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Constant-Varlet C, Nakai T, Prado J. Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings. Brain Struct Funct 2024:10.1007/s00429-024-02804-5. [PMID: 38710874 DOI: 10.1007/s00429-024-02804-5] [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: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs.
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Affiliation(s)
- Charlotte Constant-Varlet
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
| | - Tomoya Nakai
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France
- Araya Inc., Tokyo, Japan
| | - Jérôme Prado
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
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Schetter M, Romascano D, Gaujard M, Rummel C, Denervaud S. Learning by Heart or with Heart: Brain Asymmetry Reflects Pedagogical Practices. Brain Sci 2023; 13:1270. [PMID: 37759871 PMCID: PMC10526483 DOI: 10.3390/brainsci13091270] [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: 07/26/2023] [Revised: 08/14/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Brain hemispheres develop rather symmetrically, except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared the CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated the CTh AI of the students as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC region has a functional lateralization, with the right PHC region having a stronger involvement in spatiotemporal context encoding, while the left PHC region is involved in semantic encoding. We observed CTh asymmetry toward the left PHC region for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home-life and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.
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Affiliation(s)
- Martin Schetter
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - David Romascano
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Mathilde Gaujard
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Solange Denervaud
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
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Chauvin L, Kumar K, Desrosiers C, Wells W, Toews M. Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:836-845. [PMID: 34699353 PMCID: PMC9022638 DOI: 10.1109/tmi.2021.3123252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between O (N 2) image pairs in [Formula: see text] operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC = 0.97. Software is provided for efficient fine-grained curation of large, generic image datasets.
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Zhong S, Wei L, Zhao C, Yang L, Di Z, Francks C, Gong G. Interhemispheric Relationship of Genetic Influence on Human Brain Connectivity. Cereb Cortex 2020; 31:77-88. [DOI: 10.1093/cercor/bhaa207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/03/2020] [Accepted: 07/07/2020] [Indexed: 12/25/2022] Open
Abstract
Abstract
To understand the origins of interhemispheric differences and commonalities/coupling in human brain wiring, it is crucial to determine how homologous interregional connectivities of the left and right hemispheres are genetically determined and related. To address this, in the present study, we analyzed human twin and pedigree samples with high-quality diffusion magnetic resonance imaging tractography and estimated the heritability and genetic correlation of homologous left and right white matter (WM) connections. The results showed that the heritability of WM connectivity was similar and coupled between the 2 hemispheres and that the degree of overlap in genetic factors underlying homologous WM connectivity (i.e., interhemispheric genetic correlation) varied substantially across the human brain: from complete overlap to complete nonoverlap. Particularly, the heritability was significantly stronger and the chance of interhemispheric complete overlap in genetic factors was higher in subcortical WM connections than in cortical WM connections. In addition, the heritability and interhemispheric genetic correlations were stronger for long-range connections than for short-range connections. These findings highlight the determinants of the genetics underlying WM connectivity and its interhemispheric relationships, and provide insight into genetic basis of WM connectivity asymmetries in both healthy and disease states.
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Affiliation(s)
- Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Long Wei
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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Adams HH, Roshchupkin GV, DeCarli C, Franke B, Grabe HJ, Habes M, Jahanshad N, Medland SE, Niessen W, Satizabal CL, Schmidt R, Seshadri S, Teumer A, Thompson PM, Vernooij MW, Wittfeld K, Ikram MA. Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:286-290. [PMID: 30976649 PMCID: PMC6441785 DOI: 10.1016/j.dadm.2019.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme-Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.
