边缘系统的大脑性别模型是女性精神障碍的表型。

IF 4.9 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Biology of Sex Differences Pub Date : 2024-05-15 DOI:10.1186/s13293-024-00615-1
Gloria Matte Bon, Dominik Kraft, Erika Comasco, Birgit Derntl, Tobias Kaufmann
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引用次数: 0

摘要

背景:几种精神障碍的发病率和临床表现存在性别差异,这表明特定性别的大脑表型可能起着关键作用。以往的研究使用机器学习模型对全脑成像数据进行性别分类,并研究分类概率与精神健康的关联,但可能忽略了特定区域的特征:方法:我们在此研究了大脑容积成像数据的区域约束模型能否提供比基于全脑的估计值对心理健康更敏感的估计值。鉴于边缘系统在情绪处理和情绪障碍中的已知作用,我们重点研究了边缘系统。我们利用人类连接组项目和昆士兰双胞胎成像这两个不同的健康受试者队列,研究了边缘结构与非边缘结构的脑容量的性别差异和遗传率,随后应用了仅根据边缘或非边缘特征训练的区域约束机器学习模型。为了研究这些模型的生物学基础,我们评估了所获得的性别类别概率估计值的遗传率,并在一个独立的临床样本中调查了与重度抑郁症诊断的关联。所有分析均在控制或不控制估计颅内总容积(eTIV)的情况下进行:结果:在有和没有 eTIV 控制的两种分析中,肢体结构与非肢体结构相比显示出更大的性别差异和遗传性。因此,尽管边缘系统的特征数量要少得多,但机器学习模型仅根据边缘结构就能很好地进行性别分类,其性能与非边缘系统或全脑数据的性能相当。由此得出的分类概率是可遗传的,这表明潜在的生物信息可能是有意义的。我们将研究结果应用于患有重度抑郁症的独立人群,发现抑郁症与男性和女性的分类概率有关,边缘系统模型的影响最大。在不控制 eTIV 的模型中,这种关联是显著的,而在控制 eTIV 的模型中,这种关联没有通过显著性校正:总之,我们的研究结果凸显了大脑性别区域限制模型在更好地理解大脑性别差异与精神障碍之间的联系方面的潜在作用。
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Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

Background: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.

Methods: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).

Results: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.

Conclusions: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

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来源期刊
Biology of Sex Differences
Biology of Sex Differences ENDOCRINOLOGY & METABOLISM-GENETICS & HEREDITY
CiteScore
12.10
自引率
1.30%
发文量
69
审稿时长
14 weeks
期刊介绍: Biology of Sex Differences is a unique scientific journal focusing on sex differences in physiology, behavior, and disease from molecular to phenotypic levels, incorporating both basic and clinical research. The journal aims to enhance understanding of basic principles and facilitate the development of therapeutic and diagnostic tools specific to sex differences. As an open-access journal, it is the official publication of the Organization for the Study of Sex Differences and co-published by the Society for Women's Health Research. Topical areas include, but are not limited to sex differences in: genomics; the microbiome; epigenetics; molecular and cell biology; tissue biology; physiology; interaction of tissue systems, in any system including adipose, behavioral, cardiovascular, immune, muscular, neural, renal, and skeletal; clinical studies bearing on sex differences in disease or response to therapy.
期刊最新文献
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