Delayed cerebral atrophy after cerebellar stroke: topographical relation and clinical impact

Impaired dynamic cerebral autoregulation measured in the middle cerebral artery in patients with vertebrobasilar ischemia is associated with autonomic failure

OBJECTIVES

To assess whether vertebrobasilar artery ischemia (VBI) affects cortical cerebral blood flow (CBF) regulation.

MATERIAL AND METHODS

107 consecutive patients (mean age 65 ± 15 years; women 21) with VBI underwent structured stroke care with assessment of dynamic cerebral autoregulation (dCA) in both middle cerebral arteries (MCAs) by transfer function analysis using spontaneous oscillations of blood pressure (BP) and CBF velocity that yields by extraction of phase and gain information in the very low (0.02-0.07 Hz), low (0.07-0.15 Hz) and high frequency (0.15-0.5 Hz) ranges. Additionally, power spectrum analysis of BP and heart rate variability (HRV) was performed. The control group consists of 29 age- and sex-matched healthy persons.

RESULTS

Compared to controls, phase in the VBI patients was significantly reduced and gain increased in the very low frequencies (VLF), in the low (LF), phase was significantly reduced only ipsilaterally. In the high frequencies (HF), phase reduction was only marginally significant. BP power spectral density (PSD) was much higher in the patients than in the controls across all frequencies. In the PSD of heart rate variability the controls but not the patients exhibited a strong peak around 0.11Hz, while the patients, but not the controls, exhibit a strong peak around 0.36 Hz. In regression analysis, patient's phase and gain results were not related to age, sex, arterial hypertension, diabetes mellitus, renal dysfunction, heart failure as indicated by left ventricular ejection fraction, stroke subtype, presence or absence of cerebral small vessel disease.

CONCLUSION

Patients with VBI exhibit bilateral cortical autoregulation impairment in association with an autonomic nervous system disbalance.

CLINICALTRIALS

GOV IDENTIFIER

NCT04611672.

Cognitive impairment in essential tremor assessed by the cerebellar cognitive affective syndrome scale

Background

Essential tremor (ET) is a movement disorder characterized by cerebellar neurodegenerative changes. ET is also associated with non-motor symptoms including cognitive impairment. The neuropsychologic profile of a patient with ET could relate to cerebellar cognitive affective syndrome (CCAS).

Objective

This study aimed to assess the prevalence of cognitive impairment in patients with ET and identify whether the cognitive impairment in ET corresponds to a CCAS.

Methods

Cognitive functions were evaluated with the CCAS-Scale (CCAS-S) in 20 patients with ET and 20 controls matched for age, sex, and level of education. The results of the CCAS-S were compared between patients and controls. The underlying determinant of CCAS inpatients with ET was identified through the correlation between the results of the CCAS-S and age at onset of symptoms, disease duration, and the Essential Tremor Rating Assessment Scale (TETRAS).

Results

On a group level, ET patients performed significantly worse than matched controls. In total, 13 individuals with ET had a definite CCAS (CCAS-S failed items ≥ 3). ASO and TETRAS scores significantly correlated with CCAS-S performances in ET patients.

Conclusion

CCAS is highly prevalent in patients with ET which supports the cerebellar pathophysiology of associated cognitive impairment and supports a more systematic use of the CCAS-S to cognitively assessed patients with ET.

Determinant of the cerebellar cognitive affective syndrome in Friedreich's ataxia

BACKGROUND

Individuals with Friedreich's ataxia (FRDA) display significantly lower performances in many cognitive domains with a pattern of impairment that falls within the cerebellar cognitive affective syndrome (CCAS).

OBJECTIVE

To assess in a large cohort of individuals with FRDA, the main determinant of the CCAS using multiple variable regression models.

METHODS

This is a monocentric observational study that included 39 individuals with FRDA. Ataxic motor symptoms were evaluated with the SARA and cognitive functions with the CCAS-Scale (CCAS-S). Age, SARA, GAA1, Age of symptoms onset (ASO), Age and disease duration (DD) were chosen as covariates in a linear regression model to predict CCAS-S failed items and covariates in a logistic regression model to predict definite CCAS.

RESULTS

Patients mean age, SARA score, ASO, DD and GAA1 were respectively of 29 ± 14, 22 ± 10, 14 ± 11, 15 ± 9 and 712 ± 238 (4 point-mutations). Mean CCAS-S raw score was of 86 ± 16, mean number of failed items was 2.9 ± 1.6. Twenty-three individuals had definite CCAS. The multiple linear regression model with age, SARA, ASO, DD & GAA1 as covariates was statistically significant to predict CCAS-S failed items. The SARA was the only significant coefficient in regression models for predicting CCAS-S failed items number and the definite CCAS occurrence.

CONCLUSIONS

CCAS is highly prevalent in adult individuals with FRDA. CCAS is predicted by ataxic motor symptoms severity. This finding supports common core cerebellar pathophysiology in both cognitive and motor symptoms in FRDA and warrants screening for CCAS, especially in patients with SARA > 20.

Cerebellar cognitive affective syndrome after acute cerebellar stroke

Introduction

The cerebellum modulates both motor and cognitive behaviors, and a cerebellar cognitive affective syndrome (CCAS) was described after a cerebellar stroke in 1998. Yet, a CCAS is seldom sought for, due to a lack of practical screening scales. Therefore, we aimed at assessing both the prevalence of CCAS after cerebellar acute vascular lesion and the yield of the CCAS-Scale (CCAS-S) in an acute stroke setting.

Materials and methods

All patients admitted between January 2020 and January 2022 with acute onset of a cerebellar ischemic or haemorrhagic first stroke at the CUB-Hôpital Erasme and who could be evaluated by the CCAS-S within a week of symptom onset were included.

Results

Cerebellar acute vascular lesion occurred in 25/1,580 patients. All patients could complete the CCAS-S. A definite CCAS was evidenced in 21/25 patients. Patients failed 5.2 ± 2.12 items out of 8 and had a mean raw score of 68.2 ± 21.3 (normal values 82–120). Most failed items of the CCAS-S were related to verbal fluency, attention, and working memory.

Conclusion

A definite CCAS is present in almost all patients with acute cerebellar vascular lesions. CCAS is efficiently assessed by CCAS-S at bedside tests in acute stroke settings. The magnitude of CCAS likely reflects a cerebello-cortical diaschisis.

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

Objective

Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods

A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.

Results

Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion

We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.