1
|
Kobel CM, Merkesvik J, Burgos IMT, Lai W, Øyås O, Pope PB, Hvidsten TR, Aho VTE. Integrating host and microbiome biology using holo-omics. Mol Omics 2024; 20:438-452. [PMID: 38963125 DOI: 10.1039/d4mo00017j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Holo-omics is the use of omics data to study a host and its inherent microbiomes - a biological system known as a "holobiont". A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host-microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host-microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host-microbiome interplay to make meaningful interpretations and biotechnological applications.
Collapse
Affiliation(s)
- Carl M Kobel
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
| | - Jenny Merkesvik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | | | - Wanxin Lai
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
| | - Phillip B Pope
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Torgeir R Hvidsten
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Velma T E Aho
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
| |
Collapse
|
2
|
Di Tullio RW, Wei L, Balasubramanian V. Slow and steady: auditory features for discriminating animal vocalizations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.20.599962. [PMID: 39005308 PMCID: PMC11244870 DOI: 10.1101/2024.06.20.599962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
We propose that listeners can use temporal regularities - spectro-temporal correlations that change smoothly over time - to discriminate animal vocalizations within and between species. To test this idea, we used Slow Feature Analysis (SFA) to find the most temporally regular components of vocalizations from birds (blue jay, house finch, American yellow warbler, and great blue heron), humans (English speakers), and rhesus macaques. We projected vocalizations into the learned feature space and tested intra-class (same speaker/species) and inter-class (different speakers/species) auditory discrimination by a trained classifier. We found that: 1) Vocalization discrimination was excellent (> 95%) in all cases; 2) Performance depended primarily on the ~10 most temporally regular features; 3) Most vocalizations are dominated by ~10 features with high temporal regularity; and 4) These regular features are highly correlated with the most predictable components of animal sounds.
Collapse
Affiliation(s)
- Ronald W Di Tullio
- David Rittenhouse Laboratory, Department of Physics and Astronomy, University of Pennsylvania, USA
- Computational Neuroscience Initiative, University of Pennsylvania, USA
| | - Linran Wei
- David Rittenhouse Laboratory, Department of Physics and Astronomy, University of Pennsylkvania, USA
| | - Vijay Balasubramanian
- David Rittenhouse Laboratory, Department of Physics and Astronomy, University of Pennsylvania, USA
- Computational Neuroscience Initiative, University of Pennsylvania, USA
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| |
Collapse
|
3
|
Martini KM, Nemenman I. Data Efficiency, Dimensionality Reduction, and the Generalized Symmetric Information Bottleneck. Neural Comput 2024; 36:1353-1379. [PMID: 38669695 DOI: 10.1162/neco_a_01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 02/06/2024] [Indexed: 04/28/2024]
Abstract
The symmetric information bottleneck (SIB), an extension of the more familiar information bottleneck, is a dimensionality-reduction technique that simultaneously compresses two random variables to preserve information between their compressed versions. We introduce the generalized symmetric information bottleneck (GSIB), which explores different functional forms of the cost of such simultaneous reduction. We then explore the data set size requirements of such simultaneous compression. We do this by deriving bounds and root-mean-squared estimates of statistical fluctuations of the involved loss functions. We show that in typical situations, the simultaneous GSIB compression requires qualitatively less data to achieve the same errors compared to compressing variables one at a time. We suggest that this is an example of a more general principle that simultaneous compression is more data efficient than independent compression of each of the input variables.
Collapse
Affiliation(s)
- K Michael Martini
- Department of Physics and Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA 30322, U.S.A.
| | - Ilya Nemenman
- Department of Physics, Department of Biology, and Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA 30322, U.S.A.
| |
Collapse
|
4
|
Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
Collapse
Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
5
|
Lee H, Ma T, Ke H, Ye Z, Chen S. dCCA: detecting differential covariation patterns between two types of high-throughput omics data. Brief Bioinform 2024; 25:bbae288. [PMID: 38888456 PMCID: PMC11184902 DOI: 10.1093/bib/bbae288] [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: 02/21/2024] [Revised: 05/01/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes. RESULTS We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions. AVAILABILITY AND IMPLEMENTATION The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.
Collapse
Affiliation(s)
- Hwiyoung Lee
- Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD 21201, United States
- The University of Maryland Institute for Health Computing (UM-IHC), North Bethesda, MD 20852, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, United States
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, United States
| | - Zhenyao Ye
- The University of Maryland Institute for Health Computing (UM-IHC), North Bethesda, MD 20852, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD 21201, United States
- The University of Maryland Institute for Health Computing (UM-IHC), North Bethesda, MD 20852, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, United States
| |
Collapse
|
6
|
Andrés-Camazón P, Diaz-Caneja CM, Ballem R, Chen J, Calhoun VD, Iraji A. Neurobiology-based Cognitive Biotypes Using Multi-scale Intrinsic Connectivity Networks in Psychotic Disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.14.24307341. [PMID: 38798576 PMCID: PMC11118619 DOI: 10.1101/2024.05.14.24307341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective Understanding the neurobiology of cognitive dysfunction in psychotic disorders remains elusive, as does developing effective interventions. Limited knowledge about the biological heterogeneity of cognitive dysfunction hinders progress. This study aimed to identify subgroups of patients with psychosis with distinct patterns of functional brain alterations related to cognition (cognitive biotypes). Methods B-SNIP consortium data (2,270 participants including participants with psychotic disorders, relatives, and controls) was analyzed. Researchers used reference-informed independent component analysis and the NeuroMark 100k multi-scale intrinsic connectivity networks (ICN) template to obtain subject-specific ICNs and whole-brain functional network connectivity (FNC). FNC features associated with cognitive performance were identified through multivariate joint analysis. K-means clustering identified subgroups of patients based on these features in a discovery set. Subgroups were further evaluated in a replication set and in relatives. Results Two biotypes with different functional brain alteration patterns were identified. Biotype 1 exhibited brain-wide alterations, involving hypoconnectivity in cerebellar-subcortical and somatomotor-visual networks and worse cognitive performance. Biotype 2 exhibited hyperconnectivity in somatomotor-subcortical networks and hypoconnectivity in somatomotor-high cognitive processing networks, and better preserved cognitive performance. Demographic, clinical, cognitive, and FNC characteristics of biotypes were consistent in discovery and replication sets, and in relatives. 70.12% of relatives belonged to the same biotype as their affected family members. Conclusions These findings suggest two distinctive psychosis-related cognitive biotypes with differing functional brain patterns shared with their relatives. Patient stratification based on these biotypes instead of traditional diagnosis may help to optimize future research and clinical trials addressing cognitive dysfunction in psychotic disorders.
