372 research outputs found
Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus
Aims/hypothesis: Type 2 diabetes mellitus has been associated with brain atrophy and cognitive decline, but the association with ischaemic white matter lesions is unclear. Previous neuroimaging studies have mainly used semiquantitative rating scales to measure atrophy and white matter lesions (WMLs). In this study we used an automated segmentation technique to investigate the association of type 2 diabetes, several diabetes-related risk factors and cognition with cerebral tissue and WML volumes. Subjects and methods: Magnetic resonance images of 99 patients with type 2 diabetes and 46 control participants from a population-based sample were segmented using a k-nearest neighbour classifier trained on ten manually segmented data sets. White matter, grey matter, lateral ventricles, cerebrospinal fluid not including lateral ventricles, and WML volumes were assessed. Analyses were adjusted for age, sex, level of education and intracranial volume. Results: Type 2 diabetes was associated with a smaller volume of grey matter (-21.8 ml; 95% CI -34.2, -9.4) and with larger lateral ventricle volume (7.1 ml; 95% CI 2.3, 12.0) and with larger white matter lesion volume (56.5%; 95% CI 4.0, 135.8), whereas white matter volume was not affected. In separate analyses for men and women, the effects of diabetes were only significant in women. Conclusions/interpretation: The combination of atrophy with larger WML volume indicates that type 2 diabetes is associated with mixed pathology in the brain. The observed sex differences were unexpected and need to be addressed in further studies. © 2007 Springer-Verlag
NMDA receptor subunits are modified transcriptionally and post-translationally in the brain of streptozotocin-diabetic rats
A 4 year follow-up study of cognitive functioning in patients with type 2 diabetes mellitus
Contains fulltext :
90777.pdf (publisher's version ) (Open Access)AIMS/HYPOTHESIS: Type 2 diabetes mellitus is associated with moderate decrements in cognitive functioning, mainly in verbal memory, information-processing speed and executive functions. How this cognitive profile evolves over time is uncertain. The present study aims to provide detailed information on the evolution of cognitive decrements in type 2 diabetes over time.
METHODS: Sixty-eight patients with type 2 diabetes and 38 controls matched for age, sex and estimated IQ performed an elaborate neuropsychological examination in 2002-2004 and again in 2006-2008, including 11 tasks covering five cognitive domains. Vascular and metabolic determinants were recorded. Data were analysed with repeated measures analysis of variance, including main effects for group, time and the group x time interaction.
RESULTS: Patients with type 2 diabetes showed moderate decrements in information-processing speed (mean difference in z scores [95% CI] -0.37 [-0.69, -0.05]) and attention and executive functions (-0.25 [-0.49, -0.01]) compared with controls at both the baseline and the 4 year follow-up examination. After 4 years both groups showed a decline in abstract reasoning (-0.16 [-0.30, -0.02]) and attention and executive functioning (-0.29 [-0.40, -0.17]), but there was no evidence for accelerated cognitive decline in the patients with type 2 diabetes as compared with controls (all p > 0.05).
CONCLUSIONS/INTERPRETATION: In non-demented patients with type 2 diabetes, cognitive decrements are moderate in size and cognitive decline over 4 years is largely within the range of what can be viewed in normal ageing. Apparently, diabetes-related cognitive changes develop slowly over a prolonged period of time.8 p
Perforating artery flow velocity and pulsatility in patients with carotid occlusive disease: a 7 tesla MRI study
Patients with carotid occlusive disease express altered hemodynamics in the post-occlusive vasculature and lesions commonly attributed to cerebral small vessel disease (SVD). We addressed the question if cerebral perforating artery flow measures, using a novel 7T MRI technique, are altered and related to SVD lesion burden in patients with carotid occlusive disease. 21 patients were included with a uni- (18) or bilateral (3) carotid occlusion (64±7 years) and 19 controls (65 ±10 years). Mean flow velocity and pulsatility in the perforating arteries in the semi-oval center (CSO) and basal ganglia (BG), measured with a 2D phase contrast 7T MRI sequence, were compared between patients and controls, and between hemispheres in patients with unilateral carotid occlusive disease. In patients, relations were assessed between perforating artery flow measures and SVD burden score and white matter hyperintensity (WMH) volume. CSO perforating artery flow velocity was lower in patients than controls, albeit non-significant (mean difference [95% confidence interval] 0.08 cm/s [0.00–0.16]; p = 0.053), but pulsatility was similar (0.07 [-0.04–0.18]; p = 0.23). BG flow velocity and pulsatility did not differ between patients and controls (velocity = 0.28 cm/s [-0.32–0.88]; p = 0.34; pulsatility = 0.00 [-0.10–0.11]; p = 0.97). Patients with unilateral carotid occlusive disease showed no significant interhemispheric flow differences. Though non-significant, within patients lower CSO (p = 0.06) and BG (p = 0.11) flow velocity related to larger WMH volume. Our findings suggest that carotid occlusive disease may be associated with abnormal cerebral perforating artery flow and that this relates to SVD lesion burden in these patients, although our observations need corroboration in larger study populations.</p
Efficacy and safety of two 5 day insulin dosing regimens to achieve strict glycaemic control in patients with acute ischaemic stroke
Short-Term Environmental Enrichment Enhances Adult Neurogenesis, Vascular Network and Dendritic Complexity in the Hippocampus of Type 1 Diabetic Mice
Background: Several brain disturbances have been described in association to type 1 diabetes in humans. In animal models, hippocampal pathological changes were reported together with cognitive deficits. The exposure to a variety of environmental stimuli during a certain period of time is able to prevent brain alterations and to improve learning and memory in conditions like stress, aging and neurodegenerative processes. Methodology/Principal Findings: We explored the modulation of hippocampal alterations in streptozotocin-induced type 1 diabetic mice by environmental enrichment. In diabetic mice housed in standard conditions we found a reduction of adult neurogenesis in the dentate gyrus, decreased dendritic complexity in CA1 neurons and a smaller vascular fractional area in the dentate gyrus, compared with control animals in the same housing condition. A short exposure-10 days- to an enriched environment was able to enhance proliferation, survival and dendritic arborization of newborn neurons, to recover dendritic tree length and spine density of pyramidal CA1 neurons and to increase the vascular network of the dentate gyrus in diabetic animals. Conclusions/Significance: The environmental complexity seems to constitute a strong stimulator competent to rescue th
Earlier age of dementia onset and shorter survival times in dementia patients with diabetes
Diabetes is a risk factor for dementia, but relatively little is known about the epidemiology of the association. A retrospective population study using Western Australian hospital inpatient, mental health outpatient, and death records was used to compare the age at index dementia record (proxy for onset age) and survival outcomes in dementia patients with and without preexisting diabetes (n = 25,006; diabetes, 17.3%). Inpatient records from 1970 determined diabetes history in this study population with incident dementia in years 1990–2005. Dementia onset and death occurred an average 2.2 years and 2.6 years earlier, respectively, in diabetic compared with nondiabetic patients. Age-specific mortality rates were increased in patients with diabetes. In an adjusted proportional hazard model, the death rate was increased with long-duration diabetes, particularly with early age onset dementia. In dementia diagnosed before age 65 years, those with a ≥15-year history of diabetes died almost twice as fast as those without diabetes (hazard ratio = 1.9, 95% confidence interval: 1.3, 2.9). These results suggest that, in patients with diabetes, dementia onset occurs on average 2 years early and survival outcomes are generally poorer. The effect of diabetes on onset, survival, and mortality is greatest when diabetes develops before middle age and after 15 years’ diabetes duration. The impact of diabetes on dementia becomes progressively attenuated in older age groups
Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease
This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice
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