Cognitive impairment in benign multiple sclerosis: a multiparametric structural and functional MRI study

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.

Predictive factors and treatment challenges in malignant progression of relapsing-remitting multiple sclerosis

Objective

Our objective was to uncover the predictive factors that can help anticipate the malignant progression of individuals with Relapsing-Remitting Multiple Sclerosis (RRMS). Additionally, we sought to analyze and compare the response to treatment between patients with benign and malignant forms of RRMS.

Methods

This cohort study included RRMS patients categorized as benign (≥10 years since disease onset, Expanded Disability Status Scale (EDSS) ≤ 1) or malignant (≤5 years since disease onset, EDSS ≥6). Patients' data, including demographics, medical history, treatment, and MRI (Magnetic Resonance Imaging) scans, were collected and statistically analyzed.

Results

Among the 254 patients diagnosed with RRMS, 174 were found to have benign RRMS, while the remaining 80 were diagnosed with malignant RRMS. Notably, patients with malignant RRMS exhibited a significantly higher mean age of onset (32.00 ± 7.96 vs. 25.70 ± 17.19; P < 0.001) and a greater prevalence of males (40% vs. 18.4%; P = 0.014). Additionally, within the initial five years of diagnosis, patients with malignant RRMS experienced a higher number of relapses (median: 4 vs. 2; P < 0.001) and hospitalizations (median: 2 vs. 1; P = 0.006) compared to those with benign RRMS. Clinical presentations of malignant RRMS were predominantly characterized by multifocal attacks, whereas unifocal attacks were more prevalent in patients with benign RRMS. MRI scans revealed that malignant RRMS patients displayed a higher burden of plaques in the infratentorial and cord regions, as well as a greater number of black hole lesions. Conversely, benign RRMS patients exhibited a higher number of Gadolinium-enhanced lesions. Utilizing Disease-Modifying Therapies (DMTs) with an escalating approach has shown effectiveness in managing benign RRMS. However, it has proven insufficient in addressing malignant RRMS, resulting in frequent transitions to higher-line DMTs. As a result, it places a considerable burden on patients with malignant RRMS, consuming valuable time and resources, and ultimately yielding subpar outcomes.

Conclusion

Our study identifies prognostic factors for malignant progression in RRMS, including older age of onset, male gender, increased relapses and hospitalizations, multifocal attacks, higher plaque load, and black hole lesions. The current escalation strategy for DMTs is insufficient for managing malignant RRMS, requiring alternative approaches for improved outcomes. In other words, MS is a spectrum rather than a single disease, and some patients progress to a malignant phenotype of MS that is not effectively treated by the current approach.

Functional and structural brain MRI changes associated with cognitive worsening in multiple sclerosis: a 3-year longitudinal study

BACKGROUND

Heterogeneous processes may contribute to cognitive impairment in multiple sclerosis (MS).

OBJECTIVE

To apply a longitudinal multiparametric MRI approach to identify mechanisms associated with cognitive worsening in MS patients.

METHODS

3 T brain functional and structural MRI scans were acquired at baseline and after a median follow-up of 3.4 years in 35 MS patients and 22 healthy controls (HC). Associations between cognitive worsening (reliable change index score < - 1.25 at the Rao's battery) and longitudinal changes in regional T2-hyperintense white matter (WM) lesions, diffusion tensor microstructural WM damage, gray matter (GM) atrophy and resting state (RS) functional connectivity (FC) were explored.

RESULTS

At follow-up, HC showed no clusters of significant microstructural WM damage progression, GM atrophy or changes in RS FC. At follow-up, 10 MS patients (29%) showed cognitive worsening. Compared to cognitively stable, cognitively worsened MS patients showed more severe GM atrophy of the right anterior cingulate cortex and bilateral supplementary motor area (p < 0.001). Cognitively worsened vs cognitively stable MS patients showed also decreased RS FC in the right hippocampus of the right working memory network and in the right insula of the default mode network. Increased RS FC in the left insula of the executive control network was found in the opposite comparison (p < 0.001). No significant regional accumulation of focal WM lesions nor microstructural WM abnormalities occurred in both patients' groups.

