Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis

Early MS Identification Using Non-linear Functional Connectivity and Graph-theoretic Measures of Cognitive Task-fMRI Data

Introduction

Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting the brain network differences among relapsing-remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study.

Methods

In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information based on the automated anatomical labeling atlas (AAL). Subsequently, a statistical test was carried out to determine the variation in brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with the Fisher score as a hybrid feature selection method.

Results

The results of the classification performance measures showed that the construction of a brain network using a new nonlinear connectivity measure in task-fMRI performs better than the linear connectivity measures in terms of classification. The Wilcoxon rank-sum test also demonstrated a superior result for clinical applications.

Conclusion

We believe that non-linear connectivity measures, like KMI, outperform linear connectivity measures, like correlation coefficient in finding the biomarkers of MS disease according to classification performance metrics.

Highlights

The performance of some brain regions (the hippocampus, parahippocampus, cuneus, pallidum, and two segments of the cerebellum) is different between healthy and MS people.Non-linear connectivity measures, such as Kernel mutual information, perform better than linear connectivity measures, such as correlation coefficient, in finding the biomarkers of MS disease.

Plain Language Summary

Multiple sclerosis (MS) can disrupt the function of the central nervous system. The function of brain network is impaired in these patients. In this study, we evaluated the change in brain network based on a non-linear connectivity measure using cognitive task-based fMRI data between MS patients and healthy controls. We used Kernel mutual information (KMI) and designed a graph network based on the results of connectivity analysis. The the paced auditory serial addition test was used to activate cognitive functions of the brain. The classification was employed for the results using different decision tree -based technique and support vector machine. KMI can be considered a valid measure of connectivity over linear measures, like the correlation coefficient. KMI does not have the drawbacks of mutual information technique. However, further studies should be implemented on brain data of MS patients to draw more definite conclusions.

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.

Assessment and Impact of Cognitive Impairment in Multiple Sclerosis: An Overview

Cognitive impairment affects 40⁻60% of patients with multiple sclerosis. It may be present early in the course of the disease and has an impact on a patient's employability, social interactions, and quality of life. In the last three decades, an increasing interest in diagnosis and management of cognitive impairment has arisen. Neuropsychological assessment and neuroimaging studies focusing on cognitive impairment are now being incorporated as primary outcomes in clinical trials. However, there are still key uncertainties concerning the underlying mechanisms of damage, neural basis, sensitivity and validity of neuropsychological tests, and efficacy of pharmacological and non-pharmacological interventions. The present article aimed to present an overview of the assessment, neural correlates, and impact of cognitive impairment in multiple sclerosis.

Yakovlev's Basolateral Limbic Circuit in Multiple Sclerosis Related Cognitive Impairment

BACKGROUND AND PURPOSE

In 1948, Paul Yakovlev described an additional limbic circuit located basolateral to James Papez's circuit (1937) and included orbitofrontal cortex, amygdala, and dorsomedial nucleus of thalamus. This circuit is shown to be an important component of subcortical cognitive abilities. We aimed to demonstrate this circuit in a multiple sclerosis (MS) cohort using diffusion tensor imaging (DTI) and evaluate its role in MS-related cognitive impairment (CI).

METHODS

We enrolled cognitively intact (n = 10) and impaired (n = 36) MS patients who underwent a comprehensive cognitive assessment; the minimal assessment of cognitive function in MS (MACFIMS) and structural magnetic resonance imaging. Correlation analyses between volumetric and DTI-derived values of the orbitofrontothalamic (OFT), amygdalothalamic tracts (ATTs), and dorsomedial nucleus of thalamus and CI index derived from MACFIMS were computed after adjustment for age, education, and lesion load.

RESULTS

We observed a consistent trend between CI index and bilateral dorsomedial nucleus' mean diffusivity (MD) (r = .316; P = .02), left OFT Fractional anisotropy (FA) (r = -.302; P = .02), MD (r = .380; .006), and radial diffusivities (RDs) (r = .432; P = .002), also with right ATT FA (r = -.475; P = .0006) and left ATT FA ( = -.487; P = .0005). After Bonferroni correction, correlations of left OFT RD and right and left ATT FA with CI were found to be significant.

CONCLUSIONS

Our study provides in vivo DTI delineation of Yakovlev's historical basolateral limbic circuit and establishes a role in MS-related CI. These findings may potentially pave the way for future clinical studies using targeted invasive and noninvasive neurostimulation modalities for CI in MS.

