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      Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

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          Abstract

          Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or “idiosynchrony”. Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

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          The minimal preprocessing pipelines for the Human Connectome Project.

          The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Copyright © 2013 Elsevier Inc. All rights reserved.
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            The WU-Minn Human Connectome Project: an overview.

            The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.

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                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                23 April 2020
                07 April 2020
                15 July 2020
                15 July 2021
                : 215
                : 116828
                Affiliations
                [a ]Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
                [b ]Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
                [c ]Mood Brain & Development Unit, National Institute of Mental Health, Bethesda, MD, USA
                Author notes

                CRediT authorship contribution statement

                Emily S. Finn: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Visualization, Writing - original draft. Enrico Glerean: Conceptualization, Formal analysis, Methodology, Visualization, Writing - review & editing. Arman Y. Khojandi: Data curation, Software. Dylan Nielson: Software, Visualization, Validation. Peter J. Molfese: Data curation, Resources. Daniel A. Handwerker: Methodology, Validation, Writing - review & editing. Peter A. Bandettini: Funding acquisition, Supervision, Writing - review & editing.

                [* ]Corresponding author. emily.finn@ 123456nih.gov , emily.s.finn@ 123456dartmouth.edu (E.S. Finn).
                Article
                NIHMS1585696
                10.1016/j.neuroimage.2020.116828
                7298885
                32276065
                dd8fc65f-e03c-47d6-a1ee-ea4dd140db53

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Categories
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                Neurosciences
                individual differences,naturalistic,inter-subject correlation,imri,behavior,representational similarity analysis

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