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Harp NR, Wager TD, Kober H. Neuromarkers in addiction: definitions, development strategies, and recent advances. J Neural Transm (Vienna) 2024; 131:509-523. [PMID: 38630190 DOI: 10.1007/s00702-024-02766-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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2
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Xie L, Raj Y, Varathan P, He B, Yu M, Nho K, Salama P, Saykin AJ, Yan J. Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease. J Alzheimers Dis 2024; 99:715-727. [PMID: 38728189 DOI: 10.3233/jad-240098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Background There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.
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Affiliation(s)
- Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Yash Raj
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Pradeep Varathan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Bing He
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Meichen Yu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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4
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Nemali A, Vockert N, Berron D, Maas A, Bernal J, Yakupov R, Peters O, Gref D, Cosma N, Preis L, Priller J, Spruth E, Altenstein S, Lohse A, Fliessbach K, Kimmich O, Vogt I, Wiltfang J, Hansen N, Bartels C, Schott BH, Maier F, Meiberth D, Glanz W, Incesoy E, Butryn M, Buerger K, Janowitz D, Pernecky R, Rauchmann B, Burow L, Teipel S, Kilimann I, Göerß D, Dyrba M, Laske C, Munk M, Sanzenbacher C, Müller S, Spottke A, Roy N, Heneka M, Brosseron F, Roeske S, Dobisch L, Ramirez A, Ewers M, Dechent P, Scheffler K, Kleineidam L, Wolfsgruber S, Wagner M, Jessen F, Duzel E, Ziegler G. Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation. Med Image Anal 2023; 90:102913. [PMID: 37660483 DOI: 10.1016/j.media.2023.102913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/28/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023]
Abstract
Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.
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Affiliation(s)
- A Nemali
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
| | - N Vockert
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - D Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - A Maas
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - J Bernal
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - R Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - O Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - D Gref
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - N Cosma
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - L Preis
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - J Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany; School of Medicine, Technical University of Munich; Department of Psychiatry and Psychotherapy, Munich, Germany; University of Edinburgh and UK DRI, Edinburgh, UK
| | - E Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - S Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - A Lohse
- Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - K Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - O Kimmich
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - I Vogt
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - J Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - N Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - C Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - B H Schott
- Leibniz Institute for Neurobiology, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - F Maier
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
| | - D Meiberth
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
| | - W Glanz
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany
| | - E Incesoy
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - M Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - K Buerger
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - D Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - R Pernecky
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany; Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - B Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - L Burow
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - S Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - I Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - D Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - M Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - C Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - M Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - C Sanzenbacher
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - S Müller
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - A Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - N Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - M Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Psychiatry and Psychotherapy, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - F Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - S Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - L Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - A Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany; Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
| | - M Ewers
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - P Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Germany
| | - K Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
| | - L Kleineidam
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - S Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - M Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - F Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany
| | - E Duzel
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - G Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
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5
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Thomas AW, Ré C, Poldrack RA. Benchmarking explanation methods for mental state decoding with deep learning models. Neuroimage 2023; 273:120109. [PMID: 37059157 DOI: 10.1016/j.neuroimage.2023.120109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/16/2023] Open
Abstract
Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal features of brain activity that allow to accurately identify (i.e., decode) these states. Once a DL model has been trained to accurately decode a set of mental states, neuroimaging researchers often make use of methods from explainable artificial intelligence research to understand the model's learned mappings between mental states and brain activity. Here, we benchmark prominent explanation methods in a mental state decoding analysis of multiple functional Magnetic Resonance Imaging (fMRI) datasets. Our findings demonstrate a gradient between two key characteristics of an explanation in mental state decoding, namely, its faithfulness and its alignment with other empirical evidence on the mapping between brain activity and decoded mental state: explanation methods with high explanation faithfulness, which capture the model's decision process well, generally provide explanations that align less well with other empirical evidence than the explanations of methods with less faithfulness. Based on our findings, we provide guidance for neuroimaging researchers on how to choose an explanation method to gain insight into the mental state decoding decisions of DL models.
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Affiliation(s)
- Armin W Thomas
- Stanford Data Science, Stanford University, 450 Serra Mall, 94305, Stanford, USA.
| | - Christopher Ré
- Dept. of Computer Science, Stanford University, 450 Serra Mall, 94305, Stanford, USA
| | - Russell A Poldrack
- Dept. of Psychology, Stanford University, 450 Serra Mall, Stanford, 94305, USA
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6
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Buch AM, Vértes PE, Seidlitz J, Kim SH, Grosenick L, Liston C. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat Neurosci 2023; 26:650-663. [PMID: 36894656 DOI: 10.1038/s41593-023-01259-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/17/2023] [Indexed: 03/11/2023]
Abstract
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.
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Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - So Hyun Kim
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA
- School of Psychology, Korea University, Seoul, South Korea
| | - Logan Grosenick
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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7
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Speer SPH, Keysers C, Barrios JC, Teurlings CJS, Smidts A, Boksem MAS, Wager TD, Gazzola V. A multivariate brain signature for reward. Neuroimage 2023; 271:119990. [PMID: 36878456 DOI: 10.1016/j.neuroimage.2023.119990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 03/07/2023] Open
Abstract
The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.
