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arXiv 2022年

作者： Goyal, Nikhil Moraczewski, Dustin Bandettini, Peter A. Finn, Emily S. Thomas, Adam G. Data Science and Sharing Team National Institute of Mental Health BethesdaMD United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD United States Department of Psychological and Brain Sciences Dartmouth College HanoverNH United States

In mental health research, it has proven difficult to find measures of brain function that provide reliable indicators of mental health and well-being, including susceptibility to mental health disorders. Recently, a family of data-driven analyses have provided such reliable measures when applied to large, population-level datasets. In the current pre-registered replication study, we show that the canonical correlation analysis (CCA) methods previously developed using resting-state MRI functional connectivity and subject measures of cognition and behavior from healthy adults are also effective in measuring well-being (a "positive-negative axis") in an independent developmental dataset. Our replication was successful in two out of three of our pre-registered criteria, such that a primary CCA mode’s weights displayed a significant positive relationship and explained a significant amount of variance in both functional connectivity and subject measures. The only criteria that was not successful was that compared to other modes the magnitude of variance explained by the primary CCA mode was smaller than predicted, a result which could indicate a developmental trajectory of a primary mode. This replication establishes a signature neurotypical relationship between connectivity and phenotype, opening new avenues of research in neuroscience with clear clinical applications. © 2022, CC0.

关键词： Population statistics

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Revealing interpretable object representations from human behavior 7

Revealing interpretable object representations from human be...

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7th International Conference on Learning Representations, ICLR 2019

作者： Zheng, Charles Y. Baker, Chris I. Pereira, Francisco Hebart, Martin N. Section on Functional Imaging Methods National Institute of Mental Health Section on Learning and Plasticity National Institute of Mental Health

To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Embeddings

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Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas

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Pediatric radiology 2021年第4期51卷 671-672页

作者： Peter J Molfese Daniel Glen Laura Mesite Robert W Cox Fumiko Hoeft Stephen J Frost W Einar Mencl Kenneth R Pugh Peter A Bandettini Haskins Laboratories 300 George St. Suite 900 New Haven CT 06511 USA. peter.molfese@nih.gov. Section on Functional Imaging Methods National Institutes of Mental Health National Institutes of Health Bethesda MD USA. peter.molfese@nih.gov. Scientific and Statistical Computing Core National Institutes of Mental Health National Institutes of Health Bethesda MD USA. Haskins Laboratories 300 George St. Suite 900 New Haven CT 06511 USA. Brain Imaging Research Center (BIRC) University of Connecticut Storrs CT USA. Section on Functional Imaging Methods National Institutes of Mental Health National Institutes of Health Bethesda MD USA.

A Correction to this paper has been published: https://***/10.1007/s00247-020-04958-w

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Revealing interpretable object representations from human behavior

arXiv

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arXiv 2019年

作者： Zheng, Charles Y. Pereira, Francisco Baker, Chris I. Hebart, Martin N. Section on Functional Imaging Methods National Institute of Mental Health Section on Learning and Plasticity National Institute of Mental Health

To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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A measure of reliability convergence to select and optimize cognitive tasks for individual differences research

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Communications psychology 2024年第1期2卷 64页

作者： Jan Kadlec Catherine R Walsh Uri Sadé Ariel Amir Jesse Rissman Michal Ramot Department of Brain Sciences Weizmann Institute of Science Rehovot Israel. Department of Psychology University of California Los Angeles CA USA. Section on Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. Faculty of Physics Weizmann Institute of Science Rehovot Israel. Department of Psychiatry and Biobehavioral Sciences University of California Los Angeles CA USA. Department of Brain Sciences Weizmann Institute of Science Rehovot Israel. michal.ramot@weizmann.ac.il.

Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability.

关键词： Cognitive neuroscience Human behaviour

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A temporal deconvolution algorithm for multiecho functional MRI 15

A temporal deconvolution algorithm for multiecho functional ...