Collapse
Affiliation(s)
- Pablo Andrés-Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Covadonga Martínez Diaz-Caneja
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Ram Ballem
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| |
Collapse
|
7
|
Brieant A, Sisk LM, Keding TJ, Cohodes EM, Gee DG. Leveraging multivariate approaches to advance the science of early-life adversity. CHILD ABUSE & NEGLECT 2024:106754. [PMID: 38521731 DOI: 10.1016/j.chiabu.2024.106754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/12/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024]
Abstract
Since the landmark Adverse Childhood Experiences (ACEs) study, adversity research has expanded to more precisely account for the multifaceted nature of adverse experiences. The complex data structures and interrelated nature of adversity data require robust multivariate statistical methods, and recent methodological and statistical innovations have facilitated advancements in research on childhood adversity. Here, we provide an overview of a subset of multivariate methods that we believe hold particular promise for advancing the field's understanding of early-life adversity, and discuss how these approaches can be practically applied to explore different research questions. This review covers data-driven or unsupervised approaches (including dimensionality reduction and person-centered clustering/subtype identification) as well as supervised/prediction-based approaches (including linear and tree-based models and neural networks). For each, we highlight studies that have effectively applied the method to provide novel insight into early-life adversity. Taken together, we hope this review serves as a resource to adversity researchers looking to expand upon the cumulative approach described in the original ACEs study, thereby advancing the field's understanding of the complexity of adversity and related developmental consequences.
Collapse
Affiliation(s)
- Alexis Brieant
- University of Vermont, Department of Psychological Science, 2 Colchester Avenue, Burlington, VT 05402, USA; Yale University, Department of Psychology, 100 College Street, New Haven, CT 06510, USA.
| | - Lucinda M Sisk
- Yale University, Department of Psychology, 100 College Street, New Haven, CT 06510, USA
| | - Taylor J Keding
- Yale University, Department of Psychology, 100 College Street, New Haven, CT 06510, USA
| | - Emily M Cohodes
- Yale University, Department of Psychology, 100 College Street, New Haven, CT 06510, USA
| | - Dylan G Gee
- Yale University, Department of Psychology, 100 College Street, New Haven, CT 06510, USA
| |
Collapse
|
8
|
Helmer M, Warrington S, Mohammadi-Nejad AR, Ji JL, Howell A, Rosand B, Anticevic A, Sotiropoulos SN, Murray JD. On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations. Commun Biol 2024; 7:217. [PMID: 38383808 PMCID: PMC11245620 DOI: 10.1038/s42003-024-05869-4] [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: 05/07/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.
Collapse
Affiliation(s)
- Markus Helmer
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
| | - Ali-Reza Mohammadi-Nejad
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom
| | - Jie Lisa Ji
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Amber Howell
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Benjamin Rosand
- Department of Physics, Yale University, New Haven, CT, 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychology, Yale University, New Haven, CT, 06511, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom.
| | - John D Murray
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA.
- Manifest Technologies, New Haven, CT, 06510, USA.
- Department of Physics, Yale University, New Haven, CT, 06511, USA.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA.
| |
Collapse
|
9
|
Ishihara T, Hashimoto S, Tamba N, Hyodo K, Matsuda T, Takagishi H. The links between physical activity and prosocial behavior: an fNIRS hyperscanning study. Cereb Cortex 2024; 34:bhad509. [PMID: 38183181 DOI: 10.1093/cercor/bhad509] [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: 09/25/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 01/07/2024] Open
Abstract
The prevalence of physically inactive lifestyles in modern society raises concerns about the potential association with poor brain health, particularly in the lateral prefrontal cortex, which is crucial for human prosocial behavior. Here, we explored the relationship between physical activity and prosocial behavior, focusing on potential neural markers, including intra-brain functional connectivity and inter-brain synchrony in the lateral prefrontal cortex. Forty participants, each paired with a stranger, completed two experimental conditions in a randomized order: (i) face-to-face and (ii) face stimulus (eye-to-eye contact with a face stimulus of a fictitious person displayed on the screen). Following each condition, participants played economic games with either their partner or an assumed person displayed on the screen. Neural activity in the lateral prefrontal cortex was recorded by functional near-infrared spectroscopy hyperscanning. Sparse multiset canonical correlation analysis showed that a physically inactive lifestyle was covaried with poorer reciprocity, greater trust, shorter decision-making time, and weaker intra-brain connectivity in the dorsal lateral prefrontal cortex and poorer inter-brain synchrony in the ventral lateral prefrontal cortex. These associations were observed exclusively in the face-to-face condition. Our findings suggest that a physically inactive lifestyle may alter human prosocial behavior by impairing adaptable prosocial decision-making in response to social factors through altered intra-brain functional connectivity and inter-brain synchrony.
Collapse
Affiliation(s)
- Toru Ishihara
- Graduate School of Human Development and Environment, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, Japan
| | - Shinnosuke Hashimoto
- Graduate School of Human Development and Environment, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, Japan
| | - Natsuki Tamba
- Faculty of Global Human Sciences, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, Japan
| | - Kazuki Hyodo
- Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Tobuki 150, Hachioji, Tokyo 192-0001, Japan
| | - Tetsuya Matsuda
- Tamagawa University Brain Science Institute, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan
| | - Haruto Takagishi
- Tamagawa University Brain Science Institute, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan
| |
Collapse
|
10
|
Chen B, Olson L, Rios A, Salmina M, Linke A, Fishman I. Reduced covariation between brain morphometry and local spontaneous activity in young children with ASD. Cereb Cortex 2024; 34:bhae005. [PMID: 38282456 PMCID: PMC10839841 DOI: 10.1093/cercor/bhae005] [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: 10/24/2023] [Revised: 12/21/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
While disruptions in brain maturation in the first years of life in ASD are well documented, little is known about how the brain structure and function are related in young children with ASD compared to typically developing peers. We applied a multivariate pattern analysis to examine the covariation patterns between brain morphometry and local brain spontaneous activity in 38 toddlers and preschoolers with ASD and 31 typically developing children using T1-weighted structural MRI and resting-state fMRI data acquired during natural sleep. The results revealed significantly reduced brain structure-function correlations in ASD. The resultant brain structure and function composite indices were associated with age among typically developing children, but not among those with ASD, suggesting mistiming of typical brain maturational trajectories early in life in autism. Additionally, the brain function composite indices were associated with the overall developmental and adaptive behavior skills in the ASD group, highlighting the neurodevelopmental significance of early local brain activity in autism.
