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. Independent component analysis method was employed to analyze functional connectivity variations among three groups both at rest and during the continuous reading task. A whole-brain scanning during task fulfillment and resting states revealed that there were significant differences in the regions including lateral visual, default mode, left frontoparietal, ventral attention, orbitofrontal and lateral motor network. Our results revealed the necessity of adding motor coordination exercises to the training of dyslexic participants and showed that training led to functional connectivity in some brain regions similar to the healthy group. Additionally, our findings confirmed that impulsivity is associated with motor coordination and visuality, and that the dyslexic group has weaknesses in brain connectivity related to these conditions. According to our preliminary results, the differences obtained between children with dyslexia, group of dyslexia with special education and healthy children has revealed the effect of education on brain functions as well as enabling a comprehensive examination of dyslexia.

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. From the NYU dataset, 68 (34 females, 34 males) healthy subjects in the age range of 7-18 years were selected. The female group (mean age: 12.3271±3.1380) and male group (mean age: 11.8766±2.9697) consisted of right-handed, small head motion and similar IQ values. FC was obtained by seed voxel analysis and the effect of low-frequency fluctuations on gender was examined by ALFF and fALFF analyses. Two-sample t-test was used to compare female and male groups with the significance thresholds set to FDR-corrected p<0.05. RESULTS: In the results of our study, both in the ALFF, fALFF analyses and the seed regions belonging to many network regions, higher FC rates were found in girls than boys. Our results show that the females' language functions, visual functions such as object detection and recognition, working memory, executive functions, and episodic memory are more developed than males in this age range. In addition, as another result of our study, the seed regions are statistically stronger where the higher activation of female participants than male participants has concentrated in the left hemisphere. CONCLUSIONS: Gender differences in brain networks should be taken into consideration when examining childhood cognitive and neuropsychiatric disorders and the results should also be evaluated according to gender. Evaluation of gender differences in childhood can increase the likelihood of early and definitive diagnosis and correct treatment for neurological diseases and can help doctors and scientists find new diagnostic tools to discover brain differences.

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 18F-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. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

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. fMRI scans can be used only nine patients in each groups. The findings revealed group differences between cingulate cortex and primary mortor cortex; cingulate cortex and somatosensory association cortex; angular gyrus (AG) and dorsal posterior cingulate cortex, in these networks increased activity was observed in participants with ADHD + CD compared with the ADHD. We found that lower resting state (rs)-activity was observed between left AG and dorsal posterior cingulate cortex, whereas higher rs-activity connectivity were detected between right AG and somatosensory association cortex in ADHD relative to the ones with ADHD + CD. In neuropsyhcological tasks, ADHD + CD group showed poor performance in WISC-R, WCST, Stroop, AVLT tasks compared to TDs. The ADHD + CD group displayed rs-functional abnormalities in DMN. Our results suggest that abnormalities in the intrinsic activity of resting state networks may contribute to the etiology of CD and poor prognosis of ADHD + CD.

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. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.

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. METHODS: MR scans were obtained from 30 subjects (17 males, 13 females) free of any psychiatric, neurological or cognitive impairment. MR images were analyzed by using stereological (point-counting) and semi-automatic segmentation methods. RESULTS: We did not find any significant differences among the subjects with respect to gender using both methods. This study showed no significant asymmetry in subcortical structures according to methods. Also, no significant difference was found between point-counting and semi-automated segmentation methods for the volumes of subcortical structures (p > 0.05). CONCLUSION: From these results, it can be concluded that the semi-automated segmentation method and stereological technique can be used for reliable volume estimation of subcortical structures. However, the stereological method takes less time than semi-automated segmentation; it is simple, reliable and inexpensive. Further studies are required with larger samples in order to support these data.

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. The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist's visual perception. In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades. A high correlation between enzymes and Grey Relational Grades were found. The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers. With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved.

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. Clear differences were detected in the calculated parameters between healthy and cirrhosis patients. It was seen that power spectral graphics and sonograms of portal vein Doppler signals may be used to determine cirrhosis disease.

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). While the first principal component of data matrix acquired from optic nerve patients represents 67.24% of total variance, the first principal component of the macular patients data matrix represents 76.81% of total variance. The basic differences between the two patient groups were obtained with first principal component, obviously. In addition, the graphic of second principal component vs. first principal component of optic nerve and macular subjects was analyzed. The two patient groups were separated clearly from each other without any hesitation. This research developed an auxiliary system for the interpretation of the PERG signals. The stated results show that the use of PCA of physiological waveforms is presented as a powerful method likely to be incorporated in future medical signal processing.