Enhancing cognitive rehabilitation in multiple sclerosis with a disease-specific tool

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.

Cognitive benefits of the attentional vs exergame training in older adults

OBJECTIVE

The present study aimed at comparing the effectiveness of an Attentional Computerized Cognitive Training and a commercial Exergame Training.

METHODS

Eighty-four healthy older adults took part in the study. They were randomly assigned to one of the following conditions: Attentional Computerized Cognitive Training (ATT-CCT), Exergame Training (EXERG-T), or passive Control Group (CG). Participants assigned to the experimental groups underwent 8 laboratory-based sessions-lasting approximately 45 min each-of the respective training activity. A battery of cognitive tests was assessed before, after, and 3 months following the intervention phase.

RESULTS

The results showed that just the ATT-CCT improved participants' performance, specifically within attention, processing speed, verbal learning and memory. While both intervention groups revealed improved memory self-perception and decreased self-reported absent-mindedness, only the benefits following the ATT-CCT proved to be stable over time.

CONCLUSIONS

The results suggested that our ATT-CCT may be an effective tool for enhancing cognitive abilities in older healthy adults.

Adaptive Cognitive Training with Reinforcement Learning

Computer-assisted cognitive training can help patients affected by several illnesses alleviate their cognitive deficits or healthy people improve their mental performance. In most computer-based systems, training sessions consist of graded exercises, which should ideally be able to gradually improve the trainee’s cognitive functions. Indeed, adapting the difficulty of the exercises to how individuals perform in their execution is crucial to improve the effectiveness of cognitive training activities. In this article, we propose the use of reinforcement learning (RL) to learn how to automatically adapt the difficulty of computerized exercises for cognitive training. In our approach, trainees’ performance in performed exercises is used as a reward to learn a policy that changes over time the values of the parameters that determine exercise difficulty. We illustrate a method to be initially used to learn difficulty-variation policies tailored for specific categories of trainees, and then to refine these policies for single individuals. We present the results of two user studies that provide evidence for the effectiveness of our method: a first study, in which a student category policy obtained via RL was found to have better effects on the cognitive function than a standard baseline training that adopts a mechanism to vary the difficulty proposed by neuropsychologists, and a second study, demonstrating that adding an RL-based individual customization further improves the training process.