The miRBase-21 database currently lists 1881 microRNA (miRNA) precursors and 2585 unique mature human miRNAs. Since their discovery, miRNAs have proved to present a new level of epigenetic ...post-transcriptional control of protein synthesis. Initial results point to a possible involvement of miRNA in Alzheimer's disease (AD). We applied OpenArray technology to profile the expression of 1178 unique miRNAs in cerebrospinal fluid (CSF) samples of AD patients (n = 22) and controls (n = 28). Using a Cq of 34 as cut-off, we identified positive signals for 441 miRNAs, while 729 miRNAs could not be detected, indicating that at least 37% of miRNAs are present in the brain. We found 74 miRNAs being down- and 74 miRNAs being up-regulated in AD using a 1.5 fold change threshold. By applying the new explorative "Measure of relevance" method, 6 reliable and 9 informative biomarkers were identified. Confirmatory MANCOVA revealed reliable miR-100, miR-146a and miR-1274a as differentially expressed in AD reaching Bonferroni corrected significance. MANCOVA also confirmed differential expression of informative miR-103, miR-375, miR-505#, miR-708, miR-4467, miR-219, miR-296, miR-766 and miR-3622b-3p. Discrimination analysis using a combination of miR-100, miR-103 and miR-375 was able to detect AD in CSF by positively classifying controls and AD cases with 96.4% and 95.5% accuracy, respectively. Referring to the Ingenuity database we could identify a set of AD associated genes that are targeted by these miRNAs. Highly predicted targets included genes involved in the regulation of tau and amyloid pathways in AD like MAPT, BACE1 and mTOR.
Information on circulating miRNAs in frontotemporal lobar degeneration is very limited and conflicting results have complicated an interpretation in Alzheimer's disease thus far. In the present study ...we I) collected samples from multiple clinical centers across Germany, II) defined 3 homogenous patient groups with high sample sizes (bvFTD n = 48, AD n = 48 and cognitively healthy controls n = 44), III) compared expression levels in both CSF and serum samples and IV) detected a limited set of miRNAs by using a MIQE compliant protocol based on SYBR-green miRCURY assays that have proven reliable to generate reproducible results. We included several quality controls that identified and reduced technical variation to increase the reliability of our data. We showed that the expression levels of circulating miRNAs measured in CSF did not correlate with levels in serum. Using cluster analysis we found expression pattern in serum that, in part, reflects the genomic organization and affiliation to a specific miRNA family and that were specifically altered in bvFTD, AD, and control groups. Applying factor analysis we identified a 3-factor model characterized by a miRNA signature that explained 80% of the variance classifying healthy controls with 97%, bvFTD with 77% and AD with 72% accuracy. MANOVA confirmed signals like miR-320a and miR-26b-5p at BH corrected significance that contributed most to discriminate bvFTD cases with 96% sensitivity and 90% specificity and AD cases with 89% sensitivity and specificity compared to healthy controls, respectively. Correlation analysis revealed that miRNAs from the 3-factor model also correlated with levels of protein biomarker amyloid-beta1-42 and phosphorylated neurofilament heavy chain, indicating their potential role in the monitoring of progressive neuronal degeneration. Our data show that miRNAs can be reproducibly measured in serum and CSF without pre-amplification and that serum includes higher expressed signals that demonstrate an overall better ability to classify bvFTD, AD and healthy controls compared to signals detected in CSF.
Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD) and by their caretakers. Working memory and long-term declarative memory are affected early ...during the course of the disease. The individual pattern of impaired memory functions correlates with parameters of structural or functional brain integrity. AD pathology interferes with the formation of memories from the molecular level to the framework of neural networks. The investigation of AD memory loss helps to identify the involved neural structures, such as the default mode network, the influence of epigenetic and genetic factors, such as ApoE4 status, and evolutionary aspects of human cognition. Clinically, the analysis of memory assists the definition of AD subtypes, disease grading, and prognostic predictions. Despite new AD criteria that allow the earlier diagnosis of the disease by inclusion of biomarkers derived from cerebrospinal fluid or hippocampal volume analysis, neuropsychological testing remains at the core of AD diagnosis.
Today, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this ...study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer's disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.
Cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer's disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho(181)-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.
The method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.
Motor speech disorders (MSDs) are characteristic for nonfluent primary progressive aphasia (nfvPPA). In primary progressive aphasia (PPA) of the semantic (svPPA) and of the logopenic type (lvPPA), ...speech motor function is considered typically intact. However, knowledge on the prevalence of MSDs in svPPA and lvPPA is mainly based on studies with a priori knowledge of PPA syndrome diagnosis. This fully blinded retrospective study aims to provide data on the prevalence of all types of MSDs in a large sample of German-speaking patients with different subtypes of PPA.
Two raters, blinded for PPA subtype, independently evaluated connected speech samples for MSD syndrome and severity from 161 patients diagnosed with nfvPPA, svPPA or lvPPA in the database of the German Consortium of Frontotemporal Lobar Degeneration (FTLDc). In case of disagreement, a third experienced rater re-evaluated the speech samples, followed by a consensus procedure. Consensus was reached for 160 patients (74 nfvPPA, 49 svPPA, 37 lvPPA).
