Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

•Télécharger en tant que PPTX, PDF•

2 j'aime•600 vues

Seminar given at the University of Illinois at Chicago Behavioral Neuroscience Seminar Series. The Default Network (DN) is a set of brain regions that are deactivated during the performance of externally triggered goal-drive tasks and active during spontaneous cognition. Activation of the DN during times when it should be off, has been hypothesized to be a symptom of several mental health disorders such as ADHD, depression, and anxiety. We describe the use of real-time fMRI to probe DN function in patient populations and children.

Sciences

Using RealTime fMRI Based Neurofeedback
To Probe Default Network Regulation
R. Cameron Craddock, PhD
Director of Imaging, Child Mind Institute
Research Scientist, Nathan Kline Institute
February 25, 2016

Default Network
Task based deactivation
Buckner et al. Ann. N.Y. Acad. Sci. 1124: 1-38 (2008).

Default Network Connectivity
Greicius et. al. 2007 Biol. Psychiatry

DN Dysregulation
Sheline et. al. 2009 PNAS

RT Neurofeedback of DMN
• Test hypothesis of DMN dysregulation in
depression, ADHD, aging, etc …

Exp. Design
Class Training
Labels
Training run
Time-Labeled
Scans
Image Recon and SVM
Classification
Image DataData Acquisition
Stimulus Presentation
Stimulus
Conventional FMRI
Test Data Classifier Output
Testing Run
Real-Time Tracking RSNs
LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044
Stephen LaConte August 19, 2009

Stimulus seen by volunteer
Updated fMRI
results Motion tracking and correction
Intensity (brightness) of a single voxel, changing
during stimulus conditions
Controller interface for display parameters

Modulating the DMN−2−1012
0 100 200 300 400
Best Subject
Worst Subject
TR
Z−scoreDMNActivity
−20246
0 100 200 300 400
TR
Z−scoreDMNActivity

Results
0.00.10.20.30.40.50.6
3 1 7 13 6 9 5 10 11 8 4 2 12
Subject
Accuracy
Feedback
No feedback
FB NOFB
0.10.20.30.40.50.6
1 2 1 2
Scan Number
Accuracy
p = 0.055p = 0.68
Accuracy was measured from Pearson’s correlation between task
paradigm and DMN activity extracted after post-processing.

Behavioral Correlates
Measures that were significantly associated with DN regulation include (p<0.05,
FDR corrected): the affect intensity measure (AIM), ruminative responses scale
(RRS), and the imaginal processes inventory.

Currently sharing > 10,000 datasets
• 6,422 individuals
– 4 – 90 years old
– 539 ASD
– 383 ADHD
– 72 Schizophrenia
– 29 Cocaine Dependent
– 6 Epilepsy
http://fcon_1000.projects.nitrc.org/

Sharing preprocessed data
• Make data available
to a wider audience
of researchers
• Evaluate
reproducibility of
analysis results
http://preprocessed-connectomes-project.github.io/

Software to enable a new scale of data
analysis
• Very large datasets
– Need to harness high-
performance computing to
expedite processing
• RS fMRI preprocessing is a moving
target
– Many new methods are
proposed all the time
– Need to compare outputs from
different processing strategies
• Many different toolsets have
different strengths
– Need to be able to combine
tools from different packages
http://fcp-indi.github.io/

Principles of Open Neuroscience
Data, tools and ideas should be openly shared
-The Neuro Bureau Manifesto
http://www.neurobureau.org

Acknowledgments
Child Mind Institute
Michael Milham, MD, PhD
Zarrar Shehzad
Nathan Kline Institute
Amalia McDonald
Stan Colcombe, PhD
Bennett Leventhal, MD
NYU – Child Study Center
Adriana DiMartino, MD
F. Xavier Castellanos, MD
VTCRI
Stephen LaConte, PhD
Pearl Chiu, PhD
Jonathan Lisinski, MS
Emory University
Helen Mayberg, MD
This work is funded by: A NARSAD Young
Investigator Award and NIMH R01MH101555

Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

Contenu connexe

Tendances

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in systems biology. Most methods for modeling and inferring the dynamics of GRNs, such as those based on state space models, vector autoregressive models and G1DBN algorithm, assume linear dependencies among genes. However, this strong assumption does not make for true representation of time-course relationships across the genes, which are inherently nonlinear. Nonlinear modeling methods such as the S-systems and causal structure identification (CSI) have been proposed, but are known to be statistically inefficient and analytically intractable in high dimensions. To overcome these limitations, we propose an optimized ensemble approach based on support vector regression (SVR) and dynamic Bayesian networks (DBNs). The method called SVR-DBN, uses nonlinear kernels of the SVR to infer the temporal relationships among genes within the DBN framework. The two-stage ensemble is further improved by SVR parameter optimization using Particle Swarm Optimization. Results on eight insilico-generated datasets, and two real world datasets of Drosophila Melanogaster and Escherichia Coli, show that our method outperformed the G1DBN algorithm by a total average accuracy of 12%. We further applied our method to model the time-course relationships of ovarian carcinoma. From our results, four hub genes were discovered. Stratified analysis further showed that the expression levels Prostrate differentiation factor and BTG family member 2 genes, were significantly increased by the cisplatin and oxaliplatin platinum drugs; while expression levels of Polo-like kinase and Cyclin B1 genes, were both decreased by the platinum drugs. These hub genes might be potential biomarkers for ovarian carcinoma.

Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...

Arinze Akutekwe

Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling. Abstract Summary: Learning About the Brain and Brain-Inspired Learning: Quantifying mental states and identifying statistical biomarkers of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning, with the particular focus on interpretability and reproducibility of learned models. We will discuss promises and limitations of machine-learning methods in such applications, focusing on recent applications of deep learning methods such as recurrent convnets to the analysis of “brain movies” (EEG) data. On the other hand, besides the above “AI to Brain” direction, we will also discuss the “Brain to AI”, namely, borrowing ideas from neuroscience to improve machine learning, with specific focus on adult neurogenesis and online model adaptation in representation learning.

Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

MLconf

Artificial neural networks (ANN) consider classification as one of the most dynamic research and application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by back propagation algorithm. The different combinations of functions and its effect while using ANN as a classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification with suitable combination of training, learning and transfer functions. When the maximum likelihood method was compared with backpropagation neural network method, the BPNN was more accurate than maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible. Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to be more effective than other classification algorithms.

Review on classification based on artificial

ijasa

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%

A Modified CNN-Based Face Recognition System

gerogepatton

We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behavior by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Process (NAP), which is an attention generator that can update its behavior by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost.

Cost-effective Interactive Attention Learning with Neural Attention Process

MLAI2

An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.

Pattern Recognition using Artificial Neural Network

Editor IJCATR

Neural Networks in Data Mining - “An Overview”

Dr.(Mrs).Gethsiyal Augasta

[Chung il kim] 0829 thesis

Chung-Il Kim

Neural network image recognition

Oleksii Sekundant

IRJET- Prediction of Autism Spectrum Disorder using Deep Learning: A Survey

IRJET Journal

NeuroPG - Open source software for optical pattern generation and data acquis...

Daniel Murphy

98462-234458-1-PB

Dinesh Kannan

DLD_WeightSharing_Slide

Kang-Ho Lee

Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.

Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...

Dr.NAGARAJAN. S

This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.

Comparison of Image Segmentation Algorithms for Brain Tumor Detection

IJMTST Journal

IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and Thresholding

IRJET Journal

Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures without searching for the optimal parameters. While some NAS methods handle this issue by utilizing a supernet trained on a large-scale dataset such as ImageNet, they may be suboptimal if the target tasks are highly dissimilar from the dataset the supernet is trained on. To address such limitations, we introduce a novel problem of Neural Network Search (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e.g. number of parameters), from a model zoo. Then, we propose a novel framework to tackle the problem, namely Task-Adaptive Neural Network Search (TANS). Given a model-zoo that consists of network pretrained on diverse datasets, we use a novel amortized meta-learning framework to learn a cross-modal latent space with contrastive loss, to maximize the similarity between a dataset and a high-performing network on it, and minimize the similarity between irrelevant dataset-network pairs. We validate the effectiveness and efficiency of our method on ten real-world datasets, against existing NAS/AutoML baselines. The results show that our method instantly retrieves networks that outperform models obtained with the baselines with significantly fewer training steps to reach the target performance, thus minimizing the total cost of obtaining a task-optimal network. Our code and the model-zoo are available at https://anonymous.4open.science/r/TANS-33D6

