Biology subjects

Greene, A. S.

Publications and source records attributed to Greene, A. S..

14 recordsLinked to original sources

bioRxiv2025

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorders, schizophrenia, mood disorders, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorders. To capture cognitive controls multifaceted nature, we assessed two of its components--inhibition and shift-- using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study suggests flexible engagement of brain states may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sourcesThis work was supported by F31AA032179 and R01MH121095. Data were provided in part by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410) and the Transdiagnostic Connectome Project (TCP) dataset. Datasets were accessed through OpenNeuro (https://openneuro.org/datasets/ds005237 and https://openneuro.org/datasets/ds000030/versions/00016).

neuroscience↗

bioRxiv2025

Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth

Each individuals complex, multidimensional environment, known as their "exposome", plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby childrens exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137 - 8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth. HighlightsO_LIPatterns of functional connectivity reflect multivariate environmental exposures. C_LIO_LIConsistent exposome-related connectivity patterns were observed across brain states. C_LIO_LIThe identified general exposome network was associated with cognitive performance across domains. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/688261v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@b5cc3aorg.highwire.dtl.DTLVardef@172f954org.highwire.dtl.DTLVardef@14f0d01org.highwire.dtl.DTLVardef@deccb5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Global and genetic regulation of gene expression in human endothelial and vascular smooth muscle cells

BackgroundThe understanding of genetic and epigenetic regulation of gene expression in endothelial and vascular smooth muscle cells remains fragmented with limited experimental validation. MethodsChromatin conformation (Micro-C), DNA methylation (RRBS), chromatin accessibility (ATAC-seq), and transcriptome profiles (RNA-seq) were mapped in human induced pluripotent stem cell (hiPSC)-derived, isogenic endothelial and vascular smooth muscle cells (iECs and iVSMCs). CTCF and RAD21 were depleted to assess the functional relevance of chromatin architecture, and genome editing was used to evaluate the allelic effect of a blood pressure-associated single nucleotide polymorphism (SNP). ResultsSignificant correlations were identified between gene expression levels and chromatin interactions, chromatin accessibility, and DNA methylation in iECs and iVSMCs, with chromatin interactions showing the strongest association. Chromatin contact regions displayed distinct epigenetic landscapes depending on the types of regulatory element interactions involved. Perturbation of CTCF and RAD21 revealed their differential regulatory effects, particularly on the expression of genes overlapping chromatin contacts, with RAD21 exhibiting a broader regulatory impact. SNPs associated with several vascular traits were enriched in chromatin loops or accessible regions in iECs or iVSMCs. Precise genome editing demonstrated allele-dependent effects of SNP rs9833313 on the expression of SHOX2 located 247.4 kbp away but within the same chromatin loops as the SNP. ConclusionThis study provides an extensive epigenetic landscape of vascular cells that may drive novel research on the role of genetic and epigenetic mechanisms of vascular function and disease as demonstrated by our targeted experiments.

genomics↗

bioRxiv2024

A single-cell map of hypertension

Hypertension is a leading risk factor for disease burden and death worldwide. Several organ systems are involved in the development of hypertension, which contributes to stroke, heart disease, and kidney disease. Despite the broad health relevance, our understanding of the molecular landscape in hypertension is limited and lags other major diseases. Here we report an extensive analysis of the molecular landscape in hypertension and its end-organ damage and uncover novel mechanisms linking human genetic variants to the development of these diseases. We obtained single-nucleus RNA-seq (612,984 nuclei), single-nucleus ATAC-seq (179,637 nuclei), or spatial transcriptome data from five organs (hypothalamus, kidney, heart, 3rd order mesenteric artery, middle cerebral artery) in three mouse and rat models under twelve experimental conditions. More than one third of all hypertension research in animal models involves these three models. We identified both model-specific and convergent responses in cell types, genes, and pathways. By integrating our data with human genomic data, we partitioned the blood pressure and end-organ damage traits into cell type-specific transcriptional contributions and cell types common across multiple traits. Using genomic editing in animal models and human induced pluripotent stem cells, we extended key findings and identified new mechanisms linking human genetic variants to the development of hypertension and related renal injury. We anticipate that our rich data sets and findings will broadly drive forward the research of hypertension and hypertensive end-organ damage. Our approach of integrating multi-model and multi-tissue single-cell analysis with human genetic data and in vivo and in vitro genome editing can be applied to investigate other complex traits.

