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333 results for “functional network”
Personalized Large-scale Functional Networks in the Adolescent Brain Cognitive Development (ABCD) Children
<ul><li>Data for our paper "<strong>Personalized Large-scale Functional Networks in ABCD Children: Linking Functional Network Topography with Socioeconomic Status</strong>".</li><li>This dataset contains personalized functional networks for <strong>3,921</strong> participants (age 9- and 10-year-olds) who had more than 20 min of high-quality (mean motion < 0.2mm) resting-state fMRI data from the Adolescent Brain Cognitive Development (<strong>ABCD</strong>) Study.</li><li>Using regularized non-negative matrix factorization (<strong>NMF</strong>) and preprocessed resting-state fMRI data from the ABCD-BIDS Community Collection (<strong>https://github.com/ABCD-STUDY/nda-abcd-collection-3165</strong>, NDA Collection 3165), we parcellated the cortex (<strong>fsLR_32k space</strong>) into 17 functional networks for each ABCD participant.</li><li>We provided two kinds of atlas data: discrete network parcellation (<strong>IndivAtlasLabel</strong>) and probabilistic network parcellation (<strong>IndivAtlasLoading</strong>). For discrete network parcellation, each value in the dscalar indicates which network this vertex belongs to; for probabilistic network parcellation, each value indicates the probability that this vertex belongs to each network (<strong>17 in total</strong>). We also provided group atlas label and atlas loading for comparison.</li><li>For dscalars contain individual atlas loading, we separated participants into 10 parts, each has nearly 400 individuals for easily uploading and downloading.</li><li>Each value corresponds with the following key for discrete network parcellation: 1=FP1, 2=AU, 3=VS1, 4=DM1, 5=DA, 6=DM2, 7=DA2, 8=DM3, 9=VS2, 10=SM1, 11=TMP, 12=LB 13=VA1, 14=FP2, 15=SM2, 16=SM3, 17=DA3 (VS: visual; AU: auditory; SM: somatomotor; VA: ventral attention; DA: dorsal attention; FP: fronto-parietal; DM: default mode; TM: temporo-parietal; LB: limbic).</li><li>For each participant, we provide his or her subject-key in the ABCD Study. Other related information can be found at <strong>https://wiki.abcdstudy.org</strong>.</li><li>Relevant analysis scripts can be found in <strong>https://github.com/CuiLabCIBR/SingleFuncParcel_ABCD</strong>.</li></ul>
Identification of Potential Functional Modules and Diagnostic Genes for Crohn's Disease Based on Weighted Gene Co-expression Network Analysis and LASSO Algorithm
<p>Table S1 651 DEGs between CD and control samples</p> <p>Table S2 381 ME turquoise module genes</p> <p>Table S3 Coefficients of the eight module genes analyzed by LASSO regression</p>
Figure 8a from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 8a Budget Breakdown per WP, and Budget Breakdown per country. - Budget Breakdown per WP.
Figure 8b from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 8b Budget Breakdown per WP, and Budget Breakdown per country. - Budget Breakdown per country
Figure 7 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 7 DRYvER geographical coverage.
Figure 6 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 6 DRYvER partners' complementarity.
Figure 5 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 5 DRYvER management structure.
Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning
<p>Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.</p>
Functional Connectivity of the Interoceptive Network in RLS
ClinicalTrials.gov study NCT07001891. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of Cognitive Intervention in Alzheimer's Disease (AD) on Functional Cortical Networks in fMRI
ClinicalTrials.gov study NCT01329601. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Intensive Cognitive Rehabilitation on Cognitive, Motor, and Language Functional Networks in Subacute Stroke Patient
ClinicalTrials.gov study NCT05254964. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cognitive Function and EEG Brain Network Remodeling Among Users of Hearing Aids With ARHL
ClinicalTrials.gov study NCT06893432. IPD Sharing: NO. Countries: 1. Publications: 0.
Brain Neuronal Networks, Chemosensory and Trigeminal Functions in Allo-HSCT
ClinicalTrials.gov study NCT06003660. IPD Sharing: Not stated. Countries: 1. Publications: 0.
7T Anatomo-functional Characterization of Structures in the Action Observation Network
ClinicalTrials.gov study NCT06826287. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study
ClinicalTrials.gov study NCT06751498. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Intranasal Oxytocin on Functional Brain Network in Resting-state and Tasks
ClinicalTrials.gov study NCT03428906. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effects of Mild Sedation on Motor Function Networks in Patients With Brian Gliomas
ClinicalTrials.gov study NCT03984240. IPD Sharing: NO. Countries: 1. Publications: 0.
Structural and Functional Networks in ALS: An Insight Into Pseudobulbar Affect
ClinicalTrials.gov study NCT06396260. IPD Sharing: NO. Countries: 1. Publications: 0.
Functional Brain Network Changes in Patients Undergoing Deep Brain Stimulation for Essential Tremor
ClinicalTrials.gov study NCT06293638. IPD Sharing: NO. Countries: 1. Publications: 0.
Brain Networks and Mobility Function: B-NET
ClinicalTrials.gov study NCT03430427. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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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.