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Michal Brzus, Kevin Knoernschild, Jessica C. Sieren and Hans J. Johnson
Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interve...
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Ana Coelho, Paulo Marques, Ricardo Magalhães, Nuno Sousa, José Neves, Victor Alves
Pág. 42 - 52
Multimodal neuroimaging analyses are of major interest for both research and clinical practice, enabling the combined evaluation of the structure and function of the human brain. These analyses generate large volumes of data and consequently increase the...
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Muhammad Irfan, Seyed Shahrestani and Mahmoud Elkhodr
Dementia, including Alzheimer?s Disease (AD), is a complex condition, and early detection remains a formidable challenge due to limited patient records and uncertainty in identifying relevant features. This paper proposes a machine learning approach to a...
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Maha Gharaibeh, Mothanna Almahmoud, Mostafa Z. Ali, Amer Al-Badarneh, Mwaffaq El-Heis, Laith Abualigah, Maryam Altalhi, Ahmad Alaiad and Amir H. Gandomi
Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most importa...
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Juan A. Arias-López, Carmen Cadarso-Suárez and Pablo Aguiar-Fernández
In the field of medical imaging, one of the most extended research setups consists of the comparison between two groups of images, a pathological set against a control set, in order to search for statistically significant differences in brain activity. F...
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Riccardo Iandolo, Francesca Marini, Marianna Semprini, Matteo Laffranchi, Maddalena Mugnosso, Amel Cherif, Lorenzo De Michieli, Michela Chiappalone and Jacopo Zenzeri
The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots? capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed durin...
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Lilia Lazli, Mounir Boukadoum and Otmane Ait Mohamed
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient?s condition. As part of the early diagnosis of ...
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Md Easin Hasan and Amy Wagler
Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer?s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD....
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Xiaoli Yang, Yuxin Xia, Zhenwei Li, Lipei Liu, Zhipeng Fan and Jiayi Zhou
Alzheimer?s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuro...
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Zhiqiong Wang, Zican Lin, Shuo Li, Yibo Wang, Weiying Zhong, Xinlei Wang and Junchang Xin
Alzheimer?s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To ...
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Xibin Wang, Qiong Zhou, Hui Li and Mei Chen
Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution...
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Giacomo Giacalone, Marta Zanoletti, Rebecca Re, Davide Contini, Lorenzo Spinelli, Alessandro Torricelli and Luisa Roveri
Biomarkers of microcirculation dysfunction may help in the study of cerebral small vessel disease (CSVD). Time-Domain Near-Infrared spectroscopy (TD-NIRS), estimating the oxygenation of microcirculation of cerebral outer layers, might indirectly correlat...
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Vera Afreixo, Ana Helena Tavares, Vera Enes, Miguel Pinheiro, Leonor Rodrigues and Gabriela Moura
In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature sel...
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Yuta Oda, Takanori Sato, Isao Nambu and Yasuhiro Wada
Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated wit...
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Silvia Clausi, Claudia Iacobacci, Michela Lupo, Giusy Olivito, Marco Molinari and Maria Leggio
Studies from the past three decades have demonstrated that there is cerebellar involvement in the emotional domain. Emotional processing in humans requires both unconscious and conscious mechanisms. A significant amount of evidence indicates that the cer...
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Wieslaw L. Nowinski
Although no dataset at the nanoscale for the entire human brain has yet been acquired and neither a nanoscale human whole brain atlas has been constructed, tremendous progress in neuroimaging and high-performance computing makes them feasible in the non-...
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Juan Jiang, Hong Liu, Xin Yu, Jin Zhang, Bing Xiong and Lidan Kuang
Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer?s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can...
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Nayef Alqahtani, Shadab Alam, Ibrahim Aqeel, Mohammed Shuaib, Ibrahim Mohsen Khormi, Surbhi Bhatia Khan and Areej A. Malibari
Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer?s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD,...
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Rahmeh Ibrahim, Rawan Ghnemat and Qasem Abu Al-Haija
Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer?s disease detection and brain tumor identification. While CNNs optimize their paramet...
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Mohammed Isam Al-Hiyali, Norashikin Yahya, Ibrahima Faye, Maged S. Al-Quraishi and Abdulhakim Al-Ezzi
The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic comp...
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