Task Adaptive Neural Network Search with Meta-Contrastive Learning

MLAI2

Artificial neural network

GauravPandey319

How to create your own artificial neural networks

Agrata Shukla

Ijeee 16-19-a novel approach to brain tumor classification using wavelet and ...

Kumar Goud

Tendances (20)

Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...

Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

Review on classification based on artificial

A Modified CNN-Based Face Recognition System

Cost-effective Interactive Attention Learning with Neural Attention Process

Pattern Recognition using Artificial Neural Network

Neural Networks in Data Mining - “An Overview”

[Chung il kim] 0829 thesis

Neural network image recognition

IRJET- Prediction of Autism Spectrum Disorder using Deep Learning: A Survey

NeuroPG - Open source software for optical pattern generation and data acquis...

98462-234458-1-PB

DLD_WeightSharing_Slide

Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...

Comparison of Image Segmentation Algorithms for Brain Tumor Detection

IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and Thresholding

Task Adaptive Neural Network Search with Meta-Contrastive Learning

Artificial neural network

How to create your own artificial neural networks

Ijeee 16-19-a novel approach to brain tumor classification using wavelet and ...

En vedette

David rogers ingles_bloque_5_y_6

UPN Universidad Privada del Norte

La chimie des couleurs de l'automne

Ungava Louise

Rehasoft es una empresa a la vanguardia de las Nuevas Tecnologías Educativas, desarrollando productos que mejoran la lectura, la escritura, el aprendizaje y la comunicación. El equipo Rehasoft con más de 15 años de experiencia se dedica a soluciones para personas con Dislexia, TDAH, Discalculia, Baja Visión y 3ªEdad, obteniendo muy buenos resultados en el rendimiento académico escolar. Ofrecemos productos que mejoran la calidad de vida de personas con Necesidades Educativas Especiales.

Rehasoft MeMotiva Flex

Rehasoft Tdah

Wouter Blok at Emerce Connect

Wouter Blok

Irlanda. diario de viaje

Laura Marín

Io consumatore progetto io sono io vivo

ocmmedia

L'abbandono di rifiuti e il littering

Giovanni Poletti

http://es.calameo.com/books/00021239746421e2fa60b ¿Porquélasmarcasrecurren aShowroomprive. espara vendersusproductos?” Número-01 Marzo-2012 the-eMagazine. com PhilippeLimes, Directordenegociointernacional StefanoZanetti, ResponsabledeMarketingenEspaña magazineE-Commerce | Online Marketing | Hosting & Cloud | Social Med the-eMagazine. com -PORTADA OK_Maquetación 1 06/03/12 19:20 Página 1 Archivo para uso personal. Descargar en link original.

E Magazine Num 01 Marzo 2012

Sinergia Labs

Metro count presentation

MetroCount

ReName | Servicios 2016

Jorge Harly MBA Innovation

Trivadis TechEvent 2016 Polybase challenges Hive relational access to non-rel...

Trivadis

CA Sanket Kalyani_CV

SANKET KALYANI

Origen del apellido chicote

bchicote

The Default Mode Network

Evelyn McKelvie

RICS Property Auction Guide

RICS

Datos agrupados

Juan Karlos Pc

Mercer Clin Perinatol 2004, Rpm Diagnosis And Management

Eliana Cordero

Gerardo moran presentacion richard juma

Richard Santiago Juma

Le law destiny calls (2016 gg to african markets)

Afolabi Elebiju

(24) grupo verde

Danicitafer

En vedette (20)

David rogers ingles_bloque_5_y_6

La chimie des couleurs de l'automne

Rehasoft MeMotiva Flex

Wouter Blok at Emerce Connect

Irlanda. diario de viaje

Io consumatore progetto io sono io vivo

L'abbandono di rifiuti e il littering

E Magazine Num 01 Marzo 2012

Metro count presentation

ReName | Servicios 2016

Trivadis TechEvent 2016 Polybase challenges Hive relational access to non-rel...