Across all PPA syndromes, 43.8% of the patients showed MSDs. Patients with nfvPPA demonstrated the highest proportion of MSDs (62.2%), but MSDs were also identified in svPPA (26.5%) and lvPPA (29.7%), respectively. Overall, dysarthria was the most common class of MSDs, followed by apraxia of speech. In addition, we identified speech abnormalities presenting as “syllabic speech”, “dysfluent speech”, and “adynamic speech”.
Our study confirmed MSDs as frequently occurring in PPA. The study also confirmed MSDs to be most common in patients with nfvPPA. However, MSDs were also found in substantial proportions of patients with svPPA and lvPPA. Furthermore, our study identified speech motor deficits that have not received attention in previous studies on PPA. The results are discussed against the background of the existing literature on MSDs in PPA, including theoretical considerations of the neuroanatomical conditions described for each of the different subtypes of PPA.
The progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers ...reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta
(Aβ42), amyloid-beta
(Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.
We used 115 complete datasets from MCI patients of the "Dementia Competence Network", a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.
Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80-0.83, and the four-parameter combination from AUC 0.81-0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.
A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
The applause sign, i.e., the inability to execute the same amount of claps as performed by the examiner, was originally reported as a sign specific for progressive supranuclear palsy (PSP). Recent ...research, however, has provided evidence for the occurrence of the applause sign in various conditions. The aim of this study was to determine the prevalence of the applause sign and correlate its presence with neuropsychological and MRI volumetry findings in frontotemporal lobar degeneration and related conditions. The applause sign was elicited with the three clap test (TCT), with a higher score indicating poorer performance. Data were recorded from 272 patients from the cohort of the German consortium for frontotemporal lobar degeneration (FTLDc): 111 with behavioral variant frontotemporal dementia (bvFTD), 98 with primary progressive aphasia (PPA), 30 with progressive supranuclear palsy Richardson’s syndrome, 17 with corticobasal syndrome (CBS) and 16 with amyotrophic lateral sclerosis with frontotemporal dementia (ALS/FTD). For comparison, 29 healthy elderly control subjects (HC) were enrolled in the study. All subjects underwent detailed language and neuropsychological assessment. In a subset of 156 subjects, atlas-based volumetry was performed. The applause sign occurred in all patient groups (40% in PSP, 29.5% in CBS, 25% in ALS/FTD, 13.3% in PPA and 9.0% in bvFTD) but not in healthy controls. The prevalence was highest in PSP patients. It was significantly more common in PSP as compared to bvFTD, PPA and HC. The comparison between the other groups failed to show a significant difference regarding the occurrence of the applause sign. The applause sign was highly correlated to a number of neuropsychological findings, especially to measures of executive, visuospatial, and language function as well as measures of disease severity. TCT scores showed an inverse correlation with the volume of the ventral diencephalon and the pallidum. Furthermore the volume of the ventral diencephalon and pallidum were significantly smaller in patients displaying the applause sign. Our study confirms the occurrence of the applause sign in bvFTD, PSP and CBS and adds PPA and ALS/FTD to these conditions. Although still suggestive of PSP, clinically it must be interpreted with caution. From the correlation with various cognitive measures we suggest the applause sign to be indicative of disease severity. Furthermore we suggest that the applause sign represents dysfunction of the pallidum and the subthalamic nucleus, structures which are known to play important roles in response inhibition.
The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
...Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
N.A.
Cohen's kappa, accuracy, and F1-score to assess model performance.
Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
•Diagnosis of dementia is challenging – especially in rare / orphan syndromes.•Classifiers were developed for seven dementia syndromes in 477 subjects using MRI.•Focus on frontotemporal lobar ...degeneration syndromes, and Alzheimer’s disease.•SVM performed optimal in classification vs controls, reasonable in multi-syndromes.•Automated methods for MR imaging are ready for translation to clinical routine.
Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).
Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).
The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.
Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease.
Behavioral variant frontotemporal dementia (bvFTD) is characterized by deep alterations in behavior and personality. Although revised diagnostic criteria agree for executive dysfunction as most ...characteristic, impairments in social cognition are also suggested. The study aimed at identifying those neuropsychological and behavioral parameters best discriminating between bvFTD and healthy controls. Eighty six patients were diagnosed with possible or probable bvFTD according to Rascovsky et al. (2011) and compared with 43 healthy age-matched controls. Neuropsychological performance was assessed with a modified Reading the Mind in the Eyes Test (RMET), Stroop task, Trail Making Test (TMT), Hamasch-Five-Point Test (H5PT), and semantic and phonemic verbal fluency tasks. Behavior was assessed with the Apathy Evaluation Scale, Frontal Systems Behavioral Scale, and Bayer Activities of Daily Living Scale. Each test's discriminatory power was investigated by Receiver Operating Characteristic curves calculating the area under the curve (AUC). bvFTD patients performed significantly worse than healthy controls in all neuropsychological tests. Discriminatory power (AUC) was highest in behavioral questionnaires, high in verbal fluency tasks and the RMET, and lower in executive function tests such as the Stroop task, TMT and H5PT. As fluency tasks depend on several cognitive functions, not only executive functions, results suggest that the RMET discriminated better between bvFTD and control subjects than other executive tests. Social cognition should be incorporated into diagnostic criteria for bvFTD in the future, such as in the International Classification of Diseases (ICD)-11, as already suggested in the Diagnostic and Statistical Manual for Mental Disorders (DSM)-5.
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