Migraine is a heterogeneous disorder with variable symptoms and responsiveness to therapy. Because of previous analytic shortcomings, variance in migraine symptoms has been inconsistently related to brain function. In the current analysis, we used data from two sites (n = 143, male and female humans), and performed canonical correlation analysis, relating resting-state functional connectivity (RSFC) with a broad range of migraine symptoms, ranging from headache characteristics to sleep abnormalities. This identified three dimensions of covariance between symptoms and RSFC. The first dimension related to headache intensity, headache frequency, pain catastrophizing, affect, sleep disturbances, and somatic abnormalities, and was associated with frontoparietal and dorsal attention network connectivity, both of which are major cognitive networks. Additionally, RSFC scores from this dimension, both the baseline value and the change from baseline to postintervention, were associated with responsiveness to mind-body therapy. The second dimension was related to an inverse association between pain and anxiety, and to default mode network connectivity. The final dimension was related to pain catastrophizing, and salience, sensorimotor, and default mode network connectivity. In addition to performing canonical correlation analysis, we evaluated the current clustering of migraine patients into episodic and chronic subtypes, and found no evidence to support this clustering. However, when using RSFC scores from the three significant dimensions, we identified a novel clustering of migraine patients into four biotypes with unique functional connectivity patterns. These findings provide new insight into individual variability in migraine, and could serve as the foundation for novel therapies that take advantage of migraine heterogeneity.

SIGNIFICANCE STATEMENT Using a large multisite dataset of migraine patients, we identified three dimensions of multivariate association between symptoms and functional connectivity. This analysis revealed neural networks that relate to all measured symptoms, but also to specific symptom ensembles, such as patient propensity to catastrophize painful events. Using these three dimensions, we found four biotypes of migraine informed by clinical and neural variation together. Such findings pave the way for precision medicine therapy for migraine.

偏头痛是一种异质性疾病，具有不同的症状和对治疗的反应。由于先前的分析缺陷，偏头痛症状的变化与脑功能的相关性不一致。在目前的分析中，我们使用了来自两个站点的数据（n= 143，男性和女性），并进行了典型相关分析，将静息状态功能连接 (RSFC) 与广泛的偏头痛症状联系起来，从头痛特征到睡眠异常。这确定了症状和 RSFC 之间协方差的三个维度。第一个维度与头痛强度、头痛频率、疼痛灾难化、影响、睡眠障碍和躯体异常有关，并与额顶和背侧注意力网络连接相关，这两者都是主要的认知网络。此外，该维度的 RSFC 评分，包括基线值和从基线到干预后的变化，都与身心治疗的反应相关。第二个维度与疼痛和焦虑之间的负相关有关，和默认模式网络连接。最后一个维度与疼痛灾难化、显着性、感觉运动和默认模式网络连接有关。除了执行典型相关分析外，我们还评估了当前将偏头痛患者分为发作性和慢性亚型的聚类，但没有发现支持这种聚类的证据。然而，当使用来自三个重要维度的 RSFC 分数时，我们发现了一个新的偏头痛患者聚类，分为四种具有独特功能连接模式的生物型。这些发现为偏头痛的个体差异提供了新的见解，并且可以作为利用偏头痛异质性的新疗法的基础。和默认模式网络连接。除了执行典型相关分析外，我们还评估了当前将偏头痛患者分为发作性和慢性亚型的聚类，但没有发现支持这种聚类的证据。然而，当使用来自三个重要维度的 RSFC 分数时，我们发现了一个新的偏头痛患者聚类，分为四种具有独特功能连接模式的生物型。这些发现为偏头痛的个体差异提供了新的见解，并且可以作为利用偏头痛异质性的新疗法的基础。和默认模式网络连接。除了执行典型相关分析外，我们还评估了当前将偏头痛患者分为发作性和慢性亚型的聚类，但没有发现支持这种聚类的证据。然而，当使用来自三个重要维度的 RSFC 分数时，我们发现了一个新的偏头痛患者聚类，分为四种具有独特功能连接模式的生物型。这些发现为偏头痛的个体差异提供了新的见解，并且可以作为利用偏头痛异质性的新疗法的基础。当使用来自三个重要维度的 RSFC 分数时，我们发现了一个新的偏头痛患者聚类，分为四种具有独特功能连接模式的生物型。这些发现为偏头痛的个体差异提供了新的见解，并且可以作为利用偏头痛异质性的新疗法的基础。当使用来自三个重要维度的 RSFC 分数时，我们发现了一个新的偏头痛患者聚类，分为四种具有独特功能连接模式的生物型。这些发现为偏头痛的个体差异提供了新的见解，并且可以作为利用偏头痛异质性的新疗法的基础。

重要性声明使用偏头痛患者的大型多站点数据集，我们确定了症状和功能连接之间多变量关联的三个维度。该分析揭示了与所有测量的症状相关的神经网络，也与特定的症状集合相关，例如患者将痛苦事件灾难化的倾向。使用这三个维度，我们发现了四种由临床和神经变异共同决定的偏头痛生物型。这些发现为偏头痛的精准医学治疗铺平了道路。