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Affiliation(s)
- Sebastian P H Speer
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Christian Keysers
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Brain and Cognition, Department of Psychology, University of Amsterdam, The Netherlands
| | | | - Cas J S Teurlings
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Ale Smidts
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Maarten A S Boksem
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Valeria Gazzola
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
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8
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Prigent S, Nguyen HN, Leconte L, Valades-Cruz CA, Hajj B, Salamero J, Kervrann C. SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos. Sci Rep 2023; 13:1489. [PMID: 36707688 PMCID: PMC9883505 DOI: 10.1038/s41598-022-26178-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 12/12/2022] [Indexed: 01/28/2023] Open
Abstract
Modern fluorescent microscopy imaging is still limited by the optical aberrations and the photon budget available in the specimen. A direct consequence is the necessity to develop flexible and "off-road" algorithms in order to recover structural details and improve spatial resolution, which is critical when restraining the illumination to low levels in order to limit photo-damages. Here, we report SPITFIR(e) a flexible method designed to accurately and quickly restore 2D-3D fluorescence microscopy images and videos (4D images). We designed a generic sparse-promoting regularizer to subtract undesirable out-of-focus background and we developed a primal-dual algorithm for fast optimization. SPITFIR(e) is a "swiss-knife" method for practitioners as it adapts to any microscopy techniques, to various sources of signal degradation (noise, blur), to variable image contents, as well as to low signal-to-noise ratios. Our method outperforms existing state-of-the-art algorithms, and is more flexible than supervised deep-learning methods requiring ground truth datasets. The performance, the flexibility, and the ability to push the spatiotemporal resolution limit of sub-diffracted fluorescence microscopy techniques are demonstrated on experimental datasets acquired with various microscopy techniques from 3D spinning-disk confocal up to lattice light sheet microscopy.
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Affiliation(s)
- Sylvain Prigent
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France
| | - Hoai-Nam Nguyen
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France
| | - Ludovic Leconte
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France
| | - Cesar Augusto Valades-Cruz
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France
| | - Bassam Hajj
- Laboratoire Physico-Chimie, Institut Curie, PSL Research University, Sorbonne Universités, CNRS UMR168, 75005, Paris, France
| | - Jean Salamero
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France
| | - Charles Kervrann
- SERPICO Project-Team, Inria Centre Rennes-Bretagne Atlantique, 35042, Rennes Cedex, France.
- SERPICO/STED Team, UMR144 CNRS Institut Curie, PSL Research University, Sorbonne Universités, 75005, Paris, France.
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9
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Huang W, Tan K, Zhang Z, Hu J, Dong S. A Review of Fusion Methods for Omics and Imaging Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:74-93. [PMID: 35044920 DOI: 10.1109/tcbb.2022.3143900] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.
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10
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Lee S, Parthasarathi T, Cooper N, Zauberman G, Lerman C, Kable JW. A neural signature of the vividness of prospective thought is modulated by temporal proximity during intertemporal decision making. Proc Natl Acad Sci U S A 2022; 119:e2214072119. [PMID: 36279433 PMCID: PMC9636959 DOI: 10.1073/pnas.2214072119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 09/27/2022] [Indexed: 11/18/2022] Open
Abstract
Why do people discount future rewards? Multiple theories in psychology argue that one reason is that future events are imagined less vividly than immediate events, thereby diminishing their perceived value. Here we provide neuroscientific evidence for this proposal. First, we construct a neural signature of the vividness of prospective thought, using an fMRI dataset where the vividness of imagined future events is orthogonal to their valence by design. Then, we apply this neural signature in two additional fMRI datasets, each using a different delay-discounting task, to show that neural measures of vividness decline as rewards are delayed farther into the future.
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Affiliation(s)
- Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Gal Zauberman
- Yale School of Management, Yale University, New Haven, CT 06511
| | - Caryn Lerman
- University of Southern California Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033
| | - Joseph W. Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
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11
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Thomas AW, Ré C, Poldrack RA. Interpreting mental state decoding with deep learning models. Trends Cogn Sci 2022; 26:972-986. [PMID: 36223760 DOI: 10.1016/j.tics.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 01/12/2023]
Abstract
In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. However, their widespread application in mental state decoding is hindered by their lack of interpretability, difficulties in applying them to small datasets, and in ensuring their reproducibility and robustness. We recommend approaching these challenges by leveraging recent advances in explainable artificial intelligence (XAI) and transfer learning, and also provide recommendations on how to improve the reproducibility and robustness of DL models in mental state decoding.
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Affiliation(s)
- Armin W Thomas
- Stanford Data Science, Stanford University, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Russell A Poldrack
- Stanford Data Science, Stanford University, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA
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12
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Niu X, Gou J, Chang H, Lowe M, Zhang F(Z. Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection. Stat Med 2022; 41:5046-5060. [DOI: 10.1002/sim.9553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 06/16/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Xin Niu
- Department of Psychological and Brain Sciences Drexel University Philadelphia Pennsylvania USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics Villanova University Villanova Pennsylvania USA
| | - Hansoo Chang
- Department of Psychological and Brain Sciences Drexel University Philadelphia Pennsylvania USA
| | - Michael Lowe
- Department of Psychological and Brain Sciences Drexel University Philadelphia Pennsylvania USA
| | - Fengqing (Zoe) Zhang
- Department of Psychological and Brain Sciences Drexel University Philadelphia Pennsylvania USA
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13
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Lee S, Bradlow ET, Kable JW. Fast construction of interpretable whole-brain decoders. CELL REPORTS METHODS 2022; 2:100227. [PMID: 35784649 PMCID: PMC9243546 DOI: 10.1016/j.crmeth.2022.100227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 04/11/2022] [Accepted: 05/16/2022] [Indexed: 01/15/2023]
Abstract
Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficient. We extend the partial least squares algorithm to build a regularized model with variable selection that offers a unique "fit once, tune later" approach: users need to fit the model only once and can choose the best tuning parameters post hoc. We show in real data that our method scales well with increasing data size and yields interpretable predictors. The algorithm is publicly available in multiple languages in the hope that interpretable whole-brain predictors can be implemented more widely in neuroimaging research.
