Aim: The aim of this study was to provide benchmark normal values for Addenbrooke’s cognitive examination (ACE-R) and its domains for the Czech population. Methods: The study included 143 healthy subjects (89 women and 54 men) aged 55–89 years, without brain injury, neurodegenerative disease, severe hearing or visual impairment and without a psychiatric disease, with MMSE above 27 points. Participants were interviewed in detail to ascertain any previous brain injury history and to determine the level of self-suffi ciency in daily living activities. Individuals with a history of brain injury or impaired self-suffi ciency were excluded from the study. ACE-R values and values for its domains in men and women were compared with Mann-Whitney test and values for the four age and education groups were compared using the Kruskal-Wallis test. P-values were corrected for multiple testing using Bonferroni correction. Results: Cut-off scores were set at 2 and 7 percentile. Negative correlation with age (p < 0.001, r = –0.43) and positive correlation with education (p < 0.001, r = 0.41) were statistically signifi cant for the overall test performance and also for the performance in individual cognitive domains except for the Attention and orientation domain. The eff ect of sex was not statistically signifi cant. The cut-off score for the total score in Addenbrooke’s cognitive examination for all subjects aged 55–89 years is 74 points at the 2 percentile and 79 points at the 7 percentile. Conclusion: The study suggests cut-off scores for the Czech population of ACE-R and provides the basis for the development of Czech norms for this test. Práce byla podpořena grantem IGA NT13499. Autoři deklarují, že v souvislosti s předmětem studie nemají žádné komerční zájmy. The authors declare they have no potential confl icts of interest concerning drugs, products, or services used in the study. Redakční rada potvrzuje, že rukopis práce splnil ICMJE kritéria pro publikace zasílané do biomedicínských časopisů. The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers. D. Beránková1–3, P. Krulová1,4, M. Mračková2,5, I. Eliášová2,5, M. Košťálová2,6, E. Janoušová7, I. Stehnová5, M. Bar1, P. Ressner1, P. Nilius1, M. Tomagová4,8, I. Rektorová2,5 1 Centrum pro kognitivní poruchy, Neurologická klinika LF OU a FN Ostrava 2 Výzkumná skupina Aplikované neurovědy, CEITEC – Středoevropský technologický institut MU, Brno 3 Katedra rehabilitace, LF OU v Ostravě 4 Ústav ošetřovatelství a porodní asistence, LF OU v Ostravě 5 Neurologická klinika LF MU a FN u sv. Anny v Brně 6 Neurologická klinika LF MU a FN Brno 7 Institut biostatistiky a analýz, LF MU, Brno 8 Ústav ošetrovateľstva, JLF UK v Bratislave  PhDr. Dagmar Beránková Neurologická klinika LF OU a FN Ostrava 17. listopadu 1790 708 52 Ostrava-Poruba e-mail: dagmar.berankova@fno.cz Přijato k recenzi: 8. 4. 2014 Přijato do tisku: 16. 3. 2015

The most challenging task in treating the Clostridium difficile colitis (CDC) is to deal with its fulminant form. It is often non-responding to antibiotics and, upon recurrence, necessitates surgical treatment. The primary aim of our prospective research was to evaluate surgical treatment results in patients with severe CDC in the period of 2008-2014, determining risk factors leading to serious postoperative morbidity and mortality.Our secondary objective was to assess the success of faecal microbiota transplant (FMT) treatment of the recurrent colitis caused by Clostridium difficile in the period of 2010-2014.METHODS. During 2008-2014, Clostridial toxins were detected in 1956 patients at the University Hospital Brno. From them, 37 patients underwent surgery for a severe form of colitis. The Fisher exact test and Mann-Whitney test were used to evaluate factors affecting increased mortality and incidence of serious postoperative complications. Factors affecting overall survival were assessed using the Log-rank test.From 2010 to 2014, there were 80 patients with CDC recurrence enrolled and treated with FMT at the Department of InfectiousTRANSPLANTDiseases, University Hospital Brno.RESULTS. Factors that were proven statistically significant to increase the mortality and incidence of serious postoperative complications included: Mental status changes before the surgery (p=0,008), the albumin level on the day of surgery ≤20 g/l (p=0,005) and the total serum proteins level on the day of surgery ≤45 g/l (p=0,037). Statistically significant factors negatively affecting overall survival were found to be these: circulatory instability before surgery (p-value=0,035), mental status changes or artificial lung ventilation with pharmacological attenuation of consciousness before surgery (p=0,025), CRP value on the day of surgery >75 mg/l (p=0,034), the albumin level on the day of surgery ≤18,5 g/l (p=0,007), blood urea on the day of surgery >10 mmol/l (p=0,019) and the serum creatinine on the day of surgery >120 μmol/l (p-value=0,004). Thirty-day mortality reached nearly 35%, morbidity climbed up to 89%, and the 90-day mortality was 54%.A total of 80 patients were treated for recurrent CDC with FMT and the success rate of the method was 83,1%.CONCLUSION. Early and accurate surgical intervention in the fulminant form of CDC improves significantly prognosis of patients. FMT is an effective and safe method for treatment of the recurrent form of Clostridium colitis.

