Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images.

BACKGROUND: Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. NEW METHOD: We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy. RESULTS: PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels. COMPARISON WITH EXISTENT METHODS: Does not apply. CONCLUSIONS: The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Risk evaluation of cognitive impairment in patients with heart failure: A call for action.

Background: Cognitive impairment (CI) is common in patients with heart failure (HF) and impacts treatment adherence and other aspects of patient life in HF. Recognition of CI in patients with HF is therefore important. We aimed to develop a risk model with easily available patient characteristics, to identify patients with HF who are at high risk to be cognitively impaired and in need for further cognitive investigation. Methods & results: The risk model was developed in 611 patients ≥ 60 years with HF from the TIME-CHF trial. Fifty-six (9 %) patients had CI (defined as Hodkinson Abbreviated Mental Test ≤ 7). We assessed the association between potential predictors and CI with least-absolute-shrinkage-and-selection-operator (LASSO) regression analysis. The selected predictors were: older age, female sex, NYHA class III or IV, Charlson comorbidity index ≥ 6, anemia, heart rate ≥ 70 bpm and systolic blood pressure ≥ 145 mmHg. A model that combined these variables had a c-statistic of 0.70 (0.63-0.78). The model was validated in 155 patients ≥ 60 years with HF from the ECHO study. In the validation cohort 51 (33 %) patients had CI (defined as a Mini Mental State Exam ≤ 24). External validation showed an AUC of 0.56 (0.46-0.66). Conclusions: This risk model with easily available patient characteristics has poor predictive performance in external validation, which may be due to case-mix variation. These findings underscore the need for active screening and standardized assessment for CI in patients with HF.

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions.

Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10-9, rs117204439: OR = 4.9, P = 6.0 × 10-9) and replication analysis (P < 1.1 × 10-3). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G4C2 repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10-58). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10-260). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G4C2. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.

Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification.

Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient. Approach: A pretrained CNN can be updated with a patient's previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient's previously acquired imaging.

The natural course of elevated levels of depressive symptoms in patients with vascular disease over eight years of follow-up. The SMART-Medea study.

BACKGROUND: Patients with cardiovascular disease have an increased risk for depression, and depression predicts poor prognosis in these patients, but the long-term course of depression is not known. We studied the natural course of elevated levels of depressive symptoms in patients with cardiovascular disease over eight years follow-up. METHODS: Within the Second Manifestations of ARTerial disease - Memory, depression and aging (SMART-Medea) study, depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9) in 690 patients (62±10 years) at baseline and bi-annually during 8 years follow-up. Natural course was described for symptom severity and course type (never, single episode, intermittent, and chronic) based on the cut-off point of ≥6 on the PHQ-9. Using multinomial regression analysis (reference: never depressed) we estimated age- and sex-adjusted odds ratios (OR) for the associations of demographic factors and vascular disease categories with course type. RESULTS: Of the 690 patients, 60% was never depressed, 10% had a single episode, 19% had an intermittent and 11% a chronic course of depression. Increased risk for chronic course was observed for women (OR=3.42; 95% CI=1.98-5.90), those with younger age (OR=3.20; 95% CI=1.73-5.94), and for patients with cerebrovascular disease when compared to patients with coronary artery disease (OR=2.50; 95% CI=1.31-4.78). LIMITATIONS: No information was available on clinical diagnosed major depressive disorder and/or clinical events during follow-up. CONCLUSIONS: In patients with cardiovascular disease, an intermittent or chronic course of elevated levels of depressive symptoms is very common. Patients with cardiovascular disease may require more careful clinical monitoring and management of depressive symptoms.