Altered motor plasticity in an acute relapse of multiple sclerosis

In relapsing-remitting MS (RRMS), the symptoms of a clinical relapse subside over time. Neuroplasticity is believed to play an important compensatory role. In this study, we assessed excitability-decreasing plasticity during an acute relapse of MS and 12 weeks afterwards. Motor plasticity was examined in 19 patients with clinically isolated syndrome or RRMS during a steroid-treated relapse (t1) and 12 weeks afterwards (t2) using paired-associative stimulation (PAS10). This method combines repetitive electric nerve stimulation with transcranial magnetic stimulation of the contralateral motor cortex to model long-term synaptic depression in the human cortex. Additionally, 19 age-matched healthy controls were assessed. Motor-evoked potentials of the abductor pollicis brevis muscle were recorded before and after intervention. Clinical disability was assessed by the multiple sclerosis functional composite and the subscore of the nine-hole peg test taken as a measure of hand function. The effect of PAS10 was significantly different between controls and patients; at t1, but not at t2, baseline-normalized postinterventional amplitudes were significantly higher in patients (106 [IQR 98-137] % post10-15 and 111 [IQR 88-133] % post20-25) compared to controls (92 [IQR 85-111] % and 90 [IQR 75-102] %). Additional exploratory analysis indicated a potentially excitability-enhancing effect of PAS10 in patients as opposed to controls. Significant clinical improvement between t1 and t2 was not correlated with PAS10 effects. Our results indicate an alteration of PAS10-induced synaptic plasticity during relapse, presumably reflecting a polarity shift due to metaplastic processes within the motor cortex. Further studies will need to elucidate the functional significance of such changes for the clinical course of MS.

Neuropsychology of Multiple Sclerosis: Looking Back and Moving Forward

The neuropsychological aspects of multiple sclerosis (MS) have evolved over the past three decades. What was once thought to be a rare occurrence, cognitive dysfunction is now viewed as one of the most disabling symptoms of the disease, with devastating effects on patients' quality of life. This selective review will highlight major innovations and scientific discoveries in the areas of neuropathology, neuroimaging, diagnosis, and treatment that pertain to our understanding of the neuropsychological aspects of MS. Specifically, we focus on the recent discovery that MS produces pathogical lesions of gray matter (GM) that have consequences for cognitive functions. Methods for imaging these GM lesions in MS are discussed along with multimodal imaging studies that integrate structural and functional imaging methods to provide a better understanding of the relationship between cognitive test performance and functional reserve. Innovations in the screening and comprehensive assessment of cognitive disorders are presented along with recent research that examines cognitive dysfunction in pediatric MS. Results of innovative outcome studies in cognitive rehabilitation are discussed. Finally, we highlight trends for potential future innovations over the next decade. (JINS, 2017, 23, 832-842).

The Role of Advanced Magnetic Resonance Imaging Techniques in Multiple Sclerosis Clinical Trials

Magnetic resonance imaging has been crucial in the development of anti-inflammatory disease-modifying treatments. The current landscape of multiple sclerosis clinical trials is currently expanding to include testing not only of anti-inflammatory agents, but also neuroprotective, remyelinating, neuromodulating, and restorative therapies. This is especially true of therapies targeting progressive forms of the disease where neurodegeneration is a prominent feature. Imaging techniques of the brain and spinal cord have rapidly evolved in the last decade to permit in vivo characterization of tissue microstructural changes, connectivity, metabolic changes, neuronal loss, glial activity, and demyelination. Advanced magnetic resonance imaging techniques hold significant promise for accelerating the development of different treatment modalities targeting a variety of pathways in MS.

Functional cortical changes in relapsing-remitting multiple sclerosis at amplitude configuration: a resting-state fMRI study

OBJECTIVE

The aim of this study was to explore the amplitude of spontaneous brain activity fluctuations in patients with relapsing-remitting multiple sclerosis (RRMS) using the amplitude of low-frequency fluctuation (ALFF) method.

METHODS

ALFF and SPM8 were utilized to assess alterations in regional spontaneous brain activities in patients with RRMS in comparison with healthy controls (HCs). The beta values of altered brain regions between patients with RRMS and HCs were extracted, and a receiver operating characteristic (ROC) curve was generated to calculate the sensitivities and specificities of these different brain areas for distinguishing patients with RRMS from HCs. Pearson correlation analyses were applied to assess the relationships between the beta values of altered brain regions and disease duration and Expanded Disability Status Scale (EDSS) score.

PATIENTS AND PARTICIPANTS

A total of 18 patients with RRMS (13 females; five males) and 18 sex-, age-, and education-matched HCs (14 females; four males) were recruited for this study.

MEASUREMENTS AND RESULTS

Compared with HCs, patients with RRMS showed higher ALFF responses in the right fusiform gyrus (Brodmann area [BA] 37) and lower ALFF responses in the bilateral anterior cingulate cortices (BA 24 and 32), bilateral heads of the caudate nuclei, and bilateral brainstem. The ROC analysis revealed that the beta values of these abnormal brain areas showed high degrees of sensitivity and specificity for distinguishing patients with RRMS from HCs. The EDSS score showed a significant negative Pearson correlation with the beta value of the caudate head (r=-0.474, P=0.047).

CONCLUSION

RRMS is associated with disturbances in spontaneous regional brain activity in specific areas, and these specific abnormalities may provide important information about the neural mechanisms underlying behavioral impairment in RRMS.