BACKGROUND:The genome of multiple myeloma (MM) clonal plasma cells is characterized by genetic changes of prognostic importance. Disease progression is accompanied by a number of secondary chromosomal aberrations including chromosome 8. We focused on the detection of chromosome 8 aberrations in patients with MM who were examined at 2 different phases: diagnosis and progression/relapse.PATIENTS AND METHODS:A total of 62 patients with MM were examined at the time of diagnosis and at relapse/progression. The median age was 64 years (range, 39-78 years); the study included 29 males and 33 females. We analyzed bone marrow samples for detecting aberrations on chromosome 8 by the fluorescence immunophenotyping and interphase cytogenetics as a tool for the investigation of neoplasms (FICTION) and fluorescence in situ hybridization methods with specific probes.RESULTS:Chromosome 8 aberrations were detected in 24 (38.7%) patients at diagnosis and in 29 (46.8%) patients at progression/relapse. Only 5 (8%) patients developed additional chromosome 8 changes at progression/relapse. The aberrations were heterogeneous, involving numerical and structural changes of the MYC gene. Aberrations of the short arm of chromosome 8, involving the genes TRAIL-R1/-R2, were less frequent (4 of 62 patients, 6.4%). All aberrations of chromosome 8 were accompanied with additional changes and with an advanced clinical phase of the disease. This finding significantly influenced the overall survival of patients.CONCLUSION:In the current study, chromosome 8 aberrations were highly heterogeneous, were presented at diagnosis in patients with advanced clinical stage, and were associated with worse overall survival. We have not confirmed the increase of frequency aberration of chromosome 8 in disease progression. The findings demonstrate the importance of fluorescence in situ hybridization examination of chromosome 8 in newly diagnosed patients with MM.

Computer-aided schizophrenia diagnosis is a difficult task that has been developing for last decades. Since traditional classifiers have not reached sufficient sensitivity and specificity, another possible way is combining the classifiers in ensembles. In this paper, we take advantage of random subspace ensemble method and combine it with multi-layer perceptron (MLP) and support vector machines (SVM). Our experiment employs voxel-based morphometry to extract the grey matter densities from 52 images of first-episode schizophrenia patients and 52 healthy controls. MLP and SVM are adapted on random feature vectors taken from predefined feature pool and the classification results are based on their voting. Random feature ensemble method improved prediction of schizophrenia when short input feature vector (100 features) was used, however the performance was comparable with single classifiers based on bigger input feature vector (1000 and 10000 features).