Collapse
Affiliation(s)
- Bosi Chen
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY 10016, United States
| | - Lindsay Olson
- Department of Psychiatry, University of California San Francisco, San Francisco, CA 94107, United States
| | - Adriana Rios
- Department of Psychology, Brain Development Imaging Laboratories, San Diego State University, San Diego, CA 92120, United States
| | - Madison Salmina
- Department of Psychology, Brain Development Imaging Laboratories, San Diego State University, San Diego, CA 92120, United States
| | - Annika Linke
- Department of Psychology, Brain Development Imaging Laboratories, San Diego State University, San Diego, CA 92120, United States
- SDSU Center for Autism and Developmental Disorders, San Diego State University, San Diego, CA 92120, United States
| | - Inna Fishman
- Department of Psychology, Brain Development Imaging Laboratories, San Diego State University, San Diego, CA 92120, United States
- SDSU Center for Autism and Developmental Disorders, San Diego State University, San Diego, CA 92120, United States
| |
Collapse
|
11
|
Ganjali M, Mehridehnavi A, Rakhshani S, Khorasani A. Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals. Int J Neural Syst 2024; 34:2450006. [PMID: 38063378 DOI: 10.1142/s0129065724500060] [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: 12/28/2023]
Abstract
The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.
Collapse
Affiliation(s)
- Mohammadali Ganjali
- Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Mehridehnavi
- Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sajed Rakhshani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Abed Khorasani
- Department of Neurology, Northwestern University, Chicago, IL, 60611, USA
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
12
|
Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
Collapse
Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
13
|
Zhou Z, Tarzanagh DA, Hou B, Tong B, Xu J, Feng Y, Long Q, Shen L. Fair Canonical Correlation Analysis. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2023; 36:3675-3705. [PMID: 38665178 PMCID: PMC11040228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.
Collapse
Affiliation(s)
| | | | | | | | - Jia Xu
- University of Pennsylvania
| | | | | | | |
Collapse
|
14
|
Calarco N, Oliver LD, Joseph M, Hawco C, Dickie EW, DeRosse P, Gold JM, Foussias G, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls. Schizophr Bull 2023; 49:1518-1529. [PMID: 36869812 PMCID: PMC10686342 DOI: 10.1093/schbul/sbac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s). STUDY DESIGN We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD. STUDY RESULTS Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes. CONCLUSIONS The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.
Collapse
Affiliation(s)
- Navona Calarco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Michael Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pamela DeRosse
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Miklos Argyelan
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Robert W Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
15
|
Zheng Y, Ooi MCG, Juneng L, Wee HB, Latif MT, Nadzir MSM, Hanif NM, Chan A, Li L, Ahmad NB, Tangang F. Assessing the impacts of climate variables on long-term air quality trends in Peninsular Malaysia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166430. [PMID: 37607626 DOI: 10.1016/j.scitotenv.2023.166430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/16/2023] [Accepted: 08/17/2023] [Indexed: 08/24/2023]
Abstract
Climate change is thought to influence the composition of atmospheric air, but little is known about the direct relationship between these variables, especially in a hot tropical climate like that of Malaysia. This work summarizes and analyzes the climate state and air quality of Peninsular Malaysia based on selected ground-based observations of the temperature, precipitation, relative humidity, wind speed, wind direction and concentrations of PM10, O3, CO, NO2, and SO2 over the last 20 years (2000-2019). The relationship between the climate state and air quality is analyzed using the Pearson correlation and canonical correlation analysis (CCA) methods is employed to predict the degree of change in the future air quality under different warming scenarios. It is found that the Peninsular Malaysia mainly experienced strong precipitation in the central and mountainous regions, while air pollutants are primarily concentrated in densely populated areas. Throughout the period of study (interannual, monthly, and diurnal time series analyses), Peninsular Malaysia became warmer and drier, with a significant increase in temperature (+4.2 %), decrease in the relative humidity (-4.5 %), and greater fluctuation in precipitation amount. The pollution conditions have worsened; there has been an increase in the PM10 (+16.4 %), O3 (+39.5 %), and NO2 (+2.1 %) concentration over the last 20 years. However, the amount of SO2 (-53.6 %) and CO (-20.6 %) decreased significantly. The analysis of the monthly variation shows a strong bimodality of the PM10 and O3 concentrations that corresponds to the monsoon transition. Intensive diurnal fluctuations and correlations are observed for all the variables in this study. According to the CCA, the air quality factors are strongly correlated with meteorological factors; in particular, the CO, O3, and PM10 concentrations interact strongly with the air temperature. These findings show that the future air quality in Peninsular Malaysia has high possibility to deteriorate under warming condition.
Collapse
Affiliation(s)
- Yijing Zheng
- Centre of Tropical Climate Change System, Insitute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China
| | - Maggie Chel Gee Ooi
- Centre of Tropical Climate Change System, Insitute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Liew Juneng
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Hin Boo Wee
- Centre of Tropical Climate Change System, Insitute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Mohd Talib Latif
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Mohd Shahrul Mohd Nadzir
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Norfazrin Mohd Hanif
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Andy Chan
- School of Engineering, Robert Gordon University, Aberdeen AB10 7GE, United Kingdom
| | - Li Li
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai, China
| | - Norfazilah Binti Ahmad
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Fredolin Tangang
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| |
Collapse
|
16
|
Zhao C, Keyak JH, Cao X, Sha Q, Wu L, Luo Z, Zhao LJ, Tian Q, Serou M, Qiu C, Su KJ, Shen H, Deng HW, Zhou W. Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load. Front Endocrinol (Lausanne) 2023; 14:1261088. [PMID: 38075049 PMCID: PMC10710145 DOI: 10.3389/fendo.2023.1261088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
Background Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.
Collapse
Affiliation(s)
- Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, United States
| | - Joyce H. Keyak
- Department of Radiological Sciences, Department of Biomedical Engineering, Department of Mechanical and Aerospace Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, United States
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Li Wu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Lan-Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Michael Serou
- Department of Radiology, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, United States
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, United States
| |
Collapse
|
17
|
Demnitz N, Hulme OJ, Siebner HR, Kjaer M, Ebmeier KP, Boraxbekk CJ, Gillan CM. Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. Neurobiol Aging 2023; 131:115-123. [PMID: 37619515 DOI: 10.1016/j.neurobiolaging.2023.07.023] [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: 02/09/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.
Collapse
Affiliation(s)
- Naiara Demnitz
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark.
| | - Oliver J Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; London Mathematical Laboratory, London, UK; Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kjaer
- Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Klaus P Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Carl-Johan Boraxbekk
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark; Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Radiation Sciences, Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|
18
|
Islam M, Behura SK. Role of caveolin-1 in metabolic programming of fetal brain. iScience 2023; 26:107710. [PMID: 37720105 PMCID: PMC10500482 DOI: 10.1016/j.isci.2023.107710] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/10/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Mice lacking caveolin-1 (Cav1), a key protein of plasma membrane, exhibit brain aging at an early adult stage. Here, integrative analyses of metabolomics, transcriptomics, epigenetics, and single-cell data were performed to test the hypothesis that metabolic deregulation of fetal brain due to the ablation of Cav1 is linked to brain aging in these mice. The results of this study show that lack of Cav1 caused deregulation in the lipid and amino acid metabolism in the fetal brain, and genes associated with these deregulated metabolites were significantly altered in the brain upon aging. Moreover, ablation of Cav1 deregulated several metabolic genes in specific cell types of the fetal brain and impacted DNA methylation of those genes in coordination with mouse epigenetic clock. The findings of this study suggest that the aging program of brain is confounded by metabolic abnormalities in the fetal stage due to the absence of Cav1.
