Dynamics and Predictors of Cognitive Impairment along the Disease Course in Multiple Sclerosis

Abstract

(1) Background: The evolution and predictors of cognitive impairment (CI) in multiple sclerosis (MS) are poorly understood. We aimed to define the temporal dynamics of cognition throughout the disease course and identify clinical and neuroimaging measures that predict CI. (2) Methods: This paper features a longitudinal study with 212 patients who underwent several cognitive examinations at different time points. Dynamics of cognition were assessed using mixed-effects linear spline models. Machine learning techniques were used to identify which baseline demographic, clinical, and neuroimaging measures best predicted CI. (3) Results: In the first 5 years of MS, we detected an increase in the z-scores of global cognition, verbal memory, and information processing speed, which was followed by a decline in global cognition and memory (p < 0.05) between years 5 and 15. From 15 to 30 years of disease onset, cognitive decline continued, affecting global cognition and verbal memory. The baseline measures that best predicted CI were education, disease severity, lesion burden, and hippocampus and anterior cingulate cortex volume. (4) Conclusions: In MS, cognition deteriorates 5 years after disease onset, declining steadily over the next 25 years and more markedly affecting verbal memory. Education, disease severity, lesion burden, and volume of limbic structures predict future CI and may be helpful when identifying at-risk patients.

Cortical lesions at diagnosis predict long‐term cognitive impairment in multiple sclerosis: A 20‐year study

“…This could be related to the intrinsic relationship between WM and GM damage burden and their reciprocal influence; as also shown by our cluster analysis and RF results, this underlines the complex and multifactorial relationship between CI and both WM and GM pathology. These results are in line with previous studies regarding predictive models considering MRI data with respect to CI [47, 48].…”

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

“…This was confirmed by a PubMed search using the following search strategy: "(((multiple sclerosis[MeSH Terms]) OR (multiple sclerosis)) AND ((cognit*) OR (cognition[MeSH Terms]))) AND ((((machine learning[MeSH Terms]) OR (machine learning)) OR (artificial intelligence[MeSH Terms])) OR (artificial intelligence))", which was run on 3 December 2021, and yielded 39 records. Among those, we identified two studies that used machine learning for cognitive prognosis; Kiiski et al, 2018 [56] and Lopez-Soley et al, 2021 [64]. Kiiski et al, 2018 used supervised machine learning on different combinations of multimodal data, including demographic, clinical, and electroencephalography (EEG) data to predict short-term: (1) overall cognitive performance and (2) performance on information processing speed on a combined sample of persons with MS and healthy controls [56].…”

“…Opposed to the regression approach of Kiiski et al, 2018 [56], Lopez-Soley et al, 2021 used a classification approach to predict future global-and domain-specific cognitive impairment [64]. The risk of overfitting was reduced by using Lasso regularization during logistic regression, 10-fold cross-validation, and retaining as much data as possible by imputing missing values.…”

Personalized Diagnosis and Therapy for Multiple Sclerosis

“…It has gone from being an undervalued symptom to one considered essential that requires continuous monitoring. In this Special Issue we are especially interested in cognition, and experts on the subject explain predictors of cognitive impairment helpful to identify patients at risk [ 4 ] and also confirm the presence of cognitive impairment in patients with neuromyelitis optica spectrum disorder and its impact on health-related quality of life [ 5 ]. On the other hand, it aims to boost the field of machine learning for cognitive prognosis because most investigations on machine learning for MS prognosis were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked [ 6 ].…”