Tissue microstructural changes in dementia with Lewy bodies revealed by quantitative MRI.

We aimed to characterize dementia with Lewy bodies (DLB) by the quantitative MRI parameters of longitudinal relaxation time (qT1) and transverse relaxation time (qT2). These parameters reflect potential pathological changes in tissue microstructures, which may be detectable noninvasively in brain areas without evident atrophy, so may have potential value in revealing the early neuropathological changes in DLB. We conducted a cross-sectional study of subjects with DLB (N = 35) and similarly aged control participants (N = 35). All subjects underwent a detailed clinical and neuropsychological assessment and structural and quantitative 3T MRI. Quantitative MRI maps were obtained using relaxation time mapping methods. Statistical analysis was performed on gray matter qT1 and qT2 values. We found significant alterations of quantitative parameters in DLB compared to controls. In particular, qT1 decreases in bilateral temporal lobes, right parietal lobes, basal ganglia including left putamen, left caudate nucleus and left amygdala, and left hippocampus/parahippocampus; qT2 decreases in left putamen and increases in left precuneus. These regions showed only partial overlap with areas where grey matter loss was found, making atrophy an unlikely explanation for our results. Our findings support that DLB is predominantly associated with changes in posterior regions, such as visual association areas, and subcortical structures, and that qT1 and qT2 measurement can detect subtle changes not seen on structural volumetric imaging. Hence, quantitative MRI may compliment other imaging techniques in detecting early changes in DLB and in understanding neurobiological changes associated with the disorder.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00415-014-7541-

Longitudinal and quantitative MRI in AD

Quantitative MRI provides important information about tissue properties in brain both in normal ageing and in degenerative disorders. Although it is well known that those with Alzheimer's disease (AD) show a specific pattern and faster rate of atrophy than controls, the precise spatial and temporal patterns of quantitative MRI in AD are unknown. We aimed to investigate neuroimaging correlates of AD using serial quantitative MRI. In our study, twenty-one subjects with AD and thirty-two similar-aged healthy controls underwent two serial MRI scans at baseline and 12 months. Tissue characteristics were captured using two quantitative MRI parameters: longitudinal relaxation time (qT1) and transverse relaxation time (qT2). The two groups (AD and controls) were statistically compared using a voxel based quantification (VBQ) method based on Matlab and SPM8. At baseline, subjects with AD showed a significant reduction of qT1 and qT2 compared to controls in bilateral temporal and parietal lobes, hippocampus, and basal ganglia. This pattern was also observed at follow-up. Longitudinally, in AD we found a significant increase rather than further reduction of qT1 and qT2 from the baseline in bilateral hippocampus, thalamus and right caudate nucleus. In addition, the longitudinal change of qT1 in left hippocampus was negatively correlated with cognitive decline in AD over the 1-year period, and the general disease severity significantly predicted the amount of increase of qT1 in bilateral hippocampus over 12 months. The longitudinal change of qT2 in left parahippocampus correlated with change in neuropsychiatric features over time. In summary, quantitative MRI parameters were reduced in AD cross-sectionally, but increased over time, showing distinct spatiotemporal patterns from the atrophy in AD. We also showed the clinical relevance of quantitative MRI parameters, indicating their potential promise as new imaging markers in AD.The study was funded by the Sir Jules Thorn Charitable Trust (grant ref: 05/JTA) and was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust and Newcastle University and the NIHR Biomedical Research Centre and Biomedical Research Unit in Dementia based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. L. Su, A. Blamire, R. Watson, J. He and B. Aribisala report no disclosures. J. O’Brien has been a consultant for GE Healthcare, Servier, and Bayer Healthcare and has received honoraria for talks from Pfizer, GE Healthcare, Eisai, Shire, Lundbeck, Lilly, and Novartis.This is the author accepted manuscript. The final version is available from Bentham Science via http://dx.doi.org/10.2174/156720501366615111614141

Longitudinal diffusion tensor imaging in dementia with Lewy bodies and Alzheimer's disease.

OBJECTIVE: Changes in the white matter of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have been reported using diffusion weighted MRI, though few longitudinal studies have been done. METHODS: We performed diffusion weighted MRI twice, a year apart on 23 AD, 14 DLB, and 32 healthy control subjects. Mean diffusivity (MD) and fractional anisotropy (FA) were calculated. RESULTS: In AD, there were widespread regions where the longitudinal MD increase was greater than in controls, and small areas in the parietal and temporal lobes where it was greater in AD than DLB. In AD, decrease in brain volume correlated with increased MD. There were no significant differences in progression between DLB and controls. CONCLUSIONS: In AD the white matter continues to degenerate during the disease process, whereas in DLB, changes in the white matter structure are a relatively early feature. Different mechanisms are likely to underpin changes in diffusivity.The study was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.parkreldis.2016.01.00

Time course of normalization of functional β-cell capacity in the Diabetes Remission Clinical Trial after weight loss in type 2 diabetes

