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

Reliability of visual assessment of medial temporal lobe atrophy

Background: Medial temporal lobe atrophy (MTA) has been found to be an early sign of Alzheimer’s disease (AD). Visual assessment of MTA (vaMTA) is a rapid, cost-efficient and clinically adaptable visual interpretation method for rating MTA, based on coronal magnetic resonance imaging (MRI) scans. The method was developed by Scheltens et al in the 1990s. Purpose: The aim of this thesis was to investigate the reliability of vaMTA using the Scheltens rating scale: on a long-term basis, compared with volumetric calculation, compared with multivariate analyses and, finally, tested in a clinical situation. In Study I, MRI scans of 100 patients were visually assessed six times over a 1-year period. Two radiologists, with different backgrounds, performed the assessments independently of each other. The results showed a high degree of reproducibility when performed by an experienced investigator. The reproducibility drops when assessment is rarely performed. Study II was a comparison between vaMTA and measurement of hippocampal volume in 544 non-demented elderly individuals from the SwedishNational Study of Ageing and Care in Kungsholmen (SNAC-K). A significant correlation was found between the two methods. Cut-off values for MTA scores in normal ageing were also suggested. In Study III the reliability of Scheltens’ visual assessment rating scale for assessing MTA was compared with that of a multivariate MRI classification method, orthogonal projections to latent structures (OPLS), and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). A comparison between the different techniques was also performed in predicting future developments from mild cognitive impairment (MCI) to AD. The prediction accuracies in distinguishing between AD patients and CTL were high for all three modalities. All three methods were also highly accurate in identifying subjects who converted from MCI to AD at 1-year follow-up. Finally, in Study IV, vaMTA scores were used in a validation study of the proposed new “Dubois criteria” in Alzheimer’s disease, in which MTA is one of four important biomarkers. A retrospective study of 150 patients was carried out to compare the traditional diagnostic criteria for dementia with the new criteria suggested by Dubois et al. The results showed a lack of accuracy for the new AD criteria, as they were valid for only 55% of the clinically diagnosed patients with full-blown AD in this study. Conclusion: Visual assessment of MTA using the MTA scale is reliable when performed on a daily basis. Medial temporal lobe atrophy scores have a significant correlation to hippocampal volume measurements, can predict conversion from MCI to AD with similar accuracy as can volumetric calculations and multivariate analysis, and can be used as supportive biomarker in the work-up of AD

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets–collected with different scanners, protocols and disease populations–and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens’ scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.publishedVersio

Subjective cognitive impairment subjects in our clinical practice

BACKGROUND: The clinical challenge in subjective cognitive impairment (SCI) is to identify which individuals will present cognitive decline. We created a statistical model to determine which variables contribute to SCI and mild cognitive impairment (MCI) versus Alzheimer's disease (AD) diagnoses. METHODS: A total of 993 subjects diagnosed at a memory clinic (2007-2009) were included retrospectively: 433 with SCI, 373 with MCI and 187 with AD. Descriptive statistics were provided. A logistic regression model analyzed the likelihood of SCI and MCI patients being diagnosed with AD, using age, gender, Mini-Mental State Examination score, the ratio of β-amyloid 42 divided by total tau, and phosphorylated tau as independent variables. RESULTS: The SCI subjects were younger (57.8 ± 8 years) than the MCI (64.2 ± 10.6 years) and AD subjects (70.1 ± 9.7 years). They were more educated, had less medial temporal lobe atrophy (MTA) and frequently normal cerebrospinal fluid biomarkers. Apolipoprotein E4/E4 homozygotes and apolipoprotein E3/E4 heterozygotes were significantly less frequent in the SCI group (6 and 36%) than in the AD group (28 and 51%). Within the regression model, cardiovascular risk factors, confluent white matter lesions, MTA and central atrophy increased the AD likelihood for SCI subjects. CONCLUSIONS: SCI patients form a distinct group. In our model, factors suggesting cardiovascular risk, MTA and central atrophy increased the AD likelihood for SCI subjects

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets–collected with different scanners, protocols and disease populations–and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens’ scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment

Medial temporal lobe atrophy ratings in a large 75-year-old population-based cohort : gender-corrected and education-corrected normative data

OBJECTIVES: To find cut-off values for different medial temporal lobe atrophy (MTA) measures (right, left, average, and highest), accounting for gender and education, investigate the association with cognitive performance, and to compare with decline of cognitive function over 5 years in a large population-based cohort. METHODS: Three hundred and ninety 75-year-old individuals were examined with magnetic resonance imaging of the brain and cognitive testing. The Scheltens's scale was used to assess visually MTA scores (0-4) in all subjects. Cognitive tests were repeated in 278 of them after 5 years. Normal MTA cut-off values were calculated based on the 10th percentile. RESULTS: Most 75-year-old individuals had MTA score ≤2. Men had significantly higher MTA scores than women. Scores for left and average MTA were significantly higher in highly educated individuals. Abnormal MTA was associated with worse results in cognitive test and individuals with abnormal right MTA had faster cognitive decline. CONCLUSION: At age 75, gender and education are confounders for MTA grading. A score of ≥2 is abnormal for low-educated women and a score of ≥2.5 is abnormal for men and high-educated women. Subjects with abnormal right MTA, but normal MMSE scores had developed worse MMSE scores 5 years later. KEY POINTS: • Gender and education are confounders for MTA grading. • We suggest cut-off values for 75-year-olds, taking gender and education into account. • Males have higher MTA scores than women. • Higher MTA scores are associated with worse cognitive performance

Phenotypic variability and neuropsychological findings associated with C9orf72 repeat expansions in a Bulgarian dementia cohort.

BACKGROUND:The GGGGCC repeat expansion in the C9orf72 gene was recently identified as a major cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) in several European populations. The objective of this study was to determine the frequency of C9orf72 repeat expansions in a Bulgarian dementia cohort and to delineate the associated clinical features. METHODS AND FINDINGS:PCR-based assessments of the C9orf72 hexanucleotide repeat expansion in all study samples (including 82 FTD, 37 Alzheimer's disease (AD), and 16 other neurodegenerative/dementia disorder cases) were performed. We report the clinical, neuropsychological, and neuroimaging findings obtained for the C9orf72 repeat expansion carriers. Of the 135 cases screened, 3/82 (3.7%) of all FTD cases and 1/37 (2.7%) of all clinical AD cases had a C9orf72 repeat expansion. In this cohort, the C9orf72 pathological expansion was found in clinical diagnoses bridging the FTD, parkinsonism, ALS and AD spectrum. Interestingly, we showed early writing errors without aphasia in two subjects with C9orf72 expansions. CONCLUSIONS:This study represents the first genetic screening for C9orf72 repeat expansions in a Bulgarian dementia cohort. The C9orf72 repeat expansion does not appear to be a common cause of FTD and related disorders. This report confirms the notion that C9orf72 repeat expansions underlie a broad spectrum of neurodegenerative phenotypes. Relatively isolated agraphia in two cases with C9orf72 repeat expansions is a strong motivation to provide detailed and sophisticated oral and written language assessments that can be used to more precisely characterize early cognitive deficits in these heterogeneous conditions