scholarly journals Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Dimitris A. Pinotsis ◽  
Earl K. Miller
Keyword(s):  
Computational Models ◽  
Multiple Scales ◽  
Neurological Diseases ◽  
Cortical Layer ◽  
Statistical Decision Theory ◽  
Imaging Data ◽  
Cortical Layers ◽  
Statistical Decision ◽  
Non Invasive ◽  
Brain Imaging Data

AbstractNeural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

scholarly journals Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

2020 ◽  
Author(s):  
D. A. Pinotsis ◽  
E. K. Miller
Keyword(s):  
Computational Models ◽  
Multiple Scales ◽  
Neurological Diseases ◽  
Cortical Layer ◽  
Statistical Decision Theory ◽  
Imaging Data ◽  
Cortical Layers ◽  
Statistical Decision ◽  
Non Invasive ◽  
Brain Imaging Data

AbstractNeural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

Supercomputing in the Study and Stimulation of the Brain

2021 ◽  
pp. 290-300
Author(s):  
Laura Dipietro ◽  
Seth Elkin-Frankston ◽  
Ciro Ramos-Estebanez ◽  
Timothy Wagner
Keyword(s):  
High Performance ◽  
Neurological Diseases ◽  
Data Sets ◽  
Imaging Data ◽  
Building Simulations ◽  
History Of ◽  
Brain Imaging Data ◽  
Evolution Of Science ◽  
Computational Systems ◽  
The Brain

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

scholarly journals The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments

2015 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Tibor Auer ◽  
Vince D. Calhoun ◽  
R. Cameron Craddock ◽  
Samir Das ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Non Invasive ◽  
File Formats ◽  
Neuroscience Research ◽  
Imaging Experiment ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data ◽  
The Brain

AbstractThe development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

scholarly journals The importance of standards for sharing of computational models and data

2019 ◽  
Author(s):  
Russell Poldrack ◽  
Franklin Feingold ◽  
Michael Frank ◽  
Padraig Gleeson ◽  
Gilles de Hollander ◽  
...  
Keyword(s):  
Data Structure ◽  
Computational Models ◽  
Imaging Data ◽  
Model Based ◽  
Ongoing Effort ◽  
Improve Model ◽  
Target Article ◽  
Brain Imaging Data ◽  
The Brain ◽  
Model Robustness

The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.

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 Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo-yong Park ◽  
Seok-Jun Hong ◽  
Sofie L. Valk ◽  
Casey Paquola ◽  
Oualid Benkarim ◽  
...  
Keyword(s):  
Data Exchange ◽  
Computational Models ◽  
Simulation Models ◽  
Autism Diagnostic Observation Schedule ◽  
Supervised Machine Learning ◽  
Global Network ◽  
Imaging Data ◽  
Phenotypic Data ◽  
Severity Scores ◽  
High Level

AbstractThe pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

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