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Kruper J, Hagen MP, Rheault F, Crane I, Gilmore A, Narayan M, Motwani K, Lila E, Rorden C, Yeatman JD, Rokem A. Tractometry of the Human Connectome Project: resources and insights. Front Neurosci 2024; 18:1389680. [PMID: 38933816 PMCID: PMC11199395 DOI: 10.3389/fnins.2024.1389680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. Methods We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. Results We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - McKenzie P. Hagen
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - François Rheault
- Department of Computer Science, Universitè de Sherbrooke, Sherbrooke, QC, Canada
| | - Isaac Crane
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Asa Gilmore
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Manjari Narayan
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Keshav Motwani
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Eardi Lila
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, United States
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States
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Mandal PK, Jindal K, Roy S, Arora Y, Sharma S, Joon S, Goel A, Ahasan Z, Maroon JC, Singh K, Sandal K, Tripathi M, Sharma P, Samkaria A, Gaur S, Shandilya S. SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform. Front Neurol 2023; 14:1258116. [PMID: 37859652 PMCID: PMC10582723 DOI: 10.3389/fneur.2023.1258116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/15/2023] [Indexed: 10/21/2023] Open
Abstract
Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.
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Affiliation(s)
- Pravat K. Mandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
- Florey Institute of Neuroscience and Mental Health, Melbourne School of Medicine Campus, Melbourne, VIC, Australia
| | - Komal Jindal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Saurav Roy
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Yashika Arora
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shallu Sharma
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shallu Joon
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Anshika Goel
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Zoheb Ahasan
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Joseph C. Maroon
- Department of Neurosurgery, University of Pittsburgh Medical School, Pittsburgh, PA, United States
| | - Kuldeep Singh
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Kanika Sandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Pooja Sharma
- Medanta Institute of Education and Research, Medanta-The Medicity Hospital, Gurgaon, India
| | - Avantika Samkaria
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shradha Gaur
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Sandhya Shandilya
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
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Dotolo S, Esposito Abate R, Roma C, Guido D, Preziosi A, Tropea B, Palluzzi F, Giacò L, Normanno N. Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice. Biomedicines 2022; 10:biomedicines10092074. [PMID: 36140175 PMCID: PMC9495893 DOI: 10.3390/biomedicines10092074] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 11/22/2022] Open
Abstract
The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).
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Affiliation(s)
- Serena Dotolo
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Riziero Esposito Abate
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Cristin Roma
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Davide Guido
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Alessia Preziosi
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Beatrice Tropea
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Fernando Palluzzi
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Luciano Giacò
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
- Correspondence:
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DuPre E, Holdgraf C, Karakuzu A, Tetrel L, Bellec P, Stikov N, Poline JB. Beyond advertising: New infrastructures for publishing integrated research objects. PLoS Comput Biol 2022; 18:e1009651. [PMID: 34990466 PMCID: PMC8735620 DOI: 10.1371/journal.pcbi.1009651] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Elizabeth DuPre
- NeuroDataScience–ORIGAMI Laboratory, McGill University, Montreal, Quebec, Canada
| | - Chris Holdgraf
- The International Interactive Computing Collaboration (2i2c), Berkeley, California, United States of America
- International Computer Science Institute, Berkeley, California, United States of America
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
- Montreal Heart Institute, Montreal, Quebec, Canada
| | - Loïc Tetrel
- Centre de recherche de l’Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l’Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Psychology, Université de Montréal, Montreal, Quebec, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
- Montreal Heart Institute, Montreal, Quebec, Canada
| | - Jean-Baptiste Poline
- NeuroDataScience–ORIGAMI Laboratory, McGill University, Montreal, Quebec, Canada
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Klapwijk ET, van den Bos W, Tamnes CK, Raschle NM, Mills KL. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev Cogn Neurosci 2021; 47:100902. [PMID: 33383554 PMCID: PMC7779745 DOI: 10.1016/j.dcn.2020.100902] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 01/08/2023] Open
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Affiliation(s)
- Eduard T Klapwijk
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, Eugene, OR, USA
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