Vaughan G. Macefield

The cortical and subcortical circuitry underpinning motion sickness is poorly understood. However, certain regions – including the nucleus of the solitary tract (NTS) and the parieto-insular vestibular cortex (PIVC) – are known to be involved in the generation of nausea. The PIVC, in particular, is believed to serve an important role in the processing of vestibular inputs. We have previously used low-frequency sinusoidal galvanic vestibular stimulation (sGVS) to induce perceptions of side-to-side motion, which also induces nausea in ~ 40% of people. While sGVS has been previously used with brain imaging to investigate vestibular pathways, those studies used shorter bouts of stimulation (~ 30 s) which did not induce nausea. Here, we have used functional magnetic resonance imaging (fMRI) to record blood-oxygen-level-dependent (BOLD) signal-intensity changes at baseline (5 min), during low-frequency sGVS for a sustained period of time (± 2 mA, 0.08 Hz, 100 cycles; ~21 min) and during a recovery period (9 min) in 17 participants. Surprisingly, however, only four participants reported incipient or overt nausea during stimulation. Nevertheless, two regions displayed BOLD signal-intensity changes associated with the long-form (as opposed to previously used short-form) stimulation: the nucleus accumbens and locus coeruleus. Though sensations of nausea were not as apparent as expected (perhaps due to the necessity of participants adopting a supine position with their heads fixed in place), these regions would appear to be more associated with a longer period of vestibular stimulation, perhaps required to evoke sensations of nausea.

Introduction: Stroke is a leading cause of mortality and morbidity worldwide. Magnetic resonance imaging-visible perivascular spaces (PVS) are an emerging marker of cerebral small vessel disease and may have prognostic value in stroke. We aimed to characterise longitudinal changes in PVS volume and cluster count following ischaemic stroke. Patients and methods: Perivascular space volumes and cluster counts were compared between stroke survivors (n = 124; 39 women; median [Q1, Q3] age = 70 [62, 76] years) and healthy controls (n = 39; 15 women; median age = 69 [66, 72.5] years). Magnetic resonance imaging scans were acquired at 3 months, 12 months and 36 months post-stroke. Perivascular spaces were automatically segmented from T1-weighted MRI using a validated deep learning algorithm (nnU-Net). Generalised linear mixed-effects models were used to assess group differences and longitudinal changes in PVS, adjusting for baseline age, sex, total intracranial volume and body mass index. Results: At the 3-month timepoint, whole-brain PVS metrics were significantly greater in the stroke group than in controls. By 36 months, stroke patients demonstrated significant PVS reductions relative to controls in the frontal lobe (PVS volume: exp(β) = 0.93; 95% CI, 0.82, 0.99, P = .032; PVS cluster count: exp(β) = 0.91; 95% CI, 0.83, 1; P = .037). Discussion: Contrary to age-related PVS enlargement observed in controls, stroke survivors showed significant longitudinal reductions in PVS volume and cluster counts over 36 months. These findings suggest that MRI-visible PVS trajectories after stroke are dynamic and region-specific, challenging the interpretation of PVS enlargement as a simple marker of cerebrovascular disease burden. Conclusion: Ischaemic stroke is associated with dynamic and regional changes in PVS volume and count.

Heart rate variability (HRV) is a widely utilized approach for investigating cardiovascular health and human performance. Methodological approaches, analyses, and interpretations of HRV can vary extensively, and scientific debates on HRV reliability and utility remain. These debates have been amplified in recent years by the rapid growth and ubiquity of wearable devices and their incorporation into scientific research studies. The present guidelines paper takes an evidence-based approach to propose more consistent and rigorous measurement and interpretation of HRV within human cardiovascular research and application. First, HRV has some utility as a cardiovascular risk stratification tool but is not appropriate to employ as a specific marker of cardiac sympathetic outflow or sympathovagal balance. Although time-domain and spectral analysis can more accurately reflect respiratory modulation of cardiac vagal (parasympathetic) activity, caution is necessary to avoid overinterpretation, particularly with respect to the concept of “vagal tone.” Numerous experimental, demographic, and environmental factors influence HRV assessment, interpretation, and reliability. Specifically, it is critical to account for nonmodifiable (age, sex, race/ethnicity, etc.) and modifiable (i.e., exercise, physical fitness, etc.) factors within participant samples. Furthermore, technical approaches such as the HRV input signal (e.g., electrocardiography vs. photoplethysmography), length of recording, location of recordings (i.e., laboratory vs. field), respiratory rate and depth, and analytical approaches (i.e., time vs. frequency domain) can all impact rigor, reliability, and study interpretations. Finally, with respect to the rapid advancements of HRV assessment using wearable technology, investigators should interpret and contextualize findings within the limitations outlined in this guideline review.

