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      A multi-modal MRI analysis of brain structure and function in relation to OXT methylation in maltreated children and adolescents

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          Abstract

          Child maltreatment dysregulates the brain’s oxytocinergic system, resulting in dysfunctional attachment patterns. However, how the oxytocinergic system in children who are maltreated (CM) is epigenetically affected remains unknown. We assessed differences in salivary DNA methylation of the gene encoding oxytocin ( OXT) between CM ( n = 24) and non-CM ( n = 31), alongside its impact on brain structures and functions using multi-modal brain imaging (voxel-based morphometry, diffusion tensor imaging, and task and resting-state functional magnetic resonance imaging). We found that CM showed higher promoter methylation than non-CM, and nine CpG sites were observed to be correlated with each other and grouped into one index ( OXTmi). OXTmi was significantly negatively correlated with gray matter volume (GMV) in the left superior parietal lobule (SPL), and with right putamen activation during a rewarding task, but not with white matter structures. Using a random forest regression model, we investigated the sensitive period and type of maltreatment that contributed the most to OXTmi in CM, revealing that they were 5–8 years of age and physical abuse (PA), respectively. However, the presence of PA (PA+) was meant to reflect more severe cases, such as prolonged exposure to multiple types of abuse, than the absence of PA. PA+ was associated with significantly greater functional connectivity between the right putamen set as the seed and the left SPL and the left cerebellum exterior. The results suggest that OXT promoter hypermethylation may lead to the atypical development of reward and visual association structures and functions, thereby potentially worsening clinical aspects raised by traumatic experiences.

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          Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

          There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
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            Large-scale automated synthesis of human functional neuroimaging data

            The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
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              Adult attachment, working models, and relationship quality in dating couples.

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                Author and article information

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                Journal
                Translational Psychiatry
                Transl Psychiatry
                Springer Science and Business Media LLC
                2158-3188
                December 2021
                November 18 2021
                December 2021
                : 11
                : 1
                Article
                10.1038/s41398-021-01714-y
                4462e5e9-60ef-473d-99dc-4345c5aa265f
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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