CONCLUSIONS

GM atrophy progression in cognitively relevant brain regions combined with functional impoverishment in networks involved in cognitive functions may represent the substrates underlying cognitive worsening in MS.

Depressive symptoms, anxiety and cognitive impairment: emerging evidence in multiple sclerosis

Neuropsychiatric abnormalities may be broadly divided in two categories: disorders of mood, affect, and behavior and abnormalities affecting cognition. Among these conditions, clinical depression, anxiety and neurocognitive disorders are the most common in multiple sclerosis (MS), with a substantial impact on patients' quality of life and adherence to treatments. Such manifestations may occur from the earliest phases of the disease but become more frequent in MS patients with a progressive disease course and more severe clinical disability. Although the pathogenesis of these neuropsychiatric manifestations has not been fully defined yet, brain structural and functional abnormalities, consistently observed with magnetic resonance imaging (MRI), together with genetic and immunologic factors, have been suggested to be key players. Even though the detrimental clinical impact of such manifestations in MS patients is a matter of crucial importance, at present, they are often overlooked in the clinical setting. Moreover, the efficacy of pharmacologic and non-pharmacologic approaches for their amelioration has been poorly investigated, with the majority of studies showing marginal or no beneficial effect of different therapeutic approaches, possibly due to the presence of multiple and heterogeneous underlying pathological mechanisms and intrinsic methodological limitations. A better evaluation of these manifestations in the clinical setting and improvements in the understanding of their pathophysiology may offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. This review provides an updated overview regarding the pathophysiology of the most common neuropsychiatric symptoms in MS, the clinical and MRI characteristics that have been associated with mood disorders (i.e., depression and anxiety) and cognitive impairment, and the treatment approaches currently available or under investigation.

Women in the field of multiple sclerosis: How they contributed to paradigm shifts

History is full of women who made enormous contributions to science. While there is little to no imbalance at the early career stage, a decreasing proportion of women is found as seniority increases. In the multiple sclerosis (MS) field, 44% of first authors and only 35% of senior authors were female. So, in this review, we highlight ground-breaking research done by women in the field of MS, focusing mostly on their work as principal investigators. MS is an autoimmune disorder of the central nervous system (CNS), with evident paradigm shifts in the understating of its pathophysiology. It is known that the immune system becomes overactivated and attacks myelin sheath surrounding axons. The resulting demyelination disrupts the communication signals to and from the CNS, which causes unpredictable symptoms, depending on the neurons that are affected. Classically, MS was reported to cause mostly physical and motor disabilities. However, it is now recognized that cognitive impairment affects more than 50% of the MS patients. Another shifting paradigm was the involvement of gray matter in MS pathology, formerly considered to be a white matter disease. Additionally, the identification of different T cell immune subsets and the mechanisms underlying the involvement of B cells and peripheral macrophages provided a better understanding of the immunopathophysiological processes present in MS. Relevantly, the gut-brain axis, recognized as a bi-directional communication system between the CNS and the gut, was found to be crucial in MS. Indeed, gut microbiota influences not only different susceptibilities to MS pathology, but it can also be modulated in order to positively act in MS course. Also, after the identification of the first microRNA in 1993, the role of microRNAs has been investigated in MS, either as potential biomarkers or therapeutic agents. Finally, concerning MS therapeutical approaches, remyelination-based studies have arisen on the spotlight aiming to repair myelin loss/neuronal connectivity. Altogether, here we emphasize the new insights of remarkable women that have voiced the impact of cognitive impairment, white and gray matter pathology, immune response, and that of the CNS-peripheral interplay on MS diagnosis, progression, and/or therapy efficacy, leading to huge breakthroughs in the MS field.

NODDI, diffusion tensor microstructural abnormalities and atrophy of brain white matter and gray matter contribute to cognitive impairment in multiple sclerosis

BACKGROUND

Pathologically specific MRI measures may elucidate in-vivo the heterogeneous processes contributing to cognitive impairment in multiple sclerosis (MS).

PURPOSE

Using diffusion tensor and neurite orientation dispersion and density imaging (NODDI), we explored the contribution of focal lesions and normal-appearing (NA) tissue microstructural abnormalities to cognitive impairment in MS.

