Abnormalities of the executive control network in multiple sclerosis phenotypes: An fMRI effective connectivity study

Abstract

The Stroop interference task is a cognitively demanding task of executive control, a cognitive ability that is often impaired in patients with multiple sclerosis (MS). The aim of this study was to compare effective connectivity patterns within a network of brain regions involved in the Stroop task performance between MS patients with three disease clinical phenotypes [relapsing-remitting (RRMS), benign (BMS), and secondary progressive (SPMS)] and healthy subjects. Effective connectivity analysis was performed on Stroop task data using a novel method based on causal Bayes networks. Compared with controls, MS phenotypes were slower at performing the task and had reduced performance accuracy during incongruent trials that required increased cognitive control. MS phenotypes also exhibited connectivity abnormalities reflected as weaker shared connections, presence of extra connections (i.e., connections absent in the HC connectivity pattern), connection reversal, and loss. In SPMS and the BMS groups but not in the RRMS group, extra connections were associated with deficits in the Stroop task performance. In the BMS group, the response time associated with correct responses during the congruent condition showed a positive correlation with the left posterior parietal → dorsal anterior cingulate connection. In the SPMS group, performance accuracy during the congruent condition showed a negative correlation with the right insula → left insula connection. No associations between extra connections and behavioral performance measures were observed in the RRMS group. These results suggest that, depending on the phenotype, patients with MS use different strategies when cognitive control demands are high and rely on different network connections. Hum Brain Mapp, 37:2293-2304, 2016. © 2016 Wiley Periodicals, Inc.

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

“…While the shared connections represent qualitative similarities between the PPMS and HC groups, quantitative differences in connection strengths were also observed. This reversal of connections might also be interpreted as maladaptive in light of poor performance of the PPMS group compared to the HC group [Dobryakova et al, 2016], however, more research is needed to better understand the significance and consequences of reversed connections. The connections between the PCC and left PPL and between the right PPL and left PPL were stronger in the PPMS group than in the HC group.…”

“…However, a potentially, more direct representation of brain function and information integration between regions can be provided by examining effective connectivity during a specific task. We recently showed that the effective connectivity patterns during a cognitive control task differ between MS phenotypes [Dobryakova et al, 2016]. We recently showed that the effective connectivity patterns during a cognitive control task differ between MS phenotypes [Dobryakova et al, 2016].…”

“…Effective connectivity allows one to examine the influence of one brain region on another in the network that is engaged in a specific task performance [Friston, 1994]. We recently showed that the effective connectivity patterns during a cognitive control task differ between MS phenotypes [Dobryakova et al, 2016]. However, our previous study was limited to patients with relapse-onset disease, including relapsing-remitting, secondary progressive and benign MS. We expect that exploring differences in information integration through effective connectivity in PPMS subjects will provide important pieces of information about reorganization of cognitive control function, given the unique clinical profile of PPMS [Miller and Leary, 2007].…”

“…The parameter values of the SEM model (representing connection strengths for each study subject) were estimated using a regression optimizer and were exported from Tetrad as t-scores for formal statistical analysis[Dobryakova et al, 2016]. Abbreviations: SD 5 standard deviation; EDSS 5 Expanded Disability Status Scale; T2 LV 5 T2 lesion volume; NBV 5 normalized brain volume; RT 5 reaction time.the goodness-of-fit of each dataset to the outcome connectivity model of the LOFS algorithm.…”

Neurorehabilitation in Multiple Sclerosis – Resilience in Practice

“…[26][27][28][29] In some conditions, such as stroke, there is restoration towards the original physiological network over time, whereas in MS this does not seem to occur and different and more complex patterns of network connections are established. [30][31][32][33][34][35] Studies have also shown that, following an initial increase in brain functional connectivity, it then declines over the following 2 years, resulting in a decreased ability to compensate for neuronal damage, which leads to disability progression in MS. 36 These studies collectively provide evidence of functional change at brain sites remote from the injury or lesions in MS and stress the importance of treatments aimed at maintaining neuroplasticity and brain reserve to inhibit or prevent irreversible disability progression. In order to harness neuroplasticity to achieve neurorehabilitation, we need interventions that combine a strong scientific rationale and a strong biological rationale with monitoring of clinically meaningful functional and structural changes in the brain.…”