Event Abstract Back to Event Recognition of First-episode Schizophrenia from MRI Data with the Use of Artificial Neural Networks Roman Vyskovsky1, 2, Daniel Schwarz1*, Eva Janousova1 and Tomas Kasparek3 1 Masaryk University, Institute of Biostatistics and Analyses, Czechia 2 Masaryk University, Research Centre for Toxic Compounds in the Environment, Czechia 3 Masaryk University and University Hospital Brno, Department of Psychiatry, Czechia INTRODUCTION Schizophrenia as a severe disabling psychiatry disorder. If the neuroscientific community invented an algorithm that could find the information about first-episode schizophrenia (FES) in the brain images, it could help to objectively establish the diagnosis as soon as the patient’s brain is scanned in the particular imaging device. Early diagnosis would help to tackle the symptoms threatening the patients and their surroundings in the early stage of the disease by deployment of antipsychotics. Since modern computers provide sufficient power to calculate even computationally expensive tasks, self-adaptive models such as neural networks get into the foreground. We explore three types of neural networks – multilayer perceptron (MLP), radial basis function network (RBF), learning vector quantization network (LVQ) initialized using Kohonen’s self-organizing map [1] – for a classification of first-episode schizophrenia based on structural MRI. METHODS The dataset used consisted of 52 patients and 52 healthy control subjects structural MR images acquired on 1.5 T magnetic resonance imaging device Siemens at University Hospital Brno. All the images were preprocessed using optimized voxel-based morphometry in several steps [2]: correction for bias-field inhomogeneity, spatial normalization and segmentation, modulation and Gaussian smoothing. Only the gray matter densities were extracted and used to adapt the models. To further reduce the data dimensionality, two-sample t-tests were applied and only the several lengths of the most significant voxels that carry the information about grey matter densities were used to construct feature vectors. The extracted and selected features provided the information for the adaptation of the artificial neural networks (ANNs) and support vector machines (SVM) that were used as a reference method. The power of all the network types is directly affected by their parameters’ settings. Although the setting of weights is optimized during the training phase, other parameters such as the number of input and hidden neurons, the spread of RBF neurons and LVQ learning algorithms must be specified by the modeler. We explored several of these parameters and observed how they can affect the classifiers’ performance measures – overall accuracy (OA), sensitivity (Sen) and specificity (Spe). RESULTS As expected, the settings of the explored parameters matter. The most successful architectures among the investigated range of parameter settings of each neural network type were as follows: MLP which consisted of 700 input neurons and 10 hidden neurons had OA 0.70 (Sen 0.68, Spe 0.72), RBF network with 600 input neurons, 10 hidden neurons and spread equal to 3 reached OA 0.76 (Sen 0.77, Spe 0.75), LVQ network with 700 input and 10 hidden neurons adapted by LVQ 1 learning algorithm achieved OA 0.67 (Sen 0.72, Spe 0.63) and by LVQ2.1 learning algorithm achieved OA 0.69 (Sen 0.73, Spe 0.65). Since SVM reached only OA 0.64 (Sen 0.56, Spe 0.71), all the ANN types improved diagnosis of FES compared to this commonly used method. Although the success of the classifiers differs, McNemar’s test revealed significant difference only between RBF network and SVM (p = 0.02) in behalf of the former one. CONCLUSION Artificial neural networks are tools that can keep up with or can be even better than the traditional method for pattern recognition which is SVM and therefore, they can help with computer-aided schizophrenia diagnosis. The accuracy required for potential clinical practice in psychiatry has not been reached here though and hence our further work will focus on ensemble learning and deep learning methods that can potentially improve the classification performance. References [1] T. Kohonen, Self-organizing maps, 3rd ed. Berlin ; New York: Springer, 2001. [2] C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. Henson, K. J. Friston, and R. S. Frackowiak, “A voxel-based morphometric study of ageing in 465 normal adult human brains,” Neuroimage, vol. 14, no. 1 Pt 1, pp. 21–36, Jul. 2001. Keywords: MRI, Schizophrenia, artificial neural networks, Classification, Computer-aided diagnostics Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016. Presentation Type: Poster Presentation in SAN2016 Conference Topic: Posters Citation: Vyskovsky R, Schwarz D, Janousova E and Kasparek T (2016). Recognition of First-episode Schizophrenia from MRI Data with the Use of Artificial Neural Networks. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00073 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Jul 2016; Published Online: 01 Aug 2016. * Correspondence: Prof. Daniel Schwarz, Masaryk University, Institute of Biostatistics and Analyses, Brno, Czechia, schwarz@iba.muni.cz Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Roman Vyskovsky Daniel Schwarz Eva Janousova Tomas Kasparek Google Roman Vyskovsky Daniel Schwarz Eva Janousova Tomas Kasparek Google Scholar Roman Vyskovsky Daniel Schwarz Eva Janousova Tomas Kasparek PubMed Roman Vyskovsky Daniel Schwarz Eva Janousova Tomas Kasparek Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Background: Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment. Objectives: We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination.Methods: Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression.Results: The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 73%.Conclusions: Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period. (C) 2016 Elsevier Ltd. All rights reserved.

