1
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Pho B, Stevenson RA, Saljoughi S, Mohsenzadeh Y, Stojanoski B. Identifying developmental changes in functional brain connectivity associated with cognitive functioning in children and adolescents with ADHD. Dev Cogn Neurosci 2024; 69:101439. [PMID: 39182418 PMCID: PMC11385464 DOI: 10.1016/j.dcn.2024.101439] [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: 01/18/2024] [Revised: 08/14/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024] Open
Abstract
Youth diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, executive functioning. Poorer cognitive functioning in children with ADHD has been associated with differences in functional connectivity across the brain. However, little is known about the developmental changes to the brain's functional properties linked to different cognitive abilities in this cohort. To characterize these changes, we analyzed fMRI data (ADHD = 373, NT = 106) collected while youth between the ages of 6 and 16 watched a short movie-clip. We applied machine learning models to identify patterns of network connectivity in response to movie-watching that differentially predict cognitive abilities in our cohort. Using out-of-sample cross validation, our models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children (ages 6 - 11), but not in adolescents with ADHD (ages 12-16). Connections with the default mode, memory retrieval, and dorsal attention were driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that machine learning approaches can identify distinct functional connectivity profiles associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.
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Affiliation(s)
- Brian Pho
- Program in Neuroscience, University of Western Ontario, London, ON, Canada
| | - Ryan Andrew Stevenson
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Psychology, University of Western Ontario, London, ON, Canada; Western Institute for Neuroscience, University of Western Ontario, London, ON, Canada
| | - Sara Saljoughi
- Faculty of Social Science and Humanities, Ontario Tech University, Oshawa, ON, Canada
| | - Yalda Mohsenzadeh
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Computer Science, Western University, London, ON N6A 5B7, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Bobby Stojanoski
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Psychology, University of Western Ontario, London, ON, Canada.
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2
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Yuan AE, Shou W. A rigorous and versatile statistical test for correlations between stationary time series. PLoS Biol 2024; 22:e3002758. [PMID: 39146390 PMCID: PMC11398661 DOI: 10.1371/journal.pbio.3002758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 09/13/2024] [Accepted: 07/17/2024] [Indexed: 08/17/2024] Open
Abstract
In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.
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Affiliation(s)
- Alex E Yuan
- Molecular and Cellular Biology PhD program, University of Washington, Seattle, Washington, United States of America
- Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Wenying Shou
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
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3
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Weinstein SM, Tu D, Hu F, Pan R, Zhang R, Vandekar SN, Baller EB, Gur RC, Gur RE, Alexander-Bloch AF, Satterthwaite TD, Park JY. Mapping Individual Differences in Intermodal Coupling in Neurodevelopment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600817. [PMID: 38979274 PMCID: PMC11230458 DOI: 10.1101/2024.06.26.600817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA
| | - Danni Tu
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruyi Pan
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Erica B. Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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4
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Weinstein SM, Vandekar SN, Li B, Alexander‐Bloch AF, Raznahan A, Li M, Gur RE, Gur RC, Roalf DR, Park MTM, Chakravarty M, Baller EB, Linn KA, Satterthwaite TD, Shinohara RT. Network enrichment significance testing in brain-phenotype association studies. Hum Brain Mapp 2024; 45:e26714. [PMID: 38878300 PMCID: PMC11179683 DOI: 10.1002/hbm.26714] [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: 11/10/2023] [Revised: 04/08/2024] [Accepted: 05/04/2024] [Indexed: 06/19/2024] Open
Abstract
Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and BiostatisticsTemple University College of Public HealthPhiladelphiaPennsylvaniaUSA
| | - Simon N. Vandekar
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Bin Li
- Department of Computer and Information SciencesTemple University College of Science and TechnologyPhiladelphiaPennsylvaniaUSA
| | - Aaron F. Alexander‐Bloch
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
- Department of Child and Adolescent Psychiatry and Behavioral ScienceChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMarylandUSA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Integrated Program in NeuroscienceMcGill UniversityQCCanada
| | - Mallar Chakravarty
- Department of PsychiatryMcGill UniversityQCCanada
- Cerebral Imaging Centre, Douglas Research Centre, McGill UniversityQCCanada
| | - Erica B. Baller
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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5
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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6
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Calissano A, Papadopoulo T, Pennec X, Deslauriers‐Gauthier S. Graph alignment exploiting the spatial organization improves the similarity of brain networks. Hum Brain Mapp 2024; 45:e26554. [PMID: 38224543 PMCID: PMC10789220 DOI: 10.1002/hbm.26554] [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: 04/26/2023] [Revised: 10/19/2023] [Accepted: 11/22/2023] [Indexed: 01/17/2024] Open
Abstract
Every brain is unique, having its structural and functional organization shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under the common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. We investigate this assumption: can the structural properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects, considering parcellations of different granularity, to understand the connectivity misalignment between regions. First, we compare the obtained permutations with four different algorithm initializations: Spatial Adjacency, Identity, Barycenter, and Random. Our results suggest that applying an alignment strategy improves the similarity across subjects when the number of parcels is above 100 and when using Spatial Adjacency and Identity initialization (the most plausible priors). Second, we characterize the obtained permutations, revealing that the majority of permutations happens between neighbors parcels. Lastly, we study the spatial distribution of the permutations. By visualizing the results on the cortex, we observe no clear spatial patterns on the permutations and all the regions across the context are mostly permuted with first and second order neighbors.
