Measuring axial length of the eye from magnetic resonance brain imaging

BACKGROUND: Metrics derived from the human eye are increasingly used as biomarkers and endpoints in studies of cardiovascular, cerebrovascular and neurological disease. In this context, it is important to account for potential confounding that can arise from differences in ocular dimensions between individuals, for example, differences in globe size. METHODS: We measured axial length, a geometric parameter describing eye size from T(2)-weighted brain MRI scans using three different image analysis software packages (Mango, ITK and Carestream) and compared results to biometry measurements from a specialized ophthalmic instrument (IOLMaster 500) as the reference standard. RESULTS: Ninety-three healthy research participants of mean age 51.0 ± SD 5.4 years were analyzed. The level of agreement between the MRI-derived measurements and the reference standard was described by mean differences as follows, Mango − 0.8 mm; ITK − 0.5 mm; and Carestream − 0.1 mm (upper/lower 95% limits of agreement across the three tools ranged from 0.9 mm to − 2.6 mm). Inter-rater reproducibility was between − 0.03 mm and 0.45 mm (ICC 0.65 to 0.93). Intra-rater repeatability was between 0.0 mm and − 0.2 mm (ICC 0.90 to 0.95). CONCLUSIONS: We demonstrate that axial measurements of the eye derived from brain MRI are within 3.5% of the reference standard globe length of 24.1 mm. However, the limits of agreement could be considered clinically significant. Axial length of the eye obtained from MRI is not a replacement for the precision of biometry, but in the absence of biometry it could provide sufficient accuracy to act as a proxy. We recommend measuring eye axial length from MRI in studies that do not have biometry but use retinal imaging to study neurodegenerative changes so as to control for differing eye size across individuals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12886-022-02289-y

A novel knockout mouse for the small EDRK-rich factor 2 (Serf2) showing developmental and other deficits

The small EDRK-rich factor 2 (SERF2) is a highly conserved protein that modifies amyloid fibre assembly in vitro and promotes protein misfolding. However, the role of SERF2 in regulating age-related proteotoxicity remains largely unexplored due to a lack of in vivo models. Here, we report the generation of Serf2 knockout mice using an ES cell targeting approach, with Serf2 knockout alleles being bred onto different defined genetic backgrounds. We highlight phenotyping data from heterozygous Serf2^{+/-} mice, including unexpected male-specific phenotypes in startle response and pre-pulse inhibition. We report embryonic lethality in Serf2^{-/-} null animals when bred onto a C57BL/6 N background. However, homozygous null animals were viable on a mixed genetic background and, remarkably, developed without obvious abnormalities. The Serf2 knockout mice provide a powerful tool to further investigate the role of SERF2 protein in previously unexplored pathophysiological pathways in the context of a whole organism

Cerebral Small Vessel Disease burden is increased in Systemic Lupus Erythematosus

BACKGROUND AND PURPOSE—: Systemic lupus erythematosus (SLE) increases stroke risk, but the mechanism is uncertain. This study aimed to determine the association between SLE and features on neuroimaging of cerebral small vessel disease (SVD), a risk factor for stroke. METHODS—: Consecutive patients attending a clinic for SLE were recruited. All patients underwent brain magnetic resonance imaging; had blood samples taken for markers of inflammation, endothelial dysfunction, cholesterol, and autoantibodies; and underwent cognitive and psychiatric testing. The data were compared with sex- and age-matched healthy controls and patients with minor stroke. Features of SVD were measured, a total SVD score calculated, and associations sought with vascular risk factors, cognition, SLE activity, and disease duration. RESULTS—: Fifty-one SLE patients (age: 48.8 years; SD: 14.3 years) had a greater total SVD score compared with healthy controls (1 versus 0; P<0.0001) and stroke patients (1 versus 0; P=0.02). There were higher perivascular spaces and deep white matter hyperintensity scores and more superficial brain atrophy in SLE patients versus healthy controls. Despite fewer vascular risk factors than similarly aged stroke patients, SLE patients had similar or more of some SVD features. The total SVD score was not associated with SLE activity, cognition, disease duration, or any blood measure. CONCLUSIONS—: In this data set, SLE patients had a high burden of SVD features on magnetic resonance imaging, particularly perivascular spaces. A larger longitudinal study is warranted to determine the causes of SVD features in SLE and clinical implications

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study. We evaluate our framework on paired low- and high-resolution brain MRI structural full scans (i.e., T1-, T2-weighted, and FLAIR sequences are simultaneously input) obtained in clinical and research settings from scanners manufactured by Siemens, Phillips, and GE. We show that the upsampled MRIs are qualitatively faithful to the ground-truth high-quality scans (PSNR = 35.39; MAE = 3.78E−3; NMSE = 4.32E−10; SSIM = 0.9852; mean normal-appearing gray/white matter ratio intensity differences ranging from 0.0363 to 0.0784 for FLAIR, from 0.0010 to 0.0138 for T1-weighted and from 0.0156 to 0.074 for T2-weighted sequences). The automatic raw segmentation of tissues and lesions using the super-resolved images has fewer false positives and higher accuracy than those obtained from interpolated images in protocols represented with more than three sets in the training sample, making our approach a strong candidate for practical application in clinical and collaborative research

The Brain Health Index: Towards a combined measure of neurovascular and neurodegenerative structural brain injury

Background: A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia. Aim: We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index. Materials and methods: We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke’s Cognitive Exam Revised in patients and with reaction time in healthy participants. Results: The brain health index (standard beta = 0.20–0.59, P &#60; 0.05) was significantly and more strongly associated with Addenbrooke’s Cognitive Exam Revised, including at one year follow-up, than white matter hyperintensity volume (standard beta = 0.04–0.08, P &#62; 0.05) and small vessel disease score (standard beta = 0.02–0.27, P &#62; 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57–0.59, P &#60; 0.05) was more strongly associated with reaction time than measures of brain atrophy alone (standard beta = 0.04–0.13, P &#62; 0.05) in healthy participants. Conclusions: The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease

Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces

BACKGROUND: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.NEW METHOD: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.RESULTS: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.COMPARISON WITH EXISTING METHODS: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.CONCLUSIONS: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.</p