METHODS

One hundred and fifty-two MS patients underwent 3 T brain MRI and a neuropsychological evaluation. Forty-eight healthy controls (HC) were also scanned. Fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICV_f) and orientation dispersion index (ODI) were assessed in cortical and white matter (WM) lesions, thalamus, NA cortex and NAWM. Predictors of cognitive impairment were identified using random forest.

RESULTS

Fifty-two MS patients were cognitively impaired. Compared to cognitively preserved, impaired MS patients had higher WM lesion volume (LV), lower normalized brain volume (NBV), cortical volume (NCV), thalamic volume (NTV), and WM volume (p ≤ 0.021). They also showed lower NAWM FA, higher NAWM, NA cortex and thalamic MD, lower NAWM ICV_f, lower WM lesion ODI, and higher NAWM ODI (false discovery rate-p ≤ 0.026). Cortical lesion number and microstructural abnormalities were not significantly different. The best MRI predictors of cognitive impairment (relative importance) (out-of-bag area under the curve = 0.727) were NAWM FA (100%), NTV (96.0%), NBV (84.7%), thalamic MD (43.4%), NCV (40.6%), NA cortex MD (26.0%), WM LV (23.2%) and WM lesion ODI (17.9%).

CONCLUSIONS

Our multiparametric MRI study including NODDI measures suggested that neuro-axonal damage and loss of microarchitecture integrity in focal WM lesions, NAWM, and GM contribute to cognitive impairment in MS.

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.

Integrated Cognitive Rehabilitation Home-Based Protocol to Improve Cognitive Functions in Multiple Sclerosis Patients: A Randomized Controlled Study

Cognitive impairment (CI) occurs in about 40-65% of people with multiple sclerosis (MS) during the disease course. Cognitive rehabilitation has produced non-univocal results in MS patients.

OBJECTIVE

The present study aimed to evaluate whether an Integrated Cognitive Rehabilitation Program (ICRP) in MS patients might significantly improve CI.

METHODS

Forty patients with three phenotypes of MS were randomly assigned into two groups: the experimental group (EG, n = 20), which participated in the ICRP for 10 weeks of training; and the control group (CG, n = 20). All participants' cognitive functions were assessed at three timepoints (baseline, post-treatment, and 3-month follow-up) with the California Verbal Learning (CVLT), Brief Visuospatial Memory (BVMTR), Numerical Stroop, and Wisconsin tests.

RESULTS

When compared to CG patients, EG patients showed significant improvements in several measures of cognitive performance after ICRP, including verbal learning, visuospatial memory, attention, and executive functions.

CONCLUSIONS

Home-based ICRP can improve cognitive functions and prevent the deterioration of patients' cognitive deficits. As an integrated cognitive rehabilitation program aimed at potentiation of restorative and compensatory mechanisms, this approach might suggest an effective role in preserving neuronal flexibility as well as limiting the progression of cognitive dysfunction in MS.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

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Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages.

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Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease.

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Collapse of long-range connections and impaired hub integration characterize MS network reorganization.

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Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity.

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New perspectives might be the use of multimodal MRI and artificial intelligence.

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

Unraveling the substrates of cognitive impairment in multiple sclerosis: A multiparametric structural and functional magnetic resonance imaging study

BACKGROUND

Cognitive impairment frequently affects multiple sclerosis (MS) patients. However, its neuroanatomical correlates still need to be fully explored. We investigated the contribution of structural and functional magnetic resonance imaging (MRI) abnormalities in explaining cognitive impairment in MS.

METHODS

Brain dual-echo, diffusion tensor, 3D T1-weighted and resting-state (RS) MRI sequences were acquired from 276 MS patients and 102 healthy controls. Using random forest analysis, the contribution of regional white matter (WM) lesions, WM fractional anisotropy (FA) abnormalities, gray matter (GM) atrophy and RS functional connectivity (FC) alterations to cognitive impairment in MS patients was investigated.

