Data-driven exploratory method investigation on the effect of dyslexia education at brain connectivity in Turkish children: a preliminary study.

Dyslexia is a specific learning disability that is neurobiological in origin and is characterized by reading and/or spelling problems affecting the development of language-related skills. The aim of this study is to reveal functional markers based on dyslexia by examining the functions of brain regions in resting state and reading tasks and to analyze the effects of special education given during the treatment process of dyslexia. A total of 43 children, aged between 7 and 12, whose native language was Turkish, participated in the study in three groups including those diagnosed with dyslexia for the first time, those receiving special education for dyslexia, and healthy children.

Gender-based functional connectivity differences in brain networks in childhood.

Background And Objective: Understanding the effect of gender differences on the brain can provide important information to characterize normal changes throughout life and to increase the likelihood of sex-specific approaches for neurological and psychiatric diseases. In this study, Functional Connectivity (FC), Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) analyzes will be compared between female and male brains between the ages of 7 and 18 years using resting state-functional magnetic resonance imaging (rs-fMRI).

Methods: The rs-fMRI data in this study has been provided by The New York University (NYU) Child Study Center of the publicly shared ADHD200 database.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

We investigated the association between the textural features obtained from F-FDG images, metabolic parameters (SUVmax SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters.

Default mode network activity and neuropsychological profile in male children and adolescents with attention deficit hyperactivity disorder and conduct disorder.

It is known that patients with Attention Deficit and Hyperactivity disorder (ADHD) and Conduct disorder (CD) commonly shows greater symptom severity than those with ADHD alone and worse outcomes. This study researches whether Default mode network (DMN) is altered in adolescents with ADHD + CD, relative to ADHD alone and controls or not. Ten medication-naïve boys with ADHD + CD, ten medication-naïve boys with ADHD and 10-age-matched typically developing (TD) controls underwent functional magnetic resonance imaging (fMRI) scans in the resting state and neuropsychological tasks such as the Wisconsin Card Sorting Test (WCST), Stroop Test TBAG Form (STP), Auditory Verbal learning Test (AVLT), Visual Auditory Digit Span B (VADS B) were applied to all the subjects included.

Automatic segmentation of corpus callosum using Gaussian mixture modeling and Fuzzy C means methods.

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers.

Comparison of two methods for the estimation of subcortical volume and asymmetry using magnetic resonance imaging: a methodological study.

Purpose: The subcortical brain structures are associated with other structures of nervous system; therefore, they have major influence on sensory-motor, limbic and cognitive information processing. Magnetic resonance imaging provides a detailed knowledge of normal and diseased anatomical structures for medical research. The aim of the current study was to compare the volumes of subcortical brain structures and determine the probable volumetric asymmetry in healthy subjects using stereological (point-counting) and semi-automatic segmentation methods.

Quantitative grading using Grey Relational Analysis on ultrasonographic images of a fatty liver.

A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images. We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist. In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma.

Spectral analysing of portal vein Doppler signals in the cirrhosis patients.

In this study, we have researched the efficacy of short-time Fourier transformation (STFT) of Doppler signals from the portal veins of healthy volunteers and cirrhosis patients. Sonogram and power spectral distribution for portal vein Doppler spectral waveform changes in the cirrhosis disease were utilized and these graphics compared with healthy volunteers. Five parameters were used to compare power spectrum graphics.

Classification of macular and optic nerve disease by principal component analysis.

In this study, pattern electroretinography (PERG) signals were obtained by electrophysiological testing devices from 70 subjects. The group consisted of optic nerve and macular diseases subjects. Characterization and interpretation of the physiological PERG signal was done by principal component analysis (PCA).