Background: Enlargement of perivascular spaces (PVS) is common in cerebral small vessel disease, Alzheimer’s disease, and Parkinson’s disease, reflecting impaired clearance pathways. While MRI provides a means to quantify perivascular spaces, manual annotation remains time-consuming and labour-intensive. Thus, there is a need for accurate automated MRI-based PVS segmentation methods. Aim: To optimise the nnU-Net, a deep-learning framework, for PVS segmentation. Methods: 30 T1-weighted (T1w) MRI images acquired on three different scanners were used. PVS in the white matter (WM) and basal ganglia (BG) were manually labelled via a sparse annotation strategy and used to optimise the nnU-Net for T1w PVS segmentation. The same pipeline was applied to T2-weighted (T2w) images. Additionally, we trained T1w+FLAIR and T2w+FLAIR models to simultaneously segment PVS and white matter hyperintensities (WMH). Performance was assessed with 5-fold cross validation using the Dice similarity coefficient (DSC). Results: A voxel-spacing agnostic model (mean DSC = 64.3 ± 3.3%) outperformed models that resampled images to a common resolution (DSC = 40.5–55%). Training on PVS segmentations derived from preprocessed T1w images substantially improved performance (DSC = 78.3 ± 1.7%). The T2w model performed best overall (DSC = 84.7 ± 1.3%), especially for WM-PVS (DSC = 90.4 ± 0.9%) compared to BG-PVS (DSC = 79.1 ± 2%). Multimodal models achieved DSCs of 75.6 ± 3.4% (T1w+FLAIR) and 77.5 ± 2.7% (T2w+FLAIR). Conclusions: Our deep learning models provide a robust framework for automated PVS quantification across MRI modalities.

Migraine is a common and disabling neurological disorder linked to alterations in neuronal activity and waste clearance in the brain. MRI-visible perivascular spaces (PVS) are key components of the glymphatic system which may serve as imaging biomarker of such disorder. We hypothesised that higher frequency of migraine episodes would be associated with increased PVS burden, reflecting greater levels of impaired glymphatic clearance. In this retrospective case-control study of 90 participants (20 episodic migraineurs, 18 chronic migraineurs, and 52 age– and sex-matched healthy controls; 58 females, median [Q1, Q3] age=28.6 [25.1, 39.4] years) we investigated PVS alterations in episodic migraineurs ( n =20) and 18 chronic migraineurs ( n =18). PVS volumes and cluster counts were quantified in the white matter (WM), basal ganglia (BG), midbrain, and hippocampus. We stratified PVS metrics by white matter lobes and arterial vascular territories. After adjusting for age, sex, and total brain volume, episodic migraineurs exhibited significantly lower BG-PVS volumes (exp(β)=0.76, 95%CI [0.61, 0.94], p =0.01) compared to controls. Chronic migraineurs exhibited significantly lower PVS cluster counts in the parietal (exp(β)=0.8, 95%CI [0.68, 0.94], p =0.01) and temporal lobes (exp(β)=0.72, 95%CI [0.53, 0.96], p =0.03) and middle cerebral artery territory (exp(β)=0.82, 95%CI [0.68, 0.97], p =0.03) compared to healthy controls. Within migraineurs, those with aura ( n =20) exhibited significantly lower PVS burden in all brain regions, vascular territories, and across the frontal, parietal, and temporal lobes (all p _FDR <0.05). Our findings suggest that the aura symptom, rather than the migraine disorder itself, may primarily drive changes in perivascular spaces, with effects varying across brain regions.

