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Your search keyword '"Alexandre R. Franco"' showing total 8 results
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8 results on '"Alexandre R. Franco"'

1. An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI

Author

Qawi K. Telesford, Eduardo Gonzalez-Moreira, Ting Xu, Yiwen Tian, Stanley J. Colcombe, Jessica Cloud, Brian E. Russ, Arnaud Falchier, Maximilian Nentwich, Jens Madsen, Lucas C. Parra, Charles E. Schroeder, Michael P. Milham, and Alexandre R. Franco

Subjects
Science
Abstract

Abstract In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23–51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.

Published
2023
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2. An analysis-ready and quality controlled resource for pediatric brain white-matter research

Author

Adam Richie-Halford, Matthew Cieslak, Lei Ai, Sendy Caffarra, Sydney Covitz, Alexandre R. Franco, Iliana I. Karipidis, John Kruper, Michael Milham, Bárbara Avelar-Pereira, Ethan Roy, Valerie J. Sydnor, Jason D. Yeatman, The Fibr Community Science Consortium, Theodore D. Satterthwaite, and Ariel Rokem

Subjects
Science
Abstract

Abstract We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Published
2022
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3. A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Author

Russell H. Tobe, Anna MacKay-Brandt, Ryan Lim, Melissa Kramer, Melissa M. Breland, Lucia Tu, Yiwen Tian, Kristin Dietz Trautman, Caixia Hu, Raj Sangoi, Lindsay Alexander, Vilma Gabbay, F. Xavier Castellanos, Bennett L. Leventhal, R. Cameron Craddock, Stanley J. Colcombe, Alexandre R. Franco, and Michael P. Milham

Subjects
Science
Abstract

Measurement(s) Cognition • Psychiatric Diagnosis • Development Technology Type(s) MRI • phenotypic battery • actigraphy • laboratory analysis Sample Characteristic - Organism Homo sapien Sample Characteristic - Environment Community-ascertained non-diagnostic sample Sample Characteristic - Location United States

Published
2022
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4. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Author

Sook-Lei Liew, Bethany P. Lo, Miranda R. Donnelly, Artemis Zavaliangos-Petropulu, Jessica N. Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P. Simon, Julia M. Juliano, Anisha Suri, Zhizhuo Wang, Aisha Abdullah, Jun Kim, Tyler Ard, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Lei Cao, Jessica M. Cassidy, Valentina Ciullo, Adriana B. Conforto, Steven C. Cramer, Rosalia Dacosta-Aguayo, Ezequiel de la Rosa, Martin Domin, Adrienne N. Dula, Wuwei Feng, Alexandre R. Franco, Fatemeh Geranmayeh, Alexandre Gramfort, Chris M. Gregory, Colleen A. Hanlon, Brenton G. Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Jan S. Kirschke, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, Maria Mataró, Feroze B. Mohamed, Jan E. Nordvik, Gilsoon Park, Amy Pienta, Fabrizio Piras, Shane M. Redman, Kate P. Revill, Mauricio Reyes, Andrew D. Robertson, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Alison Sweet, Maria Telenczuk, Gregory Thielman, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Kristin A. Wong, and Chunshui Yu

Subjects
Science
Abstract

Measurement(s) stroke lesion Technology Type(s) manual segmentation in ITK-SNAP Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment brain

Published
2022
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8. A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Author

Russell H, Tobe, Anna, MacKay-Brandt, Ryan, Lim, Melissa, Kramer, Melissa M, Breland, Lucia, Tu, Yiwen, Tian, Kristin Dietz, Trautman, Caixia, Hu, Raj, Sangoi, Lindsay, Alexander, Vilma, Gabbay, F Xavier, Castellanos, Bennett L, Leventhal, R Cameron, Craddock, Stanley J, Colcombe, Alexandre R, Franco, and Michael P, Milham

Subjects
Diffusion Magnetic Resonance Imaging, Mental Health, Adolescent, Connectome, Brain, Humans, Child
Abstract

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Published
2021

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