• MeSH
• Discriminant Analysis MeSH
• Radiation Injuries, Experimental MeSH
• Forecasting MeSH
• Survival MeSH
• Prognosis methods   MeSH
• Dogs MeSH
• Radiobiology methods   MeSH
• Animals MeSH
• Check Tag
• Dogs MeSH
• Animals MeSH

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.

• MeSH
• Discriminant Analysis MeSH
• Adult MeSH
• Humans MeSH
• Linear Models MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain Mapping MeSH
• Young Adult MeSH
• Brain pathology   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Psychiatric Status Rating Scales MeSH
• Schizophrenia diagnosis   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Publication type
• Journal Article MeSH

The hyoid bone is characterized by sexually dimorphic features, enabling it to occasionally be used in the sex determination aspect of establishing the biological profile in skeletal remains. Based on a sample of 298 fused and non-fused hyoid bones, the present paper compares several methodological approaches to sexing human hyoid bones in order to test the legitimacy of osteometrics-based linear discriminant equations and to explore the potentials of symbolic regression and methods of geometric morphometrics. In addition, two sets of published predictive models, one of which originated in an indigenous population, were validated on the studied sample. The results showed that the hyoid shape itself is a moderate sex predictor and a combination of linear measurements is a better representation of sex-related differences. The symbolic regression was shown to exceed the predictive powers of linear discriminant function analysis when two models based on a logistic and step regression reached 96% of correctly classified cases. There was a positive correlation between discriminant scores and an individual's age as the sex assessment was highly skewed in favour of males. This suggests that the human hyoid undergoes age-related modifications which facilitates determination of male bones and complicates determination of females in older individuals. The validation of discriminant equations by Komenda and Černý (1990) and Kindschud et al. (2010) revealed that there are marked inter-population and inter-sample differences which lessened the power to correctly determine female hyoid bones.

• MeSH
• Analysis of Variance MeSH
• Discriminant Analysis MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Linear Models MeSH
• Young Adult MeSH
• Multivariate Analysis MeSH
• Observer Variation MeSH
• Hyoid Bone anatomy & histology   MeSH
• Reproducibility of Results MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Software MeSH
• Forensic Anthropology MeSH
• Sex Determination by Skeleton methods   MeSH
• Imaging, Three-Dimensional MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• Keywords
• Analýza dat, Analýza statistická, Regrese,

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika

BACKGROUND: There is initial evidence suggesting that biomarker neurogranin (Ng) may distinguish Alzheimer's disease (AD) from other neurodegenerative diseases. Therefore, we assessed (a) the discriminant ability of cerebrospinal fluid (CSF) Ng levels to distinguish between AD and frontotemporal lobar degeneration (FTLD) pathology and between different stages within the same disease, (b) the relationship between Ng levels and cognitive performance in both AD and FTLD pathology, and (c) whether CSF Ng levels vary by apolipoprotein E (APOE) polymorphism in the AD continuum. METHODS: Participants with subjective cognitive decline (SCD) (n = 33), amnestic mild cognitive impairment (aMCI) due to AD (n = 109), AD dementia (n = 67), MCI due to FTLD (n = 25), and FTLD dementia (n = 29) were recruited from the Czech Brain Aging Study. One-way analysis of covariance (ANCOVA) assessed Ng levels in diagnostic subgroups. Linear regressions evaluated the relationship between CSF Ng levels, memory scores, and APOE polymorphism. RESULTS: Ng levels were higher in aMCI-AD patients compared to MCI-FTLD (F[1, 134] = 15.16, p < .001), and in AD-dementia compared to FTLD-dementia (F[1, 96] = 4.60, p = .029). Additionally, Ng levels were higher in FTLD-dementia patients compared to MCI-FTLD (F[1, 54]= 4.35, p = .034), lower in SCD participants compared to aMCI-AD (F[1, 142] = 10.72, p = .001) and AD-dementia (F[1, 100] = 20.90, p < .001), and did not differ between SCD participants and MCI-FTLD (F[1, 58]= 1.02, p = .491) or FTLD-dementia (F[1, 62]= 2.27, p = .051). The main effect of diagnosis across the diagnostic subgroups on Aβ1-42/Ng ratio was significant too (F[4, 263]=, p < .001). We found a non-significant association between Ng levels and memory scores overall (β=-0.25, p = .154) or in AD diagnostic subgroups, and non-significant differences in this association between overall AD APOE ε4 carriers and non-carriers (β=-0.32, p = .358). CONCLUSIONS: In this first study to-date to assess MCI and dementia due to AD or FTLD within one study, elevated CSF Ng appears to be an early biomarker of AD-related impairment, but its role as a biomarker appears to diminish after dementia diagnosis, whereby dementia-related underlying processes in AD and FTLD may begin to merge. The Aβ1-42/Ng ratio discriminated AD from FTLD patients better than Ng alone. CSF Ng levels were not related to memory in AD or FTLD, suggesting that Ng may be a marker of the biological signs of disease state rather than cognitive deficits.

