to detect extreme injection and projection sizes and misalignments, while manual QC was used to detect subtle off-target tracer spreading. Using the remaining n=381 experiments, we rebuilt two different connectomes using previously-published methods; specifically: the regionalized voxel model from Knox et al. (2018), and the homogeneous model from Oh et al. (2014). Our rebuilt connectomes show strong losses in connectivity between regions with limited evidence of structural connectivity by other methods (e.g. hippocampus-medulla, cerebellum-isocortex) and gains in connectivity between regions with strong connectivity evidence (hypothalamus-cerebellum, hypothalamus-isocortex). Finally, we analyzed the rich club and community organization to demonstrate the potential downstream impacts on the representation of the overall structural connectome architectures of our QCd connectomes and observed subtle whole-brain organizational changes. We present our rebuilt connectomes, and particularly highlight the regionalized voxel model, as more accurate representations of structural connectivity derived from the AMBCA.

neuroscience↗

bioRxiv2025

Pore-Based RNA Evaluation for Control of Integrity, Sequence, and Errors - Quality Control (PRECISE-QC)

RNA is at the forefront of therapeutics and gene editing technologies. Yet, RNA synthesis remains expensive and low-yield. Consequently, most oligo manufacturers abstain from synthesizing RNA oligos longer than 60-mers. Solid-phase synthesis is the current standard production method but is often fraught with low coupling yields for canonical nucleotides and even poorer coupling for modifications. This results in high levels of byproducts such as truncations and RNA infidelity. Existing analytical methods can only provide quality control metrics such as RNA length distribution or limited composition information for short oligos. Here, we developed a standard quality control metric using Oxford Nanopore direct RNA sequencing to obtain direct insight into RNA length distribution, sequence, and presence of RNA modification sites. Our pipeline identifies error-prone regions and truncation sites that occur during synthesis. Furthermore, problematic steps in the synthesis are identified and repaired. We show that our platform can produce and assess CRISPR guide RNAs with high-fidelity and higher cleavage activity, and further, that modifications can be reliably detected. We envision that our tool will serve as an integral method for quality control pipelines that assess the integrity and accuracy of synthetic RNAs and guide the improved synthesis and yield of synthesized RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/677417v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1163a7forg.highwire.dtl.DTLVardef@5a709corg.highwire.dtl.DTLVardef@19737e6org.highwire.dtl.DTLVardef@d71a60_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Accurate spatial localization of Allen Human Brain Atlas gene expression data for human neuroimaging

The Allen Human Brain Atlas has been a tremendously impactful resource in neuroimaging. The usefulness of this resource in neuroimaging arises from spatial coordinates of dissected tissue samples being provided in relation to a Montreal Neurological Institute (MNI)-space standard brain template, thereby allowing for the integration of gene expression and spatially standardized neuroimaging data. In fact, two previous sets of spatial coordinates exist, and surprisingly, the accuracy of these coordinates in placing dissected tissue samples in correct anatomical locations within MNI-space have not been examined. Here, we show that there are significant inaccuracies in the two previous sets of coordinates, and provide a refined set of coordinates as a resource to the neuroscience community. We show (through analyses of meta-analytic data and a re-analysis of real study data) that using previous inaccurate coordinates can result in dramatically different genes being identified, which could compromise further downstream analyses.

