Biology subjects

Anderson, K. M.

Publications and source records attributed to Anderson, K. M..

13 recordsLinked to original sources

bioRxiv2025

The cellular associates of late life changes in white matter microstructure

The microstructural architecture of white matter supporting information flow across local circuits and large-scale networks changes throughout the lifespan. However, the genetic and cellular factors underlying age-related variations in white matter microstructure have yet to be established. Here, we examined the genetic associates of individual differences in diffusion-based measures of white matter in a population-based cohort (N=29,862) from the UK Biobank. Estimates of heritability from Genome-Wide Association Study (GWAS) data revealed that genetic factors are linked to population variability in 96.1% of 432 tract microstructural measures. The presence of shared genetic influences was observed to be greater within, relative to between, broad tract classes (commissural, association, projection, and complex cerebellar). Age associations with microstructural changes were estimated across diffusivity measures, with association class tracts showing the greatest vulnerability to age-related decline in older adults. Analyses of imputed cellular associates of age-related changes in white matter revealed a preferential relationship with cell gene markers of oligodendrocytes and other glial cell types, with sparse relationships observed for inhibitory and excitatory cells. These data indicate that white matter tract microstructure is shaped by genetic factors and suggest a role for glial cell-related transcripts in late-life changes in the structural wiring properties of the human brain. Significance StatementThe structural wiring of the human brain supports interregional communication and is essential for cognition, yet the genetic and cellular drivers underlying age-related changes are not well understood. Here, we characterize the extent to which genetic factors follow the major organizational patterns of white matter connectivity and associated age-related changes in late life. Age-related changes were most pronounced in late-developing association tracts, key pathways for higher cognitive functions. By integrating genetic and transcriptomic data, we show that genetic variation linked to glial cell markers accounts for a substantial proportion of age-related white matter changes. These findings provide a mechanistic framework for understanding genetic and cellular determinants of structural brain aging.

neuroscience↗

bioRxiv2023

The Cellular Underpinnings of the Human Cortical Connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cortex. The cortical sheet can be broadly divided into distinct networks, which are further embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here, using transcriptional data from the Allen Human Brain Atlas, we demonstrate that imputed cell type distributions are spatially coupled to the functional organization of cortex, as estimated through fMRI. Cortical cellular profiles follow the macro-scale organization of the functional gradients as well as the associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on post-mortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

neuroscience↗

bioRxiv2022

Three transcriptional axes underpin anatomy, development, and disorders of the human cortex

Human brain organisation 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. Here, optimised 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 cytoarchitecture, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas, and BrainSpan), we found that C1-C3 represent generalisable transcriptional programmes that are coordinated within cells, and differentially phased during foetal 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 programme for adolescent brain development, which can lead to atypical supra-granular cortical connectivity in people at high genetic risk for schizophrenia.

neuroscience↗

bioRxiv2022

The LEGO theory of the developing functional connectome

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine-scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9 - 10 years; n = 1,115 individuals, both sexes, 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition. Significance statementYears of human magnetic resonance imaging (MRI) research demonstrate that individual variability in resting-state functional connectivity relates to genetics and cognition. However, the various spatial scales where individual differences in connectivity could occur have yet to be considered in childhood brain- behavior association studies. Here, we use novel machine learning approaches to examine the reliability, heritability, and behavioral relevance of different spatial scales of the resting-state functional connectome during childhood. We show that broad features of the connectome are strongly related to heritability, whereas fine details are more reliable and strongly associated with neurocognitive performance. These data indicate that reliable, heritable, and behaviorally-relevant individual differences exist at dissociable scales of the functional connectome.

neuroscience↗

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2021

Anatomical and Functional Gradients Shape Dynamic Functional Connectivity in the Human Brain

Large-scale biophysical circuit models can provide mechanistic insights into the fundamental micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. By allowing local synaptic properties to vary across brain regions, recent large-scale circuit models have demonstrated better fit to empirical observations, such as inter-regional synchrony averaged over several minutes, i.e. static functional connectivity (FC). However, most previous models do not capture how inter-regional synchrony patterns vary over timescales of seconds, i.e., time-varying FC dynamics. Here we developed a spatially-heterogeneous large-scale dynamical circuit model that allowed for variation in local circuit properties across the human cortex. We showed that parameterizing local circuit properties with both anatomical and functional gradients was necessary for generating realistic static and dynamical properties of resting-state fMRI activity. Furthermore, empirical and simulated FC dynamics demonstrated remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. We found that time-varying regional fMRI amplitude tracked multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggested that sensory-motor regions were a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matched the principal gradient of gene expression that encompassed certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

neuroscience↗

bioRxiv2020

Local and distributed cortical markers of effort expenditure during sustained goal pursuit

