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913 results on '"Biswal, Bharat B."'

13. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F, Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A, Adcock, R Alison, Avesani, Paolo, Baczkowski, Blazej M, Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G, Berkers, Ruud MWJ, Bhanji, Jamil P, Biswal, Bharat B, Bobadilla-Suarez, Sebastian, Bortolini, Tiago, Bottenhorn, Katherine L, Bowring, Alexander, Braem, Senne, Brooks, Hayley R, Brudner, Emily G, Calderon, Cristian B, Camilleri, Julia A, Castrellon, Jaime J, Cecchetti, Luca, Cieslik, Edna C, Cole, Zachary J, Collignon, Olivier, Cox, Robert W, Cunningham, William A, Czoschke, Stefan, Dadi, Kamalaker, Davis, Charles P, Luca, Alberto De, Delgado, Mauricio R, Demetriou, Lysia, Dennison, Jeffrey B, Di, Xin, Dickie, Erin W, Dobryakova, Ekaterina, Donnat, Claire L, Dukart, Juergen, Duncan, Niall W, Durnez, Joke, Eed, Amr, Eickhoff, Simon B, Erhart, Andrew, Fontanesi, Laura, Fricke, G Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, Genon, Sarah, Glatard, Tristan, Glerean, Enrico, Goeman, Jelle J, Golowin, Sergej AE, González-García, Carlos, Gorgolewski, Krzysztof J, Grady, Cheryl L, Green, Mikella A, Guassi Moreira, João F, Guest, Olivia, Hakimi, Shabnam, Hamilton, J Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B, Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A, Hughes, Matthew E, Iacovella, Vittorio, Iordan, Alexandru D, Isager, Peder M, Isik, Ayse I, Jahn, Andrew, Johnson, Matthew R, Johnstone, Tom, Joseph, Michael JE, Juliano, Anthony C, Kable, Joseph W, Kassinopoulos, Michalis, Koba, Cemal, and Kong, Xiang-Zhen

Subjects
Generic health relevance, Brain, Data Analysis, Data Science, Datasets as Topic, Female, Functional Neuroimaging, Humans, Logistic Models, Magnetic Resonance Imaging, Male, Meta-Analysis as Topic, Models, Neurological, Reproducibility of Results, Research Personnel, Software, General Science & Technology
Abstract

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Published
2020

31. Compressed cerebro‐cerebellar functional gradients in children and adolescents with attention‐deficit/hyperactivity disorder.

Author

Cao, Qingquan, Wang, Pan, Zhang, Ziqian, Castellanos, F. Xavier, and Biswal, Bharat B.

Subjects
DEFAULT mode network, LARGE-scale brain networks, ORGANIZATIONAL structure, FUNCTIONAL connectivity, COGNITION disorders
Abstract

Both cortical and cerebellar developmental differences have been implicated in attention‐deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient‐based resting‐state functional connectivity analysis was applied to investigate the cerebro‐cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co‐occurrence of both low‐ (visual processing) and high‐order (self‐related thought) cognitive dysfunction manifesting in abnormal cerebro‐cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co‐occurrence and interaction of both low‐level and high‐level functional abnormalities in the cortex and cerebellum in ADHD. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Estimation of static and dynamic functional connectivity in resting‐state fMRI using zero‐frequency resonator.

Author

Das, Sukesh Kumar, Sao, Anil K., and Biswal, Bharat B.

Subjects
FUNCTIONAL connectivity, FUNCTIONAL magnetic resonance imaging, RESONATORS, OXYGEN in the blood
Abstract

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level‐dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs‐fMRI is spontaneous event‐related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low‐valued inflection points. Here, we extract the event‐related temporal onsets from the rs‐fMRI time courses using a zero‐frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal‐to‐noise ratio (SNR) segments are also obtained in the rs‐fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR‐based CR distinguishes the coactivation and non‐coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR‐driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points. [ABSTRACT FROM AUTHOR]

Published
2024
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