physiology↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗

bioRxiv2024

Chromatin interaction maps of human arterioles reveal new mechanisms for the genetic regulation of blood pressure

Arterioles are small blood vessels located just upstream of capillaries in nearly all tissues. The constriction and dilation of arterioles regulate tissue perfusion and are primary determinants of systemic blood pressure (BP). Abnormalities in arterioles are central to the development of major diseases such as hypertension, stroke, and microvascular complications of diabetes. Despite the broad and essential role of arterioles in physiology and disease, current knowledge of the functional genomics of arterioles is largely absent, partly because it is challenging to obtain and analyze human arteriole samples. Here, we report extensive maps of chromatin interactions, single-cell expression, and other molecular features in human arterioles and uncover new mechanisms linking human genetic variants to gene expression in vascular cells and the development of hypertension. Compared to large arteries, arterioles exhibited a higher proportion of pericytes which were strongly associated with BP traits. BP-associated single nucleotide polymorphisms (SNPs) were enriched in chromatin interaction regions in arterioles, particularly through enhancer SNP-promoter interactions, which were further linked to gene expression specificity across tissue components and cell types. Using genomic editing in animal models and human induced pluripotent stem cells, we discovered novel mechanisms linking BP-associated noncoding SNP rs1882961 to gene expression through long-range chromatin contacts and revealed remarkable effects of a 4-bp noncoding genomic segment on hypertension in vivo. We anticipate that our rich data and findings will advance the study of the numerous diseases involving arterioles. Moreover, our approach of integrating chromatin interaction mapping in trait-relevant tissues with SNP analysis and in vivo and in vitro genome editing can be applied broadly to bridge the critical gap between genetic discoveries and physiological understanding.

genomics↗

bioRxiv2024

Physiological role and mechanisms of action for a long noncoding haplotype region

Most common sequence variants associated with human traits are in noncoding regions of the genome, form haplotypes with other noncoding variants, and exhibit small effect sizes in the general population. Determining the physiological roles and mechanisms of action for these noncoding variants, particularly large haplotypes containing multiple variants, is both critical and challenging. To address this challenge, we developed an approach that integrates physiological studies in genetically engineered and phenotypically permissive animal models, precise editing of large haplotypes in human induced pluripotent stem cells (hiPSCs), and targeted chromatin conformation analysis. We applied this approach to examine the blood pressure associated rs1173771 locus, which includes a haplotype containing 11 single nucleotide polymorphisms (SNPs) spanning 17.4 kbp. Deleting the orthologous noncoding region in the genome of the Dahl salt-sensitive rat attenuated the salt-induced increase in systolic blood pressure by nearly 10 mmHg. This attenuation of hypertension appeared to be mediated by upregulation of the adjacent gene Npr3 (natriuretic peptide receptor 3) in arteries, enhancing vasodilation. The blood pressure-elevating and -lowering haplotypes were precisely reconstituted in hiPSCs using an efficient, two-step genome editing technique. The blood pressure-elevating haplotype decreased NPR3 expression in endothelial cells and vascular smooth muscle cells derived from the edited, isogenic hiPSCs. The influence of the haplotype was partially recapitulated by the sentinel SNP rs1173771. Additionally, the blood pressure-elevating haplotype showed significantly greater chromatin interactions with the NPR3 promoter region. This study illustrates the feasibility of ascertaining the physiological roles and mechanisms of action for large noncoding haplotypes. Our efficient, integrated, and targeted approach can be applied to investigate other noncoding variants.

genetics↗

bioRxiv2023

The effects of excess salt intake on the kidney metabolism in Sprague-Dawley rats