• MeSH
• Alzheimer Disease * cerebrospinal fluid   diagnosis   MeSH
• Amyloid beta-Peptides cerebrospinal fluid   MeSH
• Apolipoproteins E genetics   cerebrospinal fluid   MeSH
• Biomarkers * cerebrospinal fluid   MeSH
• Frontotemporal Lobar Degeneration * cerebrospinal fluid   diagnosis   MeSH
• Cognitive Dysfunction * cerebrospinal fluid   diagnosis   MeSH
• Middle Aged MeSH
• Humans MeSH
• Neurogranin * cerebrospinal fluid   MeSH
• Neuropsychological Tests MeSH
• Cross-Sectional Studies MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Cíle: Cílem této studie bylo zhodnotit analýzu textury (AT) na snímcích MR před podáním kontrastní látky z hlediska zlepšení přesnosti a rozlišení jemných rozdílů mezi enhancujícími lézemi (EL), neenhancujícími lézemi (NEL) a perzistentními černými dírami (persistant black holes; PBH). Materiál a metodika: Databáze zobrazení MR zahrnovala 90 pacientů, z nichž 30 mělo pouze PBH, 25 mělo pouze EL a 35 nemělo ani EL ani PBH. Tato zobrazení byla zhodnocena pomocí navrhované metody AT. Bylo extrahováno na 300 statistických texturních znaků jako deskriptorů každého ROI/léze. Byly analyzovány rozdíly mezi skupinami lézí a byla změřena plocha pod křivkou (Az) pro každý významný texturní znak. K analýze signifikantních znaků a ke zvýšení síly odlišení byla použita lineární diskriminantní analýza (LDA). Výsledky: Nejméně 14 texturních znaků prokázalo významný rozdíl mezi NEL a EL, NEL a PBH a EL a PBH. Při použití všech významných znaků naznačila LDA slibnou schopnost klasifikace NEL a PBH s hodnotou Az 0,975, která odpovídá senzitivitě 94,3 %, specificitě 96,3 % a přesnosti 95,5 %. U klasifikace EL a NEL (nebo PBH) prokázala LDA diskriminační výkon odpovídající senzitivitě, specificitě a přesnosti 100 % a Az 1. Závěry: AT byla vyhodnocena jako spolehlivá metoda s potenciálem charakterizovat NEL, EL a PBH a jako metoda, kterou mohou lékaři použít k rozlišení NEL, EL a PBH na snímcích MR před podáním kontrastní látky.