CA Sanket Kalyani_CV

Origen del apellido chicote

The Default Mode Network

RICS Property Auction Guide

Datos agrupados

Mercer Clin Perinatol 2004, Rpm Diagnosis And Management

Gerardo moran presentacion richard juma

Le law destiny calls (2016 gg to african markets)

(24) grupo verde

Similaire à Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

AnLSTMbasedDeeplearningmodelforvoice-baseddetectionofParkinsonsdisease.pdf

Danish Raza Rizvi

Pioneers advancing health research, prevention and treatment will help us understand emerging best practices where targeted assessments, monitoring and interventions can transfer into significant healthcare and quality of life outcomes. -- Chair: Alvaro Fernandez, CEO & Co-Founder of SharpBrains -- Dr. Madeleine S Goodkind, staff psychologist at New Mexico VA Health Care System -- Dr. Randy McIntosh, Vice-president of Research and Director of Baycrest’s Rotman Research Institute -- Chris Berka, CEO and Co-Founder of Advanced Brain Monitoring (ABM) Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda

How to measure and improve brain-based outcomes that matter in health care

SharpBrains

Open repositories for neuroimaging research

Cameron Craddock

Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.

Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15

MLconf

The general cell quantification and identification have technical limitations concerning the fast and accurate detection of complex morphological cells, especially for overlapping cells, irregular cell shapes, bad focal planes, among other factors. We use the deep convolutional neural networks (DCNN) to classify the annotated images of five types of white blood cells. The accuracy and performance of the proposed framework are evaluated for the blood cell classifications. The results demonstrate that the DCNN model performs close to the accuracy of 80% and provides an accurate and fast method for hematological laboratories.

Automated Analysis of Microscopy Images using Deep Convolutional Neural Network

AdetayoOkunoye

While we have seen a tremendous growth in machine learning methods over the last two decades there is still no one fits all solution. The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as Deep Learning (DL). There has been increasing use of DL recently which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts. It was therefore our goal to develop a DL framework embedded into a general research data management platform (Open Science Data Repository) which can be used as an API, standalone tool or integrated in new software as an autonomous module. In this poster we will present results of comparing performance of classic machine learning methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) with Deep Learning and will discuss challenges associated with Ddeep Learning Neural Networks (DNN). The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/) and Tensorflow (www.tensorflow.org) and applied to various use cases connected to prediction of physicochemical properties, ADME, toxicity and calculating properties of materials. It was also shown that using nVidia GPUs significantly accelerates calculations, although memory consumption puts some limits on performance and applicability of standard toolkits 'as is'.

Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictions

Valery Tkachenko

Analogy, Causality, and Discovery in Science: The engines of human thought

CITE

Spatial and Temporal Features of Noise in fMRI

Vanessa S

CSU_comp

Adrian Smith

Prediction of Neurological Disorder using Classification Approach

BRNSSPublicationHubI

Access to primary data and the provenance of derived data are increasingly recognized as an essential aspect of reproducibility in biomedical research. While productive data sharing has become the norm in some biomedical communities, human brain imaging has lagged in open data and descriptions of provenance. The overarching goal of my dissertation was to identify barriers to neuroimaging data sharing and to develop a fundamentally new, granular data exchange standard that incorporates provenance as a primitive to document cognitive neuroimaging workflow. For my dissertation research, I led the development of the Neuroimaging Data Model (NIDM), an extension to the W3C PROV standard for the domain of human brain imaging. NIDM provides a language to communicate provenance by representing primary data, computational workflow, and derived data as bundles of linked Agents, Activities, and Entities. Similar to the way a sentence conveys a standalone thought, a bundle contains provenance statements that parsimoniously express the way a given piece of data was produced. To demonstrate a system that implements NIDM, I developed a modern, semantic Web application platform that provides neuroimaging workflow as a service and captures provenance statements as NIDM bundles. The course of this work necessitated interaction with an international community, which adopted and extended central elements of this work into prevailing brain imaging software. My dissertation contributes neuroinformatics standards to advance the current state of computational infrastructure available to the cognitive neuroimaging community.