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Affiliation(s)
- Anna Calissano
- Inria Center at Université Côte d'AzurValbonneFrance
- Present address:
Imperial College London
| | | | - Xavier Pennec
- Inria Center at Université Côte d'AzurValbonneFrance
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7
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Chen AA, Weinstein SM, Adebimpe A, Gur RC, Gur RE, Merikangas KR, Satterthwaite TD, Shinohara RT, Shou H. Similarity-based multimodal regression. Biostatistics 2023:kxad033. [PMID: 38058018 DOI: 10.1093/biostatistics/kxad033] [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: 11/22/2022] [Revised: 10/07/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.
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Affiliation(s)
- Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Sarah M Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA 19122, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Schwartz E, Nenning KH, Heuer K, Jeffery N, Bertrand OC, Toro R, Kasprian G, Prayer D, Langs G. Evolution of cortical geometry and its link to function, behaviour and ecology. Nat Commun 2023; 14:2252. [PMID: 37080952 PMCID: PMC10119184 DOI: 10.1038/s41467-023-37574-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023] Open
Abstract
Studies in comparative neuroanatomy and of the fossil record demonstrate the influence of socio-ecological niches on the morphology of the cerebral cortex, but have led to oftentimes conflicting theories about its evolution. Here, we study the relationship between the shape of the cerebral cortex and the topography of its function. We establish a joint geometric representation of the cerebral cortices of ninety species of extant Euarchontoglires, including commonly used experimental model organisms. We show that variability in surface geometry relates to species' ecology and behaviour, independent of overall brain size. Notably, ancestral shape reconstruction of the cortical surface and its change during evolution enables us to trace the evolutionary history of localised cortical expansions, modal segregation of brain function, and their association to behaviour and cognition. We find that individual cortical regions follow different sequences of area increase during evolutionary adaptations to dynamic socio-ecological niches. Anatomical correlates of this sequence of events are still observable in extant species, and relate to their current behaviour and ecology. We decompose the deep evolutionary history of the shape of the human cortical surface into spatially and temporally conscribed components with highly interpretable functional associations, highlighting the importance of considering the evolutionary history of cortical regions when studying their anatomy and function.
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Affiliation(s)
- Ernst Schwartz
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Katja Heuer
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Nathan Jeffery
- Institute of Life Course & Medical Sciences, University of Liverpool, Liverpool, England
| | - Ornella C Bertrand
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona Edifici ICTA-ICP, c/ Columnes s/n, Campus de la UAB, 08193 Cerdanyola del Vallès., Barcelona, Spain
- School of GeoSciences, University of Edinburgh, Grant Institute, Edinburgh, Scotland, EH9 3FE, United Kingdom
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria.
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
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9
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Weinstein SM, Vandekar SN, Baller EB, Tu D, Adebimpe A, Tapera TM, Gur RC, Gur RE, Detre JA, Raznahan A, Alexander-Bloch AF, Satterthwaite TD, Shinohara RT, Park JY. Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence. Neuroimage 2022; 264:119712. [PMID: 36309332 PMCID: PMC10062374 DOI: 10.1016/j.neuroimage.2022.119712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/16/2022] [Accepted: 10/25/2022] [Indexed: 11/05/2022] Open
Abstract
With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.
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Affiliation(s)
- Sarah M Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Erica B Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Danni Tu
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Strategy Innovation & Deployment Section, Johnson and Johnson, Raritan, NJ, 08869, USA
| | - Tinashe M Tapera
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jun Young Park
- Department of Statistical Sciences and Department of Psychology, University of Toronto, Toronto, ON, M5G 1Z5, Canada.
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10
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Gruskin DC, Patel GH. Brain connectivity at rest predicts individual differences in normative activity during movie watching. Neuroimage 2022; 253:119100. [PMID: 35304263 PMCID: PMC9491116 DOI: 10.1016/j.neuroimage.2022.119100] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
When exposed to the same sensory event, some individuals are bound to have less typical experiences than others. Previous research has investigated this phenomenon by showing that the typicality of one's sensory experience is associated with the typicality of their stimulus-evoked brain activity (as measured by intersubject correlation, or ISC). Individual differences in ISC have recently been attributed to variability in focal neural processing. However, the extent to which these differences reflect purely intra-regional variability versus variation in the brain's baseline ability to transmit information between regions has yet to be established. Here, we show that an individual's degree and spatial distribution of ISC are closely related to their brain's functional organization at rest. Using resting state and movie watching fMRI data from the Human Connectome Project, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC. Similar region-level analyses demonstrate that the levels of ISC exhibited by brain regions during movie watching are associated with their connectivity to other regions at rest, and that the nature of these connectivity-activity relationships varies as a function of regional roles in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings contextualize reports of localized individual differences in ISC as potentially reflecting larger, network-level alterations in resting brain function and detail how the brain's ability to process complex sensory information is linked to its baseline functional organization.
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Affiliation(s)
- David C Gruskin
- Medical Scientist Training Program, Columbia University Irving Medical Center, NY 10032, USA.
| | - Gaurav H Patel
- New York State Psychiatric Institute, NY 10032, USA; Department of Psychiatry, Columbia University Irving Medical Center, NY 10032, USA.
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11
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Abstract
Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.
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12
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Weinstein SM, Vandekar SN, Adebimpe A, Tapera TM, Robert‐Fitzgerald T, Gur RC, Gur RE, Raznahan A, Satterthwaite TD, Alexander‐Bloch AF, Shinohara RT. A simple permutation-based test of intermodal correspondence. Hum Brain Mapp 2021; 42:5175-5187. [PMID: 34519385 PMCID: PMC8519855 DOI: 10.1002/hbm.25577] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/25/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.
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Affiliation(s)
- Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | | | - Azeez Adebimpe
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Tinashe M. Tapera
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Timothy Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Raquel E. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMaryland
| | - Theodore D. Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Aaron F. Alexander‐Bloch
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
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