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Zhen Y, Yang Y, Zheng Y, Wang X, Liu L, Zheng Z, Zheng H, Tang S. The heritability and structural correlates of resting-state fMRI complexity. Neuroimage 2024; 296:120657. [PMID: 38810892 DOI: 10.1016/j.neuroimage.2024.120657] [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: 03/09/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024] Open
Abstract
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.
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Affiliation(s)
- Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing 100085, China.
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.
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2
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Qi T, Mandelli ML, Pereira CLW, Wellman E, Bogley R, Licata AE, Chang EF, Oganian Y, Gorno-Tempini ML. Anatomical and behavioral correlates of auditory perception in developmental dyslexia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.09.539936. [PMID: 37214875 PMCID: PMC10197694 DOI: 10.1101/2023.05.09.539936] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Developmental dyslexia is typically associated with difficulties in basic auditory processing and in manipulating speech sounds. However, the neuroanatomical correlates of auditory difficulties in developmental dyslexia (DD) and their contribution to individual clinical phenotypes are still unknown. Recent intracranial electrocorticography findings associated processing of sound amplitude rises and speech sounds with posterior and middle superior temporal gyrus (STG), respectively. We hypothesize that regional STG anatomy will relate to specific auditory abilities in DD, and that auditory processing abilities will relate to behavioral difficulties with speech and reading. One hundred and ten children (78 DD, 32 typically developing, age 7-15 years) completed amplitude rise time and speech in noise discrimination tasks. They also underwent a battery of cognitive tests. Anatomical MRI scans were used to identify regions in which local cortical gyrification complexity correlated with auditory behavior. Behaviorally, amplitude rise time but not speech in noise performance was impaired in DD. Neurally, amplitude rise time and speech in noise performance correlated with gyrification in posterior and middle STG, respectively. Furthermore, amplitude rise time significantly contributed to reading impairments in DD, while speech in noise only explained variance in phonological awareness. Finally, amplitude rise time and speech in noise performance were not correlated, and each task was correlated with distinct neuropsychological measures, emphasizing their unique contributions to DD. Overall, we provide a direct link between the neurodevelopment of the left STG and individual variability in auditory processing abilities in neurotypical and dyslexic populations.
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Zhang S, Zhang T, Cao G, Zhou J, He Z, Li X, Ren Y, Liu T, Jiang X, Guo L, Han J, Liu T. Species -shared and -unique gyral peaks on human and macaque brains. eLife 2024; 12:RP90182. [PMID: 38635322 PMCID: PMC11026093 DOI: 10.7554/elife.90182] [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] [Indexed: 04/19/2024] Open
Abstract
Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, our research defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. Shared and unique gyral peaks in human and macaque are identified in this study, and their similarities and differences in spatial distribution, anatomical morphology, and functional connectivity were also dicussed.
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Affiliation(s)
- Songyao Zhang
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Guannan Cao
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Jingchao Zhou
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of ChinaChengduChina
| | - Zhibin He
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Xiao Li
- School of Information Technology, Northwest UniversityXi'anChina
| | - Yudan Ren
- School of Information Technology, Northwest UniversityXi'anChina
| | - Tao Liu
- College of Science, North China University of Science and TechnologyTangshanChina
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of ChinaChengduChina
| | - Lei Guo
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Junwei Han
- School of Automation, Northwestern Polytechnical UniversityXi’anChina
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthensUnited States
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4
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Zhang S, Zhang T, Cao G, Zhou J, He Z, Li X, Ren Y, Liu T, Jiang X, Guo L, Han J, Liu T. Species -Shared and -Unique Gyral Peaks on Human and Macaque Brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.26.550760. [PMID: 37546923 PMCID: PMC10402126 DOI: 10.1101/2023.07.26.550760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, we defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. In this study, we identified shared and unique gyral peaks in human and macaque, and investigated the similarities and differences in the spatial distribution, anatomical morphology, and functional connectivity of them.