We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies.

Aim: Deep brain stimulation of subthalamic nucleus (DBS STN) is considered to be a clinically established treatment method to manage the symptoms of advanced stage Parkinson's disease. Despite the strict inclusion criteria, it may have negative impact on the quality of cognitive functions. Our research aimed to identify predictive neuropsychological factors that signal risk of postoperative deterioration of cognitive functions before the DBS STN. Patients and methods: Forty-six patients with idiopathic Parkinson's disease were included in the study (mean age at the time of operation 59.61 years; SD = 7.06). The patients were examined by a neuropsychologist before and after the DBS STN implantation. The neuropsychological test battery included Wechsler Adult Intelligence Scale short form, Mattis Dementia Rating Scale, Word list, Rey-Oster-rieth Complex Figure Test, Stroop Colour Word Test and verbal fluency tests. Results: The quality of visual memory proved to be a sensitive predictive factor related to risk of cognitive changes after DBS STN implantation, as before the implantation the level of visual memory statistically negatively cor responded to impairment of cognitive performance in neuropsychological tests after the implantation. Conclusion: Low performance in the area of visual memory before the implantation may predict an increased risk of cognitive deterioration after the implantation of DBS STN in Parkinson's disease. We assume that the changes in visual memory reflect progression of degenerative process in Parkinson's disease into other brain areas away from the frontostriatal circuit, mainly to posterior temporoparietal areas.

Objective: The aim of this study was to assess clinical and electrophysiological differences within a group of patients with magnetic-resonance-imaging-negative temporal lobe epilepsy (MRI-negative TLE) according to seizure onset zone (SOZ) localization in invasive EEG (IEEG).Methods: According to SOZ localization in IEEG, 20 patients with MRI-negative TLE were divided into either having mesial SOZ-mesial MRI-negative TLE or neocortical SOZ-neocortical MRI-negative TLE. We evaluated for differences between these groups in demographic data, localization of interictal epileptiform discharges (IEDs), and the ictal onset pattern in semiinvasive EEG and in ictal semiology.Results: Thirteen of the 20 patients (65%) had mesial MRI-negative TLE and 7 of the 20 patients (35%) had neocortical MRI-negative TLE. The differences between mesial MRI-negative TLE and neocortical MRI-negative TLE were identified in the distribution of IEDs and in the ictal onset pattern in semiinvasive EEG. The patients with neocortical MRI-negative TLE tended to have more IEDs localized outside the anterotemporal region (p = 0.031) and more seizures without clear lateralization of ictal activity (p = 0.044). No other differences regarding demographic data, seizure semiology, surgical outcome, or histopathological findings were found.Conclusions: According to the localization of the SOZ, MRI-negative TLE had two subgroups: mesial MRI-negative TLE and neocortical MRI-negative TLE. The groups could be partially distinguished by an analysis of their noninvasive data (distribution of IEDs and lateralization of ictal activity). This differentiation might have an impact on the surgical approach. 2016 Elsevier Inc. All rights reserved.

