The medial fronto-parietal network of the macaque: Different specializations for spatial parameters of reaching? by Marina De Vitis, Matteo Filippini, Kostas Hadjidimitrakis, Francesco Edoardo Vaccari, Stefano Diomedi, Michela Gamberini, Martina Gori, Michele Testa, Claudio Galletti, Patrizia Fattori

“…Therefore, it is unknown whether tuning by spatial and motor variables, such as reach direction and depth, is similar in frontal and parietal cortices and whether there are differences in the temporal evolution of activity that reflects functional specializations. To fill this gap, we recorded single-neuron activity from two regions of the medial fronto-parietal circuit in macaques performing an instructed delay reaching task toward targets varying in both direction and depth. …”
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Reward-related neural activation during social media exposure in young women with non-suicidal self-injury: evidence for a continuum of severity in the reward network by Stella Nicolaou, Anna Julià, Daniela Otero, Carlos Schmidt, Juan Carlos Pascual, Joaquim Soler, Josep Marco-Pallarés, Daniel Vega

“…To address this gap, we investigated social media-related reward processing in NSSI by recruiting ninety-one young women, divided into three groups: a clinical group (NSSI with borderline personality disorder), a subclinical group (NSSI without co-occurring disorders), and a healthy control group. While undergoing functional magnetic resonance imaging (fMRI), participants received positive and negative comments on their own Instagram photos in a naturalistic task simulating real-life social media interactions. …”
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Harnessing conversion bridge strategy by organic semiconductor in polymer matrix memristors for high‐performance multi‐modal neuromorphic signal processing by Weijia Dong, Xuan Ji, Chuanbin An, Chenhui Xu, Xuwen Zhang, Bin Zhao, Yuqian Liu, Shiyu Wang, Xi Yu, Xinjun Liu, Yang Han, Yanhou Geng

“…A fully analog RC hardware system is further demonstrated by integrating the distinct volatile and non‐volatile modes of the EDCB‐based memristor into the dynamic neuron network and the linear regression layer of the RC respectively, achieving high accuracy in online arrhythmia detection tasks. Our work paves the way for high‐yield organic memristors with mechanical flexibility, advancing efficient multi‐mode neuromorphic computing within a unified memristor system integrating volatile and non‐volatile functionalities.…”
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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…The module applies a learnable activation function at the edge of the network, which not only reduces the number of model parameters but also significantly improves the generalization performance of the network. …”
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Individualising Galvanic Vestibular Stimulation Further Improves Visuomotor Performance in Parkinson’s Disease by Anjali Menon, Madhini Vigneswaran, Tina Zhang, Varsha Sreenivasan, Christina Kim, Martin J. McKeown

“…Impaired motor function is a defining characteristic of Parkinson’s disease (PD). …”
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Within- and cross-language semantic effects on oral word translation with a word flanker paradigm by Yong Zhang, Ziqian Yu, Jieyang Yu, Qianyu Ye, Yan Jing Wu

“…The target-flanker relations were manipulated as a function of semantic relatedness (identical, related and unrelated) and language congruency (congruent and incongruent). …”
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MFN: Multi-Scale Frequency Feature Fusion Network for Multi-Classification Image Segmentation by Ji Xiao, Li Jianfang, Zhao Peng, Li Xiaochen, Zhang Chengchun, Zheng Junyi, Pang Yonghui, Huang Xiangsheng

“…And a combined loss function, integrating cross-entropy loss and the Dice coefficient, is designed to improve model training and achieve optimal segmentation performance. …”
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Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou, Jun Zhao

“…The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. …”
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MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

“…We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. MFHG-DDI reframes the heterogeneous network as a graph classification task, capturing DDI information efficiently through an enhanced hybrid graph. …”
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A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points by Suyu Wang, Quan Yue, Zhenlei Xu, Peihong Qiao, Zhentao Lyu, Feng Gao

“…The study was conducted in standard and non-stationary navigation and communication task scenarios. Ablation studies confirmed the positive contributions of each component and their synergistic effects. …”
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A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm by Ziqi Liu, Ziqiao Yin, Zhilong Mi, Binghui Guo, Zhiming Zheng