RESULTS

Eighty-four MS patients (30.4%) were cognitively impaired. The best MRI predictors of cognitive impairment (relative importance [%]) (out-of-bag area under the curve [AUC] = 0.795) were (a) WM lesions in the right superior longitudinal fasciculus (100%), left anterior thalamic radiation (93.4%), left posterior corona radiata (78.5%), left medial lemniscus (74.2%), left inferior longitudinal fasciculus (70.4%), left optic radiation (68.7%), right middle cerebellar peduncle (60.6%) and right optic radiation (53.5%); (b) decreased FA in the splenium of the corpus callosum (64.3%), left optic radiation (61.0%), body of the corpus callosum (51.9%) and fornix (50.9%); and (c) atrophy of the left precuneus (91.4%), right cerebellum crus I (84.4%), right caudate nucleus (78.6%), left thalamus (76.2%) and left supplementary motor area (59.8%). The relevance of these MRI measures in explaining cognitive impairment was confirmed in a cross-validation analysis (AUC =0.765).

CONCLUSION

Structural damage in strategic WM and GM regions explains cognitive impairment in MS patients more than RS FC abnormalities.

Mild gray matter atrophy in patients with long-standing multiple sclerosis and favorable clinical course

The mechanisms responsible for the favorable clinical course in multiple sclerosis (MS) remain unclear. In this longitudinal study, we assessed whether magnetic resonance imaging (MRI)-based changes in focal and diffuse brain damage are associated with a long-term favorable MS diseases course. We found that global brain and gray matter (GM) atrophy changes were milder in MS patients with long-standing disease (⩾30 years from onset) and favorable (no/minimal disability) clinical course than in sex-age-matched disable MS patients, independently of lesions accumulation. Data showed that different trajectories of volume changes, as reflected by mild GM atrophy, may characterize patients with long-term favorable evolution.

Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach

BACKGROUND

The volume change of multiple sclerosis (MS) lesion is related to its activity and can be used to assess disease progression. Therefore, the purpose of this study was to develop radiomics models for predicting the evolution of unenhanced MS lesions by using different kinds of machine learning algorithms and explore the optimal model.

METHODS

In this prospective observation, 45 follow-up MR images obtained in 36 patients with MS (mean age 32.53±10.91; 23 women, 13 men) were evaluated. The lesions will be defined as interval activity and interval inactivity, respectively, based on the percentage of enlargement or reduction of the lesion >20% in the follow-up MR images. We extracted radiomic features of lesions on FLAIR images, and used recursive feature elimination (RFE), ReliefF algorithm and least absolute shrinkage and selection operator (LASSO) for feature selection, then three classification models including logistic regression, random forest and support vector machine (SVM) were used to build predictive models. The performance of the models were evaluated based on the sensitivity, specificity, precision, negative predictive value (NPV) and receiver operating characteristic curve (ROC) curves analyses.

RESULTS

135 interval inactivity lesions and 110 interval activity lesions were registered in our study. A total of 972 radiomics features were extracted, of which 265 were robust. The consistency and effectiveness of model performance were compared and verified by different combinations of feature selection and machine learning methods in different K-fold cross-validation strategies where K ranges from 5 to 10, thus demonstrating the stability and robustness. SVM classifier with ReliefF algorithm had the best prediction performance with an average accuracy of 0.827, sensitivity of 0.809, specificity of 0.841, precision of 0.921, NPV of 0.948 and the areas under the ROC curves (AUC) of 0.857 (95% CI: 0.812-0.902) in the cohorts.

CONCLUSION

The results demonstrated that the radiomics-based machine learning model has potential in predicting the evolution of MS lesions.

Neuroimaging Correlates of Cognitive Dysfunction in Adults with Multiple Sclerosis

Cognitive impairment is a frequent and meaningful symptom in multiple sclerosis (MS), caused by the accrual of brain structural damage only partially counteracted by effective functional reorganization. As both these aspects can be successfully investigated through the application of advanced neuroimaging, here, we offer an up-to-date overview of the latest findings on structural, functional and metabolic correlates of cognitive impairment in adults with MS, focusing on the mechanisms sustaining damage accrual and on the identification of useful imaging markers of cognitive decline.