neuroscience↗

bioRxiv2025

Evidence-based guidelines for improving network detectability in rodent fMRI

Mouse resting-state functional magnetic resonance imaging (rs-fMRI) is an increasingly popular tool for probing brain activity under experimental manipulations; however, there remains considerable variability in data quality throughout the field. There is a need for an accessible set of acquisition guidelines such that a baseline level of data quality can be attained regardless of domain expertise or specialized equipment (i.e. in anesthetized, free-breathing mice). In particular, there is a gap in the literature regarding the interpretation of physiological parameters as markers of anesthetic depth, and ultimately, data quality. To this end, we developed a set of acquisition guidelines after examining whether continuous physiological variables predict network detectability above and beyond categorical external variables (anesthetic dose, session, time) in C57Bl/7 and C3HeB/FeJ mice anesthetized with isoflurane-dexmedetomidine. Standard physiological metrics (respiration rate and heart rate) did not predict network detectability above and beyond anesthetic dose but instead depended strongly on strain and subject, thus we advise against tuning anesthesia based on respiration or heart rate when the goal is obtaining clear resting-state networks. The most important predictor of improved network detectability was a low isoflurane dose of 0.23%, hence we recommend that researchers prioritize piloting the minimal possible isoflurane dose for their mouse model. In summary, our work examines the contributions from sources of variability that impact rs-fMRI data quality and synthesizes the findings into practical guidelines to help experimenters adapt their acquisition protocols and improve data quality.

neuroscience↗

bioRxiv2025

Whole brain dimensional approach identifies shared and sex-specific networks of stress susceptibility in male and female mice.

Stress is a major risk factor for depression and anxiety, two highly comorbid disorders with sex differences in symptom presentation and prevalence. Accumulating evidence on the chronic variable stress (CVS) mouse model has revealed that stress susceptibility is sex-specific, as females require less stress exposure (6 days) compared to males (21-28 days) to develop comparable adaptations in depressive- and anxiety-like behaviors. However, the neuroanatomical changes associated with stress susceptibility remain unclear in each sex. Given this differential susceptibility, elucidating these mechanisms requires a phenotype-matched approach in which stress duration is titrated in a sex-specific manner to induce comparable behavioral adaptations. Here, we used a multi-variate brain-behavior and connectomic analysis to examine signatures of stress susceptibility in female and male mice. Using structural magnetic resonance imaging (MRI), we investigate whole-brain neuroanatomical changes associated with behavioral susceptibility within each sex. We then examined the structural connectome underlying neuroanatomical changes associated with behavioral susceptibility within each sex, and putative molecular basis for behaviorally-relevant changes using spatial gene expression analyses. CVS induced significant neuroanatomical changes in regions commonly implicated in anxiety- and depressive-like phenotypes in both sexes (e.g. nucleus accumbens and hippocampus); however qualitative differences between sexes in the direction of change were observed. In addition to these changes, in females stress-induced neuroanatomical changes were associated with both depressive- and anxiety-like behavior, whereas in males we observed two orthogonal dimensions of neuroanatomical changes associated with anxiety-like behavior or social preference. The connectomic topology underlying these brain-behavior dimensions revealed the cortical hub regions for females and subcortical hub regions for males. In females, neuroanatomical changes were associated with genes enriched for protein localization to the cell surface, suggesting alterations in cholinergic signaling By implementing a phenotype-matched CVS protocol, our findings indicate that the neuroanatomical signature of stress susceptibility encompasses similar brain regions across both sexes, however the direction of change, association to behavior and connectomic properties may differ in each sex.

neuroscience↗

bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

neuroscience↗

bioRxiv2025

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

The heterogeneity of autism and related neurodevelopmental conditions has impeded accurate prognoses and treatment discovery. Using translational neuroimaging across 135 mouse models (3,515 mice) and two human MRI datasets (n = 1,234 and n = 1,015), we derived participant subgroups from shared neuroanatomical features. These subgroups did not distinguish autism, ADHD, or OCD diagnoses and only modestly differentiated cognitive and behavioural phenotypes. Instead, they mapped onto four molecular pathways: (1) synaptic function; (2) MAPK and Wnt signalling; (3) chromatin modification and cellular stress responses; and (4) broader chromatin, immune, and second-messenger signalling pathways. This framework bridges preclinical models and idiopathic human neurodevelopmental conditions, linking patients to biologically relevant molecular mechanisms.

neuroscience↗

bioRxiv2025

Examining Alzheimer's Disease Modifiable Risk Factors: Impact of Physical Activity and Diet on Neuroanatomy and Behaviour in Mice