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Affiliation(s)
- Sangil Lee
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
- Social Science Matrix, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Eric T. Bradlow
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
| | - Joseph W. Kable
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
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14
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Dashtestani H, Miguel HO, Condy EE, Zeytinoglu S, Millerhagen JB, Debnath R, Smith E, Adali T, Fox NA, Gandjbakhche AH. Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network. Sci Rep 2022; 12:6878. [PMID: 35477980 PMCID: PMC9046278 DOI: 10.1038/s41598-022-10942-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 04/13/2022] [Indexed: 11/25/2022] Open
Abstract
The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of action observation and action execution are still unclear due to lack of ecologically valid neuroimaging measures. In this study, we used concurrent EEG and functional Near Infrared Spectroscopy (fNIRS) to examine the AON during a live-action observation and execution paradigm. We developed structured sparse multiset canonical correlation analysis (ssmCCA) to perform EEG-fNIRS data fusion. MCCA is a generalization of CCA to more than two sets of variables and is commonly used in medical multimodal data fusion. However, mCCA suffers from multi-collinearity, high dimensionality, unimodal feature selection, and loss of spatial information in interpreting the results. A limited number of participants (small sample size) is another problem in mCCA, which leads to overfitted models. Here, we adopted graph-guided (structured) fused least absolute shrinkage and selection operator (LASSO) penalty to mCCA to conduct feature selection, incorporating structural information amongst the variables (i.e., brain regions). Benefitting from concurrent recordings of brain hemodynamic and electrophysiological responses, the proposed ssmCCA finds linear transforms of each modality such that the correlation between their projections is maximized. Our analysis of 21 right-handed participants indicated that the left inferior parietal region was active during both action execution and action observation. Our findings provide new insights into the neural correlates of AON which are more fine-tuned than the results from each individual EEG or fNIRS analysis and validate the use of ssmCCA to fuse EEG and fNIRS datasets.
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Affiliation(s)
- Hadis Dashtestani
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Helga O Miguel
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Emma E Condy
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Selin Zeytinoglu
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - John B Millerhagen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | | | - Elizabeth Smith
- Behavioral Medicine and Clinical Psychology Department, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Amir H Gandjbakhche
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA.
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15
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Duan Y, Zhao W, Luo C, Liu X, Jiang H, Tang Y, Liu C, Yao D. Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning. Front Hum Neurosci 2022; 15:765517. [PMID: 35273484 PMCID: PMC8902595 DOI: 10.3389/fnhum.2021.765517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.
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Affiliation(s)
- YuMei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - WeiDong Zhao
- College of Computer, Chengdu University, Chengdu, China
| | - Cheng Luo
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - XiaoJu Liu
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - YiQian Tang
- College of Computer, Chengdu University, Chengdu, China
| | - Chang Liu
- College of Computer, Chengdu University, Chengdu, China
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - DeZhong Yao
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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16
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Kim M, Min EJ, Liu K, Yan J, Saykin AJ, Moore JH, Long Q, Shen L. Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics. Med Image Anal 2022; 76:102297. [PMID: 34871929 PMCID: PMC8792314 DOI: 10.1016/j.media.2021.102297] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/08/2021] [Accepted: 10/29/2021] [Indexed: 02/03/2023]
Abstract
The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer's or Parkinson's disease to demonstrate the generalizability of our method.
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Affiliation(s)
- Mansu Kim
- Department of Artificial Intelligence, Catholic University of Korea, Bucheon, Republic of Korea
| | - Eun Jeong Min
- College of Medicine, Catholic University of Korea, Seoul, Republic of Korea
| | - Kefei Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University, IN, USA
| | | | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA.
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17
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Wang S, Wu X, Wei K, Kong W. An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease. Front Aging Neurosci 2022; 13:817520. [PMID: 35069181 PMCID: PMC8770861 DOI: 10.3389/fnagi.2021.817520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 12/14/2021] [Indexed: 01/01/2023] Open
Abstract
Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.
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18
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Wu A, Nastase SA, Baldassano CA, Turk-Browne NB, Norman KA, Engelhardt BE, Pillow JW. Brain kernel: A new spatial covariance function for fMRI data. Neuroimage 2021; 245:118580. [PMID: 34740792 DOI: 10.1016/j.neuroimage.2021.118580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/30/2021] [Accepted: 09/14/2021] [Indexed: 11/20/2022] Open
Abstract
A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel's usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.
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Affiliation(s)
- Anqi Wu
- Center for Theoretical Neuroscience, Columbia University, New York City, NY, USA.
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
| | | | | | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
| | | | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
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19
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Kim M, Kim J, Qu J, Huang H, Long Q, Sohn KA, Kim D, Shen L. Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:1381-1384. [PMID: 35299717 PMCID: PMC8922159 DOI: 10.1109/bibm52615.2021.9669504] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.
