Eva Janoušová

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease.

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimers disease (AD). Using a sample from the Alzheimers Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression.

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimers disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene–gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimers Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3, PIK3CG, PRKCA and PRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.

Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects.

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.

Robust and complex approach of pathological speech signal analysis

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0 ? 0.0 % ) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Principe de Asturias (PdA) Hospital in Alcala de Henares of Madrid database we made improvements in classification accuracy ( 82.1 ? 3.3 % ) and specificity ( 83.8 ? 5.1 % ) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney U test as significant ( p < 0.05 ) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a cepstral peak prominence extracted from the first intrinsic mode function ( p = 6.9443 i? 10 - 32 ) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view.

Retrospective analysis of 235 unselected patients with mantle cell lymphoma confirms prognostic relevance of Mantle Cell Lymphoma International Prognostic Index and Ki-67 in the era of rituximab: long-term data from the Czech Lymphoma Project Database

Abstract Although a prognostic model (MIPI, Mantle Cell Lymphoma International Prognostic Index) for patients with mantle cell lymphoma (MCL) has been established, its clinical significance for daily practice in the rituximab era remains controversial. Data of 235 unselected patients with MCL from the Czech Lymphoma Project Database were analyzed. MIPI, simplified MIPI (s-MIPI) and Ki-67 proliferation index were assessed for all patients and for a subgroup of 155 rituximab-treated (RT) patients. MIPI divided all patients into subgroups of low-risk (22%), intermediate-risk (29%) and high-risk (49%), with median overall survival 105.8 vs. 54.1 vs. 24.6 months, respectively (p < 0.001). s-MIPI revealed similar results. The validity of both indexes was confirmed in RT patients. We confirmed the Ki-67 index to be a powerful single prognostic factor for overall survival (64.4 vs. 20.1 months, p < 0.001) for all patients and for the RT subset. Our results confirm the clinical relevance of MIPI, s-MIPI and Ki-67 for risk stratification in MCL also in the rituximab era.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases.

Addenbrooke's Cognitive Examination and Individual Domain Cut-Off Scores for Discriminating between Different Cognitive Subtypes of Parkinson's Disease

Objective. The main aim of this study was to verify the sensitivity and specificity of Addenbrookes Cognitive Examination-Revised (ACE-R) in discriminating between Parkinsons disease (PD) with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) and between PD-MCI and PD with dementia (PD-D). We also evaluated how ACE-R correlates with neuropsychological cognitive tests in PD. Methods. We examined three age-matched groups of PD patients diagnosed according to the Movement Disorder Society Task Force criteria: PD-NC, PD-MCI, and PD-D. ROC analysis was used to establish specific cut-off scores of ACE-R and its domains. Correlation analyses were performed between ACE-R and its subtests with relevant neuropsychological tests. Results. Statistically significant differences between groups were demonstrated in global ACE-R scores and subscores, except in the language domain. ACE-R cut-off score of 88.5 points discriminated best between PD-MCI and PD-NC (sensitivity 0.68, specificity 0.91); ACE-R of 82.5 points distinguished best between PD-MCI and PD-D (sensitivity 0.70, specificity 0.73). The verbal fluency domain of ACE-R demonstrated the best discrimination between PD-NC and PD-MCI (cut-off score 11.5; sensitivity 0.70, specificity 0.73) while the orientation/attention subscore was best between PD-MCI and PD-D (cut-off score 15.5; sensitivity 0.90, specificity 0.97). ACE-R scores except for ACE-R language correlated with specific cognitive tests of interest.

