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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; 229:1327-1348. [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] [MESH Headings] [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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Yamaguchi R, Matsudaira I, Takeuchi H, Imanishi T, Kimura R, Tomita H, Kawashima R, Taki Y. RELN rs7341475 associates with brain structure in japanese healthy females. Neuroscience 2022; 494:38-50. [DOI: 10.1016/j.neuroscience.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/06/2022] [Accepted: 05/06/2022] [Indexed: 11/25/2022]
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Brain areas associated with resilience to depression in high-risk young women. Brain Struct Funct 2021; 226:875-888. [PMID: 33458784 DOI: 10.1007/s00429-021-02215-w] [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: 07/25/2020] [Accepted: 01/05/2021] [Indexed: 10/22/2022]
Abstract
Previous structural brain-imaging studies in first-degree relatives of depressed patients showed alterations that are generally accepted as vulnerability markers for depression. However, only half of the relatives had depression at follow-up, while the other half did not. The aim of this study was to identify the brain areas associated with resilience to depression in high-risk subjects with familial depression. We recruited 59 young women with a history of depressed mothers. Twenty-nine of them (high-risk group [HRG]) had no depression history, while 30 (depressive group) had at least 1 depressive episode in adolescence. The brain structures of the groups were compared through voxel-based morphometry and analysis of cortical thickness. Individual amygdala nuclei and hippocampal subfield volumes were measured. The analysis showed larger amygdala volume, thicker subcallosal cortex and bilateral insula in the women in the HRG compared with those in the depressive group. In addition, we detected more gray matter in the left temporal pole in the HRG. The larger gray matter volume and increased cortical thickness in the key hub regions of the salience network (amygdala and insula) and structurally connected regions in the limbic network (subcallosal area and temporal pole) might prevent women in the HRG from converting to depression.
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Jiang R, Calhoun VD, Zuo N, Lin D, Li J, Fan L, Qi S, Sun H, Fu Z, Song M, Jiang T, Sui J. Connectome-based individualized prediction of temperament trait scores. Neuroimage 2018; 183:366-374. [PMID: 30125712 DOI: 10.1016/j.neuroimage.2018.08.038] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 12/16/2022] Open
Abstract
Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA; Dept. of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongdong Lin
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Hailun Sun
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zening Fu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; University of Electronic Science and Technology of China, Chengdu, 610054, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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