Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties. Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study. Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution. We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects

FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI

The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) MRI, there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep learning method named HypVINN for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g. sex-differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank. Finally, HypVINN can perform the segmentation in less than a minute (GPU) and will be available in the open source FastSurfer neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.Comment: Submitted to Imaging Neuroscienc

Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator

Dietary Fat Intake and Cognitive Decline in Women With Type 2 Diabetes

OBJECTIVE: Individuals with type 2 diabetes have high risk of late-life cognitive impairment, yet little is known about strategies to modify risk. Targeting insulin resistance and vascular complications—both associated with cognitive decline—may be a productive approach. We investigated whether dietary fat, which modulates glucose and lipid metabolism, might influence cognitive decline in older adults with diabetes. RESEARCH DESIGN AND METHODS: Beginning in 1995–1999, we evaluated cognitive function in 1,486 Nurses' Health Study participants, aged ≥70 years, with type 2 diabetes; second evaluations were conducted 2 years later. Dietary fat intake was assessed regularly beginning in 1980; we considered average intake from 1980 (at midlife) through initial cognitive interview and also after diabetes diagnosis. We used multivariate-adjusted linear regression models to obtain mean differences in cognitive decline across tertiles of fat intake. RESULTS: Higher intakes of saturated and trans fat since midlife, and lower polyunsaturated to saturated fat ratio, were each highly associated with worse cognitive decline in these women. On a global score averaging all six cognitive tests, mean decline among women in the highest trans fat tertile was 0.15 standard units worse than that among women in the lowest tertile (95% CI −0.24 to −0.06, P = 0.002); this mean difference was comparable with the difference we find in women 7 years apart in age. Results were similar when we analyzed diet after diabetes diagnosis. CONCLUSIONS: These findings suggest that lower intakes of saturated and trans fat and higher intake of polyunsaturated fat relative to saturated fat may reduce cognitive decline in individuals with type 2 diabetes.Statistic

Cognitive dysfunction in naturally occurring canine idiopathic epilepsy

Globally, epilepsy is a common serious brain disorder. In addition to seizure activity, epilepsy is associated with cognitive impairments including static cognitive impairments present at onset, progressive seizure-induced impairments and co-morbid dementia. Epilepsy occurs naturally in domestic dogs but its impact on canine cognition has yet to be studied, despite canine cognitive dysfunction (CCD) recognised as a spontaneous model of dementia. Here we use data from a psychometrically validated tool, the canine cognitive dysfunction rating (CCDR) scale, to compare cognitive dysfunction in dogs diagnosed with idiopathic epilepsy (IE) with controls while accounting for age. An online cross-sectional study resulted in a sample of 4051 dogs, of which n = 286 had been diagnosed with IE. Four factors were significantly associated with a diagnosis of CCD (above the diagnostic cut-off of CCDR ≥50): (i) epilepsy diagnosis: dogs with epilepsy were at higher risk; (ii) age: older dogs were at higher risk; (iii) weight: lighter dogs (kg) were at higher risk; (iv) training history: dogs with more exposure to training activities were at lower risk. Impairments in memory were most common in dogs with IE, but progression of impairments was not observed compared to controls. A significant interaction between epilepsy and age was identified, with IE dogs exhibiting a higher risk of CCD at a young age, while control dogs followed the expected pattern of low-risk throughout middle age, with risk increasing exponentially in geriatric years. Within the IE sub-population, dogs with a history of cluster seizures and high seizure frequency had higher CCDR scores. The age of onset, nature and progression of cognitive impairment in the current IE dogs appear divergent from those classically seen in CCD. Longitudinal monitoring of cognitive function from seizure onset is required to further characterise these impairments

Ankle brachial index combined with Framingham risk score to predict cardiovascular events and mortality - A meta-analysis

CONTEXT: Prediction models to identify healthy individuals at high risk of cardiovascular disease have limited accuracy. A low ankle brachial index (ABI) is an indicator of atherosclerosis and has the potential to improve prediction. OBJECTIVE: To determine if the ABI provides information on the risk of cardiovascular events and mortality independently of the Framingham risk score (FRS) and can improve risk prediction. DATA SOURCES: Relevant studies were identified. A search of MEDLINE (1950 to February 2008) and EMBASE (1980 to February 2008) was conducted using common text words for the term ankle brachial index combined with text words and Medical Subject Headings to capture prospective cohort designs. Review of reference lists and conference proceedings, and correspondence with experts was conducted to identify additional published and unpublished studies. STUDY SELECTION: Studies were included if participants were derived from a general population, ABI was measured at baseline, and individuals were followed up to detect total and cardiovascular mortality. DATA EXTRACTION: Prespecified data on individuals in each selected study were extracted into a combined data set and an individual participant data meta-analysis was conducted on individuals who had no previous history of coronary heart disease. RESULTS: Sixteen population cohort studies fulfilling the inclusion criteria were included. During 480,325 person-years of follow-up of 24,955 men and 23,339 women, the risk of death by ABI had a reverse J-shaped distribution with a normal (low risk) ABI of 1.11 to 1.40. The 10-year cardiovascular mortality in men with a low ABI (< or = 0.90) was 18.7% (95% confidence interval [CI], 13.3%-24.1%) and with normal ABI (1.11-1.40) was 4.4% (95% CI, 3.2%-5.7%) (hazard ratio [HR], 4.2; 95% CI, 3.3-5.4). Corresponding mortalities in women were 12.6% (95% CI, 6.2%-19.0%) and 4.1% (95% CI, 2.2%-6.1%) (HR, 3.5; 95% CI, 2.4-5.1). The HRs remained elevated after adjusting for FRS (2.9 [95% CI, 2.3-3.7] for men vs 3.0 [95% CI, 2.0-4.4] for women). A low ABI (< or = 0.90) was associated with approximately twice the 10-year total mortality, cardiovascular mortality, and major coronary event rate compared with the overall rate in each FRS category. Inclusion of the ABI in cardiovascular risk stratification using the FRS would result in reclassification of the risk category and modification of treatment recommendations in approximately 19% of men and 36% of women. CONCLUSION: Measurement of the ABI may improve the accuracy of cardiovascular risk prediction beyond the FRS

Study on COgnition and Prognosis in the Elderly (SCOPE)

Blood Press. 1999;8(3):177-83. Study on COgnition and Prognosis in the Elderly (SCOPE). Hansson L, Lithell H, Skoog I, Baro F, Bánki CM, Breteler M, Carbonin PU, Castaigne A, Correia M, Degaute JP, Elmfeldt D, Engedal K, Farsang C, Ferro J, Hachinski V, Hofman A, James OF, Krisin E, Leeman M, de Leeuw PW, Leys D, Lobo A, Nordby G, Olofsson B, Zanchetti A, et al. University of Uppsala, Department of Public Health, Sweden. Abstract The Study on COgnition and Prognosis in the Elderly (SCOPE) is a multicentre, prospective, randomized, double-blind, parallel-group study designed to compare the effects of candesartan cilexetil and placebo in elderly patients with mild hypertension. The primary objective of the study is to assess the effect of candesartan cilexetil on major cardiovascular events. The secondary objectives of the study are to assess the effect of candesartan cilexetil on cognitive function and on total mortality, cardiovascular mortality, myocardial infarction, stroke, renal function, hospitalization, quality of life and health economics. Male and female patients aged between 70 and 89 years, with a sitting systolic blood pressure (SBP) of 160-179 mmHg and/or diastolic blood pressure (DBP) of 90-99 mmHg, and a Mini-Mental State Examination (MMSE) score of 24 or above, are eligible for the study. The overall target study population is 4000 patients, at least 1000 of whom are also to be assessed for quality of life and health economics data. After an open run-in period lasting 1-3 months, during which patients are assessed for eligibility and those who are already on antihypertensive therapy at enrolment are switched to hydrochlorothiazide 12.5 mg o.d., patients are randomized to receive either candesartan cilexetil 8 mg once daily (o.d.) or matching placebo o.d. At subsequent study visits, if SBP remains >160 mmHg, or has decreased by 85 mmHg, study treatment is doubled to candesartan cilexetil 16 mg o.d. or two placebo tablets o.d. Recruitment was completed in January 1999. At that time 4964 patients had been randomized. All randomized patients will be followed for an additional 2 years. If the event rate is lower than anticipated, the follow-up will be prolonged. PMID: 10595696 [PubMed - indexed for MEDLINE

Early diagnosis of dementia based on intersubject whole-brain dissimilarities

This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia