Normal cohorts in automated brain atrophy estimation: how many healthy subjects to include?

OBJECTIVES: This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation. METHODS: A pooled NC of 3945 subjects (NCpool) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55-82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NCpool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC. RESULTS: The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19). CONCLUSION: At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used. CLINICAL RELEVANCE STATEMENT: The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation. KEY POINTS: • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation.

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model's transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach.

Impact of defacing on automated brain atrophy estimation.

BACKGROUND: Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation. METHODS: In total, 268 Alzheimer's disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs's test. RESULTS: Benchmark RMSE was 0.28 ± 0.1 (range 0.12-0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs's test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12). CONCLUSION: Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.

Differential effects of TSPO ligands on mitochondrial function in mouse microglia cells.

The translocator protein 18 kDa (TSPO), initially characterized as peripheral benzodiazepine receptor, is a conserved outer mitochondrial membrane protein, implicated in cholesterol transport thereby affecting steroid hormone biosynthesis, as well as in general mitochondrial function related to bioenergetics, oxidative stress, and Ca2+ homeostasis. TSPO is highly expressed in steroidogenic tissues such as adrenal glands, but shows low expression in the central nervous system. During various disease states such as inflammation, neurodegeneration or cancer, the expression of mitochondrial TSPO in affected tissues is upregulated. The expression of TSPO can be traced for diagnostic purpose by high affinity radio-ligands. Moreover, the function of TSPO is modulated by synthetic as well as endogenous ligands with agonistic or antagonistic properties. Thus, TSPO ligands serve functions as both important biomarkers and putative therapeutic agents. In the present study, we aimed to characterize the effects of TSPO ligands on mouse BV-2 microglia cells, which express significant levels of TSPO, and analyzed the effect of XBD173, PK11195, and Ro5-4864, as well as the inflammatory reagent Lipopolysaccharides (LPS) on neurosteroid synthesis and on basic mitochondrial functions such as oxidative phosphorylation, mitochondrial membrane potential and Ca2+ homeostasis. Specific TSPO-dependent effects were separated from off-target effects by comparing lentiviral TSPO knockdown with shRNA scramble-controls and wild-type BV-2 cells. Our data demonstrate ligand-specific effects on different cellular functions in a TSPO-dependent or independent manner, providing evidence for both specific TSPO-mediated, as well as off-target effects.

Role of anoctamins in cancer and apoptosis.

Anoctamin 1 (TMEM16A, Ano1) is a recently identified Ca(2+)-activated chloride channel and a member of a large protein family comprising 10 paralogues. Before Ano1 was identified as a chloride channel protein, it was known as the cancer marker DOG1. DOG1/Ano1 is expressed in gastrointestinal stromal tumours (GIST) and particularly in head and neck squamous cell carcinoma, at very high levels never detected in other tissues. It is now emerging that Ano1 is part of the 11q13 locus, amplified in several types of tumour, where it is thought to augment cell proliferation, cell migration and metastasis. Notably, Ano1 is upregulated through histone deacetylase (HDAC), corresponding to the known role of HDAC in HNSCC. As Ano1 does not enhance proliferation in every cell type, its function is perhaps modulated by cell-specific factors, or by the abundance of other anoctamins. Thus Ano6, by regulating Ca(2+)-induced membrane phospholipid scrambling and annexin V binding, supports cellular apoptosis rather than proliferation. Current findings implicate other cellular functions of anoctamins, apart from their role as Ca(2+)-activated Cl(-) channels.

Airway epithelial cells--functional links between CFTR and anoctamin dependent Cl- secretion.

Airways consist of a heterogeneous population of cells, comprising ciliated cells, Clara cells and goblet cells. Electrolyte secretion by the airways is necessary to produce the airway surface liquid that allows for mucociliary clearance of the lungs. Secretion is driven by opening of Cl(-) selective ion channels in the apical membrane of airway epithelial cells, through either receptor mediated increase in intracellular cAMP or cytosolic Ca(2+). Traditionally cAMP-dependent and Ca(2+)-dependent secretory pathways are regarded as independent. However, this concept has been challenged recently. With identification of the Ca(2+) activated Cl(-) channel TMEM16A (anoctamin 1) and with detailed knowledge of the cAMP-regulated cystic fibrosis transmembrane conductance regulator (CFTR), it has become possible to look more closely into this relationship.