Dementia is a global public health challenge, with obesity emerging as an important modifiable risk factor. Here, we examined whether lifestyle interventions can mitigate the effects of diet-induced obesity on body weight, behaviour, and brain anatomy in mouse models. Using a longitudinal design, wild-type and triple-transgenic (3xTgAD) mouse models of Alzheimers disease were exposed to a high-fat diet and assigned to one of three interventions: voluntary physical activity, a low-fat diet, and their combination. A high-fat diet significantly increased body weight and induced widespread neuroanatomical changes, with effects modulated by sex and genotype. The combined intervention led to significant weight loss in males of both genotypes. Neuroanatomical analyses revealed that a high-fat diet significantly reduced hippocampal and cerebellar volumes in wild-type mice but had a less pronounced effect on 3xTgAD mice; nevertheless, interventions, particularly the combined approach, increased localized brain volumes in these regions regardless of genotype. Multivariate integration of behavioural and neuroanatomical measures identified a brain pattern linking hippocampal and cerebellar volumes to intervention and behavioural performance. Spatial gene enrichment analysis of this pattern identified biological processes, including glucose homeostasis, as potential biological mechanisms underlying intervention effects. Overall, these findings suggest that voluntary physical activity and a low-fat diet can modulate brain structure and behaviour, partially counteracting the effects of a high-fat diet, and potentially recruiting biological processes that may support brain health.

neuroscience↗

bioRxiv2023

A new mouse model of ATR-X syndrome carrying a common patient mutation exhibits neurological and morphological defects

ATRX is a chromatin remodelling ATPase that is involved in transcriptional regulation, DNA damage repair and heterochromatin maintenance. It has been widely studied for its role in ALT-positive cancers, but its role in neurological function remains elusive. Hypomorphic mutations in the X-linked ATRX gene cause a rare form of intellectual disability combined with alpha-thalassemia called ATR-X syndrome in hemizygous males. Patients also have facial dysmorphism, microcephaly, musculoskeletal defects and genital abnormalities. Since complete deletion of ATRX in mice results in early embryonic lethality, the field has largely relied on conditional knockout models to assess the role of ATRX in multiple tissues. Given that null alleles are not found in patients, a more patient-relevant model was needed. Here, we have produced and characterised the first patient mutation knock-in model of ATR-X syndrome, carrying the most common patient mutation, R246C. This is one of a cluster of missense mutations located in the chromatin interaction domain that disrupts its function. The knock-in mice recapitulate several aspects of the patient disorder, including craniofacial defects, microcephaly and impaired neurological function. They provide a powerful model for understanding the molecular mechanisms underlying ATR-X syndrome and for testing potential therapeutic strategies.

neuroscience↗

bioRxiv2022

Transcriptomic similarity in the mouse and human brain

The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Using openly accessible brain-wide transcriptomic data sets, we evaluated the similarity of mouse and human brain regions on the basis of homologous gene expression. Our results suggest that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but that the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal sub-divisions, and that mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.

neuroscience↗

bioRxiv2022

Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus

For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles. Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions. We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions. Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.

neuroscience↗

bioRxiv2020

Characterization of Mice Bearing Humanized Androgen Receptor Genes (h/mAr) Varying in Polymorphism Length

The androgen receptor (AR) is known for masculinization of behavior and brain. To better understand the role that AR plays, mice bearing humanized Ar genes with varying lengths of a polymorphic N-terminal glutamine (Q) tract were created (Albertelli et al 2006). The length of the Q tract is inversely proporitional to AR activity. Biological studies of the Q tract length may also provide a window into potential AR contributions to sex-biases in disease risk. Here we take a multi-pronged approach to characterizing AR signaling effects on brain and behavior in mice using the humanized Ar Q tract model. We first map effects of Q tract length on regional brain anatomy, and consider if these are modified by gonadal sex. We then test the notion that spatial patterns of anatomical variation related to Q tract length could be organized by intrinsic spatiotemporal patterning of AR gene expression in the mouse brain. Finally, we test influences of Q tract length on four behavioral tests. Altering Q tract length led to neuroanatomical differences in a non-linear dosage-dependent fashion. Gene expression analyses indicated that adult neuroanatomical changes due to Q tract length are only associated with neurodevelopment (as opposed to adulthood). No significant effect of Q tract length was found on the behavior of the three mouse models. These results indicate that AR activity differentially mediates neuroanatomy and behavior, that AR activity alone does not mediate sex differences, and that neurodevelopmental processes are associated with spatial patterns of volume changes due to Q tract length in adulthood. They also indicate that androgen sensitivity in adulthood does not directly lead to autism-related behaviors or neuroanatomy, although neurodevelopmental processes may play a role earlier. Further study into sex differences, development, other behaviors, and other sex-specific mechanisms are needed to better understand AR sensitivity, neurodevelopmental disorders, and the sex difference in their prevalence.