Collapse
Affiliation(s)
- Maliha Islam
- Division of Animal Sciences, 920 East Campus Drive, University of Missouri, Columbia, MO 65211, USA
| | - Susanta K. Behura
- Division of Animal Sciences, 920 East Campus Drive, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
- Interdisciplinary Reproduction and Health Group, University of Missouri, Columbia, MO, USA
- Interdisciplinary Neuroscience Program, University of Missouri, Columbia, MO, USA
| |
Collapse
|
19
|
Pham TQ, Matsui T, Chikazoe J. Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review. BIOLOGY 2023; 12:1330. [PMID: 37887040 PMCID: PMC10604784 DOI: 10.3390/biology12101330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/22/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023]
Abstract
Artificial neural networks (ANNs) that are heavily inspired by the human brain now achieve human-level performance across multiple task domains. ANNs have thus drawn attention in neuroscience, raising the possibility of providing a framework for understanding the information encoded in the human brain. However, the correspondence between ANNs and the brain cannot be measured directly. They differ in outputs and substrates, neurons vastly outnumber their ANN analogs (i.e., nodes), and the key algorithm responsible for most of modern ANN training (i.e., backpropagation) is likely absent from the brain. Neuroscientists have thus taken a variety of approaches to examine the similarity between the brain and ANNs at multiple levels of their information hierarchy. This review provides an overview of the currently available approaches and their limitations for evaluating brain-ANN correspondence.
Collapse
Affiliation(s)
| | - Teppei Matsui
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0321, Japan
| | | |
Collapse
|
20
|
Carbonell F, McNicoll C, Zijdenbos AP, Bedell BJ. Spatial association between distributed β-amyloid and tau varies with cognition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559737. [PMID: 37808643 PMCID: PMC10557646 DOI: 10.1101/2023.09.27.559737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Several PET studies have explored the relationship between β-amyloid load and tau uptake at the early stages of Alzheimer's disease (AD) progression. Most of these studies have focused on the linear relationship between β-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between β-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and β-amyloid, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based β-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of β-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of β-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in β-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.
Collapse
|
21
|
Wang JM, Hong R, Demicco EG, Tan J, Lazcano R, Moreira AL, Li Y, Calinawan A, Razavian N, Schraink T, Gillette MA, Omenn GS, An E, Rodriguez H, Tsirigos A, Ruggles KV, Ding L, Robles AI, Mani DR, Rodland KD, Lazar AJ, Liu W, Fenyö D. Deep learning integrates histopathology and proteogenomics at a pan-cancer level. Cell Rep Med 2023; 4:101173. [PMID: 37582371 PMCID: PMC10518635 DOI: 10.1016/j.xcrm.2023.101173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/17/2023]
Abstract
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
Collapse
Affiliation(s)
- Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Elizabeth G Demicco
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital and Laboratory Medicine and Pathobiology, University of Toronto, Toronto M5G 1X5, ON, Canada
| | - Jimin Tan
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Rossana Lazcano
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Andre L Moreira
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Narges Razavian
- Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Tobias Schraink
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Michael A Gillette
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Massachusetts General Hospital Division of Pulmonary and Critical Care Medicine, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Aristotelis Tsirigos
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Li Ding
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA
| | - Alexander J Lazar
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
| |
Collapse
|
22
|
Ling Q, Liu A, Li Y, Mi T, Chan P, Liu Y, Chen X. Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3556-3565. [PMID: 37682656 DOI: 10.1109/tnsre.2023.3310340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.
Collapse
|
23
|
Sircar K, Kernich N, Herren C, Bratke G, Eysel P, Jung N, Yagdiran A. Evaluation of Classification Systems and Their Correlation With Clinical and Quality-of-life Parameters in Patients With Surgically Treated Spondylodiskitis. J Am Acad Orthop Surg 2023; 31:914-922. [PMID: 37015106 DOI: 10.5435/jaaos-d-22-01199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/02/2023] [Indexed: 04/06/2023] Open
Abstract
INTRODUCTION Surgical classifications for spondylodiscitis (SD) typically include radiologic features and the status of neurologic impairment. Clinical factors such as preoperative pain, function/disability, overall quality of life (QoL), and risk of recurrence and mortality, which are necessary for a comprehensive assessment of SD and measurement of treatment success, are not considered. There is a lack of external validation of SD classifications. The aim of this study was to validate classifications of SD and to correlate these classifications with the above clinical factors. METHODS One hundred fourteen patients from a prospective SD register (2008-2020) with available imaging, preoperative neurologic status, backpain, function/disability data (Oswestry Disability Index and Core Outcome Measures Index), QoL data (Short Form 36, European Quality-of-life Questionnaire), and a 1-year follow-up were retrospectively classified according to Akbar, Homagk, and Pola classifications. Interrater reliability, correlation among classifications, and correlation between classifications and QoL were calculated. RESULTS Interrater reliability was κ = 0.83 for Akbar, κ = 0.94 for Homagk, and κ = 0.99 for Pola. The correlation of Akbar with Pola and Homagk was moderate (ρ s = 0.47; ρ s = 0.46) and high between Pola and Homagk (ρ s = 0.7). No notable correlation was observed between any of the classifications and preoperative Oswestry Disability Index, Core Outcome Measures Index, QoL, mortality, and recurrence within 1 year. Only a weak correlation was observed between Homagk and preoperative leg pain and back pain. CONCLUSION Available SD classifications have a very good interrater reliability and moderate-to-high correlation with each other but lack correlation with preoperative pain, function/disability, and overall QoL. Because these factors are important for a comprehensive assessment of SD in severity, decision making, and prognosis, they should be included in future SD classifications. This could allow for more comprehensive treatment algorithms. LEVEL OF EVIDENCE Level II. Diagnostic study = prospective cohort study; development of diagnostic criteria. DATA AVAILABILITY The data sets used and analyzed during this study are available from the corresponding author on reasonable request.
