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

Abnormalities in cardiac-induced brain tissue deformations are now detectable with MRI: A case-report of a patient who underwent craniotomy after trauma

Background: Heartbeat and respiration induce cyclic brain tissue deformations, which receive increasing attention as potential driving force for brain clearance. These deformations can now be assessed using a novel 3D strain tensor imaging (STI) method at 7 T MRI. Methods: An 18-year-old man had suffered a traumatic brain injury and was treated with a craniotomy with a maximal diameter of 12 cm. STI was employed to capture cardiac-induced brain tissue deformations and additional time-resolved 2D flow measurements were acquired to capture cerebrospinal fluid (CSF) flow towards the spinal canal. Results: The craniotomy caused major changes in all aspects of the brain's mechanical dynamics as compared to healthy volunteer references. Tissue strains increased, particularly around the craniotomy, and directionality of deformations showed large abnormalities, also in the contralateral hemisphere. As the brain tissue could pulsate outward from the skull, physiological pulsatile CSF flow at the foramen magnum was abolished. Conclusions: This work illustrates how STI can assess physiological patterns of brain tissue deformation and how craniotomy leads to widespread deformation abnormalities that can be detected at a single patient level. While this case is meant to provide proof of concept, application of STI in other conditions of abnormal brain mechanical dynamics warrants further study

When the central integrator disintegrates: A review of the role of the thalamus in cognition and dementia

The thalamus is a complex neural structure with numerous anatomical subdivisions and intricate connectivity patterns. In recent decades, the traditional view of the thalamus as a relay station and "gateway to the cortex" has expanded in recognition of its role as a central integrator of inputs from sensory systems, cortex, basal ganglia, limbic systems, brain stem nuclei, and cerebellum. As such, the thalamus is critical for numerous aspects of human cognition, mood, and behavior, as well as serving sensory processing and motor functions. Thalamus pathology is an important contributor to cognitive and functional decline, and it might be argued that the thalamus has been somewhat overlooked as an important player in dementia. In this review, we provide a comprehensive overview of thalamus anatomy and function, with an emphasis on human cognition and behavior, and discuss emerging insights on the role of thalamus pathology in dementia

When the central integrator disintegrates: A review of the role of the thalamus in cognition and dementia

The thalamus is a complex neural structure with numerous anatomical subdivisions and intricate connectivity patterns. In recent decades, the traditional view of the thalamus as a relay station and “gateway to the cortex” has expanded in recognition of its role as a central integrator of inputs from sensory systems, cortex, basal ganglia, limbic systems, brain stem nuclei, and cerebellum. As such, the thalamus is critical for numerous aspects of human cognition, mood, and behavior, as well as serving sensory processing and motor functions. Thalamus pathology is an important contributor to cognitive and functional decline, and it might be argued that the thalamus has been somewhat overlooked as an important player in dementia. In this review, we provide a comprehensive overview of thalamus anatomy and function, with an emphasis on human cognition and behavior, and discuss emerging insights on the role of thalamus pathology in dementia

Females with type 2 diabetes are at higher risk for accelerated cognitive decline than males: CAROLINA-COGNITION study

BACKGROUND AND AIM: Cognitive dysfunction is increasingly recognized as an important comorbidity of type 2 diabetes (T2D). We aimed to establish if the risk of accelerated cognitive decline (ACD) is higher in females with T2D than males. METHODS AND RESULTS: 3163 participants (38% female) with T2D from the cognition substudy of CAROLINA® (NCT01243424) were included (mean age 64.4 ± 9.2 years; T2D duration 7.6 ± 6.1 years). The cognitive outcome was occurrence of ACD at end of follow-up, defined as a regression based index score ≤16th percentile on either the Mini-Mental State Examination (MMSE) or a composite measure of attention and executive functioning (Trail Making and Verbal Fluency Test). Potential confounders, were taken into account at an individual patient level. Logistic regression analysis was used to investigate ACD risk by sex. We assessed potential mediators for sex differences in ACD using Causal Mediation Analysis (CMA). After a median follow-up duration of 6.1 ± 0.7 years, 361 (30.0%) females compared to 494 (25.2%) males exhibited ACD (OR 1.27 [95%CI 1.08-1.49], p = .003). Depressive symptoms, which were more common in females (24.3% vs 12.5%), mediated between sex and ACD (mediation effect 20.3%, p = 0.03). There were no other significant mediators. CONCLUSION: Females with T2D had a higher risk of ACD compared to males. This was partly explained by depressive symptoms. After evaluation of vascular and diabetes-related risk factors, complications and treatment, a major share of the higher risk of ACD in females remained unexplained. Our results highlight the need for further research on causes of sex-specific ACD in T2D

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)