Dynamic Causal Modeling of Neural Responses to an Orofacial Pneumotactile Velocity Array

AbstractThe effective connectivity of neuronal networks during orofacial pneumotactile stimulation with different velocities is still unknown. The present study aims to characterize the effectivity connectivity elicited by three different saltatory velocities (5, 25, and 65 cm/s) over the lower face using dynamic causal modeling on functional magnetic resonance imaging data of twenty neurotypical adults. Our results revealed the contralateral SI and SII as the most likely sources of the driving inputs within the sensorimotor network for the pneumotactile stimuli, suggesting parallel processing of the orofacial pneumotactile stimuli. The 25 cm/s pneumotactile stimuli modulated forward interhemispheric connection from the contralateral SII to the ipsilateral SII, suggesting a serial interhemispheric connection between the bilateral SII. Moreover, the velocity pneumotactile stimuli influenced the contralateral M1 through both contralateral SI and SII, indicating that passive pneumotactile stimulation may positively impact motor function rehabilitation. Furthermore, the slow velocity 5 cm/s pneumotactile stimuli modulated both forward and backward connections between the right cerebellar lobule VI and the contralateral left SI, SII, and M1, while the medium velocity 25 cm/s pneumotactile stimuli modulated both forward and backward connections between the right cerebellar lobule VI and the contralateral left SI and M1. Our findings suggest that the right cerebellar lobule VI plays a role in the sensorimotor network through feedforward and feedback neuronal pathways.

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Using rDCM Method in the Mixed Model in order to Inference Effective Connectivity in Emotions

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v6i2.1692 ◽

2019 ◽

Cited By ~ 1

Author(s):

Naemeh Farahani ◽

Emad Fatemizadeh ◽

Ali Motie Nasrabadi

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Mixed Model ◽

Effective Connectivity ◽

Causal Modeling ◽

Imaging Data ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Dynamic Causal Modeling

Purpose: Recently, data from functional magnetic resonance imaging in the field of neuroscience have been strongly considered for the modeling of cognitive activities. Therefore, the use of a suitable method is important for evaluating functional magnetic resonance imaging data. Regression dynamic causal modeling is introduced as a new version of dynamic causal modeling in order to extract and derive effective connectivity in functional magnetic resonance imaging data. We used this method to investigate the distinction between effective connectivity and the pair of emotional states. Materials and Methods: In this article, the effective connectivity between regions and activity of brain regions of interest during the application of a particular type of stimulation, which simulates the emotions created during human life, is examined in the form of an audio-movie. To do this, we applied the regression dynamic causal modeling method to a network consisting of 18 regions of interest that named the mixed model. Results: In the mixed model, the distinction between happiness-anger, happiness-fear, and happiness-love was more intense. Finally, significant effective connectivities were observed in the auditory regions and regions related to emotion processing. Conclusion: Ultimately, we could represent the distinction between emotions by applying the regression dynamic causal modeling to the mixed model.

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