Collapse
Affiliation(s)
- Krishnan Sircar
- From the Department of Orthopaedics and Trauma Surgery, University Clinics, Faculty of Medicine, University of Cologne, Cologne, Germany (Sircar, Kernich, Eysel, and Yagdiran), Clinic for Trauma and Reconstructive Surgery, University Hospital Aachen, Germany (Herren), Institute of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Cologne, Cologne, Germany (Bratke), and the Department I of Internal Medicine (Jung), University Clinics, Faculty of Medicine, University of Cologne, Cologne, Germany
| | | | | | | | | | | | | |
Collapse
|
24
|
Durham EL, Ghanem K, Stier AJ, Cardenas-Iniguez C, Reimann GE, Jeong HJ, Dupont RM, Dong X, Moore TM, Berman MG, Lahey BB, Bzdok D, Kaczkurkin AN. Multivariate analytical approaches for investigating brain-behavior relationships. Front Neurosci 2023; 17:1175690. [PMID: 37583413 PMCID: PMC10423877 DOI: 10.3389/fnins.2023.1175690] [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: 02/27/2023] [Accepted: 07/13/2023] [Indexed: 08/17/2023] Open
Abstract
Background Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. Method We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. Results The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. Conclusion Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.
Collapse
Affiliation(s)
- E. Leighton Durham
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Karam Ghanem
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | | | - Hee Jung Jeong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Randolph M. Dupont
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Xiaoyu Dong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, United States
- The University of Chicago Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Benjamin B. Lahey
- Department of Health Studies, University of Chicago, Chicago, IL, United States
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
| | - Danilo Bzdok
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | | |
Collapse
|
25
|
Issever D, Catalbas MC, Duran F. Examining Factors Influencing Cognitive Load of Computer Programmers. Brain Sci 2023; 13:1132. [PMID: 37626489 PMCID: PMC10452396 DOI: 10.3390/brainsci13081132] [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: 06/16/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.
Collapse
Affiliation(s)
- Didem Issever
- Department of Computer Engineering, Faculty of Technology, Gazi University, 06560 Ankara, Turkey;
| | - Mehmet Cem Catalbas
- Department of Electronics and Automation, 1st Organized Industrial Zone Vocational School, Ankara University, 06935 Ankara, Turkey;
| | - Fecir Duran
- Department of Computer Engineering, Faculty of Technology, Gazi University, 06560 Ankara, Turkey;
| |
Collapse
|
26
|
Weinstein SM, Davatzikos C, Doshi J, Linn KA, Shinohara RT. Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables. Biostatistics 2023; 24:653-668. [PMID: 35950944 PMCID: PMC10345990 DOI: 10.1093/biostatistics/kxac031] [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: 01/25/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.
Collapse
Affiliation(s)
- Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 108/109B, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jimit Doshi
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | |
Collapse
|
27
|
Affaticati F, Bartholomeus E, Mullan K, Damme PV, Beutels P, Ogunjimi B, Laukens K, Meysman P. Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response. Vaccines (Basel) 2023; 11:1236. [PMID: 37515051 PMCID: PMC10384938 DOI: 10.3390/vaccines11071236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system's diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; <2 months) and late converters (n = 9; <6 months) and was defined based on the anti-HBs titers (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness.
Collapse
Affiliation(s)
- Fabio Affaticati
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
| | - Esther Bartholomeus
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, 2610 Antwerp, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp (VAXINFECTIO), 2610 Antwerp, Belgium
| | - Kerry Mullan
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
| | - Pierre Van Damme
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Centre for the Evaluation of Vaccination (CEV), Vaccine and Infectious Disease Institute, University of Antwerp, 2610 Antwerp, Belgium
| | - Philippe Beutels
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, 2610 Antwerp, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, 2610 Antwerp, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp (VAXINFECTIO), 2610 Antwerp, Belgium
- Department of Paediatrics, Antwerp University Hospital, 2650 Edegem, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
| |
Collapse
|
28
|
Tuzhilina E, Tozzi L, Hastie T. Canonical correlation analysis in high dimensions with structured regularization. STAT MODEL 2023; 23:203-227. [PMID: 37334164 PMCID: PMC10274416 DOI: 10.1177/1471082x211041033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.
Collapse
Affiliation(s)
- Elena Tuzhilina
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Trevor Hastie
- Department of Statistics, Stanford University, Stanford, CA, USA
| |
Collapse
|
29
|
Guardia T, Mazloum-Farzaghi N, Olsen RK, Tsvetanov KA, Campbell KL. Associative memory is more strongly predicted by age-related differences in the prefrontal cortex than medial temporal lobes. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
|
30
|
Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic functional connectomes represent largely similar information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.24.525348. [PMID: 36747845 PMCID: PMC9900764 DOI: 10.1101/2023.01.24.525348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
Collapse
Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
- Department of Psychiatry, Yale University, New Haven, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana
| |
Collapse
|
31
|
Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [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: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
Collapse
Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
| |
Collapse
|
32
|
Mahzarnia A, Stout JA, Anderson RJ, Moon HS, Yar Han Z, Beck K, Browndyke JN, Dunson DB, Johnson KG, O’Brien RJ, Badea A. Identifying vulnerable brain networks associated with Alzheimer's disease risk. Cereb Cortex 2023; 33:5307-5322. [PMID: 36320163 PMCID: PMC10399292 DOI: 10.1093/cercor/bhac419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 12/23/2022] Open
Abstract
The selective vulnerability of brain networks in individuals at risk for Alzheimer's disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.
Collapse
Affiliation(s)
- Ali Mahzarnia
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jacques A Stout
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
| | - Robert J Anderson
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Hae Sol Moon
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
| | - Zay Yar Han
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Kate Beck
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jeffrey N Browndyke
- Psychiatry and Behavioral Sciences Department, Duke University Medical School, Durham, 27710 NC, USA
| | - David B Dunson
- Statistical Sciences, Trinity College, Duke University, Durham, 27710 NC, USA
| | - Kim G Johnson
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Richard J O’Brien
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Alexandra Badea
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| |
Collapse
|
33
|
Jaeger C, Nuttall R, Zimmermann J, Dowsett J, Preibisch C, Sorg C, Wohlschlaeger A. Targeted rhythmic visual stimulation at individual participants' intrinsic alpha frequency causes selective increase of occipitoparietal BOLD-fMRI and EEG functional connectivity. Neuroimage 2023; 270:119981. [PMID: 36848971 DOI: 10.1016/j.neuroimage.2023.119981] [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] [Received: 02/23/2022] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 02/28/2023] Open
Abstract
Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12 Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12 Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.