Distinct Pattern of Gray Matter Atrophy in Mild Alzheimer’s Disease Impacts on Cognitive Outcomes of Noninvasive Brain Stimulation

BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is a promising tool to study and modulate brain plasticity. OBJECTIVE Our aim was to investigate the effects of rTMS on cognitive functions in patients with mild cognitive impairment and Alzheimers disease (MCI/AD) and assess the effect of gray matter (GM) atrophy on stimulation outcomes. METHODS Twenty MCI/AD patients participated in the proof-of-concept controlled study. Each patient received three sessions of 10 Hz rTMS of the right inferior frontal gyrus (IFG), the right superior temporal gyrus (STG), and the vertex (VTX, a control stimulation site) in a randomized order. Cognitive functions were tested prior to and immediately after each session. The GM volumetric data of patients were: 1) compared to healthy controls (HC) using source-based morphometry; 2) correlated with rTMS-induced cognitive improvement. RESULTS The effect of the stimulated site on the difference in cognitive scores was statistically significant for the Word part of the Stroop test (ST-W, p = 0.012, linear mixed models). As compared to the VTX stimulation, patients significantly improved after both IFG and STG stimulation in this cognitive measure. MCI/AD patients had significant GM atrophy in characteristic brain regions as compared to HC (p = 0.029, Bonferroni corrected). The amount of atrophy correlated with the change in ST-W scores after rTMS of the STG. CONCLUSION rTMS enhanced cognitive functions in MCI/AD patients. We demonstrated for the first time that distinct pattern of GM atrophy in MCI/AD diminishes the cognitive effects induced by rTMS of the temporal neocortex.

Sequestration of MBNL1 in tissues of patients with myotonic dystrophy type 2

The pathogenesis of myotonic dystrophy type 2 includes the sequestration of MBNL proteins by expanded CCUG transcripts, which leads to an abnormal splicing of their target pre-mRNAs. We have found CCUG(exp) RNA transcripts of the ZNF9 gene associated with the formation of ribonuclear foci in human skeletal muscle and some non-muscle tissues present in muscle biopsies and skin excisions from myotonic dystrophy type 2 patients. Using RNA-FISH and immunofluorescence-FISH methods in combination with a high-resolution confocal microscopy, we demonstrate a different frequency of nuclei containing the CCUG(exp) foci, a different expression pattern of MBNL1 protein and a different sequestration of MBNL1 by CCUG(exp) repeats in skeletal muscle, vascular smooth muscle and endothelia, Schwann cells, adipocytes, and ectodermal derivatives. The level of CCUG(exp) transcription in epidermal and hair sheath cells is lower compared with that in other tissues examined. We suppose that non-muscle tissues of myotonic dystrophy type 2 patients might be affected by a similar molecular mechanism as the skeletal muscle, as suggested by our observation of an aberrant insulin receptor splicing in myotonic dystrophy type 2 adipocytes.

The prevalence of obstructive sleep apnea in patients hospitalized for COPD exacerbation

BACKGROUND The concurrence of obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) is generally identified as an overlap syndrome. Only limited evidence is available on the prevalence of OSA in patients with stable COPD, and essentially no data on the prevalence of OSA in patients hospitalized for COPD exacerbation. The aims of the study were to determine the ratio of concurrence of OSA in patients hospitalized for COPD exacerbation and to identify the confounders of OSA detected in COPD subjects. METHODS 101 patients were hospitalized for COPD exacerbation at the Department of Respiratory Diseases in the course of four months. Seventy-nine consecutive patients were enrolled in the study and in 35 of these subjects polygraphy was performed. Descriptive statistics, Mann-Whitney test, Kruskal-Wallis test, Spearman correlation and Fishers test were used to summarize and evaluate results. RESULTS In 18 (51.4%) subjects with polygraphy examination, the apnea-hypopnea index (AHI) ≥ 5 indicated the presence of OSA. The AHI value, and thus the severity of the sleep disorder, correlated with the class of the Mallampati score, presence of snoring, apnea, coronary heart disease, diabetes mellitus in patients history, height, body mass index, neck, waist and hip circumferences, and the value of the Epworth sleepiness scale. CONCLUSION Polygraphy performed in patients hospitalized for exacerbation of COPD indicated an increased prevalence of OSA compared to the general population and stable COPD patients.