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Affiliation(s)
- Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Guannan Cao
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Jingchao Zhou
- College of Science, North China University of Science and Technology, Tangshan, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Xiao Li
- School of Information Technology, Northwest University, Xi’an, China
| | - Yudan Ren
- School of Information Technology, Northwest University, Xi’an, China
| | - Tao Liu
- College of Science, North China University of Science and Technology, Tangshan, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA, USA
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Akula SK, Exposito-Alonso D, Walsh CA. Shaping the brain: The emergence of cortical structure and folding. Dev Cell 2023; 58:2836-2849. [PMID: 38113850 PMCID: PMC10793202 DOI: 10.1016/j.devcel.2023.11.004] [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: 09/13/2022] [Revised: 04/08/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023]
Abstract
The cerebral cortex-the brain's covering and largest region-has increased in size and complexity in humans and supports higher cognitive functions such as language and abstract thinking. There is a growing understanding of the human cerebral cortex, including the diversity and number of cell types that it contains, as well as of the developmental mechanisms that shape cortical structure and organization. In this review, we discuss recent progress in our understanding of molecular and cellular processes, as well as mechanical forces, that regulate the folding of the cerebral cortex. Advances in human genetics, coupled with experimental modeling in gyrencephalic species, have provided insights into the central role of cortical progenitors in the gyrification and evolutionary expansion of the cerebral cortex. These studies are essential for understanding the emergence of structural and functional organization during cortical development and the pathogenesis of neurodevelopmental disorders associated with cortical malformations.
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Affiliation(s)
- Shyam K Akula
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - David Exposito-Alonso
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Christopher A Walsh
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
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6
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [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: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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Larsen B, Sydnor VJ, Keller AS, Yeo BTT, Satterthwaite TD. A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment. Trends Neurosci 2023; 46:847-862. [PMID: 37643932 PMCID: PMC10530452 DOI: 10.1016/j.tins.2023.07.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/23/2023] [Accepted: 07/25/2023] [Indexed: 08/31/2023]
Abstract
To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC), and Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Pretzsch CM, Ecker C. Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review. Front Neurosci 2023; 17:1172779. [PMID: 37457001 PMCID: PMC10347684 DOI: 10.3389/fnins.2023.1172779] [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: 02/23/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.
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Affiliation(s)
- Charlotte M. Pretzsch
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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9
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da Rocha JLD, Kepinska O, Schneider P, Benner J, Degano G, Schneider L, Golestani N. Multivariate Concavity Amplitude Index (MCAI) for characterizing Heschl's gyrus shape. Neuroimage 2023; 272:120052. [PMID: 36965861 DOI: 10.1016/j.neuroimage.2023.120052] [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: 09/15/2022] [Revised: 03/21/2023] [Accepted: 03/22/2023] [Indexed: 03/27/2023] Open
Abstract
Heschl's gyrus (HG), which includes primary auditory cortex, is highly variable in its shape (i.e. gyrification patterns), between hemispheres and across individuals. Differences in HG shape have been observed in the context of phonetic learning skill and expertise, and of professional musicianship, among others. Two of the most common configurations of HG include single HG, where a single transverse temporal gyrus is present, and common stem duplications (CSD), where a sulcus intermedius (SI) arises from the lateral aspect of HG. Here we describe a new toolbox, called 'Multivariate Concavity Amplitude Index' (MCAI), which automatically assesses the shape of HG. MCAI works on the output of TASH, our first toolbox which automatically segments HG, and computes continuous indices of concavity, which arise when sulci are present, along the outer perimeter of an inflated representation of HG, in a directional manner. Thus, MCAI provides a multivariate measure of shape, which is reproducible and sensitive to small variations in shape. We applied MCAI to structural magnetic resonance imaging (MRI) data of N=181 participants, including professional and amateur musicians and from non-musicians. Former studies have shown large variations in HG shape in the former groups. We validated MCAI by showing high correlations between the dominant (i.e. highest) lateral concavity values and continuous visual assessments of the degree of lateral gyrification of the first gyrus. As an application of MCAI, we also replicated previous visually obtained findings showing a higher likelihood of bilateral CSDs in musicians. MCAI opens a wide range of applications in evaluating HG shape in the context of individual differences, expertise, disorder and genetics.
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Affiliation(s)
- Josué Luiz Dalboni da Rocha
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, USA; Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland.