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Affiliation(s)
- Hieab H.H. Adams
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Gennady V. Roshchupkin
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Disease (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Mohamad Habes
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mark Stevens Institute for Neuroimaging and Infomatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Herston, Australia
| | - Wiro Niessen
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- Department of Neurology, Boston University, Boston, MA, USA
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Graz, Austria
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- Department of Neurology, Boston University, Boston, MA, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mark Stevens Institute for Neuroimaging and Infomatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
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Kennedy JT, Astafiev SV, Golosheykin S, Korucuoglu O, Anokhin AP. Shared genetic influences on adolescent body mass index and brain structure: A voxel-based morphometry study in twins. Neuroimage 2019; 199:261-272. [PMID: 31163268 DOI: 10.1016/j.neuroimage.2019.05.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/17/2019] [Accepted: 05/19/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Previous research has demonstrated significant relationships between obesity and brain structure. Both phenotypes are heritable, but it is not known whether they are influenced by common genetic factors. We investigated the genetic etiology of the relationship between individual variability in brain morphology and BMIz using structural MRI in adolescent twins. METHOD The sample (n = 258) consisted of 54 monozygotic and 75 dizygotic twin pairs (mean(SD) age = 13.61(0.505), BMIz = 0.608(1.013). Brain structure (volume and density of gray and white matter) was assessed using VBM. Significant voxelwise heritability of brain structure was established using the Accelerated Permutation inference for ACE models (APACE) program, with structural heritability varying from 15 to 97%, depending on region. Bivariate heritability analyses were carried out comparing additive genetic and unique environment models with and without shared genetics on BMIz and the voxels showing significant heritability in the APACE analyses. RESULTS BMIz was positively related to gray matter volume in the brainstem and thalamus and negatively related to gray matter volume in the bilateral uncus and medial orbitofrontal cortex, gray matter density in the cerebellum, prefrontal lobe, temporal lobe, and limbic system, and white matter density in the brainstem. Bivariate heritability analyses showed that BMIz and brain structure share ∼1/3 of their genes and that ∼95% of the phenotypic correlation between BMIz and brain structure is due to shared additive genetic influences. These regions included areas related to decision-making, motivation, liking vs. wanting, taste, interoception, reward processing/learning, caloric evaluation, and inhibition. CONCLUSION These results suggested genetic factors are responsible for the relationship between BMIz and heritable BMIz related brain structure in areas related to eating behavior.
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Affiliation(s)
- James T Kennedy
- Department of Psychiatry, Washington University School of Medicine, United States.
| | - Serguei V Astafiev
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Semyon Golosheykin
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Ozlem Korucuoglu
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, United States
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Jollant F, Wagner G, Richard-Devantoy S, Köhler S, Bär KJ, Turecki G, Pereira F. Neuroimaging-informed phenotypes of suicidal behavior: a family history of suicide and the use of a violent suicidal means. Transl Psychiatry 2018; 8:120. [PMID: 29921964 PMCID: PMC6008434 DOI: 10.1038/s41398-018-0170-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 04/23/2018] [Accepted: 05/11/2018] [Indexed: 11/25/2022] Open
Abstract
The identification of brain markers of suicidal risk is highly expected. However, neuroimaging studies have yielded mixed results, possibly due to phenotypic heterogeneity. In the present study, we addressed this issue using structural brain imaging. First, two independent samples of suicide attempters (n = 17 in Montreal, 32 in Jena), patient controls (n = 26/34), and healthy controls (n = 66/34) were scanned with magnetic resonance imaging. Groups were compared with FSL. We then reviewed the literature and run a GingerALE meta-analysis of 12 structural imaging studies comparing suicide attempters and patient controls with whole-brain analyses (n = 693). Finally, we explored the potential contribution of two variables previously associated with biological/cognitive deficits: a family history of suicide (FHoS), and the use of a violent suicidal means (VSM). Here, we added two groups of healthy first-degree biological relatives of suicide victims and depressed patients (n = 32). When comparing all suicide attempters and controls, very limited between-group differences were found in the two samples, and none in the meta-analysis. In contrast, a FHoS was associated with reduced volumes in bilateral temporal regions, right dorsolateral prefrontal cortex, and left putamen, several of these differences being observed across groups. VSM was associated with increased bilateral caudate (and left putamen) volumes. Some morphometric variations in cortico-subcortical networks may therefore be endophenotypes increasing the suicidal vulnerability, while others (notably in striatum) may modulate action selection. These results therefore confirm at the neural level two phenotypes at high lethal risk with a strong biological background, and uncover motives of heterogeneous findings in neuroimaging studies of suicidal behavior.
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Affiliation(s)
- Fabrice Jollant
- McGill Group for Suicide Studies (MGSS), McGill University & Douglas Mental Health University Institute, Montréal, Canada.
- Department of Psychiatry, Academic Hospital (CHU) of Nîmes, Nîmes, France.
- Paris Descartes University & Sainte-Anne Hospital, Paris, France.
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Stéphane Richard-Devantoy
- McGill Group for Suicide Studies (MGSS), McGill University & Douglas Mental Health University Institute, Montréal, Canada
| | - Stefanie Köhler
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Karl-Jürgen Bär
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Gustavo Turecki
- McGill Group for Suicide Studies (MGSS), McGill University & Douglas Mental Health University Institute, Montréal, Canada
| | - Fabricio Pereira
- Department of Radiology, Academic Hospital (CHU) of Nîmes & Research Team EA2415, Nîmes, France
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Wolters FJ, Adams HH, Bos D, Licher S, Ikram MA. Three Decades of Dementia Research: Insights from One Small Community of Indomitable Rotterdammers. J Alzheimers Dis 2018; 64:S145-S159. [DOI: 10.3233/jad-179938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Frank J. Wolters
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Neurology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Hieab H.H. Adams
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Silvan Licher
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
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