PURPOSE OF THE STUDY Scapholunate dissociation is a clinically most frequently diagnosed form of carpal instability. The aim of this study was to compare high resolution MRI using a microscopic coil with direct MRI arthrography in patients with suspected scapholunate ligament lesions and compare the results with arthroscopy findings and Geissler's arthroscopy classification. MATERIAL AND METHODS A prospective study was carried out in 47 patients (average age, 30.7 years) with clinical symptoms of wrist instability from 2013 to 2014. The patients were examined with the MR device Philips Achieva 1.5T using a microscopic coil and subsequently by direct MR arthrography. The results of examination were evaluated independently by two groups of physicians using a modified arthroscopic classification. The results were verified arthroscopically. For evaluation, an adjusted Geissler's classification was used. The study was approved by the Multicentre Ethics Committee of the Faculty of Medicine in Brno and informed consent was obtained from each patient. RESULTS A total of The MRI examination was evaluated and included in the study in 44 patients (three were excluded for the presence of motion artefacts). Only 20 patients underwent arthroscopy. Examination with a microscopic coil correctly classified 14 of them; an accuracy of 70 % (95 % CI: 45.7 % - 88.1 %) and p = 0.021. Direct MR arthrography correctly classified 16 of 20 injured ligaments, i.e., an accuracy of 80 % (95 % CI: 56.3 % - 94.3 %) and p = 0.002. DISCUSSION Currently, the diagnosis of pathological changes in the wrist is made by routine MRI especially when there is the possibility of using sequences with high spatial resolution. Even though we achieved poorer results by native examination using these techniques, when they were compared with the results of direct MR arthrography, they were still better than those reported in the recent literature. CONCLUSION The optimal method for an examination algorithm of scapholunate ligament lesions is direct MR arthrography. In our study correct findings of direct MR arthrography using Geissler's classification were shown in 80 % of the patients. Key words: scapholunate ligament, scapholunate ligament lesion, direct MR arthrography, microscopic coil, Geissler's classification.

The aim of this study was to investigate the effects of rTMS on cognitive functions in patients with mild cognitive impairment and Alzheimer’s disease (MCI/AD) and assess the effect of gray matter (GM) atrophy on stimulation outcomes. Twenty MCI/AD patients participated in the placebo-controlled study. Each patient received 3 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. 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). As compared to the VTX stimulation, patients significantly improved after both IFG and STG stimulation. The amount of atrophy in MCI/AD patients correlated with the change in ST-W scores after rTMS of the STG. We demonstrated for the first time that GM atrophy in MCI/AD diminishes the cognitive effects induced by rTMS of the temporal neocortex.

This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.

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.

Simultaneous EEG-fMRI is increasingly used for the noninvasive pre-surgical evaluation of epileptic patients to localize the epileptogenic zone. In this retrospective study of EEG-fMRI data in patients with pharmacoresistant epilepsy, we compared a wide range of data processing strategies using validation with resection masks after successful epilepsy surgery. The aims of this study were to find how various data processing strategies influence EEG-fMRI results, and to identify the best approach for data processing. Thirteen subjects (9 F, 4 M) with pharmacoresistant epilepsy and good outcome after epilepsy surgery were included in the study. Simultaneous EEG-fMRI data (1.5 T scanner) was acquired before the surgery (300 scans per session, TR = 3 s). The position of interictal discharges (IED) was marked and used as onsets for event-related regressors in statistical model. SPM8 software was used for data processing. In total, 240 statistical analyses were calculated for each subject comprising all possible combinations of the used variants of preprocessing and GLM settings. The resection mask was created individually for each patient using clinical MR images acquired 3 months after the surgery. Several parameters (e.g. sensitivity, cosine criterion) were calculated for each dataset and processing pipeline to evaluate the concordance between spike-informed EEG-fMRI results and the resection mask. Multivariate statistical analysis was performed in SPSS software. We found that the preprocessing type (mainly basic pipeline vs. correction for cardiac artifact) does not affect the results. The study revealed two main findings. The first is the optimal processing pipeline – only canonical HRF as a basis function, IED stimulation time series shifted 2 s earlier than positions from EEG description, and massive filtering of artifact (24 movement regressors, signals from white matter and CSF, and global signal). The second finding is related to a more general understanding of the influence of various processing options on results. The superiority of canonical HRF over more flexible basis functions is probably due to our concordance measure, which is based on a single epileptic focus represented with a resection mask, and some type of similarity between the activation map and the mask. The finding of earlier BOLD responses is in concordance with the predominantly prespiking character of the BOLD response presented in previous studies.

