In the field of physiological study of human intelligence, strong evidence of a more efficient operation (i.e., less activation) of the brain in brighter individuals (the neural efficiency hypothesis) can be found. Most studies in this field have used single, homogeneous tasks and have not examined sex differences. In analyzing the extent of Event-related Desynchronization (ERD) in the EEG during the performance of a verbal and a visuo-spatial task, we recently found that males and females display neural efficiency primarily in the domain where they usually perform better (i.e., verbal in females and spatial in males; cf. A.C. Neubauer, A. Fink, D.G. Schrausser, Intelligence and neural efficiency: the influence of task content and sex on brain-IQ relationship. Intelligence, 30 (2002) 515-536). However, this interpretation was complicated by differences in the complexity of the two tasks. By using a verbal (semantic) and a spatial (rotation) task of comparable complexity in this research, we sought to replicate and extend our earlier findings by additionally considering the individual differences in intelligence structure and the topographical distribution over the cortex. Findings were similar to the previous study: Females (n = 35) display neural efficiency (i.e., less brain activation in brighter individuals) primarily during the verbal task, males (n = 31) in the spatial task. However, the strength of this brain activation-IQ relationship varies with the intelligence factor: In males, the highest correlations were observed for spatial IQ, in females for verbal IQ. Furthermore, the sexes displayed topographical differences of neural efficiency patterns.
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http://dx.doi.org/10.1016/j.cogbrainres.2005.05.011 | DOI Listing |
Nat Commun
January 2025
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.
View Article and Find Full Text PDFSci Rep
January 2025
D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India.
Indian agriculture is vital sector in the country's economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers' livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management.
View Article and Find Full Text PDFAnal Chim Acta
March 2025
School of Automation, Central South University, 410083, Changsha, China. Electronic address:
In spectral analysis, selecting the right spectral variables is crucial for effective modeling. It reduces data dimensionality, removes irrelevant wavelength points, and improves both the generalization ability and computational efficiency of the model. However, the number of available samples often falls short of the total possible combinations of wavelengths, making variable selection a non-deterministic polynomial-time (NP) hard optimization problem.
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January 2025
Department of Spine Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University.
Background: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intelligence (AI) has been explored for MRI image recognition in LDH, existing methods often focus solely on disc herniation presence.
View Article and Find Full Text PDFInt J Cardiol
January 2025
Department of Computer Center, Zigong Fourth People's Hospital, Zigong, Sichuan 643000, China.
Background: Acute Stanford Type A aortic dissection (AAD-type A) and acute myocardial infarction (AMI) present with similar symptoms but require distinct treatments. Efficient differentiation is critical due to limited access to radiological equipment in many primary healthcare. This study develops a multimodal deep learning model integrating electrocardiogram (ECG) signals and laboratory indicators to enhance diagnostic accuracy for AAD-type A and AMI.
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