JNS.jpgThe January issue of the Journal of the Neurological Sciences Vol 444 is now available online.


Click here to access


Issue highlights

Full length articleOpen Access

Clinical features, risk factors and survival in cardiac myxoma-related ischemic stroke: A multicenter case-control study

Qiao et al.

Published online: December 6, 2022


Cardiac myxoma (CM) is an important etiology of stroke in young adults, but studies on CM-related ischemic stroke (CM-IS) are limited and conflicting. Hence, we investigated clinical characterizations, risk factors of CM-IS, and short-term survival after surgical resection.

We performed a retrospective analysis of data from all CM patients at three referral management centers and conducted follow-up examination.

Full length article

The interaction between metaplastic neuromodulation and fatigue in multiple sclerosis

Xian et al.

Published online: December 11, 2022


Neuromuscular fatigue contributes to decrements in quality of life in Multiple Sclerosis (MS), yet available treatments demonstrate limited efficacy. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique which presents promise in managing fatigue, possibly related to its capacity to modulate corticospinal excitability. There is evidence for capitalising on metaplasticity using tDCS for improving outcomes. However, this remains to be explored with fatigue in people with MS (pwMS). We investigated cathodal tDCS (ctDCS) priming on anodal tDCS (atDCS)-induced corticospinal excitability and fatigue modulation in pwMS.

ctDCS primed atDCS induced MEP elevation in healthy participants but not in pwMS, possibly indicating impaired metaplasticity in pwMS. No tDCS-mediated change in the magnitude of fatigue was observed, implying that development of fatigue may not rely on changes in corticospinal excitability.

These findings expand understanding of tDCS effects in pwMS, highlighting differences that may be relevant in the disease pathophysiology.

Review Article

Stroke mortality prediction using machine learning: systematic review

Schwartz et al.

Published online: December 20, 2022


Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning–based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.

Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.

Full length article - Open Access

The diagnostic experience for people with MND and their caregivers in the U.K.

O'Brien et al.

Published online: October 28, 2022


How an MND diagnosis is communicated has implications for how individuals adapt to their illness. The consultation process with the neurologist, diagnosis delivery, and adherence to UK guidelines, were explored from the perspectives of people diagnosed with MND and family caregivers.

While there is evidence of satisfaction with the delivery of the diagnosis amongst people with MND and caregivers, there is room for improvement. There is a need for greater awareness of the requirements of people with MND and caregivers. There is also a need to raise awareness of the NICE MND guideline and ensure adequate training, time and funding to ensure communication at this difficult time is acceptable and effective. Where possible it would be preferable for referrals to be made to MND centres.