scholarly journals Broken detailed balance and entropy production in the human brain

2021 ◽  
Vol 118 (47) ◽  
pp. e2109889118
Author(s):  
Christopher W. Lynn ◽  
Eli J. Cornblath ◽  
Lia Papadopoulos ◽  
Maxwell A. Bertolero ◽  
Danielle S. Bassett
Keyword(s):  
Human Brain ◽  
Entropy Production ◽  
Large Scale ◽  
Detailed Balance ◽  
Neural Systems ◽  
Imaging Data ◽  
Cellular Functions ◽  
Diverse Range ◽  
Brain Imaging Data ◽  
Dynamic Ising Model

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition and provide a general tool for quantifying entropy production in macroscopic systems.

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.

Linking brain imaging signals to visual perception

2013 ◽  
Vol 30 (5-6) ◽  
pp. 229-241 ◽  
Author(s):  
ANDREW E. WELCHMAN ◽  
ZOE KOURTZI
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Imaging Data ◽  
Neural Basis ◽  
3D Perception ◽  
Multimodal Data ◽  
Psychological States ◽  
Perceptual Judgments ◽  
Brain Circuits ◽  
Brain Imaging Data

AbstractThe rapid advances in brain imaging technology over the past 20 years are affording new insights into cortical processing hierarchies in the human brain. These new data provide a complementary front in seeking to understand the links between perceptual and physiological states. Here we review some of the challenges associated with incorporating brain imaging data into such “linking hypotheses,” highlighting some of the considerations needed in brain imaging data acquisition and analysis. We discuss work that has sought to link human brain imaging signals to existing electrophysiological data and opened up new opportunities in studying the neural basis of complex perceptual judgments. We consider a range of approaches when using human functional magnetic resonance imaging to identify brain circuits whose activity changes in a similar manner to perceptual judgments and illustrate these approaches by discussing work that has studied the neural basis of 3D perception and perceptual learning. Finally, we describe approaches that have sought to understand the information content of brain imaging data using machine learning and work that has integrated multimodal data to overcome the limitations associated with individual brain imaging approaches. Together these approaches provide an important route in seeking to understand the links between physiological and psychological states.

scholarly journals COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1512 ◽  
Author(s):  
Jing Ming ◽  
Eric Verner ◽  
Anand Sarwate ◽  
Ross Kelly ◽  
Cory Reed ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Sharing ◽  
Large Scale ◽  
Data Transfer ◽  
Imaging Data ◽  
Sensitive Data ◽  
Large Scale Data ◽  
Decentralized Algorithms ◽  
Neuroimaging Data ◽  
Brain Imaging Data

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

scholarly journals Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Rayus Kuplicki ◽  
James Touthang ◽  
Obada Al Zoubi ◽  
Ahmad Mayeli ◽  
Masaya Misaki ◽  
...  
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Laboratory Data ◽  
Imaging Data ◽  
Common Data Elements ◽  
Level Data ◽  
Reproducible Analysis ◽  
Brain Imaging Data ◽  
Data Elements ◽  
The Brain

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

scholarly journals The modular and integrative functional architecture of the human brain

2015 ◽  
Vol 112 (49) ◽  
pp. E6798-E6807 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
B. T. Thomas Yeo ◽  
Mark D’Esposito
Keyword(s):  
Human Brain ◽  
Cognitive Functions ◽  
Topic Model ◽  
Brain Activity ◽  
Modular Function ◽  
Imaging Data ◽  
Functional Architecture ◽  
Brain Imaging Data ◽  
The Brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules’ processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author–topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network’s modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules’ functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain’s modular yet integrated implementation of cognitive functions.

scholarly journals The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

2013 ◽  
Vol 19 (6) ◽  
pp. 659-667 ◽  
Author(s):  
A Di Martino ◽  
C-G Yan ◽  
Q Li ◽  
E Denio ◽  
F X Castellanos ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Exchange ◽  
Large Scale ◽  
Imaging Data ◽  
Scale Evaluation ◽  
Brain Architecture ◽  
Brain Imaging Data

scholarly journals FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

2021 ◽  
Vol 15 ◽  
Author(s):  
Tinashe M. Tapera ◽  
Matthew Cieslak ◽  
Max Bertolero ◽  
Azeez Adebimpe ◽  
Geoffrey K. Aguirre ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Curation ◽  
Imaging Data ◽  
Large Scale Data ◽  
Neuroscience Research ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
Database Platform ◽  
The Brain ◽  
Scale Data