Reproducibility in human cognitive neuroimaging: a community-driven data sha...

Nolan Nichols

Ferree Resume

Thomas Ferree

Share and Reuse: how data sharing can take your research to the next level

Krzysztof Gorgolewski

The Pacific Research Platform (PRP) aims to achieve transparent and rapid data access among collaborating scientists at multiple institutions through an integrated implementation of data-focused networking that extends the university campus Science DMZ model to a regional, national, and, eventually, a global scale. PRP researchers are routinely achieving high-performance end-to-end networking from their labs to their collaborators’ labs and data centers, traversing multiple, heterogeneous Science DMZs and wide-area networks connecting multiple campus gateways, enabling researchers across the partnership to transfer data over dedicated optical lightpaths at speeds from 10Gb/s to 100Gb/s.

NERSC, AI and the Superfacility, Debbie Bard

PacificResearchPlatform

An Evolutionary-based Neural Network for Distinguishing between Genuine and P...

Md Rakibul Hasan

Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.

Deep learning methods applied to physicochemical and toxicological endpoints

Valery Tkachenko

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.

Top Cited Articles in Data Mining - International Journal of Data Mining & Kn...

IJDKP

Machine Learning in Healthcare Diagnostics

Larry Smarr

Oxford_15-03-22.pptx

SaigeRutherford

International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.

C0344023028

inventionjournals

Similaire à Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation (20)

AnLSTMbasedDeeplearningmodelforvoice-baseddetectionofParkinsonsdisease.pdf

How to measure and improve brain-based outcomes that matter in health care

Open repositories for neuroimaging research

Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15

Automated Analysis of Microscopy Images using Deep Convolutional Neural Network

Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictions

Analogy, Causality, and Discovery in Science: The engines of human thought

Spatial and Temporal Features of Noise in fMRI

CSU_comp

Prediction of Neurological Disorder using Classification Approach

Reproducibility in human cognitive neuroimaging: a community-driven data sha...

Ferree Resume

Share and Reuse: how data sharing can take your research to the next level

NERSC, AI and the Superfacility, Debbie Bard

An Evolutionary-based Neural Network for Distinguishing between Genuine and P...

Deep learning methods applied to physicochemical and toxicological endpoints

Top Cited Articles in Data Mining - International Journal of Data Mining & Kn...

Machine Learning in Healthcare Diagnostics

Oxford_15-03-22.pptx

C0344023028

Dernier

Module for Grade 9 for Asynchronous/Distance learning

levieagacer

Zoology 5th semester notes( Sumit_yadav).pdf

Sumit Kumar yadav

Forensic Biology & Its biological significance.pdf

rohankumarsinghrore1

Conjugation, transduction and transformation

Areesha Ahmad

Just Call Vip call girls Srinagar Jammu Kashmir Escorts ☎️ 8617697112 Starting From 5K to 15K High Profile Escorts In Srinagar Jammu Kashmir ❤Personal Whatsapp Number Jammu Kashmir Call Girls 8617697112 💦✅. There are a number of Srinagar Jammu Kashmir Escorts willing to meet you at an affordable rate, which also possesses high moral standards and humanitarian tendencies. These girls can help satisfy the sexual desires of clients without fail; it is therefore essential that clients select an established service. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call

❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.

Nitya salvi

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

Alex Henderson

Clean In Place(CIP).pptx .