Obstructive sleep apnoea (OSA) is a disorder marked by repeated episodes of airway collapse during sleep, leading to hypoxaemia and sympathoexcitation. Its impact on brain fluid transport remains unclear. We investigated MRI-visible perivascular spaces (PVS) in healthy controls ( n =20; 5 females, mean±SD age = 52.1±9.9 years) and OSA patients ( n =20; 3 females, mean±SD age = 54.6±9.6 years) before and after continuous positive airway pressure (CPAP) therapy in a longitudinal case-control study. MRI-PVS were automatically quantified using a deep learning model called the nnU-Net. At baseline, OSA patients had significantly greater whole-brain PVS volumes and cluster counts than controls (volume: exp(β)=1.65, 95%CI [1.07, 2.51], p =0.01; cluster counts: exp(β)=1.51, 95%CI [1.1, 2.04], p =0.01). However, after 12 months of CPAP, these differences were no longer significant (volume: exp(β)=1.56, 95%CI [1.03, 2.39], p =0.054; cluster counts: exp(β)=1.39, 95%CI [0.97, 1.92], p =0.072). Similarly, PVS metrics were significantly greater in OSA patients than controls at baseline in the frontal (volume: exp(β)=1.66, 95%CI [1.02, 2.64], p =0.04; cluster counts: exp(β)=1.46, 95%CI [1.02, 2.08], p =0.04) and temporal lobe (volume: exp(β)=1.92, 95%CI [1.2, 3.03], p =0.01; cluster counts: exp(β)=1.68, 95%CI [1.1, 2.55], p =0.02). After 12 months of CPAP, PVS metrics remained significantly higher in the OSA patients compared to controls in the frontal (volume: exp(β)=1.68, 95%CI [0.94, 2.87], p =0.085; cluster counts: exp(β)=1.4, 95%CI [0.92, 2.05], p =0.12) and temporal lobes (volume: exp(β)=1.54, 95%CI [0.94, 2.38], p =0.085; cluster counts: exp(β)=1.44, 95%CI [0.95, 2.12], p =0.089). These findings suggest that OSA is associated with PVS enlargement, which may be regionally reversible with CPAP treatment.

Stroke is a leading cause of mortality and morbidity worldwide. MRI-visible perivascular spaces (PVS) are an emerging marker of cerebral small vessel disease and may have prognostic value in stroke. We investigated longitudinal changes in PVS volume and cluster count following ischaemic stroke. PVS volumes and cluster counts were compared between stroke survivors ( n =124; 39 women; median [Q1, Q3] age=70 [62, 76] years) and healthy controls ( n =39; 15 women; median age=69 [66, 72.5] years). MRI scans were acquired at 3 months, 12 months, and 36 months post-stroke. PVS were automatically segmented from T1-weighted MRI using a validated deep learning algorithm (nnU-Net). Generalised linear mixed-effects models were used to assess group differences and longitudinal changes in PVS, adjusting for baseline age, sex, total intracranial volume, and BMI. At the 12-month timepoint, no significant differences in PVS metrics were observed between stroke and control groups. However, at the 36-month timepoint we observed a significant brain-wide reduction in PVS volume (exp(β)=0.93, 95%CI [0.87, 1], p =0.035) and cluster count (exp(β)=0.92, 95%CI [0.85, 0.99], p =0.003) in the stroke group compared to control. Regionally, by 36 months, stroke patients demonstrated significant PVS reductions relative to controls in the frontal (PVS volume: exp(β)=0.93, 95%CI [0.82, 0.99], p =0.032; PVS cluster counts: exp(β)=0.91, 95%CI [0.83, 1], p =0.037) and parietal lobes (PVS volume: exp(β)=0.93, 95%CI [0.85, 1.01], p =0.10; PVS cluster counts: exp(β)=0.84, 95%CI [0.68, 1.08], p <0.001). These findings suggest that ischaemic stroke is associated with dynamic and regional changes in PVS volume and counts.

As a result of its location (facilitating ease of access) and its multimodal nature, the dorsolateral prefrontal cortex (dlPFC) is a commonly targeted site for transcranial electrical and magnetic stimulation in the treatment of psychiatric disorders. However, this cortical region has many interconnections throughout the brain, overlapping with other systems. One such example is the vestibular system. Starting at the vestibular organs within the inner ear, the vestibular system plays a major role in nauseogenic responses, and many vestibular disorders do not have well‐documented treatment plans. Given the overlapping circuitry between the dlPFC and vestibular processing networks, it has been hypothesised that the two would have interactions with one another when stimulated. Indeed, recent work has shown transcranial alternating current stimulation of the dlPFC to have an inhibitory action on sinusoidal galvanic vestibular stimulation‐induced nausea and perceptions of motion. Furthermore, stimulating the dlPFC through transcranial direct current stimulation has reduced dizziness in a cohort of phobic postural vertigo patients. This review aims to introduce the vestibular system and vestibular‐based nausea, including the cortical processing network, before discussing the dlPFC as a potential site for therapeutic targeting in various vestibular‐related disease states. This targeting focusses on transcranial stimulation, first in vestibular disorders, then in generalised motion sickness, concluding with speculation upon its possible (though perhaps less probable) ‘blue‐sky’ use in chemotherapy‐induced nausea and vomiting. image