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15th IEEE International Symposium on Biomedical imaging, ISBI 2018

作者： Gaudes, Cesar Caballero Bandettini, Peter A. Gonzalez-Castillo, Javier Basque Center on Cognition Brain and Language San Sebastian Spain Section on Functional Imaging Methods NIMH NIH BethesdaMD United States Functional MRI Core NIH BethesdaMD United States

ISBN: (纸本)9781538636367

This work introduces a novel method for the temporal deconvolution of the BOLD signal for multiecho functional MRI (fMRI) data: Multiecho Paradigm Free Mapping (ME-PFM). By solving an inverse problem with the Generalized LASSO, this method obtains voxelwise time-varying estimates of the changes in the transverse relaxation (R) and the net magnetization (So) of the fMRI signal without prior knowledge of the timings of the BOLD responses. Our results with experimental data demonstrate that the maps of R changes obtained with ME-PFM at the times of the stimulus trials closely resemble the maps obtained with a standard approach that is aware of the onsets and durations of the events. Compared with previous deconvolution approaches for BOLD fMRI, this method yields estimates of R changes in physiologically interpretable units, which is particularly relevant for the quantification of the dynamics of the BOLD fMRI signal during tasks and resting state. © 2018 IEEE.

关键词： Deconvolution

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Correction to: Global surface features contribute to human haptic roughness estimations

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Experimental brain research 2022年第4期240卷 1293页

作者： Huazhi Li Jiajia Yang Yinghua Yu Wu Wang Yulong Liu Mengni Zhou Qingqing Li Jingjing Yang Shiping Shao Satoshi Takahashi Yoshimichi Ejima Jinglong Wu Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. School of Psychological and Cognitive Sciences Peking University Beijing China. Department of Teacher Education Wenzhou University Wenzhou China. School of Computer Science and Technology Changchun University of Science and Technology Changchun China. School of Social Welfare Yonsei University Seoul Korea. School of Mechatronical Engineering Beijing Institute of Technology Beijing China.

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Knowing what you know in brain segmentation using Bayesian deep neural networks

arXiv

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arXiv 2018年

作者： McClure, Patrick Rho, Nao Lee, John A. Kaczmarzyk, Jakub R. Zheng, Charles Ghosh, Satrajit S. Nielson, Dylan M. Thomas, Adam G. Bandettini, Peter Pereira, Francisco Machine Learning Team National Institute of Mental Health BethesdaMD United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD United States Data Sharing and Science Team National Institute of Mental Health BethesdaMD United States McGovern Institute for Brain Research Massachusetts Institute of Technology CambridgeMA United States

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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Extrapolating expected accuracies for large multi-class problems

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2018年第1期19卷

作者： Charles Zheng Rakesh Achanta Yuval Benjamini Section on Functional Imaging Methods National Institute of Mental Health Bethesda MD Department of Statistics Stanford University Palo Alto CA Department of Statistics The Hebrew University of Jerusalem Jerusalem Israel

The difficulty of multi-class classification generally increases with the number of classes. Using data for a small set of the classes, can we predict how well the classifier scales as the number of classes increases? We propose a framework for studying this question, assuming that classes in both sets are sampled from the same population and that the classifier is based on independently learned scoring functions. Under this framework, we can express the classification accuracy on a set of k classes as the (k-1)st moment of a discriminability function; the discriminability function itself does not depend on k. We leverage this result to develop a non-parametric regression estimator for the discriminability function, which can extrapolate accuracy results to larger unobserved sets. We also formalize an alternative approach that extrapolates accuracy separately for each class, and identify tradeoffs between the two methods. We show that both methods can accurately predict classifier performance on label sets up to ten times the size of the original set, both in simulations as well as in realistic face recognition or character recognition tasks.

关键词： face recognition multi-class problems nonparametric models object recognition transfer learning

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Quantum computing at the frontiers of biological sciences

arXiv

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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