Mapping Complex Brain Torque Components and Their Genetic and Phenomic Architecture in 24,112 healthy individuals

AbstractThe mechanisms determining the development and individual variability of brain torque (BT) remain unclear. Here, all relevant components of BT were analyzed using neuroimaging data of up to 24,112 individuals from 6 cohorts. Our large-scale data confirmed the population-level predominance of the typical anticlockwise torque and suggested a “first attenuating, then enlarging” dynamic across the lifespan primarily for frontal, occipital and perisylvian BT features. Sex/handedness differences in BT were found and were related to cognitive sex/handedness differences in verbal-numerical reasoning. We observed differential heritability of up to 56% for BT, especially in temporal language areas, and identified numerous genome- and phenome-wide significant associations pointing to neurodevelopment, cognitive functions, lifestyle, neurological and psychiatric disorders, sociodemographic, cardiovascular and anthropometric traits. This study provides a comprehensive description of BT and insights into biological and other factors that may contribute to the development and individual variations of BT.

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COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽

10.12688/f1000research.12353.1 ◽

2017 ◽

Vol 6 ◽

pp. 1512 ◽

Cited By ~ 8

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.

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FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

Frontiers in Neuroinformatics ◽

10.3389/fninf.2021.678403 ◽

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.

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TRANSPARENCY AND REPRODUCIBILITY IN THE NEUROIMAGING OF AGING

Innovation in Aging ◽

10.1093/geroni/igz038.088 ◽

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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231 COVID-19 Lockdown Policies Across 20 Countries Modulate Sleep and Resting Heart Rate Measures

SLEEP ◽

10.1093/sleep/zsab072.230 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A92-A92

Author(s):

Ju Lynn Ong ◽

Teyang Lau ◽

Mari Karsikas ◽

Hannu Kinnunen ◽

Michael Chee

Keyword(s):

Heart Rate ◽

Large Scale ◽

Time Window ◽

Population Level ◽

Multilevel Modelling ◽

Resting Heart Rate ◽

Sleep Patterns ◽

Growth Curve Models ◽

Large Scale Data ◽

Unit Increase

Abstract Introduction Lockdowns imposed to stem the spread of COVID-19 have disrupted the lifestyles of many worldwide, but studies to date are mostly confined to observations within a limited number of countries, based on subjective reports and survey from a narrow time window. In the present study, we investigate associations between the severity of lockdown policies and objective sleep and resting-heart rate measures. Methods Data from 113,000 users of a consumer sleep tracker across 20 countries were gathered between Jan–Jul 2020 and compared with an equivalent period in 2019 as a control for naturally occurring seasonal fluctuations. Lockdown stringency was derived using scores from the Oxford Government Response Tracker. Multilevel growth curve models were used to quantify the effect of lockdown stringency on changes to sleep patterns (midsleep time and midsleep variability) and resting heart rate changes, and to predict changes in resting heart rate from changes to sleep patterns. Results Lockdown severity modulated the size of shifts in sleep midpoint and regularity during this period. Midsleep times were delayed in all countries during strict lockdowns, particularly on weekdays, while midsleep variability reduced. The largest shifts in midsleep time (+0.09 to +0.58 hours), midsleep variability (–0.12 to –0.26 hours) and resting heart rate (–0.35 to –2.08 bpm) occurred in April and May - when most countries imposed their strictest lockdown measures. In addition, multilevel modelling revealed that for each unit increase in stringency index, midsleep time was delayed by 0.96 min, midsleep variability decreased by 0.46 min and resting heart rate decreased by 0.06 bpm. Finally, in models predicting changes in resting heart rate from changes to sleep patterns, midsleep variability was shown to be the strongest predictor of resting heart rate, wherein an hour increase in the standard deviation of midsleep variability predicted a 5.12 increase in bpm, while an hour increase in midsleep time only predicted a 1.25 decrease in bpm. Conclusion Our findings demonstrate the utility of large-scale data from consumer wearables in providing population-level insights into how lockdown severity directly impacts sleep health during this pandemic period. Support (if any) Work conducted at NUS is supported by a grant awarded to Michael Chee (NMRC/STAR19may-0001).

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FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

10.1101/2021.03.12.434998 ◽

2021 ◽

Author(s):

Tinashe M. Tapera ◽

Matthew Cieslak ◽

Max Bertolero ◽

Azeez Adebimpe ◽

Geoffrey K. Aguirre ◽

...

Keyword(s):

Data Storage ◽

Large Scale ◽

Data Curation ◽

Imaging Data ◽

Large Scale Data ◽

Neuroscience Research ◽

Neuroimaging Data ◽

Brain Imaging Data ◽

Database Platform ◽

The Brain

ABSTRACTThe 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 a data storage specification 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 not designed for use on cloud-based systems such as 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.

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Heritability of individualized cortical network topography

10.1101/2020.07.30.229427 ◽

2020 ◽

Cited By ~ 1

Author(s):

Kevin Michael Anderson ◽

Tian Ge ◽

Ru Kong ◽

Lauren M Patrick ◽

R. Nathan Spreng ◽

...

Keyword(s):

Genetic Control ◽

Large Scale ◽

Posterior Parietal Cortex ◽

Genetic Factors ◽

Population Level ◽

Individual Variability ◽

Cortical Networks ◽

Brain Organization ◽

Human Connectome Project ◽

Sensory Motor

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

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Heritability of individualized cortical network topography

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2016271118 ◽

2021 ◽

Vol 118 (9) ◽

pp. e2016271118

Author(s):

Kevin M. Anderson ◽

Tian Ge ◽

Ru Kong ◽

Lauren M. Patrick ◽

R. Nathan Spreng ◽

...

Keyword(s):

Genetic Control ◽

Large Scale ◽

Posterior Parietal Cortex ◽

Genetic Factors ◽

Population Level ◽

Individual Variability ◽

Cortical Networks ◽

Brain Organization ◽

Human Connectome Project ◽

Sensory Motor

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

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