Thomasson Marine, Ceravolo Leonardo, Corradi-Dell'Acqua Corrado, Mantelli Amélie, Saj Arnaud, Assal Frédéric, Grandjean Didier, Péron Julie
Clinical and Experimental Neuropsychology Laboratory, Department of Psychology, University of Geneva, 40 bd du Pont d'Arve, Geneva 1205, Switzerland.
Neuroscience of Emotion and Affective Dynamics Laboratory, Department of Psychology and Swiss Centre for Affective Sciences, University of Geneva, 40 bd du Pont d'Arve, Geneva 1205, Switzerland.
Cereb Cortex Commun. 2023 Jan 11;4(1):tgad002. doi: 10.1093/texcom/tgad002. eCollection 2023.
Vocal emotion recognition, a key determinant to analyzing a speaker's emotional state, is known to be impaired following cerebellar dysfunctions. Nevertheless, its possible functional integration in the large-scale brain network subtending emotional prosody recognition has yet to be explored. We administered an emotional prosody recognition task to patients with right versus left-hemispheric cerebellar lesions and a group of matched controls. We explored the lesional correlates of vocal emotion recognition in patients through a network-based analysis by combining a neuropsychological approach for lesion mapping with normative brain connectome data. Results revealed impaired recognition among patients for neutral or negative prosody, with poorer sadness recognition performances by patients with right cerebellar lesion. Network-based lesion-symptom mapping revealed that sadness recognition performances were linked to a network connecting the cerebellum with left frontal, temporal, and parietal cortices. Moreover, when focusing solely on a subgroup of patients with right cerebellar damage, sadness recognition performances were associated with a more restricted network connecting the cerebellum to the left parietal lobe. As the left hemisphere is known to be crucial for the processing of short segmental information, these results suggest that a corticocerebellar network operates on a fine temporal scale during vocal emotion decoding.
语音情感识别是分析说话者情绪状态的关键因素,已知在小脑功能障碍后会受损。然而,其在支持情感韵律识别的大规模脑网络中的可能功能整合尚未得到探索。我们对患有右半球和左半球小脑病变的患者以及一组匹配的对照组进行了情感韵律识别任务。我们通过将基于神经心理学的病变映射方法与规范的脑连接组数据相结合的基于网络的分析,探索了患者语音情感识别的病变相关性。结果显示,患者对中性或负面韵律的识别受损,右小脑病变患者对悲伤的识别表现更差。基于网络的病变-症状映射显示,悲伤识别表现与一个将小脑与左额叶、颞叶和顶叶皮层相连的网络有关。此外,当仅关注右小脑损伤患者的一个亚组时,悲伤识别表现与一个将小脑与左顶叶相连的更受限网络有关。由于已知左半球对短片段信息的处理至关重要,这些结果表明,皮质-小脑网络在语音情感解码过程中在精细的时间尺度上发挥作用。