| | - Olga Kepinska
- Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Peter Schneider
- Department of Neuroradiology and Department of Neurology, Section of Biomagnetism, University of Heidelberg Hospital, Heidelberg, Germany; Centre for Systematic Musicology, University of Graz, Graz, Austria; Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Jan Benner
- Department of Neuroradiology and Department of Neurology, Section of Biomagnetism, University of Heidelberg Hospital, Heidelberg, Germany
| | - Giulio Degano
- Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland
| | - Letitia Schneider
- Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Narly Golestani
- Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland; Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria; Department of Behavioral and Cognitive Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
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10
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Staginnus M, Cornwell H, Toschi N, Oosterling M, Paradysz M, Smaragdi A, González-Madruga K, Pauli R, Rogers JC, Bernhard A, Martinelli A, Kohls G, Raschle NM, Konrad K, Stadler C, Freitag CM, De Brito SA, Fairchild G. Testing the Ecophenotype Model: Cortical Structure Alterations in Conduct Disorder With Versus Without Childhood Maltreatment. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023:S2451-9022(22)00347-0. [PMID: 36925341 DOI: 10.1016/j.bpsc.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/16/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023]
Abstract
BACKGROUND Childhood maltreatment is common in youths with conduct disorder (CD), and both CD and maltreatment have been linked to neuroanatomical alterations. Nonetheless, our understanding of the contribution of maltreatment to the neuroanatomical alterations observed in CD remains limited. We tested the applicability of the ecophenotype model to CD, which holds that maltreatment-related psychopathology is (neurobiologically) distinct from psychopathology without maltreatment. METHODS Surface-based morphometry was used to investigate cortical volume, thickness, surface area, and gyrification in a mixed-sex sample of participants with CD (n = 114) and healthy control subjects (HCs) (n = 146), ages 9 to 18 years. Using vertexwise general linear models adjusted for sex, age, total intracranial volume, and site, the control group was compared with the overall CD group and the CD subgroups with (n = 49) versus without (n = 65) maltreatment (assessed by the Children's Bad Experiences interview). These subgroups were also directly compared. RESULTS The overall CD group showed lower cortical thickness in the right inferior frontal gyrus. CD youths with a history of maltreatment showed more widespread structural alterations relative to HCs, comprising lower thickness, volume, and gyrification in inferior and middle frontal regions. Conversely, CD youths with no history of maltreatment only showed greater left superior temporal gyrus folding relative to HCs. When contrasting the CD subgroups, those with maltreatment displayed lower right superior temporal gyrus volume, right precentral gyrus surface area, and gyrification in frontal, temporal, and parietal regions. CONCLUSIONS Consistent with the ecophenotype model, findings indicated that CD youths with versus without maltreatment differ neurobiologically. This highlights the importance of considering maltreatment history in neuroimaging studies of CD and other disorders.
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Affiliation(s)
| | - Harriet Cornwell
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata," Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, Massachusetts
| | | | - Michal Paradysz
- Department of Psychology, University of Bath, Bath, United Kingdom
| | | | | | - Ruth Pauli
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Jack C Rogers
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Anka Bernhard
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Anne Martinelli
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany; School of Psychology, Fresenius University of Applied Sciences, Frankfurt, Germany
| | - Gregor Kohls
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, RWTH Aachen, Aachen, Germany; Department of Child and Adolescent Psychiatry, Medical Faculty, TU Dresden, Dresden, Germany
| | - Nora Maria Raschle
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Kerstin Konrad
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, RWTH Aachen, Aachen, Germany; JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen and Research Centre Juelich, Juelich, Germany
| | - Christina Stadler
- Department of Child and Adolescent Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Stephane A De Brito
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Graeme Fairchild
- Department of Psychology, University of Bath, Bath, United Kingdom
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11
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Maes HHM, Lapato DM, Schmitt JE, Luciana M, Banich MT, Bjork JM, Hewitt JK, Madden PA, Heath AC, Barch DM, Thompson WK, Iacono WG, Neale MC. Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behav Genet 2023; 53:1-24. [PMID: 36357558 PMCID: PMC9823057 DOI: 10.1007/s10519-022-10123-w] [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: 09/19/2021] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.
