Does multivariate pattern analysis of multimodal brain MRI data accurately identify patients with Takotsubo syndrome compared to healthy controls?
Machine learning analysis of multimodal brain MRI data can distinguish Takotsubo syndrome patients from healthy controls with over 82% accuracy, supporting the concept of a brain-heart interaction in TTS.
Takotsubo syndrome (TTS) is characterized by acute left ventricular dysfunction, with a hospital-mortality rate similar to acute coronary syndrome (ACS). However, the aetiology of TTS is still unknown. In the present study, a multivariate pattern analysis using machine learning with multimodal magnetic resonance imaging (MRI) data of the human brain of TTS patients and age- and gender-matched healthy control subjects was performed. We found consistent structural and functional alterations in TTS patients compared to the control group. In particular, anatomical and neurophysiological measures from brain regions constituting the emotional-autonomic control system contributed to a prediction accuracy of more than 82%. Thus, our findings demonstrate homogeneous neuronal alterations in TTS patients and substantiate the importance of the concept of a brain-heart interaction in TTS.
Klein et al. (2017) studied this question.