“…Furthermore, we propose an approximate equation for evaluating the accuracy of early and late fusion methods as a function of sample size, feature quantity, and modality number. …”
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Robust Fusion of Time Series and Image Data for Improved Multimodal Clinical Prediction by Ali Rasekh, Reza Heidari, Amir Hosein Haji Mohammad Rezaie, Parsa Sharifi Sedeh, Zahra Ahmadi, Prasenjit Mitra, Wolfgang Nejdl

“…We also deal with imbalanced datasets and use an uncertainty loss function, yielding improved results while simultaneously providing a principled means of modeling uncertainty. …”
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Affective state-dependent effects of prefrontal rTMS on the cognitive control of negative stimuli in healthy and depressed individuals by Jasmina Paneva, Teresa Schuhmann, Stefanie De Smet, Terence De Meza, Felix Duecker, Alexander T. Sack

“…However, evidence of the cognitive effects of lDLPFC HF-rTMS, especially those cognitive functions affected by MDD, is mixed. Methods: We here assessed the cognitive effects of a single, offline, 10Hz rTMS session on task performance in an emotional faces N-back (EFNBACK) task, in both healthy and depressed individuals. …”
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Brain Reactivity and Vulnerability to Social Feedback Following Acute Stress in Early Adolescence by Zeynep Celen, Ryan J. Murray, Mariana Magnus Smith, Sondes Jouabli, Vladimira Ivanova, Eleonore Pham, Zoe Schilliger, Patrik Vuilleumier, Arnaud Merglen, Paul Klauser, Camille Piguet

“…Understanding how the brain reacts to acute and social stressors during this period might help us detect and protect those at risk. Methods We used functional magnetic resonance imaging to investigate acute social stress reactivity in non‐clinical adolescents between ages 13 and 15 years (N = 61) with a range of depression scores (Beck Depression Inventory scores 0–32). …”
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“Computational analysis on verbal fluency reveals heterogeneity in subjective language interests and brain structure” by Francilia Zengaffinen, Antje Stahnke, Stephan Furger, Roland Wiest, Thomas Dierks, Werner Strik, Yosuke Morishima

“…Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand.We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. …”
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Exploratory study of associations between monetary reward anticipation brain responses and mu-opioid signalling in alcohol dependence, gambling disorder and healthy controls by Samuel Turton, Louise M. Paterson, James FM. Myers, Inge Mick, Chen-Chia Lan, John McGonigle, Henrietta Bowden-Jones, Luke Clark, David J. Nutt, Anne R. Lingford-Hughes

“…We explored associations between reward anticipation responses, using the Monetary Incentive Delay (MID) functional magnetic resonance imaging (fMRI) task, and mu-opioid receptor (MOR) availability and endogenous opioid release capacity using [11C]carfentanil positron emission tomography (PET), in 13 AD, 15 GD and 14 heathy control (HC) participants. …”
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Artificial optoelectronic synapses based on Ga2O3 metal–semiconductor–metal solar‐blind ultraviolet photodetectors with asymmetric electrodes for neuromorphic computing by Huazhen Sun, Bingjie Ye, Mei Ge, Biao Gong, Leyang Qian, Irina N. Parkhomenko, Fadei F. Komarov, Yu Liu, Guofeng Yang

“…Abstract Research on optoelectronic synapses that can integrate both detection and processing functions is essential for the development of efficient neuromorphic computing. …”
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Electroencephalography-Based Recognition of Low Mental Resilience Using Multi-Condition Decision-Level Fusion Approach by Rumaisa Abu Hasan, Tong Boon Tang, Muhamad Saiful Bahri Yusoff, Syed Saad Azhar Ali

“…Three types of EEG features, namely spectra, functional connectivity (FC) and effective connectivity (EC) were extracted, and their correlation with a standard resilience assessment instrument – the Connor-Davidson Resilience Scale were evaluated at resting and task conditions using stepwise regression. …”
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Least Squares Method and Least Modules Method for Finding Defects in Regular Images by Volodymyr Zhydkov, Petro Stetsyuk, Olha Khomiak

“…Methods of calculating gradients of smooth functions (LSM) and subgradients of non-smooth functions (LMM) for all four problems are described. …”
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Lifetime Prediction Analysis of Proton Exchange Membrane Fuel Cells Based on Empirical Mode Decomposition—Temporal Convolutional Network by Chao Zheng, Changqing Du, Jiaming Zhang, Yiming Zhang, Jun Shen, Jiaxin Huang

“…Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. …”
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