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Affiliation(s)
- Mansu Kim
- Department of Artificial Intelligence, Catholic University of Korea, Bucheon, South Korea
| | - Jaesik Kim
- Department of Computer Engineering, Ajou University, Suwon, South Korea
| | - Jeffrey Qu
- School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA
| | - Kyung-Ah Sohn
- Department of Artificial Intelligence, Ajou University, Suwon, South Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA,Correspondence to
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20
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Xie L, He B, Varathan P, Nho K, Risacher SL, Saykin AJ, Salama P, Yan J. Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease. Brief Bioinform 2021; 22:bbab121. [PMID: 33971669 PMCID: PMC8574309 DOI: 10.1093/bib/bbab121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 11/15/2022] Open
Abstract
A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.
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Affiliation(s)
- Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA
| | - Bing He
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA
| | - Pradeep Varathan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN 46204, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN 46204, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN 46204, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN 46204, USA
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21
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Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints. Med Biol Eng Comput 2021; 60:95-108. [PMID: 34714488 DOI: 10.1007/s11517-021-02439-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/02/2021] [Indexed: 10/20/2022]
Abstract
Imaging genetics research can explore the potential correlation between imaging and genomics. Most association analysis methods cannot effectively use the prior knowledge of the original data. In this respect, we add the prior knowledge of each original data to mine more effective biomarkers. The study of imaging genetics based on the sparse canonical correlation analysis (SCCA) is helpful to mine the potential biomarkers of neurological diseases. To improve the performance and interpretability of SCCA, we proposed a penalty method based on the autocorrelation matrix for discovering the possible biological mechanism between single nucleotide polymorphisms (SNP) variations and brain regions changes of Alzheimer's disease (AD). The addition of the penalty allows the proposed algorithm to analyze the correlation between different modal features. The proposed algorithm obtains more biologically interpretable ROIs and SNPs that are significantly related to AD, which has better anti-noise performance. Compared with other SCCA-based algorithms (JCB-SCCA, JSNMNMF), the proposed algorithm can still maintain a stronger correlation with ground truth even when the noise is larger. Then, we put the regions of interest (ROI) selected by the three algorithms into the SVM classifier. The proposed algorithm has higher classification accuracy. Also, we use ridge regression with SNPs selected by three algorithms and four AD risk ROIs. The proposed algorithm has a smaller root mean square error (RMSE). It shows that proposed algorithm has a good ability in association recognition and feature selection. Furthermore, it selects important features more stably, improving the clinical diagnosis of new potential biomarkers.
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22
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Zhao Y, Caffo B, Luo X. Principal regression for high dimensional covariance matrices. Electron J Stat 2021; 15:4192-4235. [PMID: 35782590 PMCID: PMC9248851 DOI: 10.1214/21-ejs1887] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
This manuscript presents an approach to perform generalized linear regression with multiple high dimensional covariance matrices as the outcome. In many areas of study, such as resting-state functional magnetic resonance imaging (fMRI) studies, this type of regression can be utilized to characterize variation in the covariance matrices across units. Model parameters are estimated by maximizing a likelihood formulation of a generalized linear model, conditioning on a well-conditioned linear shrinkage estimator for multiple covariance matrices, where the shrinkage coefficients are proposed to be shared across matrices. Theoretical studies demonstrate that the proposed covariance matrix estimator is optimal achieving the uniformly minimum quadratic loss asymptotically among all linear combinations of the identity matrix and the sample covariance matrix. Under certain regularity conditions, the proposed estimator of the model parameters is consistent. The superior performance of the proposed approach over existing methods is illustrated through simulation studies. Implemented to a resting-state fMRI study acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identified a brain network within which functional connectivity is significantly associated with Apolipoprotein E ε4, a strong genetic marker for Alzheimer's disease.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston
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23
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Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization. J Mol Neurosci 2021; 72:255-272. [PMID: 34410569 DOI: 10.1007/s12031-021-01888-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/06/2021] [Indexed: 10/20/2022]
Abstract
Imaging genetics reveals the connection between microscopic genetics and macroscopic imaging, enabling the identification of disease biomarkers. In this work, we make full use of prior knowledge that has significant reference value for investigating the correlation between the brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose joint-connectivity-based sparse nonnegative matrix factorization (JCB-SNMF). The algorithm simultaneously projects structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information for each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROIs), SNPs, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a protein-protein interaction (PPI) network, we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also found some significant SNP-ROI and gene-ROI pairs. Among them, two SNPs rs4472239 and rs11918049 and three genes KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.
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24
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Kaiser EE, Poythress J, Scheulin KM, Jurgielewicz BJ, Lazar NA, Park C, Stice SL, Ahn J, West FD. An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model. Neural Regen Res 2021; 16:842-850. [PMID: 33229718 PMCID: PMC8178783 DOI: 10.4103/1673-5374.297079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/08/2020] [Accepted: 07/22/2020] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a clinically relevant, real-time imaging modality that is frequently utilized to assess stroke type and severity. However, specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need. Consequently, the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke. Stroke was induced via permanent middle cerebral artery occlusion. At 24 hours post-stroke, MRI analysis revealed focal ischemic lesions, decreased diffusivity, hemispheric swelling, and white matter degradation. Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke. Gaussian graphical models identified specific MRI outputs and functional recovery variables, including white matter integrity and gait performance, that exhibited strong conditional dependencies. Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance. Consequently, these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities (e.g., white matter composition) that have proven to be critical in ischemic stroke pathophysiology. The study was approved by the University of Georgia (UGA) Institutional Animal Care and Use Committee (IACUC; Protocol Number: A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5) on November 22, 2017.