The adaptive adjustment of behavior in pursuit of desired goals is critical for survival. To accomplish this complex feat, individuals must weigh the potential benefits of a given course of action against time, energy, and resource costs. Prior research in this domain has greatly advanced understanding of the cortico-striatal circuits that support the anticipation and receipt of desired outcomes, characterizing core aspects of subjective valuation at discrete points in time. However, motivated goal pursuit is not a static or cost neutral process and the brain mechanisms that underlie individual differences in the dynamic updating of effort expenditure across time remain unclear. Here, 38 healthy right-handed participants underwent functional MRI (fMRI) while completing a novel paradigm to examine their willingness to exert physical effort over a prolonged trial, either to obtain monetary rewards or avoid punishments. During sustained goal pursuit, medial prefrontal cortex (mPFC) response scaled with trial-to-trial differences in effort expenditure as a function of both monetary condition and eventual task earnings. Multivariate pattern analysis (MVPA) searchlights were used to examine relations linking prior trial-level effort expenditure to subsequent brain responses to feedback. At reward feedback, whole-brain searchlights identified signals reflecting past effort expenditure in dorsal and ventral mPFC, encompassing broad swaths of frontoparietal and dorsal attention networks. These results suggest a core role for mPFC in scaling effort expenditure during sustained goal pursuit, with the subsequent tracking of effort costs following successful goal attainment extending to incorporate distributed brain networks that support executive functioning and externally oriented attention. Significance StatementHistorically, much of the research on subjective valuation has focused on discrete points in time. Here, we examine brain responses associated with willingness to exert physical effort during the sustained pursuit of desired goals. Our analyses reveal a distributed pattern of brain activity encompassing aspects of ventral mPFC that tracks with trial-level variability in effort expenditure. Indicating that the brain represents echoes of effort at the point of feedback, searchlight analyses revealed signals associated with past effort expenditure in broad swaths of dorsal and medial PFC. These data have important implications for the study of how the brains valuation mechanisms contend with the complexity of real-world dynamic environments with relevance for the study of behavior across health and disease.

neuroscience↗

bioRxiv2020

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. SignificanceThe widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

neuroscience↗

bioRxiv2020

Convergent molecular, cellular, and neural signatures of major depressive disorder

Major depressive disorder emerges from the complex interactions of biological systems that span across genes and molecules through cells, circuits, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to the onset and maintenance of depression requires a multi-scale approach, encompassing measures of brain structure and function as well as genetic and cell-specific genomic data. Here, we examined anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Genome Superstruct Project, and ENIGMA (combined N[≥]23,723). Integrative analyses incorporated measures of cortical gene expression, post-mortem patient transcriptional data, depression GWAS, and single-cell transcription. Neuroimaging correlates of depression and negative affect were consistent across the three independent datasets. Linking ex-vivo gene downregulation with in-vivo neuroimaging, we found that genomic correlates of depression-linked neuroimaging phenotypes tracked gene downregulation in post-mortem cortical tissue samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data implicated somatostatin interneurons and astrocytes as consistent cell associates of depression, through both in-vivo imaging and ex-vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multi-scale approaches, the genomic correlates of depression-linked brain function and structure were enriched for known and novel disorder relevant molecular pathways. These findings bridge across levels to connect specific genes, cell classes, and biological pathways to in-vivo imaging correlates of depression. Key FindingsO_LIMajor depressive disorder and negative affect are associated with replicable profiles of cortical anatomy and function across independent population-level neuroimaging datasets (combined N[≥]23,723). C_LIO_LISomatostatin interneurons are consistent spatial transcriptional associates of in-vivo depression-linked imaging phenotypes. C_LIO_LIIntegrative single-cell gene expression analysis associate somatostatin interneurons and astrocytes with both in-vivo depression-linked imaging and ex-vivo gene downregulation in independent MDD cortical tissue samples. C_LIO_LITranscriptional correlates of in-vivo depression imaging phenotypes selectively capture gene downregulation in post-mortem tissue samples from patients with depression, but not other psychiatric disorders. C_LIO_LIIndicating that some cell classes are preferentially sensitive to inherited disease liability, genome-wide risk for depression is enriched among interneurons, but not glia. C_LIO_LIGene associates of depression-linked anatomy and function identify specific neurotransmitter systems, molecular signaling pathways, and receptors, suggesting possible targets for pharmaceutical intervention. C_LI

