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Jeganathan J, Koussis NC, Paton B, Sina Mansour L, Zalesky A, Breakspear M. Spurious correlations in surface-based functional brain imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602799. [PMID: 39026811 PMCID: PMC11257594 DOI: 10.1101/2024.07.09.602799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The study of functional MRI data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this bias are not well understood or widely appreciated. We explain the origins of these biases, and using in-silico models of fMRI data, illustrate how they lead to spurious results. The bias leads to leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this "gyral bias", including test-retest reliability, fingerprinting, functional parcellations, regional homogeneity, and brain-behaviour associations. Finally, we provide recommendations to avoid or remedy this spatial bias.
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Affiliation(s)
- Jayson Jeganathan
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Nikitas C Koussis
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Mark Hughes Foundation Centre for Brain Cancer Research, University of Newcastle, NSW, Australia
- School of Medicine and Public Health, College of Medicine, Health and Wellbeing, University of Newcastle, Newcastle, NSW, Australia
| | - Bryan Paton
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Mark Hughes Foundation Centre for Brain Cancer Research, University of Newcastle, NSW, Australia
| | - L Sina Mansour
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Systems Lab, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, Australia
| | - Andrew Zalesky
- Systems Lab, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, College of Medicine, Health and Wellbeing, University of Newcastle, Newcastle, NSW, Australia
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2
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Andreella A, Finos L, Lindquist MA. Enhanced hyperalignment via spatial prior information. Hum Brain Mapp 2023; 44:1725-1740. [PMID: 36541577 PMCID: PMC9921258 DOI: 10.1002/hbm.26170] [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: 06/10/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises-Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises-Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms.
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Affiliation(s)
- Angela Andreella
- Department of Economics, Ca' Foscari University of Venice, Venice, Italy
| | - Livio Finos
- Department of Developmental Psychology and Socialization, University of Padova, Padova, Italy
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
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3
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Kumar M, Anderson MJ, Antony JW, Baldassano C, Brooks PP, Cai MB, Chen PHC, Ellis CT, Henselman-Petrusek G, Huberdeau D, Hutchinson JB, Li YP, Lu Q, Manning JR, Mennen AC, Nastase SA, Richard H, Schapiro AC, Schuck NW, Shvartsman M, Sundaram N, Suo D, Turek JS, Turner D, Vo VA, Wallace G, Wang Y, Williams JA, Zhang H, Zhu X, Capota˘ M, Cohen JD, Hasson U, Li K, Ramadge PJ, Turk-Browne NB, Willke TL, Norman KA. BrainIAK: The Brain Imaging Analysis Kit. APERTURE NEURO 2022; 1. [PMID: 35939268 PMCID: PMC9351935 DOI: 10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.
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Affiliation(s)
- Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Michael J. Anderson
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - James W. Antony
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | | | - Paula P. Brooks
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Ming Bo Cai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Japan
| | - Po-Hsuan Cameron Chen
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | | | | | | | | | - Y. Peeta Li
- Department of Psychology, University of Oregon, Eugene, OR
| | - Qihong Lu
- Department of Psychology, Princeton University, Princeton, NJ
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH
| | - Anne C. Mennen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Samuel A. Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Hugo Richard
- Parietal Team, Inria, Neurospin, CEA, Université Paris-Saclay, France
| | - Anna C. Schapiro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA
| | - Nicolas W. Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Michael Shvartsman
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Narayanan Sundaram
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - Daniel Suo
- epartment of Computer Science, Princeton University, Princeton, NJ
| | - Javier S. Turek
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - David Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Vy A. Vo
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Grant Wallace
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Yida Wang
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - Jamal A. Williams
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Hejia Zhang
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Xia Zhu
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Mihai Capota˘
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Jonathan D. Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Kai Li
- Department of Computer Science, Princeton University, Princeton, NJ
| | - Peter J. Ramadge
- Department of Electrical Engineering, and the Center for Statistics and Machine Learning, Princeton University, Princeton, NJ
| | | | | | - Kenneth A. Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
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Pinho AL, Amadon A, Fabre M, Dohmatob E, Denghien I, Torre JJ, Ginisty C, Becuwe-Desmidt S, Roger S, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Pinel P, Eger E, Varoquaux G, Pallier C, Dehaene S, Hertz-Pannier L, Thirion B. Subject-specific segregation of functional territories based on deep phenotyping. Hum Brain Mapp 2020; 42:841-870. [PMID: 33368868 PMCID: PMC7856658 DOI: 10.1002/hbm.25189] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/11/2020] [Accepted: 08/04/2020] [Indexed: 11/08/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset-a high-resolution task-fMRI dataset acquired in a fixed environment-in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large-scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants.
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Affiliation(s)
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Murielle Fabre
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, France.,Criteo AI Lab, Paris, France
| | - Isabelle Denghien
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | | | | | | | | | | | | | | | | | | | - Philippe Pinel
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | | | - Christophe Pallier
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France.,Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France.,UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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Tu Y, Mi L, Zhang W, Zhang H, Zhang J, Fan Y, Goradia D, Chen K, Caselli RJ, Reiman EM, Gu X, Wang Y. Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study. Neuroinformatics 2020; 18:531-548. [PMID: 32253701 PMCID: PMC7502473 DOI: 10.1007/s12021-020-09459-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton's method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβ- normal controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker.
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Affiliation(s)
- Yanshuai Tu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Liang Mi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Haomeng Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Junwei Zhang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Yonghui Fan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | | | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | | | - Xianfeng Gu
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA.
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