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Affiliation(s)
- Erin E. Kaiser
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
- Neuroscience, Biomedical and Health Sciences Institute, University of Georgia, Athens, GA, USA
- Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA
| | - J.C. Poythress
- Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Kelly M. Scheulin
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
- Neuroscience, Biomedical and Health Sciences Institute, University of Georgia, Athens, GA, USA
- Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA
| | - Brian J. Jurgielewicz
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
- Neuroscience, Biomedical and Health Sciences Institute, University of Georgia, Athens, GA, USA
- Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA
| | - Nicole A. Lazar
- Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Cheolwoo Park
- Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Steven L. Stice
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
- Neuroscience, Biomedical and Health Sciences Institute, University of Georgia, Athens, GA, USA
- Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA
| | - Jeongyoun Ahn
- Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Franklin D. West
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
- Neuroscience, Biomedical and Health Sciences Institute, University of Georgia, Athens, GA, USA
- Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA
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25
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Kim M, Bao J, Liu K, Park BY, Park H, Baik JY, Shen L. A structural enriched functional network: An application to predict brain cognitive performance. Med Image Anal 2021; 71:102026. [PMID: 33848962 DOI: 10.1016/j.media.2021.102026] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/10/2021] [Accepted: 03/01/2021] [Indexed: 11/19/2022]
Abstract
The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.
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Affiliation(s)
- Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingxuan Bao
- School of Arts and Sciences, University of Pennsylvania, PA, USA
| | - Kefei Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Jae Young Baik
- School of Arts and Sciences, University of Pennsylvania, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA.
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26
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Pietrosanu M, Zhang L, Seres P, Elkady A, Wilman AH, Kong L, Cobzas D. Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain. Front Hum Neurosci 2021; 15:641616. [PMID: 33708081 PMCID: PMC7940836 DOI: 10.3389/fnhum.2021.641616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L 1 (lasso), total variation (TV), and L 2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.
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Affiliation(s)
- Matthew Pietrosanu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Li Zhang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Ahmed Elkady
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Dana Cobzas
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.,Department of Computer Science, MacEwan University, Edmonton, AB, Canada
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27
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Jolly E, Chang LJ. Multivariate spatial feature selection in fMRI. Soc Cogn Affect Neurosci 2021; 16:795-806. [PMID: 33501987 PMCID: PMC8343556 DOI: 10.1093/scan/nsab010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/16/2020] [Accepted: 01/25/2021] [Indexed: 01/20/2023] Open
Abstract
Multivariate neuroimaging analyses constitute a powerful class of techniques to identify psychological representations. However, not all psychological processes are represented at the same spatial scale throughout the brain. This heterogeneity is apparent when comparing hierarchically organized local representations of perceptual processes to flexible transmodal representations of more abstract cognitive processes such as social and affective operations. An open question is how the spatial scale of analytic approaches interacts with the spatial scale of the representations under investigation. In this article, we describe how multivariate analyses can be viewed as existing on a spatial spectrum, anchored by searchlights used to identify locally distributed patterns of information on one end, whole brain approach used to identify diffuse neural representations at the other and region-based approaches in between. We describe how these distinctions are an important and often overlooked analytic consideration and provide heuristics to compare these different techniques to choose based on the analyst’s inferential goals.
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Affiliation(s)
- E Jolly
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA
| | - L J Chang
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA
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28
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Overmeyer R, Berghäuser J, Dieterich R, Wolff M, Goschke T, Endrass T. The Error-Related Negativity Predicts Self-Control Failures in Daily Life. Front Hum Neurosci 2021; 14:614979. [PMID: 33584226 PMCID: PMC7873054 DOI: 10.3389/fnhum.2020.614979] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
Adaptive behavior critically depends on performance monitoring (PM), the ability to monitor action outcomes and the need to adapt behavior. PM-related brain activity has been linked to guiding decisions about whether action adaptation is warranted. The present study examined whether PM-related brain activity in a flanker task, as measured by electroencephalography (EEG), was associated with adaptive behavior in daily life. Specifically, we were interested in the employment of self-control, operationalized as self-control failures (SCFs), and measured using ecological momentary assessment. Analyses were conducted using an adaptive elastic net regression to predict SCFs from EEG in a sample of 131 participants. The model was fit using within-subject averaged response-locked EEG activity at each electrode and time point within an epoch surrounding the response. We found that higher amplitudes of the error-related negativity (ERN) were related to fewer SCFs. This suggests that lower error-related activity may relate to lower recruitment of interventive self-control in daily life. Altered cognitive control processes, like PM, have been proposed as underlying mechanisms for various mental disorders. Understanding how alterations in PM relate to regulatory control might therefore aid in delineating how these alterations contribute to different psychopathologies.
