Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state

The cost-of-illness of multiple sclerosis in Jordan

BACKGROUND

Multiple Sclerosis (MS) imposes a significant financial burden on health-care systems. This study aims to determine the cost-of-illness (COI) for MS in Jordan, a country where data on the economic impact of MS are scarce.

METHODS

Data were collected for one year, annual COI was estimated using a cross-sectional snowball sampling design. Eligible patients completed a self-reported questionnaire to provide sociodemographic, physician visit, and diagnostic and laboratory test data. Indirect costs were estimated using an adjusted Human Capital Approach.

RESULTS

This study included 383 patients, (73% females, 61% between 26-45). Eighty % took disease-modifying therapies (DMTs), and 40% had relapses in that year. One-third use non-DMTs and equipment for assistance. The average annual cost per patient was $11,719 (direct costs=$11,252, indirect costs=$467). The total annual cost for all participants was $748,299. The estimated cost of non-DMT, medical tools, diagnostic tests, and hospitalization per patient was $53, 51, 99, and 235 respectively.

CONCLUSION

High costs of DMTs state the necessity of resource optimization in Jordan public healthcare facilities. Such findings yield policy-informing actionable insights, suggesting strategic investments in more cost-effective DMTs with potential improvement in accessibility and reduction in the overall economic burden faced by both patients and governments.

The contribution of EEG to assess and treat motor disorders in multiple sclerosis

OBJECTIVE

Electroencephalography (EEG) can highlight significant changes in spontaneous electrical activity of the brain produced by altered brain network connectivity linked to inflammatory demyelinating lesions and neuronal loss occurring in multiple sclerosis (MS). In this review, we describe the main EEG findings reported in the literature to characterize motor network alteration in term of local activity or functional connectivity changes in patients with MS (pwMS).

METHODS

A comprehensive literature search was conducted to include articles with quantitative analyses of resting-state EEG recordings (spectrograms or advanced methods for assessing spatial and temporal dynamics, such as coherence, theory of graphs, recurrent quantification, microstates) or dynamic EEG recordings during a motor task, with or without connectivity analyses.

RESULTS

In this systematic review, we identified 26 original articles using EEG in the evaluation of MS-related motor disorders. Various resting or dynamic EEG parameters could serve as diagnostic biomarkers of motor control impairment to differentiate pwMS from healthy subjects or be related to a specific clinical condition (fatigue) or neuroradiological aspects (lesion load).

CONCLUSIONS

We highlight some key EEG patterns in pwMS at rest and during movement, both suggesting an alteration or disruption of brain connectivity, more specifically involving sensorimotor networks.

SIGNIFICANCE

Some of these EEG biomarkers of motor disturbance could be used to design future therapeutic strategies in MS based on neuromodulation approaches, or to predict the effects of motor training and rehabilitation in pwMS.

A review of signal processing and machine learning techniques for interictal epileptiform discharge detection

Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.