Collapse
Affiliation(s)
- Cilia Jaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Graduate School of Systemic Neuroscience, Ludwig Maximilian University, Planneg-Martinsried, Germany
| | - Rachel Nuttall
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Department of Anesthesiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Juliana Zimmermann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - James Dowsett
- Department of Psychology, Ludwig Maximilian University, Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Clinic for Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Department of Psychiatry, Technical University of Munich, Munich, Germany
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TUM Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| |
Collapse
|
34
|
Bos B, Barratt B, Batalle D, Gale-Grant O, Hughes EJ, Beevers S, Cordero-Grande L, Price AN, Hutter J, Hajnal JV, Kelly FJ, David Edwards A, Counsell SJ. Prenatal exposure to air pollution is associated with structural changes in the neonatal brain. ENVIRONMENT INTERNATIONAL 2023; 174:107921. [PMID: 37058974 PMCID: PMC10410199 DOI: 10.1016/j.envint.2023.107921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/23/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Prenatal exposure to air pollution is associated with adverse neurologic consequences in childhood. However, the relationship between in utero exposure to air pollution and neonatal brain development is unclear. METHODS We modelled maternal exposure to nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) at postcode level between date of conception to date of birth and studied the effect of prenatal air pollution exposure on neonatal brain morphology in 469 (207 male) healthy neonates, with gestational age of ≥36 weeks. Infants underwent MR neuroimaging at 3 Tesla at 41.29 (36.71-45.14) weeks post-menstrual age (PMA) as part of the developing human connectome project (dHCP). Single pollutant linear regression and canonical correlation analysis (CCA) were performed to assess the relationship between air pollution and brain morphology, adjusting for confounders and correcting for false discovery rate. RESULTS Higher exposure to PM10 and lower exposure to NO2 was strongly canonically correlated to a larger relative ventricular volume, and moderately associated with larger relative size of the cerebellum. Modest associations were detected with higher exposure to PM10 and lower exposure to NO2 and smaller relative cortical grey matter and amygdala and hippocampus, and larger relaive brainstem and extracerebral CSF volume. No associations were found with white matter or deep grey nuclei volume. CONCLUSIONS Our findings show that prenatal exposure to air pollution is associated with altered brain morphometry in the neonatal period, albeit with opposing results for NO2 and PM10. This finding provides further evidence that reducing levels of maternal exposure to particulate matter during pregnancy should be a public health priority and highlights the importance of understanding the impacts of air pollution on this critical development window.
Collapse
Affiliation(s)
- Brendan Bos
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Ben Barratt
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Sean Beevers
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Frank J Kelly
- MRC Centre for Environment and Health, Imperial College London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
| |
Collapse
|
35
|
Wan Z, Li M, Liu S, Huang J, Tan H, Duan W. EEGformer: A transformer-based brain activity classification method using EEG signal. Front Neurosci 2023; 17:1148855. [PMID: 37034169 PMCID: PMC10079879 DOI: 10.3389/fnins.2023.1148855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Background The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain. Method Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG). Results The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance. Conclusion EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.
Collapse
Affiliation(s)
- Zhijiang Wan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China
| | - Manyu Li
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Shichang Liu
- School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, China
| | - Jiajin Huang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Hai Tan
- School of Computer Science, Nanjing Audit University, Nanjing, Jiangsu, China
| | - Wenfeng Duan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
| |
Collapse
|
36
|
Xu B, Dallâ Aglio L, Flournoy J, Bortsova G, Tervo-Clemmens B, Collins P, de Bruijne M, Luciana M, Marquand A, Wang H, Tiemeier H, Muetzel RL. Multivariate brain-based dimensions of child psychiatric problems: degrees of generalizability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.12.23287158. [PMID: 36993191 PMCID: PMC10055441 DOI: 10.1101/2023.03.12.23287158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. This study aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n =8,605). Using sparse canonical correlations analysis, we identified three brain-behavior dimensions in ABCD: attention problems, aggression and rule-breaking behaviors, and withdrawn behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degree of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.
Collapse
|
37
|
A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure–function relationship. Cogn Neurodyn 2023. [DOI: 10.1007/s11571-023-09941-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023] Open
|
38
|
Peng L, Wang N, Xu J, Zhu X, Li X. GATE: Graph CCA for Temporal Self-Supervised Learning for Label-Efficient fMRI Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:391-402. [PMID: 36018878 DOI: 10.1109/tmi.2022.3201974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal sElf-supervised learning on fMRI analysis (GATE). Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis. Our code is available at https://github.com/LarryUESTC/GATE.
Collapse
|
39
|
Thompson AC, Miller ME, Handing EP, Chen H, Hugenschmidt CE, Laurienti PJ, Kritchevsky SB. Examining the intersection of cognitive and physical function measures: Results from the brain networks and mobility (B-NET) study. Front Aging Neurosci 2023; 15:1090641. [PMID: 36819728 PMCID: PMC9932333 DOI: 10.3389/fnagi.2023.1090641] [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: 11/05/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
Background and objectives Although evidence exists that measures of mobility and cognition are correlated, it is not known to what extent they overlap, especially across various domains. This study aimed to investigate the intersection of 18 different objective cognitive and physical function measures from a sample of unimpaired adults aged 70 years and older. Research design and methods Canonical correlation analysis was utilized to explore the joint cross-sectional relationship between 13 cognitive and 6 physical function measures in the baseline visit of the Brain Networks and Mobility Function (B-NET) Study (n = 192). Results Mean age of participants was 76.4 years. Two synthetic functions were identified. Function 1 explained 26.3% of the shared variability between the cognition and physical function variables, whereas Function 2 explained 19.5%. Function 1 termed "cognitive and physical speed" related the expanded Short Physical Performance Battery (eSPPB), 400-m walk speed, and Dual Task gait speed measures of physical function to semantic fluency animals scores, Digit Symbol Coding (DSC), and Trail Making Test B. Function 2 termed "complex motor tasks and cognitive tasks" related the Force Plate Postural Sway Foam Task and Dual Task to the following cognitive variables: MoCA Adjusted Score, Verbal Fluency L words, Craft story immediate and delayed recall, and Trail Making Test B. Discussion and implications We identified groups of cognitive and physical functional abilities that were linked in cross-sectional analyses, which may suggest shared underlying neural network pathway(s) related to speed (Function 1) or complexity (Function 2). Translational significance Whether such neural processes decline before measurable functional losses or may be important targets for future interventions that aim to prevent disability also remains to be determined.
Collapse
Affiliation(s)
- Atalie C Thompson
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Section on Gerontology and Geriatric Medicine, Department of Internal Medicine, Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Michael E Miller
- Section on Gerontology and Geriatric Medicine, Department of Internal Medicine, Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Elizabeth P Handing
- Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
| | - Haiying Chen
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Christina E Hugenschmidt
- Section on Gerontology and Geriatric Medicine, Department of Internal Medicine, Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Stephen B Kritchevsky
- Section on Gerontology and Geriatric Medicine, Department of Internal Medicine, Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States
| |
Collapse
|
40
|
Liu H, Tang J, Chen T, Zhu P, Sun D, Wang W. Assessment of heavy metals contamination and human health risk assessment of the commonly consumed medicinal herbs in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:7345-7357. [PMID: 36040690 DOI: 10.1007/s11356-022-22647-z] [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: 01/19/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
This study investigates heavy metal contamination of commonly consumed medicinal herbs and human health risks to the Chinese population arising from the consumption of herbs that contain potentially harmful elements. Food safety standards for Chinese residents are becoming stricter, and much work in this field needs to be performed. This study examines Co, Ba, Fe, Cr, Mn, Ni, Zn, As, Cd, Pb, Cu, Be, Sb, and Bi concentrations in four regularly consumed Chinese herb species: Radix Paeoniae Alba (RPA), Radix Angelicae Dahuricae (RAD), Rhizoma Atractylodis Macrocephalae (RAM), and Radix Puerariae (RP). A pollution status examination and evaluation of heavy metals in RPA, RAD, RAM, and RP were performed. The human health risk assessment associated with the intake of potentially harmful elements in herbs was calculated in terms of the estimated daily intake (EDI), the target hazard quotient (THQ), the estimated hazard index (HI), and the lifetime cancer risk (CR). The mean single-factor pollution index (PI) showed that in the RPA, RAD, RAM, and RP samples, approximately 10.0%, 10.0%, 30.0%, and 10.0%, respectively, were polluted by Cd. The present study indicated that the pattern of consumption of the studied herbs in China does not seem to suggest an excessive health hazard associated with any of the toxic elements studied.