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Affiliation(s)
- Rebecca Overmeyer
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Julia Berghäuser
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Raoul Dieterich
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Max Wolff
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.,Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Thomas Goschke
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.,Neuroimaging Centre, Technische Universität Dresden, Dresden, Germany
| | - Tanja Endrass
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.,Neuroimaging Centre, Technische Universität Dresden, Dresden, Germany
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29
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Adkins TJ, Lee TG. Reward modulates cortical representations of action. Neuroimage 2020; 228:117708. [PMID: 33385555 DOI: 10.1016/j.neuroimage.2020.117708] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 10/22/2022] Open
Abstract
People are capable of rapid improvements in performance when they are offered a reward. The neural mechanism by which this performance enhancement occurs remains unclear. We investigated this phenomenon by offering people monetary reward for successful performance in a sequence production task. We found that people performed actions more quickly and accurately when they were offered large reward. Increasing reward magnitude was associated with elevated activity throughout the brain prior to movement. Multivariate patterns of activity in these reward-responsive regions encoded information about the upcoming action. Follow-up analyses provided evidence that action decoding in pre-SMA and other motor planning areas was improved for large reward trials and successful action decoding in SMA was associated with improved performance. These results suggest that reward may enhance performance by enhancing neural representations of action used in motor planning.
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Affiliation(s)
- Tyler J Adkins
- Department of Psychology, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Taraz G Lee
- Department of Psychology, University of Michigan, Ann Arbor, MI, 48109, USA
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30
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Kim M, Kim JS, Youn J, Park H, Cho JW. GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105713. [PMID: 32846317 DOI: 10.1016/j.cmpb.2020.105713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinson's disease (PD). Imaging-based measurements, especially those related to the surface shape of the basal ganglia, have shown potential for explaining the severity of LID in PD. Here, we aimed to explore a novel application of the methodology to find biomarkers of LID severity in PD using regularization. METHODS We proposed an application of graph-constrained elastic net (GraphNet) regularization to detect surface-based shape biomarkers explaining the severity of LID and compared the approach with other conventional regularization methods. To examine the methods, we used two independent datasets, one as a training dataset to build the model, and the other dataset was used to validate the constructed model. RESULTS We found that the left striatum (putamen was the greatest and the caudate was second) was the most significant surface-based biomarker related to the severity of LID. Our results improved the interpretability of identified surface-based biomarkers compared to competing methods. We also found that GraphNet regularization improved prediction of the severity of LID better than the conventional regularization methods. Our model performed better in terms of root-mean-squared error and correlation coefficient between predicted and actual clinical scores. CONCLUSION The proposed algorithm offers an advantage of interpretable anatomical variations related to the deformation of the cortical surface. The experimental results showed that GraphNet regularization was robust to identify surface-based shape biomarkers related to both hypokinetic and hyperkinetic movement disorders.
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Affiliation(s)
- Mansu Kim
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea; Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jinyoung Youn
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Korea; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea.
| | - Jin Whan Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Neuroscience Center, Samsung Medical Center, Seoul, Korea
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31
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Kim M, Bao J, Liu K, Park BY, Park H, Shen L. Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2020; 12436:292-302. [PMID: 34766171 PMCID: PMC8580103 DOI: 10.1007/978-3-030-59861-7_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The connectivity analysis is a powerful technique for investigating a hard-wired brain architecture as well as flexible, functional dynamics tied to human cognition. Recent multi-modal connectivity studies had the challenge of combining functional and structural connectivity information into one integrated network. In this paper, we proposed a simplex regression model with graph-constrained Elastic Net (GraphNet) to estimate functional networks enriched by structural connectivity in a biologically meaningful way with a low model complexity. Our model constructed the functional networks using sparse simplex regression framework and enriched structural connectivity information based on GraphNet constraint. We applied our model on the real neuroimaging datasets to show its ability for predicting a clinical score. Our results demonstrated that integrating multi-modal features could detect more sensitive and subtle brain biomarkers than using a single modality.
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Affiliation(s)
- Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
| | - Jingxaun Bao
- School of Arts and Sciences, University of Pennsylvania, USA
| | - Kefei Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Canada
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Korea
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
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32
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Yılmaz Ö, Çelik E, Çukur T. Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments. Eur J Neurosci 2020; 52:3394-3410. [PMID: 32343012 PMCID: PMC9748846 DOI: 10.1111/ejn.14760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 03/18/2020] [Accepted: 04/21/2020] [Indexed: 12/16/2022]
Abstract
Voxelwise modeling is a powerful framework to predict single-voxel functional selectivity for the stimulus features that exist in complex natural stimuli. Yet, because VM disregards potential correlations across stimulus features or neighboring voxels, it may yield suboptimal sensitivity in measuring functional selectivity in the presence of high levels of measurement noise. Here, we introduce a novel voxelwise modeling approach that simultaneously utilizes stimulus correlations in model features and response correlations among voxel neighborhoods. The proposed method performs feature and spatial regularization while still generating single-voxel response predictions. We demonstrated the performance of our approach on a functional magnetic resonance imaging dataset from a natural vision experiment. Compared to VM, the proposed method yields clear improvements in prediction performance, together with increased feature coherence and spatial coherence of voxelwise models. Overall, the proposed method can offer improved sensitivity in modeling of single voxels in naturalistic functional magnetic resonance imaging experiments.
