Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research.
The processing advantage for multiword expressions over novel language has long been attested in the literature. However, the evidence pertains almost exclusively to multiword expression processing in adults. Whether or not other populations are sensitive to phrase frequency effects is largely unknown. Here, we sought to address this gap by recording the eye movements of third and fourth graders, as well as adults (first-language Mandarin) as they read phrases varying in frequency embedded in sentence context. We were interested in how phrase frequency, operationalized as phrase type (collocation vs. control) or (continuous) phrase frequency, and age might influence participants’ reading. Adults read collocations and higher frequency phrases consistently faster than control and lower frequency phrases, respectively. Critically, fourth, but not third, graders read collocations and higher frequency phrases faster than control and lower frequency sequences, respectively, although this effect was largely confined to a late measure. Our results reaffirm phrase frequency effects in adults and point to emerging phrase frequency effects in primary school children. The use of eye tracking has further allowed us to tap into early versus late stages of phrasal processing, to explore different areas of interest, and to probe possible differences between phrase frequency conceptualized as a dichotomy versus a continuum.
A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients.
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