Mapping the Human Connectome: Structure, Function, and Heritability
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA384837 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Mapping the Human Connectome: Structure, Function, and Heritability
| PRJNA384838 | ENA
Project description:<p>The Human Connectome Project (HCP) has acquired vast amounts of data about the pattern of long-distance connections (wiring) in the brains of large numbers of healthy participants aged 22-35 using cutting-edge MRI and MEG neuroimaging, and extensive behavioral testing. Types of MR data collected included diffusion MRI (dMRI), resting-state functional MRI (r-fMRI), and structural MRI at both 3T and 7T, and task-evoked functional MRI (t-fMRI) at 3T. </p> <p>Following an extensive period of development and optimization of data acquisition and analysis methods, at Washington University in St. Louis we studied 1,206 participants comprising twins and their non-twin siblings. A subset of 184 twins was scanned again at the University of Minnesota using a 7T scanner. A different subset of 95 twins was studied at St. Louis University using combined resting state and/or task-activated MEG.</p> <p>We were able to collect genetic data on 1142 of our 1206 participants, including 149 pairs of genetically-confirmed monozygotic twins (298 participants) and 94 pairs of genetically-confirmed dizygotic twins (188 participants). Overall, there are 457 different families in the study, as determined by genetic analysis.</p> <p>Our rich set of imaging, behavioral and genetic data - available through the <a href="https://www.humanconnectome.org/data/">Human Connectome</a> database - will enable many types of analysis of brain circuitry, its relationship to behavior, and the contributions of genetic and environmental factors to brain circuits. </p>
| phs001364 | dbGaP
Project description:Understanding neuronal connectivity at single-cell resolution remains a fundamental challenge in neuroscience, with current methods constrained by an inherent trade-off between throughput and synaptic precision. Here we present Connectome-seq, a high-throughput method that combines engineered synaptic proteins, RNA barcoding, and single-compartment sequencing to map neuronal connectivity at single-synapse resolution. We validate this approach in the mouse pontocerebellar circuit, successfully identifying thousands of connections including the predominant excitatory projections from pontine neurons to cerebellar granule cells, while also revealing potentially novel synaptic partnerships. By enabling systematic mapping of neuronal connectivity across brain regions with single-cell precision, Connectome-seq provides a scalable platform for comprehensive circuit analysis across different experimental conditions and biological states. This advance in connectivity mapping methodology opens new possibilities for understanding circuit organization in complex mammalian brains.
2025-12-16 | GSE312903 | GEO
Project description:Enhancer connectome was studied in melanoma cell lines that express ALK ATI
2025-05-01 | GSE232300 | GEO
Project description:Transcriptomic RNA structure mapping unveils the structure dynamic induced by DHX36 binding (Structure-seq)
| PRJNA994087 | ENA
Project description:Enhancer connectome of melanoma cell lines
| PRJNA971577 | ENA
Project description:Transcriptomic RNA structure mapping unveils the structure dynamic induced by DHX36 binding
| PRJNA994085 | ENA
Project description:The connectome, a comprehensive map of the brain's anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes "context" is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
| S-EPMC8464439 | biostudies-literature
Project description:Transcriptomic RNA structure mapping unveils the structure dynamic induced by DHX36 binding (RBNS)
| PRJNA994088 | ENA
Project description:The long non-coding RNA (lncRNA) Xist is a master regulator of X-chromosome inactivation in mammalian cells. Models for how Xist and other lncRNAs function depend on thermodynamically stable secondary and higher-order structures that RNAs can form in the context of a cell. Probing accessible RNA bases can provide data to build models of RNA conformation that provide insight into RNA function, molecular evolution, and modularity. To study the structure of Xist in cells, we built upon recent advances in RNA secondary structure mapping and modeling to develop Targeted Structure-Seq, which combines chemical probing of RNA structure in cells with target-specific massively parallel sequencing. By enriching for signals from the RNA of interest, Targeted Structure-Seq achieves high coverage of the target RNA with relatively few sequencing reads, thus providing a targeted and scalable approach to analyze RNA conformation in cells. We use this approach to probe the full-length Xist lncRNA to develop new models for functional elements within Xist, including the repeat A element in the 5'-end of Xist. This analysis also identified new structural elements in Xist that are evolutionarily conserved, including a new element proximal to the C repeats that is important for Xist function. Examination of dimethylsufate reactivity of Xist lncRNA and 18S rRNA in cells using targeted reverse transcription to determine reactivity, and comparisons with untreated control samples.
2016-02-09 | E-GEOD-67667 | biostudies-arrayexpress