Cerebral microbleeds are not associated with postoperative delirium and postoperative cognitive dysfunction in older individuals

BACKGROUND: Cerebral microbleeds (CMB) occur in the context of cerebral small vessel disease. Other brain MRI markers of cerebral small vessel disease are associated with the occurrence of postoperative delirium (POD) and postoperative cognitive dysfunction (POCD), but for CMB this is unknown. We aimed to study the association between CMB and the occurrence of POD and POCD in older individuals. METHODS: The current study consists of 65 patients (72±5 years) from the BIOCOG study, which is a prospective, observational study of patients who underwent an elective surgery of at least 60 minutes. Patients in the current study received a preoperative cerebral MRI scan including a 3D susceptibility-weighted imaging sequence to detect CMB. The occurrence of POD was screened for twice a day until postoperative day 7 by using the DSM-5, NuDesc, CAM, and CAM-ICU. The occurrence of POCD was determined by the reliable change index model at 7 days after surgery or discharge, respectively, and 3 months after surgery. Statistical analyses consisted of logistic regression adjusted for age and gender. RESULTS: A total of 39 CMB were detected in 17 patients (26%) prior to surgery. POD occurred in 14 out of 65 patients (22%). POCD at 7 days after surgery occurred in 11 out of 54 patients (20%) and in 3 out of 40 patients at the 3 month follow-up (8%). Preoperative CMB were not associated with the occurrence of POD (OR (95%-CI): 0.28 (0.05, 1.57); p = 0.147) or POCD at 7 days after surgery (0.76 (0.16, 3.54); p = 0.727) or at 3 months follow-up (0.61 (0.03, 11.64); p = 0.740). CONCLUSION: We did not find an association between preoperative CMB and the occurrence of POD or POCD. TRIAL REGISTRATION: clinicaltrials.gov (NCT02265263) on 23 September 2014

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006–0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.</p

The prevalence and severity of fatigue in meningioma patients and its association with patient-, tumor-and treatment-related factors

Background: Fatigue is a commonly reported and severe symptom in primary brain tumor patients, but the exact occurrence in meningioma patients is unknown. This study aimed to determine the frequency and severity of fatigue in meningioma patients as well as associations between the level of fatigue and patient-, tumor-, and treatment-related factors. Methods: In this multicenter cross-sectional study, meningioma patients completed questionnaires on fatigue (MFI-20), sleep (PSQI), anxiety and depression (HADS), tumor-related symptoms (MDASI-BT), and cognitive functioning (MOS-CFS). Multivariable regression models were used to evaluate the independent association between fatigue and each patient-, tumor-, and treatment-related factor separately, corrected for relevant confounders. Results: Based on predetermined in-and exclusion criteria, 275 patients, on average 5.3 (SDa=a2.0) year since diagnosis, were recruited. Most patients had undergone resection (92%). Meningioma patients reported higher scores on all fatigue subscales compared to normative data and 26% were classified as fatigued. Having experienced a complication due to resection (OR 3.6, 95% CI: 1.8-7.0), having received radiotherapy (OR 2.4, 95% CI: 1.2-4.8), a higher number of comorbidities (OR 1.6, 95% CI: 1.3-1.9) and lower educational level (low level as reference; high level OR 0.3, 95% CI: 0.2-0.7) were independently associated with more fatigue. Conclusions: Fatigue is a frequent problem in meningioma patients even many years after treatment. Both patient-and treatment-related factors were determinants of fatigue, with the treatment-related factors being the most likely target for intervention in this patient population.</p

A cluster of blood-based protein biomarkers associated with decreased cerebral blood flow relates to future cardiovascular events in patients with cardiovascular disease

Biological processes underlying decreased cerebral blood flow (CBF) in patients with cardiovascular disease (CVD) are largely unknown. We hypothesized that identification of protein clusters associated with lower CBF in patients with CVD may explain underlying processes. In 428 participants (74% cardiovascular diseases; 26% reference participants) from the Heart-Brain Connection Study, we assessed the relationship between 92 plasma proteins from the Olink® cardiovascular III panel and normal-appearing grey matter CBF, using affinity propagation and hierarchical clustering algorithms, and generated a Biomarker Compound Score (BCS). The BCS was related to cardiovascular risk and observed cardiovascular events within 2-year follow-up using Spearman correlation and logistic regression. Thirteen proteins were associated with CBF (ρSpearman range: −0.10 to −0.19, pFDR-corrected &lt;0.05), and formed one cluster. The cluster primarily reflected extracellular matrix organization processes. The BCS was higher in patients with CVD compared to reference participants (pFDR-corrected &lt;0.05) and was associated with cardiovascular risk (ρSpearman 0.42, p &lt; 0.001) and cardiovascular events (OR 2.05, p &lt; 0.01). In conclusion, we identified a cluster of plasma proteins related to CBF, reflecting extracellular matrix organization processes, that is also related to future cardiovascular events in patients with CVD, representing potential targets to preserve CBF and mitigate cardiovascular risk in patients with CVD.</p

A role for new brain magnetic resonance imaging modalities in daily clinical practice; protocol of the prediction of cognitive recovery after stroke (PROCRAS) study.

