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

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure

Rosiglitazone and Cognitive Stability in Older Individuals With Type 2 Diabetes and Mild Cognitive Impairment

OBJECTIVE— Studies have suggested that insulin resistance plays a role in cognitive impairment in individuals with type 2 diabetes. We aimed to determine whether an improvement in insulin resistance could explain cognitive performance variations over 36 weeks in older individuals with mild cognitive impairment (MCI) and type 2 diabetes. RESEARCH DESIGN AND METHODS— A total of 97 older individuals (mean +/- SD age 76 +/-6 years) who had recently (< 2 months) started an antidiabetes treatment of metformin (500 mg twice a day) (n = 30) or metformin (500 mg/day)*rosiglitazone (4 mg/day) (n = 32) or diet (n = 35) volunteered. The neuropsychological test battery consisted of the Mini-Mental State Examination (MMSE), Rey Verbal Auditory Learning Test (RAVLT) total recall, and Trail Making Tests (TMT-A and TMT-B) performed at baseline and every 12 weeks for 36 weeks along with clinical testing. RESULTS— At baseline, no significant differences were found between groups in clinical or neuropsychological parameters. Mean +/- SD values in the entire population were as follows: A1C 7.5 +/- 0.5%, fasting plasma glucose (FPG) 8.6 +/- 1.3 mmol/l, fasting plasma insulin (FPI) 148 +/- 74 pmol/l, MMSE 24.9 +/- 2.4, TMT-A 61.6 +/- 42.0, TMT-B 162.8 +/- 78.7, the difference between TMT-B and TMT-A [DIFFBA] 101.2 +/- 58.1, and RAVLT 24.3 +/- 2.1. At follow-up, ANOVA models tested changes in metabolic control parameters (FPI, FPG, and A1C). Such parameters improved in the metformin and metformin/rosiglitazone groups (Ptrend < 0.05 in both groups). ANCOVA repeated models showed that results for the metformin/rosiglitazone group remained stable for all neuropsychological tests, and results for the diet group remained stable for the MMSE and TMT-A and declined for the TMT-B (Ptrend = 0.024), executive efficiency (DIFFBA) (Ptrend = 0.026), and RAVLT memory test (Ptrend = 0.011). Results for the metformin group remained stable for the MMSE and TMTs but declined for the RAVLT (Ptrend = 0.011). With use of linear mixed-effects models, the interaction term, FPI * time, correlated with cognitive stability on the RAVLT in the metformin/rosiglitazone group (beta = -1.899; P = 0.009)

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

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