Objective: To assess functional β-cell capacity in type 2 diabetes during 2 years of remission induced by dietary weight loss. Research Design and Methods: A Stepped Insulin Secretion Test with Arginine was used to quantify functional β-cell capacity by hyperglycemia and arginine stimulation. Thirty-nine of 57 participants initially achieved remission (HbA1c <6.5% [<48 mmol/mol] and fasting plasma glucose <7 mmol/L on no antidiabetic drug therapy) with a 16.4 ± 7.7 kg weight loss and were followed up with supportive advice on avoidance of weight regain. At 2 years, 20 participants remained in remission in the study. A nondiabetic control (NDC) group, matched for age, sex, and weight after weight loss with the intervention group, was studied once. Results: During remission, median (interquartile range) maximal rate of insulin secretion increased from 581 (480–811) pmol/min/m2 at baseline to 736 (542–998) pmol/min/m2 at 5 months, 942 (565–1,240) pmol/min/m2 at 12 months (P = 0.028 from baseline), and 936 (635–1,435) pmol/min/m2 at 24 months (P = 0.023 from baseline; n = 20 of 39 of those initially in remission). This was comparable to the NDC group (1,016 [857–1,507] pmol/min/m2) by 12 (P = 0.064) and 24 (P = 0.244) months. Median first-phase insulin response increased from baseline to 5 months (42 [4–67] to 107 [59–163] pmol/min/m2; P < 0.0001) and then remained stable at 12 and 24 months (110 [59–201] and 125 [65–166] pmol/min/m2, respectively; P < 0.0001 vs. baseline) but lower than that of the NDC group (250 [226–429] pmol/min/m2; P < 0.0001). Conclusions: A gradual increase in assessed functional β-cell capacity occurred after weight loss, becoming similar to NDC participants by 12 months. This was unchanged at 2 years with continuing remission of type 2 diabetes

Remission of human type 2 diabetes requires decrease in liver and pancreas fat content but is dependent upon capacity for beta cell recovery

The Diabetes Remission Clinical Trial reported return and persistence of non-diabetic blood glucose control in 46% of people with type 2 diabetes of up to 6 years duration. Detailed metabolic studies were performed on a subgroup (intervention, n = 64; control, n = 26). In the intervention group, liver fat content decreased (16.0% ± 1.3% to 3.1% ± 0.5%, p < 0.0001) immediately after weight loss. Similarly, plasma triglyceride and pancreas fat content decreased whether or not glucose control normalized. Recovery of first-phase insulin response (0.04[−0.05–0.32] to 0.11[0.0005–0.51] nmol/min/m2, p < 0.0001) defined those who returned to non-diabetic glucose control and this was durable at 12 months (0.11[0.005–0.81] nmol/min/m2, p = 0.0001). Responders were similar to non-responders at baseline but had shorter diabetes duration (2.7 ± 0.3 versus 3.8 ± 0.4 years; p = 0.02). This study demonstrates that β cell ability to recover long-term function persists after diagnosis, changing the previous paradigm of irreversible loss of β cell function in type 2 diabetes

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images

Study funding This work was funded by the Row Fogo Charitable Trust (MVH, VGC) grant no. BRO-D.FID3668413, and the Wellcome Trust (patient recruitment, scanning, primary study Ref No. 088134/Z/09). The study was conducted independently of the funders who do not hold the data and did not participate in the study design or analyses. The Lothian Birth Cohort 1936 is funded by Age UK (Disconnected Mind grant) and the Medical Research Council (MRC; MR/M01311/1, G1001245, 82800), and the latter supported BSA. IJD was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, which is funded by the MRC and the Biotechnology and Biological Sciences Research Council (MR/K026992/1). David Moratal acknowledges financial support from the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R, and from the Conselleria d'Educació, Investigació, Cultura i Esport, Generalitat Valenciana (grants AEST/2017/013 and AEST/2018/021). Rafael Ortiz-Ramón was supported by grant ACIF/2015/078 and grant BEFPI/2017/004 from the Conselleria d’Educació, Investigació, Cultura i Esport of the Valencian Community (Spain).Peer reviewedPublisher PD

Blood Pressure, Internal Carotid Artery Flow Parameters, and Age-Related White Matter Hyperintensities

White matter hyperintensities (WMH) are associated with hypertension. We examined interactions between blood pressure (BP), internal carotid artery (ICA) flow velocity parameters and WMH. We obtained BP measurements from 694 community-dwelling subjects at mean ages 69.6 (±0.8) and again at 72.6 (±0.7) years, plus brain MRI and ICA ultrasound at age 73±1 years. Diastolic and mean BP decreased and pulse pressure increased but systolic BP did not change between 70 and 73 years. Multiple linear regression, corrected for vascular disease and risk factors, showed that WMH at age 73 were associated with history of hypertension (β=0.13, p<0.001) and with BP at age 70 (systolic β=0.08, mean β=0.09, diastolic β=0.08, all p<0.05); similar but attenuated associations were seen for BP at age 73. Lower diastolic BP and higher pulse pressure were associated with higher ICA pulsatility index at age 73 (diastolic BP: standardized β, age 70=−0.24, p<0.001; pulse pressure age 70 β=0.19, p<0.001). WMH were associated with higher ICA pulsatility index (β=0.13, p=0.002) after adjusting for BP and correction for multiple testing. Therefore falling diastolic BP and increased pulse pressure are associated with increased ICA pulsatility index, which in turn is associated with WMH. This suggests that hypertension and WMH may either associate indirectly because hypertension increases arterial stiffness which leads to WMH over time, or co-associate through advancing age and stiffer vessels, or both. Reducing vascular stiffness may reduce WMH progression and should be tested in randomised trials, in addition to testing antihypertensive therapy