neuroscience↗

bioRxiv2020

Exposure to maternal high-fat diet induces extensive changes in the brain of adult offspring

Maternal environmental exposures, such as high-fat diets, diabetes and obesity, can induce long term effects in offspring. These effects include increased risk of neurodevelopmental disorders (NDDs) including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), depression and anxiety. The mechanisms underlying these late-life neurologic effects are unknown. In this article, we measured changes in the offspring brain and determined which brain regions are sensitive to maternal metabolic milieu and therefore may mediate NDD risk. We showed that mice exposed to maternal high-fat diet display extensive brain changes in adulthood despite being switched to low-fat diet at weaning. Brain regions impacted by early-life diet include the extended amygdalar system, which plays an important role in reward-seeking behaviour. Genes preferentially expressed in these regions have functions related to feeding behavior, while also being implicated in human NDDs, such as autism. Our data demonstrated that exposure to maternal high-fat diet in early-life leads to brain alterations that persist into adulthood, even after dietary modifications.

neuroscience↗

bioRxiv2020

The gamma-Protocadherins regulate the survival of GABAergic interneurons during developmentally-regulated cell death.

Inhibitory interneurons integrate into developing circuits in specific ratios and distributions. In the cortex, the formation of inhibitory networks occurs concurrently with the apoptotic elimination of a third of GABAergic interneurons. The molecular mechanisms that select GABAergic interneurons to survive or die are unknown. Here we report that the clustered Protocadherins regulate GABAergic cell survival in the developing brain. Deletion of the Pcdh-gamma genes (Pcdhgs) from GABAergic neurons in mice causes a severe loss of inhibitory neurons in multiple brain regions and results in motor deficits and seizure activities. By focusing on the neocortex and cerebellar cortex, we demonstrate that GABAergic interneuron loss results from elevated apoptosis during the postnatal wave of Bax-dependent programmed cell death. Pro-survival AKT signals are reduced in Pcdhg-deficient interneurons, diminishing the intrinsic capacity of interneurons to compete and incorporate into developing networks. We propose that the Pcdhgs mediate selective GABAergic interneuron survival to contribute to the formation of balanced inhibitory networks.

neuroscience↗

bioRxiv2019

Reduced axonal caliber and white matter changes in a rat model of Fragile X syndrome with a deletion of a K Homology domain of Fmr1.

Fragile X syndrome (FXS) is a neurodevelopmental disorder that is caused by mutations in the FMR1 gene that are known to cause neuroanatomical alterations. The morphological underpinnings of these alterations have not been elucidated. Furthermore, while alterations have been identified in both male and female individuals, neuroanatomy in female rodent models has not been assessed. We identified structural differences in regions that are also altered in FXS in male and female rat models, including the splenium of the corpus callosum. Interestingly, different sets of regions were disrupted in male and female rat models and, remarkably, male rats had higher brain-wide diffusion than female rats overall. We found reduced axonal caliber in the splenium, offering a mechanism for its structural changes. Our results provide insight into which brain regions are vulnerable to a loss of Fmr1 expression and suggest a potential mechanism for how its loss causes white matter dysfunction in FXS.

neuroscience↗

bioRxiv2019

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

neuroscience↗

bioRxiv2017

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

An 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.\n\nHere, 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.\n\nWe 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.

neuroscience↗