neuroscience↗

bioRxiv2019

Targeting Toxic Nuclear RNA Foci with CRISPR-Cas13 to Treat Myotonic Dystrophy

Human monogenetic diseases can arise from the aberrant expansion of tandem nucleotide repeat sequences, which when transcribed into RNA, can misfold and aggregate into toxic nuclear foci1. Nuclear retention of repeat-containing RNAs can disrupt their normal expression and induce widespread splicing defects by sequestering essential RNA binding proteins. Among the most prevalent of these disorders is myotonic dystrophy type 1 (DM1), a disease occurring from the expression of a noncoding CTG repeat expansion in the 3UTR of the human dystrophia myotonica protein kinase (DMPK) gene2,3. Here we show that RNA-binding CRISPR-Cas13, with a robust non-classical nuclear localization signal, can be efficiently targeted to toxic nuclear RNA foci for either visualization or cleavage, tools we named hilightR and eraseR, respectively. HilightR combines catalytically dead Cas13b (dCas13b) with a fluorescent protein to directly visualize CUG repeat RNA foci in the nucleus of live cells, allowing for quantification of foci number and observation of foci dynamics. EraseR utilizes the intrinsic endoribonuclease activity of Cas13b, targeted to nuclear CUG repeat RNA, to disrupt nuclear foci. These studies demonstrate the potential for targeting toxic nuclear RNA foci directly with CRISPR-Cas13 for either the identification or treatment of DM1. The efficient and sequence programmable nature of CRISPR-Cas13 systems will allow for rapid targeting and manipulation of other human nuclear RNA disorders, without the associated risks of genome editing.

molecular biology↗

bioRxiv2019

Behavioral and neural signatures of working memory in childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during development, however, are not well understood. Here we establish associations between working memory, cognitive abilities, and functional MRI activation in data from over 4,000 9-10-year-olds enrolled in the Adolescent Brain Cognitive Development study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity in response to an explicit memory challenge indexes working memory ability. Furthermore, this relationship is domain-specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task, and reward processing during a monetary incentive delay task does not track memory abilities. Together these results inform our understanding of the emergence of individual differences in working memory and lay the groundwork for characterizing the ways in which they change across adolescence.

neuroscience↗

bioRxiv2019

Targeted Cleavage and Polyadenylation of RNA by CRISPR-Cas13

Post-transcriptional cleavage and polyadenylation of messenger and long noncoding RNAs is coordinated by a supercomplex of ~20 individual proteins within the eukaryotic nucleus1,2. Polyadenylation plays an essential role in controlling RNA transcript stability, nuclear export, and translation efficiency3-6. More than half of all human RNA transcripts contain multiple polyadenylation signal sequences that can undergo alternative cleavage and polyadenylation during development and cellular differentiation7,8. Alternative cleavage and polyadenylation is an important mechanism for the control of gene expression and defects in 3 end processing can give rise to myriad human diseases9,10. Here we show that fusion of catalytically dead Cas13 to a single mammalian polyadenylation factor, Nudix hydrolase 21 (NUDT21), allows for site-specific CRISPR-Cas13-guided cleavage and polyadenylation of RNA in mammalian cells. This approach, which we named Postscriptr, can be utilized for the non-genomic manipulation of gene expression and may have potential future therapeutic applications for treating human RNA processing diseases.

molecular biology↗

bioRxiv2018

The transcriptional landscape of cortical interneurons underlies in-vivo brain function and schizophrenia risk

Inhibitory interneurons orchestrate information flow across cortex and are implicated in psychiatric illness. Although classes of interneurons have unique functional properties and spatial distributions throughout the brain, the relative influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped organizational properties of somatostatin and parvalbumin related transcripts within human and non-human primates. Interneuron spatial distributions recapitulate cortico-striato-thalamic functional networks and track regional differences in functional MRI signal amplitude. In the general population (n=9,627), parvalbumin-linked genes account for an enriched proportion of genome-wide heritable variance in in-vivo functional MRI signal amplitude. This relationship is spatially dependent, following the topographic organization of parvalbumin expression in independent post-mortem brain tissue. Finally, genetic risk for schizophrenia is enriched among interneuron-linked genes and predictive of cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular genetic basis of resting-state brain function across cortex is shaped by the spatial distribution of interneuron-related transcripts and underlies individual differences in risk for schizophrenia.\n\nKey FindingsO_LISpatial distributions of somatostatin (SST) and parvalbumin (PVALB) are negatively correlated in mature human and non-human primate cortex, paralleling patterns observed in utero.\nC_LIO_LISST and PVALB are differentially expressed within distinct limbic and somato/motor cortico-striato-thalamic networks, respectively.\nC_LIO_LIIn-vivo resting-state signal amplitude is heritable in the general population and tracks relative SST/PVALB expression across cortex.\nC_LIO_LISingle-nucleotide polymorphisms tied to PVALB-related genes account for an enriched proportion of the heritable variance in resting-state signal amplitude.\nC_LIO_LIPVALB-mediated heritability of resting-state signal amplitude in the general population is spatially heterogeneous, mirroring the cortical expression of PVALB in independent post-mortem brain tissue.\nC_LIO_LIPolygenic risk for schizophrenia is enriched among interneuron-linked genes and predicts resting-state signal amplitude in a manner that also follows the cortical expression of PVALB.\nC_LI

neuroscience↗