Background: Cognitive impairment is common after acute ischemic stroke, affecting up to 75% of the patients. About half of the patients will show recovery, whereas the others will remain cognitively impaired or deteriorate. It is difficult to predict these different cognitive outcomes. Objective: The objective of this study is to investigate whether diffusion tensor imaging-based measures of brain connectivity predict cognitive recovery after 1 year, in addition to patient characteristics and stroke severity. A specific premise of the Prediction of Cognitive Recovery After Stroke (PROCRAS) study is that it is conducted in a daily practice setting. Methods: The PROCRAS study is a prospective, mono-center cohort study conducted in a large teaching hospital in the Netherlands. A total of 350 patients suffering from an ischemic stroke who screen positive for cognitive impairment on the Montreal Cognitive Assessment (MoCA<26) in the acute stage will undergo a 3Tesla-Magnetic Resonance Imaging (3T-MRI) with a diffusion-weighted sequence and a neuropsychological assessment. Patients will be classified as being unimpaired, as having a mild vascular cognitive disorder, or as having a major vascular cognitive disorder. One year after stroke, patients will undergo follow-up neuropsychological assessment. The primary endpoint is recovery of cognitive function 1 year after stroke in patients with a confirmed poststroke cognitive disorder. The secondary endpoint is deterioration of cognitive function in the first year after stroke. Results: The study is already ongoing for 1.5 years, and thus far, 252 patients have provided written informed consent. Final results are expected in June 2019. Conclusions: The PROCRAS study will show the additional predictive value of diffusion tensor imaging-based measures of brain connectivity for cognitive outcome at 1 year in patients with a poststroke cognitive disorder in a daily clinical practice setting

The association between frailty and MRI features of cerebral small vessel disease.

Frailty is a common syndrome in older individuals that is associated with poor cognitive outcome. The underlying brain correlates of frailty are unclear. The aim of this study was to investigate the association between frailty and MRI features of cerebral small vessel disease in a group of non-demented older individuals. We included 170 participants who were classified as frail (n = 30), pre-frail (n = 85) or non-frail (n = 55). The association of frailty and white matter hyperintensity volume and shape features, lacunar infarcts and cerebral perfusion was investigated by regression analyses adjusted for age and sex. Frail and pre-frail participants were older, more often female and showed higher white matter hyperintensity volume (0.69 [95%-CI 0.08 to 1.31], p = 0.03 respectively 0.43 [95%-CI: 0.04 to 0.82], p = 0.03) compared to non-frail participants. Frail participants showed a non-significant trend, and pre-frail participants showed a more complex shape of white matter hyperintensities (concavity index: 0.04 [95%-CI: 0.03 to 0.08], p = 0.03; fractal dimensions: 0.07 [95%-CI: 0.00 to 0.15], p = 0.05) compared to non-frail participants. No between group differences were found in gray matter perfusion or in the presence of lacunar infarcts. In conclusion, increased white matter hyperintensity volume and a more complex white matter hyperintensity shape may be structural brain correlates of the frailty phenotype

MRI-Based classification of neuropsychiatric systemic lupus erythematosus patients with self-supervised contrastive learning

Introduction/Purpose: Systemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous neuropsychiatric (NP) syndromes. Structural brain abnormalities are commonly found in SLE and NPSLE, but their role in diagnosis is limited, and their usefulness in distinguishing between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE (non-NPSLE) is non-existent. Self-supervised contrastive learning algorithms proved to be useful in classification tasks in rare diseases with limited number of datasets. Our aim was to apply self-supervised contrastive learning on T1-weighted images acquired from a well-defined cohort of SLE patients, aiming to distinguish between NPSLE and non-NPSLE patients. Subjects and Methods: We used 3T MRI T1-weighted images of 163 patients. The training set comprised 68 non-NPSLE and 34 NPSLE patients. We applied random geometric transformations between iterations to augment our data sets. The ML pipeline consisted of convolutional base encoder and linear projector. To test the classification task, the projector was removed and one linear layer was measured. Validation of the method consisted of 6 repeated random sub-samplings, each using a random selection of a small group of patients of both subtypes. Results: In the 6 trials, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in both groups, Gradient-weighted Class Activation Maps (Grad-CAM) were examined. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, while no such communality was found for the non-NPSLE group. Discussion/Conclusion: The self-supervised contrastive learning model was effective in capturing common brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but indicates involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T1-weighted images

