Purpose: Expertise is the product of training. Few studies have used functional connectivity or conventional diffusometric methods to identify neural underpinnings of chess expertise. Diffusometric variables of white matter might reflect these adaptive changes, along with changes in structural connectivity, which is a sensitive measure of microstructural changes. Method: Diffusometric variables of 29 professional chess players and 29 age-sex matched controls were extracted for white matter regions based on John Hopkin's Mori white matter atlas and partially correlated against professional training time and level of chess proficiency. Diffusion MRI connectometry was implemented to identify changes in structural connectivity in professional players compared to novices. Result: Compared to novices, higher planar anisotropy (CP) was observed in inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) and cingulate gyrus, in professional chess players, which correlated with higher RPM score in this group. Higher fractional anisotropy (FA) was observed in ILF, uncinate fasciculus (UF) and hippocampus and correlated with better scores in Raven's progressive matrices (RPM) score and longer duration of chess training in professional players. Consistently, radial diffusivity in bilateral IFOF, bilateral ILF and bilateral SLF was inversely correlated with level of training in professional players. DMRI connectometry analysis identified increased connectivity in bilateral UF, bilateral IFOF, bilateral cingulum, and corpus callosum in chess player's compared to controls. Conclusion: Structural connectivity of major associational subcortical white matter fibers are increased in professional chess players. FA and CP of ILF, SLF and UF directly correlates with duration of professional training and RPM score, in professional chess players.
The neuroanatomical characteristics of the brain exhibit variations between females and males, encompassing both healthy and pathological conditions [1].Bethlehem et al. (2022) recently developed a human brain chart based on Magnetic Resonance Imaging (MRI) data from over 100,000 participants ranging in age from 115 days post-conception to 100 years [2].This study discovered that males and females have significantly different brain tissue volumes throughout their lives and that these differences can also be seen in the brain growth patterns of people with psychiatric and neurologic conditions.Based on this growth chart trajectory, males have larger brain tissue volumes and more significant variance across MRI phenotypes, compared to females.It is critical to understand the effects of biological sex on brain development as it can significantly affect the physical and mental health of different psychiatric and neurologic patients.Numerous studies have been conducted to compare brain volumes between healthy males and females.These studies have consistently found that males tend to have larger Total Cerebral Volume (TCV), total Grey Matter Volume (GMV), subcortical Grey Matter Volume (sGMV), total White Matter Volume (WMV), and Cerebrospinal Fluid Volume (CSFV).On the other hand, females generally demonstrate a higher mean Cortical Thickness (CTh) [3].Abe et al. ( 2010) examined a population of individuals ranging in age from 21 to 71, revealing that males possess larger total GMV and WMV [4].Additionally, males exhibited higher rates of decline in GMV and WMV with aging compared to females.This suggests that males and females have distinct brain structures, and the patterns of age-related brain changes are also different.The precise etiology behind the lifelong disparities in brain structure between males and females remains incompletely understood.Emerging evidence suggests that these variations may have developmental origins.Certain investigations have identified different phenotypes in male and female brain cells, indicating a critical role of sex chromosomes in the emergence of sex-based brain differences.For instance, in vitro research has revealed that embryonal brain cells with XX and XY chromosomal configurations exhibit divergent behaviors [5].XY cell cultures demonstrate a propensity to yield a higher number of dopamine neurons compared to their XX counterparts [5].This emphasizes the genetic origin of the sex differences in brain structure.Consistent with this perspective, male infants exhibit larger total GMV and WMV compared to their female counterparts [6].Moreover, these discrepancies persist into puberty, with males exhibiting greater total GMV, WMV, and TCV [7].These findings underscore the significance of sex chromosomes in the development and perpetuation of brain differences between males and females.While numerous studies have provided substantial evidence supporting the genetic origin of brain differences between males and females, a considerable body of research has demonstrated significant effects of sex hormones on brain development.Previous research revealed that testosterone acts as a human fetal brain programming system, leading to fetal dimorphism in brain structure and function
Abstract Diffusion tensor imaging (DTI) has revolutionized our understanding of the neural underpinnings of alcohol teratogenesis. This technique can detect alterations in white matter in neurodevelopmental disorders, such as fetal alcohol spectrum disorder (FASD). Using Prisma guidelines, we identified 23 DTI studies conducted on individuals with prenatal alcohol exposure (PAE). These studies confirm the widespread nature of brain damage in PAE by reporting diffusivity alterations in commissural, association, and projection fibers; and in relation to increasing cognitive impairment. Reduced integrity in terms of lower fractional anisotropy (FA) and higher mean diffusivity (MD) and radial diffusivity (RD) is reported more consistently in the corpus callosum, cerebellar peduncles, cingulum, and longitudinal fasciculi connecting frontal and temporoparietal regions. Although these interesting results provide insight into FASD neuropathology, it is important to investigate the clinical diversity of this disorder for better treatment options and prediction of progression. The aim of this review is to provide a summary of different patterns of neural structure between PAE and typically developed individuals. We further discuss the association of alterations in diffusivity with demographic features and symptomatology of PAE. With the accumulated knowledge of the neural correlates of FASD presenting symptoms, a comprehensive understanding of the heterogeneity in FASD will potentially improve the disease management and will highlight the diagnostic challenges and potential areas of future research avenues, where neural markers may be beneficial.