Collapse
Affiliation(s)
- Haiping Liu
- School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China.
| | - Jianfeng Tang
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Tongjun Chen
- School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Pingping Zhu
- School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Dongdong Sun
- School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Weiyun Wang
- School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| |
Collapse
|
41
|
Shymanskaya A, Kohn N, Habel U, Wagels L. Brain network changes in adult victims of violence. Front Psychiatry 2023; 14:1040861. [PMID: 36816407 PMCID: PMC9931748 DOI: 10.3389/fpsyt.2023.1040861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Stressful experiences such as violence can affect mental health severely. The effects are associated with changes in structural and functional brain networks. The current study aimed to investigate brain network changes in four large-scale brain networks, the default mode network, the salience network, the fronto-parietal network, and the dorsal attention network in self-identified victims of violence and controls who did not identify themselves as victims. MATERIALS AND METHODS The control group (n = 32) was matched to the victim group (n = 32) by age, gender, and primary psychiatric disorder. Sparse inverse covariance maps were derived from functional resting-state measurements and from T1 weighted structural data for both groups. RESULTS Our data underlined that mostly the salience network was affected in the sample of self-identified victims. In self-identified victims with a current psychiatric diagnosis, the dorsal attention network was mostly affected underlining the potential role of psychopathological alterations on attention-related processes. CONCLUSION The results showed that individuals who identify themselves as victim demonstrated significant differences in all considered networks, both within- and between-network.
Collapse
Affiliation(s)
- Aliaksandra Shymanskaya
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN Institute Brain Structure and Function, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany
| | - Nils Kohn
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmengen, Netherlands
| | - Ute Habel
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN Institute Brain Structure and Function, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany
| | - Lisa Wagels
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN Institute Brain Structure and Function, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany
| |
Collapse
|
42
|
Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 2023; 28:17-27. [PMID: 35790874 DOI: 10.1038/s41380-022-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 01/07/2023]
Abstract
Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.
Collapse
|
43
|
Multimodal and multidomain lesion network mapping enhances prediction of sensorimotor behavior in stroke patients. Sci Rep 2022; 12:22400. [PMID: 36575263 PMCID: PMC9794717 DOI: 10.1038/s41598-022-26945-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.
Collapse
|
44
|
Pospisil DA, Bair W. Accounting for Bias in the Estimation of r 2 between Two Sets of Noisy Neural Responses. J Neurosci 2022; 42:9343-9355. [PMID: 36396403 PMCID: PMC9794365 DOI: 10.1523/jneurosci.0198-22.2022] [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: 01/24/2022] [Revised: 09/23/2022] [Accepted: 10/20/2022] [Indexed: 11/19/2022] Open
Abstract
The Pearson correlation coefficient squared, r 2, is an important tool used in the analysis of neural data to quantify the similarity between neural tuning curves. Yet this metric is biased by trial-to-trial variability; as trial-to-trial variability increases, measured correlation decreases. Major lines of research are confounded by this bias, including those involving the study of invariance of neural tuning across conditions and the analysis of the similarity of tuning across neurons. To address this, we extend an estimator, [Formula: see text], that was recently developed for estimating model-to-neuron correlation, in which a noisy signal is compared with a noise-free prediction, to the case of neuron-to-neuron correlation, in which two noisy signals are compared with each other. We compare the performance of our novel estimator to a prior method developed by Spearman, commonly used in other fields but widely overlooked in neuroscience, and find that our method has less bias. We then apply our estimator to demonstrate how it avoids drastic confounds introduced by trial-to-trial variability using data collected in two prior studies (macaque, both sexes) that examined two different forms of invariance in the neural encoding of visual inputs-translation invariance and fill-outline invariance. Our results quantify for the first time the gradual falloff with spatial offset of translation-invariant shape selectivity within visual cortical neuronal receptive fields and offer a principled method to compare invariance in noisy biological systems to that in noise-free models.SIGNIFICANCE STATEMENT Quantifying the similarity between two sets of averaged neural responses is fundamental to the analysis of neural data. A ubiquitous metric of similarity, the correlation coefficient, is attenuated by trial-to-trial variability that arises from many irrelevant factors. Spearman recognized this problem and proposed corrected methods that have been extended over a century. We show this method has large asymptotic biases that can be overcome using a novel estimator. Despite the frequent use of the correlation coefficient in neuroscience, consensus on how to address this fundamental statistical issue has not been reached. We provide an accurate estimator of the correlation coefficient and apply it to gain insight into visual invariance.
Collapse
Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540
- Department of Biological Structure, University of Washington, Seattle 98195
| | - Wyeth Bair
- Department of Biological Structure, University of Washington, Seattle 98195
- Computational Neuroscience Center, University of Washington, Seattle 98195
- Washington National Primate Research Center, University of Washington, Seattle 98195
| |
Collapse
|
45
|
Markov NT, Lindbergh CA, Staffaroni AM, Perez K, Stevens M, Nguyen K, Murad NF, Fonseca C, Campisi J, Kramer J, Furman D. Age-related brain atrophy is not a homogenous process: Different functional brain networks associate differentially with aging and blood factors. Proc Natl Acad Sci U S A 2022; 119:e2207181119. [PMID: 36459652 PMCID: PMC9894212 DOI: 10.1073/pnas.2207181119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022] Open
Abstract
Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline.