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Affiliation(s)
- Özgür Yılmaz
- National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey,Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
| | - Emin Çelik
- National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey,Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
| | - Tolga Çukur
- National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey,Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey,Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
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Cai MB, Shvartsman M, Wu A, Zhang H, Zhu X. Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. Neuropsychologia 2020; 144:107500. [PMID: 32433952 PMCID: PMC7387580 DOI: 10.1016/j.neuropsychologia.2020.107500] [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] [Received: 10/31/2019] [Revised: 05/09/2020] [Accepted: 05/15/2020] [Indexed: 01/27/2023]
Abstract
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.
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Affiliation(s)
- Ming Bo Cai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Japan; Princeton Neuroscience Institute, Princeton University, United States.
| | | | - Anqi Wu
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, United States
| | - Hejia Zhang
- Department of Electrical Engineering, Princeton University, United States
| | - Xia Zhu
- Intel Corporation, United States
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Individual differences in local functional brain connectivity affect TMS effects on behavior. Sci Rep 2020; 10:10422. [PMID: 32591568 PMCID: PMC7320140 DOI: 10.1038/s41598-020-67162-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 05/18/2020] [Indexed: 11/25/2022] Open
Abstract
Behavioral effects of transcranial magnetic stimulation (TMS) often show substantial differences between subjects. One factor that might contribute to these inter-individual differences is the interaction of current brain states with the effects of local brain network perturbation. The aim of the current study was to identify brain regions whose connectivity before and following right parietal perturbation affects individual behavioral effects during a visuospatial target detection task. 20 subjects participated in an fMRI experiment where their brain hemodynamic response was measured during resting state, and then during a visuospatial target detection task following 1 Hz rTMS and sham stimulation. To select a parsimonious set of associated brain regions, an elastic net analysis was used in combination with a whole-brain voxel-wise functional connectivity analysis. TMS-induced changes in accuracy were significantly correlated with the pattern of functional connectivity during the task state following TMS. The functional connectivity of the left superior temporal, angular, and precentral gyri was identified as key explanatory variable for the individual behavioral TMS effects. Our results suggest that the brain must reach an appropriate state in which right parietal TMS can induce improvements in visual target detection. The ability to reach this state appears to vary between individuals.
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Xie L, Varathan P, Nho K, Saykin AJ, Salama P, Yan J. Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso. PLoS One 2020; 15:e0234748. [PMID: 32555747 PMCID: PMC7299377 DOI: 10.1371/journal.pone.0234748] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 06/02/2020] [Indexed: 12/16/2022] Open
Abstract
Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level.
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Affiliation(s)
- Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Pradeep Varathan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
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36
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Gómez-Verdejo V, Parrado-Hernández E, Tohka J. Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging. Neuroinformatics 2020; 17:593-609. [PMID: 30919255 PMCID: PMC6841656 DOI: 10.1007/s12021-019-9415-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.
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Affiliation(s)
- Vanessa Gómez-Verdejo
- Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Spain
| | - Emilio Parrado-Hernández
- Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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Kohoutová L, Heo J, Cha S, Lee S, Moon T, Wager TD, Woo CW. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat Protoc 2020; 15:1399-1435. [PMID: 32203486 PMCID: PMC9533325 DOI: 10.1038/s41596-019-0289-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 12/19/2019] [Indexed: 12/15/2022]
Abstract
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories.
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Affiliation(s)
- Lada Kohoutová
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Juyeon Heo
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sungmin Cha
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sungwoo Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Taesup Moon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
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38
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Zhang Y, Peng P, Ju Y, Li G, Calhoun VD, Wang YP. Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity. IEEE J Biomed Health Inform 2020; 24:2621-2629. [PMID: 32071012 DOI: 10.1109/jbhi.2020.2972581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.
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40
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Kim M, Won JH, Youn J, Park H. Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:23-34. [PMID: 31144631 DOI: 10.1109/tmi.2019.2918839] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson's disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer's patients or healthy subjects to determine the generalizability of our algorithm.
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41
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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Thomas AW, Heekeren HR, Müller KR, Samek W. Analyzing Neuroimaging Data Through Recurrent Deep Learning Models. Front Neurosci 2019; 13:1321. [PMID: 31920491 PMCID: PMC6914836 DOI: 10.3389/fnins.2019.01321] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 11/25/2019] [Indexed: 01/25/2023] Open
Abstract
The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.
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Affiliation(s)
- Armin W. Thomas
- Machine Learning Group, Technische Universität Berlin, Berlin, Germany
- Center for Cognitive Neuroscience Berlin, Freie Universität Berlin, Berlin, Germany
- Max Planck School of Cognition, Leipzig, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Hauke R. Heekeren
- Center for Cognitive Neuroscience Berlin, Freie Universität Berlin, Berlin, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Wojciech Samek
- Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
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43
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Williams AH, Poole B, Maheswaranathan N, Dhawale AK, Fisher T, Wilson CD, Brann DH, Trautmann EM, Ryu S, Shusterman R, Rinberg D, Ölveczky BP, Shenoy KV, Ganguli S. Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping. Neuron 2019; 105:246-259.e8. [PMID: 31786013 DOI: 10.1016/j.neuron.2019.10.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/17/2019] [Accepted: 10/10/2019] [Indexed: 12/22/2022]
Abstract
Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.
