Ageing, genetic, medical and lifestyle factors contribute to the risk of Alzheimer’s disease and other dementias. Around a third of dementia cases are attributable to modifiable risk factors such as physical inactivity, smoking and hypertension. With the rising prevalence and lack of neuroprotective drugs, there is renewed focus on dementia prevention strategies across the lifespan. Neurologists encounter many people with risk factors for dementia and are frequently asked whether lifestyle changes may help. Exercise has emerged as a key intervention for influencing cognition positively, including reducing the risk of age-related cognitive decline and dementia. This article focuses on the current evidence for physical inactivity as a modifiable dementia risk factor and aims to support neurologists when discussing risk reduction.

Pulmonary arterial dissection is an extremely rare and usually lethal complication of chronic pulmonary hypertension. The condition usually manifests as cardiogenic shock or sudden death and is therefore typically diagnosed at postmortem examination rather than during life. However, recent isolated reports have described pulmonary artery dissection in surviving patients. The first case of pulmonary artery dissection in a surviving patient with cor pulmonale caused by chronic obstructive pulmonary disease is presented. The aetiology, pathophysiology, and clinical presentation of pulmonary artery dissection are reviewed and factors that may aid diagnosis during life are discussed.

When a patient presents with tremor, it can be useful to perform a few simple pen and paper tests. In this article, we explain how to maximise the value of handwriting and of drawing Archimedes spirals and straight lines as clinical assessments. These tasks take a matter of seconds to complete but provide a wealth of information that supplements the standard physical examination. They aid the diagnosis of a tremor disorder and can contribute to its longitudinal monitoring. Watching the patient’s upper limb while they write and draw may reveal abnormalities such as bradykinesia, dystonic posturing and distractibility. The finished script and drawings can then be evaluated for frequency, amplitude, direction and symmetry of oscillatory pen movements and for overall scale of penmanship. Essential, dystonic, functional and parkinsonian tremor each has a characteristic pattern of abnormality on these pen and paper tests.

Functional neurological disorder (FND) is a common and disabling disorder, often misunderstood by clinicians. Although viewed sceptically by some, FND is a diagnosis that can be made accurately, based on positive clinical signs, with clinical features that have remained stable for over 100 years. Despite some progress in the last decade, people with FND continue to suffer subtle and overt forms of discrimination by clinicians, researchers and the public. There is abundant evidence that disorders perceived as primarily affecting women are neglected in healthcare and medical research, and the course of FND mirrors this neglect. We outline the reasons why FND is a feminist issue, incorporating historical and contemporary clinical, research and social perspectives. We call for parity for FND in medical education, research and clinical service development so that people affected by FND can receive the care they need.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $$93.5\%$$93.5 % accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.