Background: Accurate characterization of benign and malignant ovarian cancers plays a critical role in decision making about the therapeutic strategy, treatment monitoring, and could highly affect the treatment outcome. In this context, dynamic contrast enhanced (DCE-) MRI has evolved into a helpful imaging technique in distinguishing complex adnexal masses by providing noninvasive and quantitative biomarkers of tumor progression. Reliable prediction of malignancy in complex adnexal masses depends on proper selection of quantitative DCE-MRI descriptive parameters and their cutoff points, which the latter is commonly carried out by threshold criteria. Objectives: In this work, we exploited an unsupervised, non-parametric clustering algorithm, which does not require any prior or expert knowledge about the thresholds to select the optimal predictor parameters, followed by introducing a classification decision-tree for accurate differentiation of malignant from benign ovarian tumors. Patients and Methods: Data Acquisition: Twenty-two patients diagnosed with solid or solid/cystic complex ovarian masses (12 benign and 10 malignant as identified with histological assessment) underwent DCE-MR imaging on a 3T MR scanner (Siemens MAGNETOM Tim TRIO) using a surface phased-array coil, TE/TR = 1.74/5 ms, flip angle = 60, image matrix = 156x192, FOV = 23x23 cm2, slice thickness = 5 mm, number of measurements = 52 at 6 s/volume, number of slices = 16. The acquisition was performed before and immediately after injection of 0.2mL/kg of Gadolinium (DOTAREM; Guerbet, Aulnay, France), followed by injection of 20 cc normal saline solution with 3 mL/min injection rate. Pre-processing: All images were corrected for motion artifacts, using an efficient non-rigid image registration approach in a groupwise setting. Data Quantification: The regions-of-interest (ROIs) were placed on the solid part of tumors and within the adjacent psoas (as an internal reference). Several semi-quantitative parameters were used for further analysis and clustering of the signal intensity curves: SImax = maximum signal intensity of tumor to that of psoas, TTP: Time-to-Peak, Wash-in-Rate (WIR) = (SImax-SI0)/TTP, IAUC60 = initial area under the time-intensity curve during the first 60 seconds in tumor to that of psoas. Clustering: Clustering was performed for each descriptive parameter, using unsupervised Hierarchical Clustering (HC) with Wards linkage method, before and after registration, to both determine the best descriptive parameters for diagnosing malignant from benign tumors and evaluate the effects of registration on the outcome of diagnosis. Results: The box-and-whisker plots for TTP, SImax, WIR, and IAUC60 for both benign and malignant tumors were shown. TTP and WIR parameters led to none and small overlaps between enhancement characteristics of benign and malignant tumors, respectively, suggesting their reliability in distinguishing cancer types. The sensitivity and specificity of each parameter in diagnosing malignancy in complex ovarian cancers are summarized. As it can be inferred, WIR parameter returns a sensitivity of 100% in distinguishing malignant tumors (both before and after registration), and TTP produces the best specificity in comparison with SImax and IAUC60 parameters. In several studies, the early enhancement (TTP) is confirmed to be an indication of malignancy, and WIR is shown to be correlated with the expression of vascular endothelial growth factor (VEGF). Also, it can be observed that registration can significantly improve the outcome of tumor characterization, in the sense that the parameters would become more reliable to characterize the cancer malignancy. In view of these results, WIR and TTP were combined to develop a decision tree for classification of malignant from benign tumors, which generated promising results on the data with 95% of accuracy before and 100% after registration. Conclusions: This result recommends that optimizing the decision approach could compensate for misalignment of data, which is essentially important when proper registration software is not available or feasible in a clinical diagnosis setting. In conclusion, we proposed a decision tree classifier developed through an unsupervised clustering approach, which is unbiased to the threshold values of the parameters and provides a more flexible framework for increasing the positive prediction rate for distinguishing malignant from benign complex ovarian tumors.
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