Poonam Aher Patil

GBSN - Biochemistry (Unit 1)

Areesha Ahmad

Justdial Call Girls In Indirapuram, Ghaziabad, 8800357707 Escorts Service Best High Class call girls Service Escorts Service in Home Hotel in Delhi NCR 24 Hours Available Service. We provide Super Class Hot and Sexy Indian Female Escorts Service, A to Z Body and Mind Satisfaction by Top Class Female Models in Delhi Gurgaon Noida NCR in Hotel 24hrs In Call and Out Call Service At Lowest Price. We Have Indian Punjabi Kashmiri Northeast Every Type Sexy Bold Beautiful Young Soft Cute Charming Female Escorts Available. Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24hrs in Delhi NCR

Justdial Call Girls In Indirapuram, Ghaziabad, 8800357707 Escorts Service

monikaservice1

Grade 7 - Lesson 1 - Microscope and Its Functions

OrtegaSyrineMay

PSYCHOSOCIAL NEEDS. in nursing II sem pptx

Suji236384

Call Girls In Safdarung Enclave Arjun Nagar Whatsapp +91 9654467111 Delhi ⛟ Open 24 Hrs, ☎ Booking Short 2000 Night 6000 ALL HOME/HOTEL SERVICE DOORSTEP SERVICE IN/CALL & OUT/CALL SERVICE WITH MANY OPTIONS AVAILABLE DELHI GURGAON & NOIDA SERVICE IN REASONABLE RATES FROM LOW TO HIGH PROFILE STAFF’S. Call Girl Number~24X7~Call Girl Services, New Delhi, Delhi OutCall Rate Call Girl Mahipalpur,Call Girl Connaught Place,Call Girl Nehru Place,Call Girl Chanakyapuri,Call Girl Paharganj,Call Girl Dhaula Kuan,Call Girl Moti Bagh,Call Girl Karol Bagh,Call Girl Greater Kailash,Call Girl Naraina, Call Girl Katwaria Sarai,Call Girl Janakpuri,Call Girl Kalkaji,Call Girl Lajpat Nagar,Call Girl Palam,Call Girl Malviya Nagar,Call Girl Mehrauli,Call Girl Govindpuri,Call Girl Sarojini Nagar ,Call Girl Neb Sarai,Call Girl South Ex,Call Girl Munirka,Call Girl Saket,Call Girl Chattarpur

9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000

Sapana Sha

www.whatsapp.com+917728919243 HOT & SEXY MODELS // COLLEGE GIRLS AVAILABLE FOR COMPLETE ENJOYMENT WITH HIGH PROFILE INDIAN MODEL AVAILABLE HOTEL & HOME ★ SAFE AND SECURE HIGH CLASS SERVICE AFFORDABLE RATE SATISFACTION,UNLIMITED ENJOYMENT. ★ All Meetings are confidential and no information is provided to any one at any cost. ★ EXCLUSIVE PROFILes Are Safe and Consensual with Most Limits Respected ★ Service Available In: - HOME *Star Hotel Service .In Call & Out call SeRvIcEs : ★ A-Level ★ Strip-tease ★ BBBJ (Bareback Blowjob)Receive advanced sexual techniques in different mode make their life more pleasurable. ★ Spending time in hotel rooms ★ BJ (Blowjob Without a Condom) ★ Completion (Oral to completion) ★ Covered (Covered blowjob Without a Condom)

Call Girls Ahmedabad +917728919243 call me Independent Escort Service

shivanisharma5244

Site Acceptance Test .

Poonam Aher Patil

GBSN - Microbiology (Unit 1)

Areesha Ahmad

Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Sérgio Sacani

GBSN - Microbiology (Unit 3)

Areesha Ahmad

High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...

chandars293

Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....

muralinath2

module for grade 9 for distance learning

levieagacer

Dernier (20)

Module for Grade 9 for Asynchronous/Distance learning

Zoology 5th semester notes( Sumit_yadav).pdf

Forensic Biology & Its biological significance.pdf

Conjugation, transduction and transformation

❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

Clean In Place(CIP).pptx .