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Affiliation(s)
- Hermine H. M. Maes
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA ,grid.224260.00000 0004 0458 8737Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA
| | - Dana M. Lapato
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA
| | - J. Eric Schmitt
- grid.25879.310000 0004 1936 8972Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Monica Luciana
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Marie T. Banich
- grid.266190.a0000000096214564Department of Psychology and Neuroscience, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA
| | - James M. Bjork
- grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
| | - John K. Hewitt
- grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute for Behavioral Genetics, University of Colorado, Boulder, USA
| | - Pamela A. Madden
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Andrew C. Heath
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Deanna M. Barch
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Wes K. Thompson
- grid.266100.30000 0001 2107 4242Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA USA
| | - William G. Iacono
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
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12
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Assessment of Characteristics of Imaging Biomarkers for Quantifying Anterior Cingulate Cortex Changes: A Twin Study of Middle- to Advanced-Aged Populations in East Asia. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121855. [PMID: 36557058 PMCID: PMC9783013 DOI: 10.3390/medicina58121855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/30/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022]
Abstract
Background and Objectives: Our aim was to assess genetic and environmental effects on surface morphological parameters for quantifying anterior cingulate cortex (ACC) changes in middle- to advanced-age East Asians using twin analysis. Materials and Methods: Normal twins over 39 years old comprising 37 monozygotic pairs and 17 dizygotic pairs underwent 3-dimensional (3D) T1-weighted imaging of the brain at 3T. Freesurfer-derived ACC parameters including thickness, standard deviation of thickness (STDthickness), volume, surface area, and sulcal morphological parameters (folding, mean, and Gaussian curvatures) were calculated from 3D T1-weighted volume images. Twin analysis with a model involving phenotype variance components of additive genetic effects (A), common environmental effects (C), and unique environmental effects (E) was performed to assess the magnitude of each genetic and environmental influence on parameters. Results: Most parameters fit best with an AE model. Both thickness (A: left 0.73/right 0.71) and surface area (A: left 0.63/right 0.71) were highly heritable. STDthickness was low to moderately heritable (A: left 0.48/right 0.29). Volume was moderately heritable (A: left 0.37). Folding was low to moderately heritable (A: left 0.44/right 0.28). Mean curvature (A: left 0.37/right 0.65) and Gaussian curvature (A: right 0.79) were moderately to highly heritable. Right volume and left Gaussian curvature fit best with a CE model, indicating a relatively weak contribution of genetic factors to these parameters. Conclusions: When assessing ACC changes in middle- to advanced-age East Asians, one must keep in mind that thickness and surface area appear to be strongly affected by genetic factors, whereas sulcal morphological parameters tend to involve environmental factors.
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13
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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14
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Troiani V, Snyder W, Kozick S, Patti MA, Beiler D. Variability and concordance of sulcal patterns in the orbitofrontal cortex: A twin study. Psychiatry Res Neuroimaging 2022; 324:111492. [PMID: 35597228 DOI: 10.1016/j.pscychresns.2022.111492] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/15/2022] [Accepted: 05/13/2022] [Indexed: 10/18/2022]
Abstract
Sulcogyral patterns have been identified in the orbitofrontal cortex (OFC) based on the continuity of the medial and lateral orbital sulci. Pattern types are named according to their frequency in the population, with Type I present in ∼60%, Type II in ∼25%, Type III in ∼10%, and Type IV in ∼5%. Previous work has demonstrated that psychiatric conditions with high estimated heritability (e.g. schizophrenia, bipolar disorder) are associated with reduced frequency of Type I patterns, but the general heritability of the OFC sulcogyral patterns is unknown. We examined concordance of OFC patterns in 304 monozygotic (MZ) twins relative to 172 dizygotic (DZ) twins using structural magnetic resonance imaging data. We find that the frequency of pattern types within MZ and DZ twins are similar and bilateral concordance rates across all pattern types in DZ twins were 14% and 21% for MZ twins. Results from follow-up analyses confirm that continuity in the rostral-caudal direction is an important source of variability within the OFC, and subtype analyses indicate that variability is present in other sulci that are not represented by overall OFC pattern type. Overall, these results suggest that OFC sulcogyral patterns may reflect important variance that is not genetic in origin.
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Affiliation(s)
- Vanessa Troiani
- Geisinger Autism and Developmental Medicine Institute, 120 Hamm Drive, Suite 2A, Lewisburg, PA 17837, United States.
| | - Will Snyder
- Geisinger Autism and Developmental Medicine Institute, 120 Hamm Drive, Suite 2A, Lewisburg, PA 17837, United States
| | - Shane Kozick
- Geisinger Autism and Developmental Medicine Institute, 120 Hamm Drive, Suite 2A, Lewisburg, PA 17837, United States
| | - Marisa A Patti
- Geisinger Autism and Developmental Medicine Institute, 120 Hamm Drive, Suite 2A, Lewisburg, PA 17837, United States
| | - Donielle Beiler
- Geisinger Autism and Developmental Medicine Institute, 120 Hamm Drive, Suite 2A, Lewisburg, PA 17837, United States
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15
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Nenning KH, Langs G. Machine learning in neuroimaging: from research to clinical practice. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 62:1-10. [PMID: 36044070 PMCID: PMC9732070 DOI: 10.1007/s00117-022-01051-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.
