Article
Computer Science, Artificial Intelligence
Caglar Cakan, Nikola Jajcay, Klaus Obermayer
Summary: neurolib is a computational framework for whole-brain modeling written in Python, which can load structural and functional datasets, simulate and analyze whole-brain models, and optimize the models.
COGNITIVE COMPUTATION
(2023)
Article
Mathematical & Computational Biology
Heidi Kleven, Ingrid Reiten, Camilla H. Blixhavn, Ulrike Schlegel, Martin ovsthus, Eszter A. Papp, Maja A. Puchades, Jan G. Bjaalie, Trygve B. Leergaard, Ingvild E. Bjerke
Summary: Brain atlases are essential resources for neuroscience research. This perspective article provides a guide on how to use mouse and rat brain atlases for data analysis and reporting in accordance with the FAIR principles. It discusses the interpretation and navigation of atlases, their applications in spatial registration and data visualization, as well as the importance of transparent reporting and comparing data mapped to different atlases. The article emphasizes the relevance of atlas-based tools and workflows for FAIR data sharing.
FRONTIERS IN NEUROINFORMATICS
(2023)
Article
Surgery
Meltem Kurt Pehlivanoglu, Eren Cem Ay, Ayse Gul Eker, Nur Banu Albayrak, Nevcihan Duru, Ahmet Serdar Mutluer, Tolga Turan Dundar, Ihsan Dogan
Summary: This study proposes a new surgical path planning framework for neurosurgery that allows independent planning of surgical paths, considers the entire structure of the brain, and allows curvilinear paths. Neurosurgeons can generate patient-specific optimal surgical pathways using this framework.
INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY
(2023)
Article
Biology
David M. Young, Siavash Fazel Darbandi, Grace Schwartz, Zachary Bonzell, Deniz Yuruk, Mai Nojima, Laurent C. Gole, John L. R. Rubenstein, Weimiao Yu, Stephan J. Sanders
Summary: The article discusses a computational approach for 3D atlas construction that reduces artifacts by identifying anatomical boundaries in imaging data. The method was applied to the Allen Developing Mouse Brain Atlas, resulting in more comprehensive and accurate atlases. Performance validation on 15 whole mouse brains showed qualitative and quantitative improvement.
Article
Multidisciplinary Sciences
Harry Carey, Michael Pegios, Lewis Martin, Chris Saleeba, Anita J. Turner, Nicholas A. Everett, Ingvild E. Bjerke, Maja A. Puchades, Jan G. Bjaalie, Simon Mcmullan
Summary: Registration of data to a common frame of reference is crucial in analyzing and integrating diverse neuroscientific data. The traditional methods for registration are time-consuming and rely on expertise. However, using the neural network DeepSlice, we were able to significantly improve the speed while maintaining the accuracy of registering mouse brain histological images to the Allen Brain Common Coordinate Framework.
NATURE COMMUNICATIONS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Markus Sack, Lei Zheng, Natalia Gass, Gabriele Ende, Alexander Sartorius, Wolfgang Weber-Fahr
Summary: Researchers can easily create mouse brain atlases with an adjustable user-defined level of detail and coverage to match specific research questions using the graphical user interface application presented to overcome the inherent weakness of predefined rigid brain regions in conventional brain atlases.
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
(2021)
Article
Biochemical Research Methods
Sacha Ichbiah, Fabrice Delbary, Alex Mcdougall, Remi Dumollard, Herve Turlier
Summary: In this study, a computational method called 'foambryo' is proposed to infer spatiotemporal atlases of cellular forces from fluorescence microscopy images. The method is validated and shown to be biologically relevant. It can be used to gain new insights into the regulation of cell mechanics in developing embryos.
Article
Chemistry, Analytical
Mauren Abreu de Souza, Daoana Carolaine Alka Cordeiro, Jonathan de Oliveira, Mateus Ferro Antunes de Oliveira, Beatriz Leandro Bonafini
Summary: This paper presents the 3D reconstruction and visualization of the neck/bust region and inner anatomical structures using thermal infrared and CT images. By combining the 3D models and segmented anatomical structures, this methodology provides correlated functional and anatomical images, which could enhance biomedical applications and future diagnosis.
Article
Computer Science, Information Systems
Yongbo Wang, Gaofeng Chen, Tao Xi, Zhaoying Bian, Dong Zeng, Habib Zaidi, Ji He, Jianhua Ma
Summary: Sparse-view helical CT images suffer from noise, artifacts, and severe anatomical distortions, reducing the applicability of existing reconstruction algorithms. A novel TDATV model is proposed for SHCT reconstruction, utilizing tensor decomposition and anisotropic total variation regularization to reduce HCT radiation dose. Results show the potential of SHCT in achieving ultra-low dose CT examinations.
