Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity.
Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity.
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DOI：
10.1016/j.nicl.2020.102517
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Savic I
中科院分区：
文献类型：
--
作者：
Moody TD;Feusner JD;Reggente N;Vanhoecke J;Holmberg M;Manzouri A;Sorouri Khorashad B;Savic I
机器学习模型预测跨性别激素治疗后的身体一致性。预测功能包括临床指标和网络功能连接。最具预测性的网络是额顶叶和扣带盖。功能连接可以提供对激素的体脑效应的见解。方法可用于增强个性化治疗。性别不一致（GI）的个体由于其性别认同与出生性别之间的不一致而经历严重的痛苦。社会学、文化、人际关系和生物学因素可能是促成因素，对某些人来说，跨性别激素治疗和性别确认手术等医疗治疗可能会有所帮助。跨性别激素治疗可以有效地减少身体不协调，但反应各不相同，并且没有可靠的方法来预测治疗结果。我们使用跨性别激素治疗前的临床和MRI数据作为特征来训练机器学习模型，以预测个体治疗后的身体一致性（他们的身体照片与自我认同的匹配程度）。25名性别不一致的跨性别女性和跨性别男性参与了研究。该模型显着预测治疗后的身体一致性，最高的预测功能来自扣带-盖（R2 = 0.41）和额顶叶（R2 = 0.30）网络。这项研究提供的证据表明，激素治疗的疗效可以从治疗前收集的信息预测，功能性大脑连接的模式可以提供对激素的体脑效应的见解，影响一个人的身体一致性的感觉。结果可以帮助确定个性化治疗的个人预测有低的身体自我一致性标准治疗后的需要。
A machine learning model predicted body congruence after cross-sex hormone therapy. Predictive features included clinical metrics and network functional connectivity. The most predictive networks were fronto-parietal and cingulo-opercular. Functional connectivity may provide insights into body-brain effects of hormones. Methods could be used to enhance personalized therapies. Individuals with gender incongruence (GI) experience serious distress due to incongruence between their gender identity and birth-assigned sex. Sociological, cultural, interpersonal, and biological factors are likely contributory, and for some individuals medical treatment such as cross-sex hormone therapy and gender-affirming surgery can be helpful. Cross-sex hormone therapy can be effective for reducing body incongruence, but responses vary, and there is no reliable way to predict therapeutic outcomes. We used clinical and MRI data before cross-sex hormone therapy as features to train a machine learning model to predict individuals’ post-therapy body congruence (the degree to which photos of their bodies match their self-identities). Twenty-five trans women and trans men with gender incongruence participated. The model significantly predicted post-therapy body congruence, with the highest predictive features coming from the cingulo-opercular (R2 = 0.41) and fronto-parietal (R2 = 0.30) networks. This study provides evidence that hormone therapy efficacy can be predicted from information collected before therapy, and that patterns of functional brain connectivity may provide insights into body-brain effects of hormones, affecting one's sense of body congruence. Results could help identify the need for personalized therapies in individuals predicted to have low body-self congruence after standard therapy.
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影响因子：
3.2
作者：
Feusner JD;Lidström A;Moody TD;Dhejne C;Bookheimer SY;Savic I
通讯作者：
Savic I
影响因子：
--
作者：
Bouman, Walter Pierre;Claes, Laurence;Arcelus, Jon
通讯作者：
Arcelus, Jon
影响因子：
3.7
作者：
Burke, Sarah M.;Manzouri, Amir H.;Savic-Berglund, Ivanka
通讯作者：
Savic-Berglund, Ivanka
影响因子：
3.8
作者：
Feusner, Jamie D.;Dervisic, Jasenko;Savic, Ivanka
通讯作者：
Savic, Ivanka
影响因子：
3.1
作者：
Herold, Dorrit;Spengler, Stephanie;Bermpohl, Felix
通讯作者：
Bermpohl, Felix