scholarly journals Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI

2020 ◽  
Author(s):  
WH Thompson ◽  
R Nair ◽  
H Oya ◽  
O Esteban ◽  
JM Shine ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Effective Connectivity ◽  
Block Design ◽  
Imaging Data ◽  
Whole Brain ◽  
Multiple Contacts ◽  
Data Collection Process ◽  
Resource Data ◽  
Brain Imaging Data ◽  
Intracranial Electrodes

AbstractMapping the causal effects of one brain region on another (effective connectivity) is a challenging problem in neuroscience, since it requires invasive direct manipulation of brain function, together with whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals Whole-Brain Reconstruction of Neurons in the Ventral Pallidum Reveals Diverse Projection Patterns

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiru Feng ◽  
Sile An ◽  
Ruiyu Wang ◽  
Rui Lin ◽  
Anan Li ◽  
...  
Keyword(s):  
Single Neuron ◽  
Neural Circuit ◽  
Imaging System ◽  
Ventral Pallidum ◽  
Imaging Data ◽  
Subcortical Structures ◽  
Optical Sectioning ◽  
Whole Brain ◽  
Brain Imaging Data ◽  
Axonal Density

The ventral pallidum (VP) integrates reward signals to regulate cognitive, emotional, and motor processes associated with motivational salience. Previous studies have revealed that the VP projects axons to many cortical and subcortical structures. However, descriptions of the neuronal morphologies and projection patterns of the VP neurons at the single neuron level are lacking, thus hindering the understanding of the wiring diagram of the VP. In this study, we used recently developed progress in robust sparse labeling and fluorescence micro-optical sectioning tomography imaging system (fMOST) to label mediodorsal thalamus-projecting neurons in the VP and obtain high-resolution whole-brain imaging data. Based on these data, we reconstructed VP neurons and classified them into three types according to their fiber projection patterns. We systematically compared the axonal density in various downstream centers and analyzed the soma distribution and dendritic morphologies of the various subtypes at the single neuron level. Our study thus provides a detailed characterization of the morphological features of VP neurons, laying a foundation for exploring the neural circuit organization underlying the important behavioral functions of VP.

scholarly journals Atypical development of subcortico-cortical effective connectivity in autism

2021 ◽  
Author(s):  
Luigi Lorenzini ◽  
Guido van Wingen ◽  
Leonardo Cerliani
Keyword(s):  
Cognitive Processing ◽  
Sensory Processing ◽  
Data Exchange ◽  
Effective Connectivity ◽  
Sensory Information ◽  
Autism Spectrum ◽  
Cortical Processing ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
Atypical Development

Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from delays in the neurotypical development of the functional segregation between cortical and subcortical brain processes, as suggested by previous findings of overconnectivity between primary sensory regions and deep brain nuclei in ASD. To test this hypothesis, we used dynamic causal modelling to quantify the effect of age on the development of (1) cortical functional segregation from subcortical activity and (2) directional influence of subcortical activity on cortical processing in 166 participants with ASD and 193 typically developing controls (TD) from the Autism Brain Imaging Data Exchange (ABIDE). We found that in TD participants age was significantly associated with increased functional segregation of cortical sensory processing from subcortical activity, paralleled by a decreased influence of subcortical activity on cortical processing. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory information relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals The Open Brain Consent : Informing research participants and obtaining consent to share brain imaging data

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Local and Distant responses to single pulse electrical stimulation reflect different forms of connectivity

2020 ◽  
Author(s):  
Britni Crocker ◽  
Lauren Ostrowski ◽  
Ziv M. Williams ◽  
Darin D. Dougherty ◽  
Emad N. Eskandar ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Resting State ◽  
Diffusion Tensor ◽  
Effective Connectivity ◽  
Clinical Care ◽  
Structural Connectivity ◽  
Intractable Epilepsy ◽  
Single Pulse ◽  
Intracranial Electrode ◽  
The Brain

AbstractBackgroundMeasuring connectivity in the human brain can involve innumerable approaches using both noninvasive (fMRI, EEG) and invasive (intracranial EEG or iEEG) recording modalities, including the use of external probing stimuli, such as direct electrical stimulation.Objective/HypothesisTo examine how different measures of connectivity correlate with one another, we compared ‘passive’ measures of connectivity during resting state conditions map to the more ‘active’ probing measures of connectivity with single pulse electrical stimulation (SPES).MethodsWe measured the network engagement and spread of the cortico-cortico evoked potential (CCEP) induced by SPES at 53 total sites across the brain, including cortical and subcortical regions, in patients with intractable epilepsy (N=11) who were undergoing intracranial recordings as a part of their clinical care for identifying seizure onset zones. We compared the CCEP network to functional, effective, and structural measures of connectivity during a resting state in each patient. Functional and effective connectivity measures included correlation or Granger causality measures applied to stereoEEG (sEEGs) recordings. Structural connectivity was derived from diffusion tensor imaging (DTI) acquired before intracranial electrode implant and monitoring (N=8).ResultsThe CCEP network was most similar to the resting state voltage correlation network in channels near to the stimulation location. In contrast, the distant CCEP network was most similar to the DTI network. Other connectivity measures were not as similar to the CCEP network.ConclusionsThese results demonstrate that different connectivity measures, including those derived from active stimulation-based probing, measure different, complementary aspects of regional interrelationships in the brain.

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