Aims: The aim of this study was to evaluate texture analysis (TA) in pre-contrast injection MR images to improve accuracy and to identify subtle differences between enhancing lesions (ELs), non-enhancing lesions (NELs) and persistent black holes (PBHs). Materials and methodology: The MR image database comprised 90 patients; 30 of whom had only PBHs, 25 had only ELs and 35 neither EL or PBH. These were assessed by the proposed TA method. Up to 300 statistical texture features were extracted as descriptors for each ROI/lesion. Differences between the lesion groups were analyzed and evaluations were made for area under the receiver operating characteristic curve (Az) for each significant texture feature. Linear discriminant analysis (LDA) was employed to analyze significant features and increase power of discrimination. Results: At least 14 texture features showed significant difference between NELs and ELs, NELs and PBHs, and ELs and PBHs. By using all significant features, LDA indicated a promising level of performance for classification of NELs and PBHs with Az value of 0.975 that corresponds to sensitivity of 94.3%, specificity of 96.3%, accuracy of 95.5%. In classification of ELs and NELs (or PBH), LDA demonstrated discrimination performance with sensitivity, specificity and accuracy of 100% and Az of 1. Conclusions: TA was determined as a reliable method, with potential for characterization and the method can be applied by physicians to differentiate NELs, ELs and PBH in pre-contrast injection MRI imaging.

• MeSH
• Data Interpretation, Statistical MeSH
• Contrast Media MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain diagnostic imaging   MeSH
• Neurogenic Inflammation MeSH
• Image Processing, Computer-Assisted * classification   MeSH
• ROC Curve MeSH
• Multiple Sclerosis * diagnostic imaging   diagnosis   MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Humans MeSH

• MeSH
• Computer Systems MeSH
• Statistics as Topic MeSH

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika
• lékařská informatika

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.

• MeSH
• Algorithms MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Discriminant Analysis MeSH
• Adult MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Adolescent MeSH
• Young Adult MeSH
• Schizophrenia diagnosis   MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

• MeSH
• Discriminant Analysis MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Mandible * diagnostic imaging   anatomy & histology   MeSH
• Young Adult MeSH
• Tomography, X-Ray Computed MeSH
• Probability * MeSH
• Aged MeSH
• Forensic Anthropology methods   MeSH
• Sex Determination by Skeleton * methods   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic MeSH

Recently, a specific role of nonlinear dynamics and complexity in neural and cognitive processes has been proposed, and there are several reported studies suggesting that smokers might display characteristic changes in the EEG dimensional complexity in comparison to non-smokers. With the aim to extend these findings to autonomic activity, we have examined dimensional complexity in bilateral electrodermal activity (EDA) that reflects limbic modulation influences and may provide information on specific emotional processes related to sympathetic activity. In the present study EDA was assessed in 35 smokers (mean age 23.4, SD=1.4) and 41 non-smokers (mean age 23.2, SD=1.8) during resting conditions. Calculation of dimensional complexity in both groups similarly as in previous reported studies was performed using an algorithm for pointwise correlation dimension (PD2). The results of nonlinear and statistical analysis of EDA records indicate increased complexity during rest conditions (indexed by PD2) in smokers compared to non-smokers (Mann-Whitney test; p<0.01), even though EDA measurement does not discriminate the groups (Mann-Whitney test; p>0.05). These results present a first supportive evidence that EDA complexity may exhibit an electrophysiological marker that could potentially explain the role of complex dynamics in the autonomic nervous system related to smoking habits and addiction.

• MeSH
• Algorithms MeSH
• Adult MeSH
• Electric Stimulation methods   instrumentation   MeSH
• Electroencephalography MeSH
• Financing, Organized MeSH
• Galvanic Skin Response physiology   MeSH
• Data Interpretation, Statistical MeSH
• Cognition physiology   drug effects   MeSH
• Smoking physiopathology   MeSH
• Limbic System physiology   drug effects   MeSH
• Brain physiology   drug effects   MeSH
• Nonlinear Dynamics MeSH
• Attention physiology   drug effects   MeSH
• Reaction Time physiology   MeSH
• Sensitivity and Specificity MeSH
• Case-Control Studies MeSH
• Check Tag
• Adult MeSH
• Male MeSH
• Female MeSH