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Affiliation(s)
- Karl-Heinz Nenning
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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16
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Darayi M, Hoffman ME, Sayut J, Wang S, Demirci N, Consolini J, Holland MA. Computational models of cortical folding: A review of common approaches. J Biomech 2021; 139:110851. [PMID: 34802706 DOI: 10.1016/j.jbiomech.2021.110851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/09/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
The process of gyrification, by which the brain develops the intricate pattern of gyral hills and sulcal valleys, is the result of interactions between biological and mechanical processes during brain development. Researchers have developed a vast array of computational models in order to investigate cortical folding. This review aims to summarize these studies, focusing on five essential elements of the brain that affect development and gyrification and how they are represented in computational models: (i) the constraints of skull, meninges, and cerebrospinal fluid; (ii) heterogeneity of cortical layers and regions; (iii) anisotropic behavior of subcortical fiber tracts; (iv) material properties of brain tissue; and (v) the complex geometry of the brain. Finally, we highlight areas of need for future simulations of brain development.
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Affiliation(s)
- Mohsen Darayi
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Mia E Hoffman
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - John Sayut
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Shuolun Wang
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jack Consolini
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria A Holland
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA.
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17
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Floris DL, Andrews DS. Differences in Degree and Form. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:851-853. [PMID: 34507628 PMCID: PMC9152983 DOI: 10.1016/j.bpsc.2021.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.
| | - Derek S Andrews
- Medical Investigation of Neurodevelopmental Disorders Institute and Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California Davis, Sacramento, California
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18
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Schön M, Nosanova A, Jacob C, Kraus JM, Kestler HAK, Mayer B, Feldengut S, Amunts K, Del Tredici K, Boeckers TM, Braak H. A comparative study of pre-alpha islands in the entorhinal cortex from selected primates and in lissencephaly. J Comp Neurol 2021; 530:683-704. [PMID: 34402535 DOI: 10.1002/cne.25233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/08/2021] [Accepted: 08/12/2021] [Indexed: 11/11/2022]
Abstract
The entorhinal cortex (EC) is the main interface between the sensory association areas of the neocortex and the hippocampus. It is crucial for the evaluation and processing of sensory data for long-term memory consolidation, and shows damage in many brain diseases, e.g., neurodegenerative diseases, such as Alzheimer's disease and developmental disorders. The pre-alpha layer of the EC in humans (layer II) displays a remarkable distribution of neurons in islands. These cellular islands give rise to a portion of the perforant path - the major reciprocal data stream for neocortical information into the hippocampal formation. However, the functional relevance of the morphological appearance of the pre-alpha layer in cellular islands and the precise timing of their initial appearance during primate evolution are largely unknown. Here, we conducted a comparative study of the EC from 38 non-human primates and Homo sapiens and found a strong relationship between gyrification index (GI) and the presence of the pre-alpha cellular islands. The formation of cellular islands also correlated wih brain and body weight as well as neopallial volume. In the two human lissencephalic cases, the cellular islands in the pre-alpha layer were lacking. These findings emphasize the relationship between cortical folding and island formation in the entorhinal cortex from an evolutionary perspective, and suggest a role in the pathomechanism of developmental brain disorders. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- M Schön
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - A Nosanova
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - C Jacob
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - J M Kraus
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - H A K Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - B Mayer
- Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany
| | - S Feldengut
- Clinical Neuroanatomy, Department of Neurology, Center for Clinical Research, Ulm University, Ulm, Germany
| | - K Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany.,C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - K Del Tredici
- Clinical Neuroanatomy, Department of Neurology, Center for Clinical Research, Ulm University, Ulm, Germany
| | - T M Boeckers
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany.,DZNE, Ulm site, Ulm, Germany
| | - H Braak
- Clinical Neuroanatomy, Department of Neurology, Center for Clinical Research, Ulm University, Ulm, Germany
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19
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Ball G, Kelly CE, Beare R, Seal ML. Individual variation underlying brain age estimates in typical development. Neuroimage 2021; 235:118036. [PMID: 33838267 DOI: 10.1016/j.neuroimage.2021.118036] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/19/2021] [Accepted: 03/26/2021] [Indexed: 12/14/2022] Open
Abstract
Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed 'brain age', this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n = 768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to identify sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regions brain development. However, while several regions are found to contribute to brain age prediction error, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia.
| | - Claire E Kelly
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Victorian Infant Brain Studies (VIBeS), Murdoch Children's Research Institute, Australia
| | - Richard Beare
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia
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