Aim:To develop and validate a new, in the Czech language still missing test of functional communication for patients with aphasia. Methods: Functional Communication Questionnaire (FCQ) comprises 20 items that evaluate communication in four areas of real situations: I. Basal communication, IL Social communication, Ill. Reading and writing, and IV. Calculation and orientation. Every item is evaluated on 6-degree scale (0-5 points), and the sum (Functional Communication Index - FCI) of 100 points represents maximum possible value. FCQ was validated in groups of healthy volunteers (n = 110, median age 63 years), patients with aphasia (n = 38, median age 62 years), and patients with Alzheimer dementia (AD) (n = 8, median age 81.5 years). Results: Values of FCI correlated significantly with age (Spearman r = -0.354, p = 0.000148); and different normal limits of FCI were established for decades: 50-59 years: >90; 60-69 years: > 85; 70-79 years: > 76. Using ROC analysis we confirmed high diagnostic validity of FCQ in discrimination between healthy controls and patients with aphasia (sensitivity of 89.5% and specificity 01 93.6% for cut-off value 86.5; AUC = 0.974; p <0.001). Degree of functional communication impairment quantified with FCI in patients with aphasia correlated with the degree of language deficit quantified with MASTcz, while there was no significant difference in FCI values between patients with aphasia and AD. Repeated evaluation in 10 patients with aphasia proved very high test-retest reliability of FCQ. New therapeutic material based on FCQ was finally introduced into clinical practice. Conclusion: FCQ extends the repertoire of diagnostic tests for patients with aphasia available in the Czech language. Our results showed very good psychometric characteristics of FCQ that should be confirmed by further research.

This article 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 $100.0\pm0.0\,\%$) 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 Pr{\'i}ncipe de Asturias (PdA) Hospital in Alcal{\'a} de Henares of Madrid database we made improvements in classification accuracy ($82.1\pm3.3\,\%$) and specificity ($83.8\pm5.1\,\%$) 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 ($p = 6.9443\cdot10^{-32}$) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The article 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.

Cil: Na zakladě existujicich cizojazycných testů sestavit a validovat vlastni, v ceskem jazyce dosud chybějici test funkcionalni komunikace u pacientů s afazii. Metodika: Byl vytvořen Dotaznik funkcionalni komunikace (DFK), který hodnoti stav komunikace v realných situacich pomoci 20 položek v oblastech: I. bazalni komunikace, II. socialni komunikace, III. cteni a psani a IV. cisla a orientace; každa položka je hodnocena sestistupňovou skalou (0– 5 bodů). Maximalni možna hodnota Indexu funkcionalni komunikace (Index FK) dosahuje 100 bodů. DFK byl validovan na souborech zdravých dobrovolniků (n = 110, median věku 63 let), pa cientů s afazii (n = 38, median věku 62 let) a s Alzheimerovou demenci (n = 8, median věku 81,5 let). Výsledky: Hodnoty Indexu FK korelovaly významně s věkem (Spearman r = – 0,354; p = 0,000148), proto byly stanoveny normativni hodnoty Indexu FK pro věk: 50– 59 let: u003e 90; 60– 69 let: u003e 85; 70 a vice let: u003e 76. Pomoci ROC analýzy byla potvrzena vysoka diagnosticka validita DFK v diskriminaci mezi normalnimi jedinci a pa cienty s afazii (senzitivita při cut-off hodnotě Indexu FK 86,5 je 89,5 % a specifi cita je 93,6 %; AUC = 0,974; p u003c 0,001). Hodnoty Indexu FK u pa cientů s afazii významně korelovaly se stupněm jazykoveho defi citu vyjadřeneho testem MASTcz a nezjistili jsme rozdil oproti hodnotam Indexu FK u pa cientů s poruchami komunikace u Alzheimerovy demence. Potvrdili jsme test- retest reliabilitu opakovaným vysetřenim 10 pacientů s afazii. Byl vytvořen terapeutický material, který navazuje na položky DFK. Zavěr: DFK je nový validovaný test v ceskem jazyce, který doplňuje paletu dia gnostických nastrojů pro pa cienty s afazii. Výzkum naznacil jeho výborne psychometricke vlastnosti, jež je třeba ověřit na větsich souborech.