GBSN - Biochemistry (Unit 1)

Justdial Call Girls In Indirapuram, Ghaziabad, 8800357707 Escorts Service

Grade 7 - Lesson 1 - Microscope and Its Functions

PSYCHOSOCIAL NEEDS. in nursing II sem pptx

9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000

Call Girls Ahmedabad +917728919243 call me Independent Escort Service

Site Acceptance Test .

GBSN - Microbiology (Unit 1)

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

GBSN - Microbiology (Unit 3)

High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...

Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....

module for grade 9 for distance learning

Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

2. Using RealTime fMRI Based Neurofeedback To Probe Default Network Regulation R. Cameron Craddock, PhD Director of Imaging, Child Mind Institute Research Scientist, Nathan Kline Institute February 25, 2016

3. Default Network Task based deactivation Buckner et al. Ann. N.Y. Acad. Sci. 1124: 1-38 (2008).

4. Default Network Connectivity Greicius et. al. 2007 Biol. Psychiatry

5. DN Dysregulation Sheline et. al. 2009 PNAS

6. ICN Competition Fox MD PNAS 2005

7. RT Neurofeedback of DMN • Test hypothesis of DMN dysregulation in depression, ADHD, aging, etc …

8. Exp. Design Class Training Labels Training run Time-Labeled Scans Image Recon and SVM Classification Image DataData Acquisition Stimulus Presentation Stimulus Conventional FMRI Test Data Classifier Output Testing Run Real-Time Tracking RSNs LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044 Stephen LaConte August 19, 2009

9. Stimulus seen by volunteer Updated fMRI results Motion tracking and correction Intensity (brightness) of a single voxel, changing during stimulus conditions Controller interface for display parameters

10. Decoding DN Activity

11. DMN Modulation Task

12. Modulating the DMN−2−1012 0 100 200 300 400 Best Subject Worst Subject TR Z−scoreDMNActivity −20246 0 100 200 300 400 TR Z−scoreDMNActivity

13. Results 0.00.10.20.30.40.50.6 3 1 7 13 6 9 5 10 11 8 4 2 12 Subject Accuracy Feedback No feedback FB NOFB 0.10.20.30.40.50.6 1 2 1 2 Scan Number Accuracy p = 0.055p = 0.68 Accuracy was measured from Pearson’s correlation between task paradigm and DMN activity extracted after post-processing.

14. Behavioral Correlates Measures that were significantly associated with DN regulation include (p<0.05, FDR corrected): the affect intensity measure (AIM), ruminative responses scale (RRS), and the imaginal processes inventory.

15. RT fMRI Neurofeedback for Children

16. All data is being prospectively shared

17. Currently sharing > 10,000 datasets • 6,422 individuals – 4 – 90 years old – 539 ASD – 383 ADHD – 72 Schizophrenia – 29 Cocaine Dependent – 6 Epilepsy http://fcon_1000.projects.nitrc.org/

18. Sharing preprocessed data • Make data available to a wider audience of researchers • Evaluate reproducibility of analysis results http://preprocessed-connectomes-project.github.io/

19. Software to enable a new scale of data analysis • Very large datasets – Need to harness high- performance computing to expedite processing • RS fMRI preprocessing is a moving target – Many new methods are proposed all the time – Need to compare outputs from different processing strategies • Many different toolsets have different strengths – Need to be able to combine tools from different packages http://fcp-indi.github.io/

20. Principles of Open Neuroscience Data, tools and ideas should be openly shared -The Neuro Bureau Manifesto http://www.neurobureau.org

21.

22. Acknowledgments Child Mind Institute Michael Milham, MD, PhD Zarrar Shehzad Nathan Kline Institute Amalia McDonald Stan Colcombe, PhD Bennett Leventhal, MD NYU – Child Study Center Adriana DiMartino, MD F. Xavier Castellanos, MD VTCRI Stephen LaConte, PhD Pearl Chiu, PhD Jonathan Lisinski, MS Emory University Helen Mayberg, MD This work is funded by: A NARSAD Young Investigator Award and NIMH R01MH101555

Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

Similaire à Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation (20)

Dernier

Dernier (20)

Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation