Cerebral hemodynamics in aging: the interplay between blood pressure, cerebral perfusion, and dementia.

Bespreking proefschrift Jurgen A.H.R. Claassen. Cerebral hemodynamics in aging: the interplay between blood pressure, cerebral perfusion, and dementia

Cerebral autoregulation assessed by near-infrared spectroscopy:validation using transcranial Doppler in patients with controlled hypertension, cognitive impairment and controls

PURPOSE: Cerebral autoregulation (CA) aims to attenuate the effects of blood pressure variation on cerebral blood flow. This study assessed the criterion validity of CA derived from near-infrared spectroscopy (NIRS) as an alternative for Transcranial Doppler (TCD). METHODS: Measurements of continuous blood pressure (BP), oxygenated hemoglobin (O(2)Hb) using NIRS and cerebral blood flow velocity (CBFV) using TCD (gold standard) were performed in 82 controls, 27 patients with hypertension and 94 cognitively impaired patients during supine rest (all individuals) and repeated sit to stand transitions (cognitively impaired patients). The BP-CBFV and BP-O(2)Hb transfer function phase shifts (TF(φ)) were computed as CA measures. Spearman correlations (ρ) and Bland Altman limits of agreement (BAloa) between NIRS- and TCD-derived CA measures were computed. BAloa separation < 50° was considered a high absolute agreement. RESULTS: NIRS- and TCD-derived CA estimates were significantly correlated during supine rest (ρ = 0.22–0.30, N = 111–120) and repeated sit-to-stand transitions (ρ = 0.46–0.61, N = 19–32). BAloa separation ranged between 87° and 112° (supine rest) and 65°–77° (repeated sit to stand transitions). CONCLUSION: Criterion validity of NIRS-derived CA measures allows for comparison between groups but was insufficient for clinical application in individuals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00421-021-04681-w

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

Association Between Blood Pressure Variability and Cerebral Small-Vessel Disease: A Systematic Review and Meta-Analysis

Background-—Research links blood pressure variability (BPV) with stroke; however, the association with cerebral small-vessel disease (CSVD) remains unclear. As BPV and mean blood pressure are interrelated, it remains uncertain whether BPV adds additional information to understanding cerebrovascular morphological characteristics. Methods and Results-—A systematic review was performed from inception until March 3, 2019. Eligibility criteria included population, adults without stroke (2=85%) independent of mean systolic pressure. Likewise, higher diastolic BPV was associated with higher odds for CSVD (OR, 1.30; 95% CI, 1.14–1.48; I2=53%) independent of mean diastolic pressure. There was no evidence of a pairwise interaction between systolic/diastolic and BPV/mean ORs (P=0.47), nor a difference between BPV versus mean pressure ORs (P=0.58). Fifty-four standardized mean differences were pooled and provided similar results for pairwise interaction (P=0.38) and difference between standardized mean differences (P=0.70). Conclusions-—On the basis of the available studies, BPV was associated with CSVD independent of mean blood pressure. However, more high-quality longitudinal data are required to elucidate whether BPV contributes unique variance to CSVD morphological characteristics

Sedentary behaviour and brain health in middle-aged and older adults: A systematic review

Sedentary behaviour may increase the risk of dementia. Studying physiological effects of sedentary behaviour on cerebral health may provide new insights into the nature of this association. Accordingly, we reviewed if and how acute and habitual sedentary behaviour relate to brain health factors in middle-aged and older adults (≥45 years). Four databases were searched. Twenty-nine studies were included, with mainly cross-sectional designs. Nine studies examined neurotrophic factors and six studied functional brain measures, with the majority of these studies finding no associations with sedentary behaviour. The results from studies on sedentary behaviour and cerebrovascular measures were inconclusive. There was a tentative association between habitual sedentary behaviour and structural white matter health. An explanatory pathway for this effect might relate to the immediate vascular effects of sitting, such as elevation of blood pressure. Nevertheless, due to the foremost cross-sectional nature of the available evidence, reverse causality could also be a possible explanation. More prospective studies are needed to understand the potential of sedentary behaviour as a target for brain health

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

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

BACKGROUND: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing. METHOD: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low ( n = 34) and high ( n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox. RESULTS: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group. CONCLUSION: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning

Dynamic cerebral autoregulation reproducibility is affected by physiological variability

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of > 0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p less than 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ? 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters. Copyright © 2019 Sanders, Elting, Panerai, Aries, Bor-Seng-Shu, Caicedo, Chacon, Gommer, Van Huffel, Jara, Kostoglou, Mahdi, Marmarelis, Mitsis, Müller, Nikolic, Nogueira, Payne, Puppo, Shin, Simpson, Tarumi, Yelicich, Zhang and Claassen

Genome-wide association study of frontotemporal dementia identifies a <i>C9ORF72</i> haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10−9, rs117204439: OR = 4.9, P = 6.0 × 10−9) and replication analysis (P &lt; 1.1 × 10−3). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G4C2 repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10−58). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10−260). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G4C2. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.</p