Advances of AI in Neuroimaging
Neuroimaging is a rapidly evolving field that involves the use of non-invasive imaging techniques to visualize and study the structure and function of the human brain. With the advent of artificial intelligence (AI), the field of neuroimaging has seen significant breakthroughs in terms of accuracy,...
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| Natura: | Online |
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| Lingua: | inglese |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Accesso online: | ONIX_20250812T110751_9783725840540_309 |
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| collection | Directory of Open Access Books |
| description | Neuroimaging is a rapidly evolving field that involves the use of non-invasive imaging techniques to visualize and study the structure and function of the human brain. With the advent of artificial intelligence (AI), the field of neuroimaging has seen significant breakthroughs in terms of accuracy, speed, and efficiency in identifying various brain disorders. AI models have been widely applied in the analysis and interpretation of neuroimaging data, aiding researchers and clinicians to diagnose, treat, and monitor patients with neurological and psychiatric disorders. An aim of this Special Issue reprint is to present some advanced AI methods for application in neuroimaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). It provides a platform for cutting-edge research at the intersection of AI and neuroimaging, aiming to revolutionize neuroscience and healthcare. It helps readers understand how AI models, coupled with neuroimaging, can advance our understanding of the human brain, its functions, and the mechanisms of brain diseases. This Special Issue reprint also helps readers learn how AI methods in neuroimaging can be used in diagnosis, the improvement of patient care, cost reduction, the enhancement of clinical decision making, as well as the treatment and monitoring of patients with neurological and psychiatric disorders. |
| format | Online |
| id | doab-20.500.12854ir-165554 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1655542025-08-12T09:54:40Z Advances of AI in Neuroimaging Beheshti, Iman Sone, Daichi Leung, Carson K. deep learning brain tumor magnetic resonance imaging classification neural network pre-trained models healthcare Parkinson’s disease motor subtypes arterial spin labelling machine learning support vector machine major depressive disorder multi-modal high and low frequencies feature fusion fundamentals of laparoscopic surgery electroencephalogram skill classification common spatial pattern temporal–spatial pattern recognition deep neural networks artificial intelligence predictive modeling spine surgery collaborative design inter-brain synchrony (IBS) hyper-scanning design cognition COVID-19 AI Long COVID neuroimaging cognition non-invasive brain stimulation human brain function whole brain activity individuality Alzheimer’s disease (AD) rTMS treatment DLPFC MRI analysis efficacy prediction artificial intelligence neurology stroke epilepsy magneticresonance imaging super-resolution mild cognitive impairment hyperparameter optimization Pareto optimality Markov blanket amyloid PET centiloid scale resting-state functional magnetic resonance imaging resting-state functional connectivity graph convolution network generative adversarial network virtual reality immersion task difficulty electroencephalography (EEG) biomarkers resting state electroencephalography source localization recurrent neural network dynamics healthy aging mind–body practice Tai Chi temporal lobe epilepsy white matter diffusion tensor imaging brain age spatial–temporal multimodal low-rank tensor fusion n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Neuroimaging is a rapidly evolving field that involves the use of non-invasive imaging techniques to visualize and study the structure and function of the human brain. With the advent of artificial intelligence (AI), the field of neuroimaging has seen significant breakthroughs in terms of accuracy, speed, and efficiency in identifying various brain disorders. AI models have been widely applied in the analysis and interpretation of neuroimaging data, aiding researchers and clinicians to diagnose, treat, and monitor patients with neurological and psychiatric disorders. An aim of this Special Issue reprint is to present some advanced AI methods for application in neuroimaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). It provides a platform for cutting-edge research at the intersection of AI and neuroimaging, aiming to revolutionize neuroscience and healthcare. It helps readers understand how AI models, coupled with neuroimaging, can advance our understanding of the human brain, its functions, and the mechanisms of brain diseases. This Special Issue reprint also helps readers learn how AI methods in neuroimaging can be used in diagnosis, the improvement of patient care, cost reduction, the enhancement of clinical decision making, as well as the treatment and monitoring of patients with neurological and psychiatric disorders. 2025-08-12T09:54:38Z 2025-08-12T09:54:38Z 2025 book ONIX_20250812T110751_9783725840540_309 9783725840540 9783725840533 https://directory.doabooks.org/handle/20.500.12854/165554 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11044 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4053-3 10.3390/books978-3-7258-4053-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725840540 9783725840533 300 open access |
| spellingShingle | deep learning brain tumor magnetic resonance imaging classification neural network pre-trained models healthcare Parkinson’s disease motor subtypes arterial spin labelling machine learning support vector machine major depressive disorder multi-modal high and low frequencies feature fusion fundamentals of laparoscopic surgery electroencephalogram skill classification common spatial pattern temporal–spatial pattern recognition deep neural networks artificial intelligence predictive modeling spine surgery collaborative design inter-brain synchrony (IBS) hyper-scanning design cognition COVID-19 AI Long COVID neuroimaging cognition non-invasive brain stimulation human brain function whole brain activity individuality Alzheimer’s disease (AD) rTMS treatment DLPFC MRI analysis efficacy prediction artificial intelligence neurology stroke epilepsy magneticresonance imaging super-resolution mild cognitive impairment hyperparameter optimization Pareto optimality Markov blanket amyloid PET centiloid scale resting-state functional magnetic resonance imaging resting-state functional connectivity graph convolution network generative adversarial network virtual reality immersion task difficulty electroencephalography (EEG) biomarkers resting state electroencephalography source localization recurrent neural network dynamics healthy aging mind–body practice Tai Chi temporal lobe epilepsy white matter diffusion tensor imaging brain age spatial–temporal multimodal low-rank tensor fusion n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Advances of AI in Neuroimaging |
| title | Advances of AI in Neuroimaging |
| title_full | Advances of AI in Neuroimaging |
| title_fullStr | Advances of AI in Neuroimaging |
| title_full_unstemmed | Advances of AI in Neuroimaging |
| title_short | Advances of AI in Neuroimaging |
| title_sort | advances of ai in neuroimaging |
| topic | deep learning brain tumor magnetic resonance imaging classification neural network pre-trained models healthcare Parkinson’s disease motor subtypes arterial spin labelling machine learning support vector machine major depressive disorder multi-modal high and low frequencies feature fusion fundamentals of laparoscopic surgery electroencephalogram skill classification common spatial pattern temporal–spatial pattern recognition deep neural networks artificial intelligence predictive modeling spine surgery collaborative design inter-brain synchrony (IBS) hyper-scanning design cognition COVID-19 AI Long COVID neuroimaging cognition non-invasive brain stimulation human brain function whole brain activity individuality Alzheimer’s disease (AD) rTMS treatment DLPFC MRI analysis efficacy prediction artificial intelligence neurology stroke epilepsy magneticresonance imaging super-resolution mild cognitive impairment hyperparameter optimization Pareto optimality Markov blanket amyloid PET centiloid scale resting-state functional magnetic resonance imaging resting-state functional connectivity graph convolution network generative adversarial network virtual reality immersion task difficulty electroencephalography (EEG) biomarkers resting state electroencephalography source localization recurrent neural network dynamics healthy aging mind–body practice Tai Chi temporal lobe epilepsy white matter diffusion tensor imaging brain age spatial–temporal multimodal low-rank tensor fusion n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | deep learning brain tumor magnetic resonance imaging classification neural network pre-trained models healthcare Parkinson’s disease motor subtypes arterial spin labelling machine learning support vector machine major depressive disorder multi-modal high and low frequencies feature fusion fundamentals of laparoscopic surgery electroencephalogram skill classification common spatial pattern temporal–spatial pattern recognition deep neural networks artificial intelligence predictive modeling spine surgery collaborative design inter-brain synchrony (IBS) hyper-scanning design cognition COVID-19 AI Long COVID neuroimaging cognition non-invasive brain stimulation human brain function whole brain activity individuality Alzheimer’s disease (AD) rTMS treatment DLPFC MRI analysis efficacy prediction artificial intelligence neurology stroke epilepsy magneticresonance imaging super-resolution mild cognitive impairment hyperparameter optimization Pareto optimality Markov blanket amyloid PET centiloid scale resting-state functional magnetic resonance imaging resting-state functional connectivity graph convolution network generative adversarial network virtual reality immersion task difficulty electroencephalography (EEG) biomarkers resting state electroencephalography source localization recurrent neural network dynamics healthy aging mind–body practice Tai Chi temporal lobe epilepsy white matter diffusion tensor imaging brain age spatial–temporal multimodal low-rank tensor fusion n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20250812T110751_9783725840540_309 |