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

scholarly journals Identification of de novo mutations in the Chinese ASD cohort via whole-exome sequencing unveils brain regions implicated in autism

2021 ◽  
Author(s):  
Bo Yuan ◽  
Mengdi Wang ◽  
Xinran Wu ◽  
Peipei Cheng ◽  
Ran Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
De Novo ◽  
Brain Regions ◽  
Imaging Data ◽  
De Novo Mutations ◽  
Genetic Studies ◽  
Single Cell Sequencing ◽  
Genome Wide ◽  
Whole Exome ◽  
Brain Imaging Data

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation and transcriptional regulation, have been identified through human genetic studies, the East Asian ASD cohorts is still under-represented in the genome-wide genetic studies. Here we performed whole-exome sequencing on 369 ASD trios including probands and unaffected parents of Chinese origin. Using a joint-calling analytical pipeline based on GATK toolkits, we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants, as well as de novo copy number variations containing known ASD-related genes. Importantly, combining with single-cell sequencing data from the developing human brain, we found that expression of genes with de novo mutations were specifically enriched in pre-, post-central gyrus (PRC, PC) and banks of superior temporal (BST) regions in the human brain. By further analyzing the brain imaging data with ASD and health controls, we found that the gray volume of the right BST in ASD patients significantly decreased comparing to health controls, suggesting the potential structural deficits associated with ASD. Finally, we found that there was decrease in the seed-based functional connectivity (FC) between BST/PC/PRC and sensory areas, insula, as well as frontal lobes in ASD patients. This work indicated that the combinatorial analysis with genome-wide screening, single-cell sequencing and brain imaging data would reveal brain regions contributing to etiology of ASD.

scholarly journals Improved bounds on entropy production in living systems

2021 ◽  
Vol 118 (18) ◽  
pp. e2024300118
Author(s):  
Dominic J. Skinner ◽  
Jörn Dunkel
Keyword(s):  
Entropy Production ◽  
Biological Systems ◽  
Detailed Balance ◽  
Calcium Oscillations ◽  
Living Systems ◽  
Entropy Production Rate ◽  
Local Order ◽  
Nonequilibrium Processes ◽  
Diverse Range ◽  
Human Embryonic Kidney Cells

Living systems maintain or increase local order by working against the second law of thermodynamics. Thermodynamic consistency is restored as they consume free energy, thereby increasing the net entropy of their environment. Recently introduced estimators for the entropy production rate have provided major insights into the efficiency of important cellular processes. In experiments, however, many degrees of freedom typically remain hidden to the observer, and, in these cases, existing methods are not optimal. Here, by reformulating the problem within an optimization framework, we are able to infer improved bounds on the rate of entropy production from partial measurements of biological systems. Our approach yields provably optimal estimates given certain measurable transition statistics. In contrast to prevailing methods, the improved estimator reveals nonzero entropy production rates even when nonequilibrium processes appear time symmetric and therefore may pretend to obey detailed balance. We demonstrate the broad applicability of this framework by providing improved bounds on the energy consumption rates in a diverse range of biological systems including bacterial flagella motors, growing microtubules, and calcium oscillations within human embryonic kidney cells.

scholarly journals TRANSPARENCY AND REPRODUCIBILITY IN THE NEUROIMAGING OF AGING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S23-S24
Author(s):  
Kendra L Seaman
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Social Science Research ◽  
Science Research ◽  
Data Sets ◽  
Imaging Data ◽  
Large Scale Data ◽  
Neuroimaging Data ◽  
Brain Imaging Data

Abstract In concert with broader efforts to increase the reliability of social science research, there are several efforts to increase transparency and reproducibility in neuroimaging. The large-scale nature of neuroimaging data and constantly evolving analysis tools can make transparency challenging. I will describe emerging tools used to document, organize, and share behavioral and neuroimaging data. These tools include: (1) the preregistration of neuroimaging data sets which increases openness and protects researchers from suspicions of p-hacking, (2) the conversion of neuroimaging data into a standardized format (Brain Imaging Data Structure: BIDS) that enables standardized scripts to process and share neuroimaging data, and (3) the sharing of final neuroimaging results on Neurovault which allows the community to do rapid meta-analysis. Using these tools improves workflows within labs, improves the overall quality of our science and provides a potential model for other disciplines using large-scale data.

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