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109 results for “cortical development”
Data from: Cell type-specific dysregulation of gene expression due to Chd8 haploinsufficiency during mouse cortical development
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Data and Code: Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development
<h1><strong>Note</strong></h1> <p>Here we provide preprocessed data and analysis code used in "Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual preprocessed volumes, normalized into the MNI template, and averaged across five TRs for each block (see paper for details about the preprocessing pipeline).</p> <p>The analysis code requires Python version 3.8.8, Nilearn version 0.8.1, and Scikit-learn version 0.24.1.</p> <p>If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp. </p> <p> </p> <h1><strong>Usage</strong></h1> <pre>import PredysDecoding5ans_SearchLight_LOOCV as pdsl5 import PredysDecoding8ans_SearchLight_LOOCV as pdsl8 import PredysDecoding5to8_SearchLight as pdsl58 import PredysDecoding8to5_SearchLight as pdsl85 </pre> <h3>Within-format decoding for 5-year-olds (Figures 2A, B):</h3> <pre>pdsl5.IntraModalDec(TaskName='Dots') pdsl5.SaveNifti_PermTest(TaskName='Dots') pdsl5.IntraModalDec(TaskName='Digits') pdsl5.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding for 8-year-olds (Figure 2C, D):</h3> <pre>pdsl8.IntraModalDec(TaskName='Dots') pdsl8.SaveNifti_PermTest(TaskName='Dots') pdsl8.IntraModalDec(TaskName='Digits') pdsl8.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding, paired tests between 5- and 8-year-olds (Figures 3A, B):</h3> <pre>pdsl5.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl5.SaveNifti_Paired_PermTest(TaskName='Digits') pdsl8.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl8.SaveNifti_Paired_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding across 5- and 8-year-olds (Figures 3C, D):</h3> <pre>pdsl85.IntraModalDec(TaskName='Dots') pdsl85.SaveNifti_PermTest(TaskName='Dots') pdsl85.IntraModalDec(TaskName='Digits') pdsl85.SaveNifti_PermTest(TaskName='Digits') pdsl58.IntraModalDec(TaskName='Dots') pdsl58.SaveNifti_PermTest(TaskName='Dots') pdsl58.IntraModalDec(TaskName='Digits') pdsl58.SaveNifti_PermTest(TaskName='Digits') pdsl58.SaveNifti_PermTest_Conj(TaskName='Dots') pdsl58.SaveNifti_PermTest_Conj(TaskName='Digits')</pre> <h3>Between-format decoding for 5-year-olds (Figures 4A, 5B):</h3> <pre>pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl5.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl5.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Letters') pdsl5.SaveNifti_PermTest(TaskName='Dots2Letters') pdsl5.CrossModalDec(TaskName1='Letters', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Letters2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Letters', TaskName2='Letters2Dots')</pre> <h3>Between-format decoding for 8-year-olds (Figure 4A):</h3> <pre>pdsl8.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl8.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl8.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl8.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl8.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <h3>Between-format decoding, paired tests between 5- and 8-year-olds (Figure 4B)</h3> <pre>pdsl5.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl8.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <p> </p>
Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity
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Functional Connectivity Development along the Sensorimotor-Association Axis Enhances the Cortical Hierarchy
<p><span>Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total <em>n </em>= 3,355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (<em>n </em>= 1,207), Nathan Kline Institute-Rockland Sample (<em>n</em> = 397), Human Connectome Project: Development (<em>n</em> = 625), and Healthy Brain Network (<em>n</em> = 1,126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development. <strong><span> </span></strong><span> </span></span></p>
Development of a Cortical Visual Neuroprosthesis for the Blind
ClinicalTrials.gov study NCT02983370. IPD Sharing: NO. Countries: 1. Publications: 13.
Data from: Development of visual cortical function in infant macaques: a BOLD fMRI study
Functional brain development is not well understood. In the visual system, neurophysiological studies in nonhuman primates show quite mature neuronal properties near birth although visual function is itself quite immature and continues to develop over many months or years after birth. Our goal was to assess the relative development of two main visual processing streams, dorsal and ventral, using BOLD fMRI in an attempt to understand the global mechanisms that support the maturation of visual behavior. Seven infant macaque monkeys (_Macaca mulatta_) were repeatedly scanned, while anesthetized, over an age range of 102 to 1431 days. Large rotating checkerboard stimuli induced BOLD activation in visual cortices at early ages. Additionally we used static and dynamic Glass pattern stimuli to probe BOLD responses in primary visual cortex and two extrastriate areas: V4 and MT-V5. The resulting activations were analyzed with standard GLM and multivoxel pattern analysis (MVPA) approaches. We analyzed three contrasts: Glass pattern present/absent, static/dynamic Glass pattern presentation, and structured/random Glass pattern form. For both GLM and MVPA approaches, robust coherent BOLD activation appeared relatively late in comparison to the maturation of known neuronal properties and the development of behavioral sensitivity to Glass patterns. Robust differential activity to Glass pattern present/absent and dynamic/static stimulus presentation appeared first in V1, followed by V4 and MT-V5 at older ages; there was no reliable distinction between the two extrastriate areas. A similar pattern of results was obtained with the two analysis methods, although MVPA analysis showed reliable differential responses emerging at later ages than GLM. Although BOLD responses to large visual stimuli are detectable, our results with more refined stimuli indicate that global BOLD activity changes as behavioral performance matures. This reflects an hierarchical development of the visual pathways. Since fMRI BOLD reflects neural activity on a population level, our results indicate that, although individual neurons might be adult-like, a longer maturation process takes place on a population level.
Developing Criteria for Cortical Resections
ClinicalTrials.gov study NCT00152659. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cortical Metrics Assessment Outcome Measure Development in Autism With Memantine Treatment
ClinicalTrials.gov study NCT02353130. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Development of visual cortical function in infant macaques: a BOLD fMRI study
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MYT1L deficiency impairs excitatory neuron trajectory during cortical development
GEO Series GSE262368. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Single-nucleus transcriptomics atlas of middle and late prenatal human cortical development
GEO Series GSE217511. Homo sapiens. 36 samples. Type: Expression profiling by high throughput sequencing.
Consequences of ZNF292 deficiency for human cortical interneuron development
GEO Series GSE249940. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Sandwich cortical lamination and single-cell analysis decodes the developing spatial processing system
GEO Series GSE134482. Sus scrofa. 10 samples. Type: Expression profiling by high throughput sequencing.
Vascularized human cortical organoids (vOrganoid) model cortical development in vivo
GEO Series GSE131094. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Autism-linked Cullin3 germline haploinsufficiency severely impacts mouse brain development and cortical neurogenesis through RhoA signaling
GEO Series GSE144046. Mus musculus. 108 samples. Type: Expression profiling by high throughput sequencing.
Dysregulated cell states revealed by single-cell multiomics in mild malformations of cortical development with oligodendroglial hyperplasia in epilepsy
GEO Series GSE284073. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Sp9 regulates medial ganglionic eminence-derived cortical interneuron development
GEO Series GSE99049. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A quantitative framework to evaluate modeling of cortical development by neural stem cells
GEO Series GSE57595. Homo sapiens. 105 samples. Type: Expression profiling by array.
Single cell epigenomics reveals mechanisms of human cortical development
GEO Series GSE163018. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
MYT1L deficiency impairs excitatory neuron trajectory during cortical development [P21]
GEO Series GSE262366. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.