Primary progressive multiple sclerosis (PPMS) leads to physical and cognitive disability. Specifically, cognitive deficits in PPMS have been explained by both grey matter atrophy and white matter lesions. However, existing research still lacks in the understanding of how the brain of a patient with PPMS functions under cognitive control demands. Thus, the aim of the current study was to examine information integration in patients with PPMS using a search-based effective connectivity method. Fourteen patients with PPMS and 22 age- and gender-matched healthy controls (HC) performed the Stroop task, a cognitively demanding interference task that taxes neural resources required for cognitive control and response inhibition. Results showed that compared to HC, PPMS patients exhibited poor behavioral performance and alterations in information flow, manifested in the form of the loss of top-down connections, reversal of connections, and hyperconnectivity. Significant correlations were observed between connection strengths and behavioral measures. The connection between the posterior parietal cortex (PCC) and left posterior parietal lobule, which was present in both groups, showed a negative correlation with performance accuracy on incongruent trials. The connection between the left dorsolateral prefrontal cortex and PCC showed a positive correlation with performance accuracy on incongruent trials. However, the adaptive nature of this connection was not significant on a behavioral level as the PPMS group performed significantly worse compared to the HC group during the Stroop task. Thus, the current study provides important evidence about effective connectivity patterns that can be characterized as maladaptive cerebral re-organization in the PPMS brain. Hum Brain Mapp 38:2580–2588, 2017. © 2017 Wiley Periodicals, Inc.

Altered neural mechanisms of cognitive control in patients with primary progressive multiple sclerosis: An effective connectivity study

Rocca, Maria Assunta;FILIPPI, MASSIMO
2017-01-01

Abstract

Primary progressive multiple sclerosis (PPMS) leads to physical and cognitive disability. Specifically, cognitive deficits in PPMS have been explained by both grey matter atrophy and white matter lesions. However, existing research still lacks in the understanding of how the brain of a patient with PPMS functions under cognitive control demands. Thus, the aim of the current study was to examine information integration in patients with PPMS using a search-based effective connectivity method. Fourteen patients with PPMS and 22 age- and gender-matched healthy controls (HC) performed the Stroop task, a cognitively demanding interference task that taxes neural resources required for cognitive control and response inhibition. Results showed that compared to HC, PPMS patients exhibited poor behavioral performance and alterations in information flow, manifested in the form of the loss of top-down connections, reversal of connections, and hyperconnectivity. Significant correlations were observed between connection strengths and behavioral measures. The connection between the posterior parietal cortex (PCC) and left posterior parietal lobule, which was present in both groups, showed a negative correlation with performance accuracy on incongruent trials. The connection between the left dorsolateral prefrontal cortex and PCC showed a positive correlation with performance accuracy on incongruent trials. However, the adaptive nature of this connection was not significant on a behavioral level as the PPMS group performed significantly worse compared to the HC group during the Stroop task. Thus, the current study provides important evidence about effective connectivity patterns that can be characterized as maladaptive cerebral re-organization in the PPMS brain. Hum Brain Mapp 38:2580–2588, 2017. © 2017 Wiley Periodicals, Inc.
2017
Bayes networks; effective connectivity; executive control; fMRI; interference; multiple sclerosis; primary progressive; Stroop; Anatomy; Radiological and Ultrasound Technology; Radiology, Nuclear Medicine and Imaging; Neurology; Neurology (clinical)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/58792
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