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Affiliation(s)
- Alex H Williams
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA.
| | - Ben Poole
- Google Brain, Google Inc., Mountain View, CA 94043, USA
| | | | - Ashesh K Dhawale
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Tucker Fisher
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA
| | - Christopher D Wilson
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA
| | - David H Brann
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Eric M Trautmann
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Stephen Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Roman Shusterman
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Dmitry Rinberg
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Krishna V Shenoy
- Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Surya Ganguli
- Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Google Brain, Google Inc., Mountain View, CA 94043, USA.
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44
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Arroyo Relión JD, Kessler D, Levina E, Taylor SF. NETWORK CLASSIFICATION WITH APPLICATIONS TO BRAIN CONNECTOMICS. Ann Appl Stat 2019; 13:1648-1677. [PMID: 33408802 DOI: 10.1214/19-aoas1252] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.
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Abstract
Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
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46
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Yousefnezhad M, Zhang D. Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis. Neuroinformatics 2019; 17:197-210. [PMID: 30094688 DOI: 10.1007/s12021-018-9394-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.
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Affiliation(s)
- Muhammad Yousefnezhad
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
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47
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Poldrack RA, Gorgolewski KJ, Varoquaux G. Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021237] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
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Affiliation(s)
- Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California 94305, USA
| | | | - Gaël Varoquaux
- Parietal Team, Inria and NeuroSpin/CEA (Atomic Energy Commission), 91191 Gif/-sur-Yvette, France
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48
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Beer JC, Aizenstein HJ, Anderson SJ, Krafty RT. Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages. Biometrics 2019; 75:1299-1309. [PMID: 31022303 DOI: 10.1111/biom.13075] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 04/16/2019] [Indexed: 01/09/2023]
Abstract
Predicting clinical variables from whole-brain neuroimages is a high-dimensional problem that can potentially benefit from feature selection or extraction. Penalized regression is a popular embedded feature selection method for high-dimensional data. For neuroimaging applications, spatial regularization using the ℓ 1 or ℓ 2 norm of the image gradient has shown good performance, yielding smooth solutions in spatially contiguous brain regions. Enormous resources have been devoted to establishing structural and functional brain connectivity networks that can be used to define spatially distributed yet related groups of voxels. We propose using the fused sparse group lasso (FSGL) penalty to encourage structured, sparse, and interpretable solutions by incorporating prior information about spatial and group structure among voxels. We present optimization steps for FSGL penalized regression using the alternating direction method of multipliers algorithm. With simulation studies and in application to real functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange, we demonstrate conditions under which fusion and group penalty terms together outperform either of them alone.
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Affiliation(s)
- Joanne C Beer
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Howard J Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Stewart J Anderson
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Robert T Krafty
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
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Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. Neuroimage 2019; 200:528-539. [PMID: 31201988 PMCID: PMC6711452 DOI: 10.1016/j.neuroimage.2019.06.017] [Citation(s) in RCA: 206] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/01/2019] [Accepted: 06/05/2019] [Indexed: 11/25/2022] Open
Abstract
It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank. It is of interest to study "brain age'', as inferred from brain imaging data. The delta between brain age and actual age is typically computed. We describe problems with the most common current approach. We present potential improvements. We evaluate methods on simulated data and data from UK Biobank.
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Affiliation(s)
- Stephen M Smith
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-OHBA), University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Big Data Institute, University of Oxford, Oxford, UK; Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
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Grosenick L, Shi TC, Gunning FM, Dubin MJ, Downar J, Liston C. Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:554-566. [PMID: 31176387 PMCID: PMC6788795 DOI: 10.1016/j.bpsc.2019.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/29/2019] [Accepted: 04/29/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Previously, we identified four depression subtypes defined by distinct functional connectivity alterations in depression-related brain networks, which in turn predicted clinical symptoms and treatment response. Optogenetic functional magnetic resonance imaging offers a promising approach for testing how dysfunction in specific circuits gives rise to subtype-specific, depression-related behaviors. However, this approach assumes that there are robust, reproducible correlations between functional connectivity and depressive symptoms-an assumption that was not extensively tested in previous work. METHODS First, we comprehensively reevaluated the stability of canonical correlations between functional connectivity and symptoms (N = 220 subjects) using optimized approaches for large-scale statistical hypothesis testing, and we validated methods for improving estimation of latent variables driving brain-behavior correlations. Having confirmed this necessary condition, we reviewed recent advances in optogenetic functional magnetic resonance imaging and illustrated one approach to formulating hypotheses regarding latent subtype-specific circuit mechanisms and testing them in animal models. RESULTS Correlations between connectivity features and clinical symptoms were robustly significant, and canonical correlation analysis solutions tested repeatedly on held-out data generalized. However, they were sensitive to data quality, preprocessing, and clinical heterogeneity, which can reduce effect sizes. Generalization could be markedly improved by adding L2 regularization, which decreased estimator variance, increased canonical correlations in left-out data, and stabilized feature selection. These improvements were useful for identifying candidate circuits for optogenetic interrogation in animal models. CONCLUSIONS Multiview, latent-variable approaches such as canonical correlation analysis offer a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic functional magnetic resonance imaging holds promise for testing hypotheses regarding latent, subtype-specific mechanisms driving depressive symptoms and behaviors.
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Affiliation(s)
- Logan Grosenick
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York; Department of Statistics, Columbia University, New York, New York; Simons Foundation, New York, New York
| | - Tracey C Shi
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M Gunning
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Marc J Dubin
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry, Toronto Western Hospital, Toronto, Ontario, Canada
| | - Conor Liston
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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