Functional Capacity May Predict Treatment Response to Cognitive Rehabilitation in Multiple Sclerosis
Using a backward logistic regression model, lower functional capacity between the default-mode network and ventral attention network accurately predicted response to cognitive rehabilitation among a cohort of patients with MS.
Hanneke E. Hulst, PhD
Using a cohort of patients with multiple sclerosis (MS) who underwent a 7-week attention training program, results showed a mild-to-moderate overall short-term working memory, with treatment response dependent on a window of opportunity defined by an intact functional capacity (FC) between the default-mode network (DMN) and attention networks.1
Patients in the intervention group were divided into responders (n = 22; 38%) and nonresponders (n = 36; 62%) based on immediate postintervention improvement compared with baseline. Responders and nonresponders did not differ on demographic variables, clinical variables, baseline cognition, and any of the questionnaires; however, responders had a significantly lower FC between DMN-ventral attention network (VAN) compared with nonresponders (0.87 vs 0.98; P = .018).
To better understand the causes of heterogeneity in cognitive training, senior investigator Hanneke E. Hulst, PhD, assistant professor of neurosciences, Amsterdam UMC, and colleagues identified specific patient characteristics associated with treatment response. Patients were randomized into an intervention group (n = 58) or a waiting-list control group (CG; n = 24). Additionally, 21 age-, sex-, and education-matched healthy controls (HCs) were included at baseline.
"In our multivariate prediction model, lower FC between DMN-VAN and between DMN-FPN (iem ‘normal FC’) were both identified as predictors of response. This indicates that the fewer deviations there are from HC-like FC, the higher the chance for successful cognitive training," Hulst et al wrote, adding that, "Consequently, it seems that the timing of cognitive training in [people with] MS is of utmost importance. One could argue that a mind-set shift from symptom management towards preventive intervention aimed at preserving cognition is needed (ie, enhancement of network functioning rather than restoring it, since the latter might be impossible)."
The intervention consisted of the C-Car computer program, previously used in the field of neuro-oncology. C-Car simulates driving a car, with tasks designed to train sustained, selective, alternating, and divided attention. Because of its adaptive design, patients practice at their own level, with difficulty increased throughout the sessions. A score of at least 90% were needed to progress to the next difficulty. Additionally, all patients underwent neuropsychological assessment at baseline (T0), post intervention (T1) and 3 months follow-up (T2). Six cognitive domains—verbal memory, information processing speed, visuospatial memory, working memory, verbal fluency, and attention—were measured. Responders were defined as scoring above the reliable change index (RCI) threshold of 1.64 on at least 2 of the 6 (33%) cognitive domains measured, on at least 1 test per domain.
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In terms of specific cognitive characteristics, 2 responders (9.1%) improved on 4 of the 6 domains, 7 responders (31.9%) improved on 3 of the 6 domains, and 13 responders (59%) improved on 2 of the 6 cognitive domains. The greatest improvements were seen on one or more measures of working memory (n = 19; 84.6%), verbal memory (n = 11; 50%), and information processing speed (n = 10; 45.5%). For the nonresponders, half (n = 18) improved on one cognitive domain, while the other half showed no improvements.
Responders did not show significant differences in FC compared with HC, but nonresponders showed significantly higher FC between DMN-dorsal attention network (0.92 vs 0.81; P = .009) and DMN-VAN (0.98 vs 0.81; P = .001) compared with both responders and HC. Using a backward logistic regression model, investigators found that lower FC between DMN-VAN (P = .004) and lower FC between DMN-frontoparietal network (P = .029) accurately predicted 81.8% of nonresponders and 54.4% of responders (Nagelkerke R2 = 0.25).
"Given the heterogeneity of MS progression, disease course and observed differential response to cognitive training, it is evident that future studies in the field now need to start exploring individualized (selection) approaches to maximize the effectiveness of cognitive training programs," Hulst et al wrote.