Collapse
Affiliation(s)
- Nikola T. Markov
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Cutter A. Lindbergh
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT06030
| | - Adam M. Staffaroni
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - Kevin Perez
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- University of Lausanne, LausanneCH-1015, Switzerland
| | - Michael Stevens
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Khiem Nguyen
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- Nguyen Tat Thanh Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City70000, Vietnam
| | - Natalia F. Murad
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Corrina Fonseca
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - Judith Campisi
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Joel Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - David Furman
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- Instituto de Investigaciones en Medicina Traslacional, Universidad Austral, Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar1629, Argentina
- Stanford 1000 Immunomes Project, Stanford University School of Medicine, Stanford, CA94305
| |
Collapse
|
46
|
Adam EM, Sur M. Algebraic approach for subspace decomposition and clustering of neural activity. STAR Protoc 2022; 3:101841. [PMID: 36386884 PMCID: PMC9664015 DOI: 10.1016/j.xpro.2022.101841] [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] [Indexed: 11/13/2022] Open
Abstract
We developed an approach to decompose neuronal signals into disjoint components, corresponding to task- or event-based epochs. This protocol describes how to project behavioral templates onto a low-dimensional subspace of neuronal responses to derive neuronal templates, then how to decompose and cluster neuronal responses using these derived templates. We outline these steps on complementary datasets of calcium imaging and spiking activity. Our approach relies on fundamental, linear algebraic principles and is adaptive to the temporal structure of the neural data. For complete details on the use and execution of this protocol, please refer to Adam et al. (2022).1 Apply singular value decomposition to derive a low-dimensional neural response subspace Project behavioral templates onto the subspace to derive neuronal templates Every neuronal response can be decomposed as a weighted sum of neuronal templates The derived weights can be used for clustering the neurons
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Collapse
|
47
|
Stout DM, Simmons AN, Nievergelt CM, Minassian A, Biswas N, Maihofer AX, Risbrough VB, Baker DG. Deriving psychiatric symptom-based biomarkers from multivariate relationships between psychophysiological and biochemical measures. Neuropsychopharmacology 2022; 47:2252-2260. [PMID: 35347268 PMCID: PMC9630445 DOI: 10.1038/s41386-022-01303-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/18/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022]
Abstract
Identification of biomarkers for psychiatric disorders remains very challenging due to substantial symptom heterogeneity and diagnostic comorbidity, limiting the ability to map symptoms to underlying neurobiology. Dimensional symptom clusters, such as anhedonia, hyperarousal, etc., are complex and arise due to interactions of a multitude of complex biological relationships. The primary aim of the current investigation was to use multi-set canonical correlation analysis (mCCA) to derive biomarkers (biochemical, physiological) linked to dimensional symptoms across the anxiety and depressive spectrum. Active-duty service members (N = 2,592) completed standardized depression, anxiety and posttraumatic stress questionnaires and several psychophysiological and biochemical assays. Using this approach, we identified two phenotype associations between distinct physiological and biological phenotypes. One was characterized by symptoms of dysphoric arousal (anhedonia, anxiety, hypervigilance) which was associated with low blood pressure and startle reactivity. This finding is in line with previous studies suggesting blunted physiological reactivity is associated with subpopulations endorsing anxiety with comorbid depressive features. A second phenotype of anxious fatigue (high anxiety and reexperiencing/avoidance symptoms coupled with fatigue) was associated with elevated blood levels of norepinephrine and the inflammatory marker C-reactive protein in conjunction with high blood pressure. This second phenotype may describe populations in which inflammation and high sympathetic outflow might contribute to anxious fatigue. Overall, these findings support the growing consensus that distinct neuropsychiatric symptom patterns are associated with differential physiological and blood-based biological profiles and highlight the potential of mCCA to reveal important psychiatric symptom biomarkers from several psychophysiological and biochemical measures.
Collapse
Affiliation(s)
- Daniel M Stout
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Alan N Simmons
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Caroline M Nievergelt
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Arpi Minassian
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Nilima Biswas
- Department of Pathology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Victoria B Risbrough
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Dewleen G Baker
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| |
Collapse
|
48
|
Nicolaisen-Sobesky E, Mihalik A, Kharabian-Masouleh S, Ferreira FS, Hoffstaedter F, Schwender H, Maleki Balajoo S, Valk SL, Eickhoff SB, Yeo BTT, Mourao-Miranda J, Genon S. A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure. Commun Biol 2022; 5:1297. [PMID: 36435870 PMCID: PMC9701210 DOI: 10.1038/s42003-022-04244-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/09/2022] [Indexed: 11/28/2022] Open
Abstract
Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.
Collapse
Affiliation(s)
- Eliana Nicolaisen-Sobesky
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Agoston Mihalik
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Shahrzad Kharabian-Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Fabio S Ferreira
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Somayeh Maleki Balajoo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Research Group "Cognitive Neurogenetics", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Translational MR Research, Centre for Sleep & Cognition, N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
49
|
Mihalik A, Chapman J, Adams RA, Winter NR, Ferreira FS, Shawe-Taylor J, Mourão-Miranda J. Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:1055-1067. [PMID: 35952973 DOI: 10.1016/j.bpsc.2022.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 06/30/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.
Collapse
Affiliation(s)
- Agoston Mihalik
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
| | - James Chapman
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Rick A Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Nils R Winter
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabio S Ferreira
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - John Shawe-Taylor
- Department of Computer Science, University College London, London, United Kingdom
| | - Janaina Mourão-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| |
Collapse
|
50
|
Catalbas MC, Burken S. The mathematical relationship between COVID-19 cases and socio-economic indicators of OECD countries. Pathog Glob Health 2022; 116:376-388. [PMID: 35037595 PMCID: PMC9387341 DOI: 10.1080/20477724.2022.2028376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
In most aspects, the COVID-19 pandemic has been the most impactful disease in the last few decades. Although two years have passed since the start of the COVID-19 pandemic, it is clear that the pandemic is far from being brought under full control. This pandemic, which was kept under control in part by the discovery of vaccines, has had a negative impact on people's daily lives as well as global economies. The transmission and death rate of this disease, which was detected in Wuhan, China in December 2019, has differed between countries. The reason for these different rates according to countries has been investigated while developing a mathematical model in this comprehensive study. The effect of 16 different socioeconomic indicators of countries on the parameters of the COVID-19 pandemic was analyzed in this study, which was performed on 35 countries, the majority of which are OECD countries. The main objective of this research is to minimize potential negative effects by improving or taking these indicators into account in future similar outbreaks. In this study, canonical correlation analysis was used to obtain the appropriate mathematical model and the performance of the obtained model has been tested statistically. According to the mathematical model obtained, the age factor comes to the fore as the most important indicator in the COVID-19 pandemic and the next important indicator is the health expenditures of the countries. The influence of 16 different socioeconomic indicators on the COVID-19 pandemic parameters are presented as a percentage with details.
Collapse
Affiliation(s)
- Mehmet Cem Catalbas
- 1st Organized Industrial Zone Vocational School, Department of Electronics and Automation, Ankara University, Ankara, Turkey,CONTACT Mehmet Cem Catalbas 1st Organized Industrial Zone Vocational School, Department of Electronics and Automation, Ankara University, 06935, Sincan, Ankara, Turkey
| | - Serkan Burken
- Faculty of Economics and Administrative Sciences, Economics, Ostim Technical University, Ankara, Turkey
| |
Collapse
|