Yung-Hui Huang

Effects of long-term practice and task complexity on brain activities when performing abacus-based mental calculations: a PET study

PurposeThe aim of this study was to examine the neural bases for the exceptional mental calculation ability possessed by Chinese abacus experts through PET imaging.MethodsWe compared the different regional cerebral blood flow (rCBF) patterns using 15O-water PET in 10 abacus experts and 12 non-experts while they were performing each of the following three tasks: covert reading, simple addition, and complex contiguous addition. All data collected were analyzed using SPM2 and MNI templates.ResultsFor non-experts during the tasks of simple addition, the observed activation of brain regions were associated with coordination of language (inferior frontal network) and visuospatial processing (left parietal/frontal network). Similar activation patterns but with a larger visuospatial processing involvement were observed during complex contiguous addition tasks, suggesting the recruitment of more visuospatial memory for solving the complex problems. For abacus experts, however, the brain activation patterns showed slight differences when they were performing simple and complex addition tasks, both of which involve visuospatial processing (bilateral parietal/frontal network). These findings supported the notion that the experts were completing all the calculation process on a virtual mental abacus and relying on this same computational strategy in both simple and complex tasks, which required almost no increasing brain workload for solving the latter.ConclusionIn conclusion, after intensive training and practice, the neural pathways in an abacus expert have been connected more effectively for performing the number encoding and retrieval that are required in abacus tasks, resulting in exceptional mental computational ability.

Investigated geometrical characteristics and image density of left ventricle of multi-detector computed tomography in early coronary artery disease patients

BACKGROUNDnCoronary artery disease (CAD) remains the leading cause of death worldwide. Currently, cardiac multi-detector computed tomography (MDCT) is widely used to diagnose CAD. The purpose in this study is to identify informative and useful predictors from left ventricular (LV) in the early CAD patients using cardiac MDCT images.nnnMATERIALS AND METHODSnStudy groups comprised 42 subjects who underwent a screening health examination, including laboratory testing and cardiac angiography by 64-slice MDCT angiography. Two geometrical characteristics and one image density were defined as shape, size and stiffness on MDCT image. The t-test, logistic regression, and receiver operating characteristic curve were applied to assess and identify the significant predictors. The Kappa statistics was used to exam the agreements with physicians judgments (i.e., Golden of True, GOT).nnnRESULTSnThe proposed three characteristics of LV MDCT images are important predictors and risk factors for the early CAD patients. These predictors present over 80% of AUC and higher odds ratio. The Kappa statistics was 0.68 for the combinations of shape and stiffness into logistic regression.nnnCONCLUSIONSnThe shape, size and stiffness of the left ventricular on MDCT can be used to be the effective indicators in the early CAD patients. Besides, the combinations of shape and stiffness into logistic regression could provide substantial agreement with physicians judgments.

Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT

Purpose. Coronary artery calcification (CAC) scores are widely used to determine risk for Coronary Artery Disease (CAD). A CAC score does not have the diagnostic accuracy needed for CAD. This work uses a novel efficient approach to predict CAD in patients with low CAC scores. Materials and Methods. The study group comprised 86 subjects who underwent a screening health examination, including laboratory testing, CAC scanning, and cardiac angiography by 64-slice multidetector computed tomographic angiography. Eleven physiological variables and three personal parameters were investigated in proposed model. Logistic regression was applied to assess the sensitivity, specificity, and accuracy of when using individual variables and CAC score. Meta-analysis combined physiological and personal parameters by logistic regression. Results. The diagnostic sensitivity of the CAC score was 14.3% when the CAC score was ≤30. Sensitivity increased to 57.13% using the proposed model. The statistically significant variables, based on beta values and P values, were family history, LDL-c, blood pressure, HDL-c, age, triglyceride, and cholesterol. Conclusions. The CAC score has low negative predictive value for CAD. This work applied a novel prediction method that uses patient information, including physiological and society parameters. The proposed method increases the accuracy of CAC score for predicting CAD.

Diagnosed chest lesion on diffusion-weighted magnetic resonance images using apparent diffusion coefficients.

PURPOSEnA novel diagnostic method using the standard deviation (SD) value of apparent diffusion coefficient (ADC) by diffusion-weighted (DWI) magnetic resonance imaging (MRI) is applied for differential diagnosis of primary chest cancers, metastatic tumors and benign tumors.nnnMATERIALS AND METHODSnThis retrospective study enrolled 27 patients (20 males, 7 female; age, 15-85; mean age, 68) who had thoracic mass lesions in the last three years and underwent an MRI chest examination at our institution. In total, 29 mass lesions were analyzed using SD of ADC and DWI. Lesions were divided into five groups: Primary lung cancers (Nu200a=u200a10); esophageal cancers (Nu200a=u200a5); metastatic tumors (Nu200a=u200a8); benign tumors (Nu200a=u200a3); and inflammatory lesions (Nu200a=u200a3). Quantitative assessment of MRI parameters of mass lesions was performed. The ADC value was acquired based on the average of the entire tumor area. The error-plot, t-test and the area under receiver operating characteristic (AUC) were applied for statistical analysis.nnnRESULTSnThe SD of ADC value (mean±SD) was (4.867±1.359)×10-4 mm2/sec in primary lung cancers, and (3.598±0.350)×10-4 mm2/sec in metastatic tumors. The SD of ADC values of primary lung cancers and metastatic tumors (Pu200a< u200a0.05) were significantly different and the AUC was 0.800 (Pu200a< u200a0.05). The means of SD of ADC values was 4.532±1.406×10-4 mm2/sec and 2.973±0.364×10-4 mm2/sec for malignant tumors (including primary lung cancers, esophageal cancers) and benign tumors with respectively. The mean of SD of ADC values between malignant chest tumors and benign chest tumors was shown significant difference (Pu200a< u200a0.01). The values of AUC was 0.967 between malignant chest tumors and benign chest tumors (Pu200a< u200a0.05). The ADC values for primary lung cancers, metastatic tumors and benign tumors were not significantly difference (Pu200a> u200a0.05).nnnCONCLUSIONSnThe mean of SD of ADC value by DWI can be used for differential diagnosis of chest lesions.