In the present study, novel methods were developed which allowed continuous (24/7) measurement of blood pressure (BP) and renal blood flow (RBF) in freely moving rats and the intermittent collection of arterial and renal venous blood to estimate kidney metabolic fluxes of O2 and metabolites. The study determined the effects of a high salt (HS) diet upon whole kidney O2 consumption and the metabolomic profiles of normal Sprague Dawley (SD) rats. A separate group of rats was studied to determine changes in the cortex (Cx) and outer medulla (OM) tissue metabolomic and mRNAseq profiles before and following the switch from a 0.4% to a 4.0% NaCl diet. Significant changes in the metabolomic and transcriptomic profiles occurred with feeding of the HS diet. A progressive increase of kidney O2 consumption was found despite a reduction in expression of most of the mRNA encoding enzymes of TCA cycle. Increased glycolysis was evident with the elevation of mRNA expression encoding key glycolytic enzymes and release of pyruvate and lactate from the kidney in the renal venous blood. Glycolytic production of NADH is used in either the production of lactate or oxidized via the malate aspartate shuttle. Aerobic glycolysis (e.g., Warburg-effect) may account for the needed increase in cellular energy. The study provides evidence that kidney metabolism responds to a HS diet enabling enhanced energy production while protecting from oxidate stress and injury.

physiology↗

bioRxiv2021

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole brain functional organization due to handedness have been relatively understudied beyond pre-specified networks of interest. Functional connectivity offers the ability to unravel differences in the functional organization of the whole brain. Here, we compared connectivity profiles of left- and right-handed individuals using data-driven parcellations of the whole brain. We explored differences in connectivity profiles of previously established regions of interest, and showed functional organization differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional organization of left- and right-handed individuals are not specific to regions of interest. In particular, we found that connections between and within-hemispheres and the cerebellum show distinct patterns of connectivity. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals.

neuroscience↗

bioRxiv2020

Behavioral and brain signatures of substance use vulnerability in childhood

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n=9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which--together with mechanistic perspectives--may inform strategies aimed at early identification of risk for addiction.

neuroscience↗

bioRxiv2019

How tasks change whole-brain functional organization to reveal brain-phenotype relationships

Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. Given this evidence that tasks change patterns of FC independent of activation to amplify brain-phenotype relationships, we develop and apply an inter-subject PPI analysis to further characterize these predictive FC changes. We find that task-induced consistency of FC patterns across individuals is useful for prediction--to a point; these results suggest that tasks improve FC-based prediction performance by de-noising the BOLD signal, revealing meaningful individual differences in brain functional organization. Together, these findings demonstrate that, when it comes to the effects of in-scanner tasks on the brain, focal activation is only the tip of the iceberg, and they offer a framework to best leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.

neuroscience↗

bioRxiv2019

Functional connectivity predicts changes in attention over minutes, days, and months

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention in single individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attention changes across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

neuroscience↗

bioRxiv2019

Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

ImportanceWe show that three common approaches to clinical deficits (cognitive phenotype, disease group, disease severity) each offer useful and perhaps complimentary explanations for the brains underlying functional architecture as affected by psychiatric disease.\n\nObjectiveTo understand how different clinical frameworks are represented in the brains functional connectome.\n\nDesignWe use an openly available dataset to create predictive models based on multiple connectomes built from task-based functional MRI data. We use these models to predict individual traits corresponding to multiple cognitive constructs across disease category. We also show that these same connectomes statistically differ depending on disease category and symptom burden.\n\nSettingThis was a population-based study with data collected in UCLA.\n\nParticipantsHealthy adults were recruited by community advertisements from the Los Angeles area. Participants with adult ADHD, bipolar disorder, and schizophrenia were recruited using a patient-oriented strategy involving outreach to local clinics and online portals (separate from the methods used to recruit healthy volunteers)

neuroscience↗

bioRxiv2018

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level, require such an atlas to assess connections between nodes and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional atlases reconfigure substantially and in a meaningful manner, according to brain state.

neuroscience↗