Article
Biology
Liyue Shen, Wei Zhao, Dante Capaldi, John Pauly, Lei Xing
Summary: This study establishes a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. The seamless inclusion of geometric priors is shown to be essential for enhancing imaging performance and provides new avenues for data-driven biomedical imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Wei Dong, Yixing Lao, Michael Kaess, Vladlen Koltun
Summary: We present ASH, a modern and high-performance framework for parallel spatial hashing on GPU, which achieves higher performance, supports richer functionality, and requires fewer lines of code (LoC) compared to existing GPU hash map implementations. ASH provides a versatile tensor interface and enables direct access to spatially varying data via indices, allowing seamless integration with modern libraries such as PyTorch.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Environmental Sciences
Chunhui Zhao, Chi Zhang, Yiming Yan, Nan Su
Summary: In this paper, a novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed. The framework consists of two convolutional neural networks, Scale-ONet and Optim-Net, which can generate water-tight mesh models with exact shape and rough scale of buildings, and reduce scale errors for these models, respectively. Experimental results show that the framework has good robustness for different input images and can achieve ideal reconstruction accuracy on both model shape and scale of buildings.
Article
Neurosciences
Tao Zhong, Jingkuan Wei, Kunhua Wu, Liangjun Chen, Fenqiang Zhao, Yuchen Pei, Ya Wang, Hongjiang Zhang, Zhengwang Wu, Ying Huang, Tengfei Li, Li Wang, Yongchang Chen, Weizhi Ji, Yu Zhang, Gang Li, Yuyu Niu
Summary: In this study, longitudinal brain atlases and associated tissue probability maps were constructed based on structural MRI scans from typically-developing cynomolgus macaques, providing dense time-points during infancy and addressing the lack of temporally densely-sampled atlases for non-human primates.
Article
Construction & Building Technology
Maria Insa-Iglesias, Mark David Jenkins, Gordon Morison
Summary: The study proposes a 2D/3D visual inspection framework to provide structural examiners with an understandable and repeatable way to detect, visualize, and evaluate structural defects.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Qiang Wu, Xunpen Qin, Kang Dong, Aixian Shi, Zeqi Hu
Summary: This paper presents a two-stage convolutional neural network (CNN) method for crack defect detection and segmentation of metal parts. The first stage detects potential cracks and crops them to a small area, while the second stage learns the context of cracks in the detected patches. A window-based stereo matching method is then used to map crack pixels to 3D world points. Experimental results show that the proposed method achieves high accuracy and efficiency in target detection and pixel-level segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemical Research Methods
Aline Silva da Cruz, Maria Margarida Drehmer, Wagner Baetas-da-Cruz, Joao Carlos Machado
Summary: This study quantified microcirculation cerebral blood flow in a rat model of ischemic stroke using ultrasound biomicroscopy and ultrasound contrast agents. The results showed high sensitivity and specificity of this method, making it a valuable tool for preclinical studies.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Christina Dalla, Ivana Jaric, Pavlina Pavlidi, Georgia E. Hodes, Nikolaos Kokras, Anton Bespalov, Martien J. Kas, Thomas Steckler, Mohamed Kabbaj, Hanno Wuerbel, Jordan Marrocco, Jessica Tollkuhn, Rebecca Shansky, Debra Bangasser, Jill B. Becker, Margaret McCarthy, Chantelle Ferland-Beckham
Summary: Many funding agencies have emphasized the importance of considering sex as a biological variable in experimental design to improve the reproducibility and translational relevance of preclinical research. Omitting the female sex from experimental designs in neuroscience and pharmacology can result in biased or limited understanding of disease mechanisms. This article provides methodological considerations for incorporating sex as a biological variable in in vitro and in vivo experiments, including the influence of age and hormone levels, and proposes strategies to enhance methodological rigor and translational relevance in preclinical research.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Wenyu Gu, Dongxu Li, Jia-Hong Gao
Summary: We developed a precise and rapid method for positioning and labelling triaxial OPMs on a wearable magnetoencephalography (MEG) system, improving the efficiency of OPM positioning and labelling.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Kai Lin, Linhang Zhang, Jing Cai, Jiaqi Sun, Wenjie Cui, Guangda Liu
Summary: The article introduces an EEG feature map processing model for emotion recognition, which achieves significantly improved accuracy by fusing EEG information at different spatial scales and introducing a channel attention mechanism.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
John E. Parker, Asier Aristieta, Aryn H. Gittis, Jonathan E. Rubin
Summary: This work presents a toolbox that implements a methodology for automated classification of neural responses based on spike train recordings. The toolbox provides a user-friendly and efficient approach to detect various types of neuronal responses that may not be identified by traditional methods.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Summary: This study compared the performance of SVM and CNN on the same datasets and found that CNN achieved consistently higher classification accuracies. The classification accuracies of SVM and CNN were generally not correlated, and the heatmaps derived from them did not overlap significantly.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Antonino Visalli, Maria Montefinese, Giada Viviani, Livio Finos, Antonino Vallesi, Ettore Ambrosini
Summary: This study introduces an analytical strategy that allows the use of mixed-effects models (LMM) in mass univariate analyses of EEG data. The proposed method overcomes the computational costs and shows excellent performance properties, making it increasingly important in the field of neuroscience.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Xavier Cano-Ferrer, Alexandra Tran -Van -Minh, Ede Rancz
Summary: This study developed a novel rotation platform for studying neural processes and spatial navigation. The platform is modular, affordable, and easy to build, and can be driven by the experimenter or animal movement. The research demonstrated the utility of the platform, which combines the benefits of head fixation and intact vestibular activity.
JOURNAL OF NEUROSCIENCE METHODS
(2024)