Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data

Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer's disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials. The online version contains supplementary material available at 10.1186/s13195-021-00946-w

The reliability of a deep learning model in external memory clinic MRI data: A multi‐cohort study

AbstractBackgroundDeep learning (DL) has provided impressive results in numerous domains in recent years, including medical image analysis. Training DL models requires large data sets to yield good performance. Since medical data can be difficult to acquire, most studies rely on public research cohorts, which often have harmonized scanning protocols and strict exclusion criteria. This is not representative of a clinical setting. In this study, we investigated the performance of a DL model in out‐of‐distribution data from multiple memory clinics and research cohorts.MethodWe trained multiple versions of AVRA: a DL model trained to predict visual ratings of Scheltens' medial temporal atrophy (MTA) scale (Mårtensson et al., 2019). This was done on different combinations of training data—starting with only harmonized MRI data from public research cohorts, and further increasing image heterogeneity in the training set by including external memory clinic data. We assessed the performance in multiple test sets by comparing AVRA's MTA ratings to an experienced radiologist's (who rated all images in this study). Data came from Alzheimer's Disease Neuroimaging Initiative (ADNI), AddNeuroMed, and images from 13 European memory clinics in the E‐DLB consortium.ResultsModels trained only on research cohorts generalized well to new data acquired with similar protocols as the training data (weighted kappa κw between 0.70‐0.72), but worse to memory clinic data with more image variability (κw between 0.34‐0.66). This was most prominent in one specific memory clinic, where the DL model systematically predicted too low MTA scores. When including data from a wider range of scanners and protocols during training, the agreement to the radiologist's ratings in external memory clinics increased (κw between 0.51‐0.71).ConclusionIn this study we showed that increasing heterogeneity in training data improves generalization to out‐of‐distribution data. Our findings suggest that studies assessing reliability of a DL model should be done in multiple cohorts, and that softwares based on DL need to be rigorously evaluated prior to being certified for deployment to clinics. References: Mårtensson, G. et al. (2019) 'AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks', NeuroImage: Clinical. Elsevier, 23(March), p. 101872

Sub-Saharan staging areas of a first-summer short-toed snake eagle Circaetus gallicus

International audienceAn immature Short-toed Snake Eagle hatched in France summered at its Sahelian wintering grounds in Mali

Satellite tracking of a booted eagle aquila pennata during migration

International audienc

Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data

Background: Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer’s disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. Results: We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. Conclusions: This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials

Identification by array comparative genomic hybridization of a new amplicon on chromosome 17q highly recurrent in BRCA1 mutated triple negative breast cancer

IntroductionTriple Negative Breast Cancers (TNBC) represent about 12% to 20% of all breast cancers (BC) and have a worse outcome compared to other BC subtypes. TNBC often show a deficiency in DNA double-strand break repair mechanisms. This is generally related to the inactivation of a repair enzymatic complex involving BRCA1 caused either by genetic mutations, epigenetic modifications or by post-transcriptional regulations.The identification of new molecular biomarkers that would allow the rapid identification of BC presenting a BRCA1 deficiency could be useful to select patients who could benefit from PARP inhibitors, alkylating agents or platinum-based chemotherapy.MethodsGenomic DNA from 131 formalin-fixed paraffin-embedded (FFPE) tumors (luminal A and B, HER2+ and triple negative BC) with known BRCA1 mutation status or unscreened for BRCA1 mutation were analysed by array Comparative Genomic Hybridization (array CGH). One highly significant and recurrent gain in the 17q25.3 genomic region was analysed by fluorescent in situ hybridization (FISH). Expression of the genes of the 17q25.3 amplicon was studied using customized Taqman low density arrays and single Taqman assays (Applied Biosystems).ResultsWe identified by array CGH and confirmed by FISH a gain in the 17q25.3 genomic region in 90% of the BRCA1 mutated tumors. This chromosomal gain was present in only 28.6% of the BRCA1 non-mutated TNBC, 26.7% of the unscreened TNBC, 13.6% of the luminal B, 19.0% of the HER2+ and 0% of the luminal A breast cancers. The 17q25.3 gain was also detected in 50% of the TNBC with BRCA1 promoter methylation. Interestingly, BRCA1 promoter methylation was never detected in BRCA1 mutated BC. Gene expression analyses of the 17q25.3 sub-region showed a significant over-expression of 17 genes in BRCA1 mutated TNBC (n¿=¿15) as compared to the BRCA1 non mutated TNBC (n¿=¿13).ConclusionsIn this study, we have identified by array CGH and confirmed by FISH a recurrent gain in 17q25.3 significantly associated to BRCA1 mutated TNBC. Up-regulated genes in the 17q25.3 amplicon might represent potential therapeutic targets and warrant further investigation