Neuropsychiatric systemic lupus erythematosus is associated with a distinct type and shape of cerebral white matter hyperintensities

OBJECTIVES: Advanced white matter hyperintensity (WMH) markers on brain MRI may help reveal underlying mechanisms and aid in the diagnosis of different phenotypes of SLE patients experiencing neuropsychiatric (NP) manifestations. METHODS: In this prospective cohort study, we included a clinically well-defined cohort of 155 patients consisting of 38 patients with NPSLE (26 inflammatory and 12 ischaemic phenotype) and 117 non-NPSLE patients. Differences in 3 T MRI WMH markers (volume, type and shape) were compared between patients with NPSLE and non-NPSLE and between patients with inflammatory and ischaemic NPSLE by linear and logistic regression analyses corrected for age, sex and intracranial volume. RESULTS: Compared with non-NPSLE [92% female; mean age 42 (13) years], patients with NPSLE [87% female; mean age 40 (14) years] showed a higher total WMH volume [B (95%-CI)]: 0.46 (0.0 7 ↔ 0.86); P = 0.021], a higher periventricular/confluent WMH volume [0.46 (0.0 6 ↔ 0.86); P = 0.024], a higher occurrence of periventricular with deep WMH type [0.32 (0.1 3 ↔ 0.77); P = 0.011], a higher number of deep WMH lesions [3.06 (1.2 1 ↔ 4.90); P = 0.001] and a more complex WMH shape [convexity: ‒0.07 (‒0.12 ↔ ‒0.02); P = 0.011, concavity index: 0.05 (0.0 1 ↔ 0.08); P = 0.007]. WMH shape was more complex in inflammatory NPSLE patients [89% female; mean age 39 (15) years] compared with patients with the ischaemic phenotype [83% female; mean age 41 (11) years] [concavity index: 0.08 (0.0 1 ↔ 0.15); P = 0.034]. CONCLUSION: We demonstrated that patients with NPSLE showed a higher periventricular/confluent WMH volume and more complex shape of WMH compared with non-NPSLE patients. This finding was particularly significant in inflammatory NPLSE patients, suggesting different or more severe underlying pathophysiological abnormalities

White matter hyperintensities associate with cognitive slowing in patients with systemic lupus erythematosus and neuropsychiatric symptoms

Objective- To compare cognitive function between patients with different phenotypes of neuropsychiatric systemic lupus erythematosus (NPSLE) and assess its association with brain and white matter hyperintensity (WMH) volumes. Methods- Patients attending the Leiden University Medical Centre NPSLE clinic between 2007 and 2015 without large brain infarcts were included (n=151; 42±13 years, 91% women). In a multidisciplinary consensus meeting, neuropsychiatric symptoms were attributed to systemic lupus erythematosus (SLE) (NPSLE, inflammatory (n=24) or ischaemic (n=12)) or to minor/non-NPSLE (n=115). Multiple regression analyses were performed to compare cognitive function between NPSLE phenotypes and to assess associations between brain and WMH volumes and cognitive function cross-sectionally. Results- Global cognitive function was impaired in 5%, learning and memory (LM) in 46%, executive function and complex attention (EFCA) in 39% and psychomotor speed (PS) in 46% of all patients. Patients with inflammatory NPSLE showed the most cognitive impairment in all domains (p≤0.05). Higher WMH volume associated with lower PS in the total group (B: −0.14 (95% CI −0.32 to −0.02)); especially in inflammatory NPSLE (B: −0.36 (95% CI −0.60 to −0.12). In the total group, lower total brain volume and grey matter volume associated with lower cognitive functioning in all domains (all: 0.00/0.01 (0.00;0.01)) and lower white matter volume associated with lower LM, EFCA and PS (all: 0.00/0.01 (0.00;0.01)). Conclusion- We demonstrated that an association between brain and WMH volumes and cognitive function is present in patients with SLE, but differs between (NP)SLE phenotypes. WMHs associated with PS especially in inflammatory NPSLE, which suggests a different, potentially more severe underlying pathophysiological mechanism of cognitive impairment in this phenotype