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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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2
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Ebrahimzadeh E, Saharkhiz S, Rajabion L, Oskouei HB, Seraji M, Fayaz F, Saliminia S, Sadjadi SM, Soltanian-Zadeh H. Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function. Front Syst Neurosci 2022; 16:934266. [PMID: 35966000 PMCID: PMC9371554 DOI: 10.3389/fnsys.2022.934266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/08/2022] [Indexed: 02/01/2023] Open
Abstract
Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh, ,
| | - Saber Saharkhiz
- Department of Pharmacology-Physiology, Faculty of Medicine, University of Sherbrooke, Sherbrooke, Canada
| | - Lila Rajabion
- School of Graduate Studies, State University of New York Empire State College, Manhattan, NY, United States
| | | | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Farahnaz Fayaz
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Sarah Saliminia
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Seyyed Mostafa Sadjadi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Kaulen N, Rajkumar R, Régio Brambilla C, Mauler J, Ramkiran S, Orth L, Sbaihat H, Lang M, Wyss C, Rota Kops E, Scheins J, Neumaier B, Ermert J, Herzog H, Langen K, Lerche C, Shah NJ, Veselinović T, Neuner I. mGluR
5
and
GABA
A
receptor‐specific parametric
PET
atlas construction—
PET
/
MR
data processing pipeline, validation, and application. Hum Brain Mapp 2022; 43:2148-2163. [PMID: 35076125 PMCID: PMC8996359 DOI: 10.1002/hbm.25778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 12/14/2021] [Accepted: 12/24/2021] [Indexed: 12/15/2022] Open
Abstract
The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR5 and GABAA are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BPND), and total distribution volume (VT) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [11C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [11C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as VT and BPND atlases of mGluR5 and GABAA, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δABP = 3.0 ± 1.0% and δFMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σABP = 4.0%–16.0%, σFMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [15O]H2O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.
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Affiliation(s)
- Nicolas Kaulen
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
| | - Ravichandran Rajkumar
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
- JARA BRAIN Translational Medicine Aachen Germany
| | - Cláudia Régio Brambilla
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
- JARA BRAIN Translational Medicine Aachen Germany
| | - Jörg Mauler
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
| | - Shukti Ramkiran
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
- JARA BRAIN Translational Medicine Aachen Germany
| | - Linda Orth
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
| | - Hasan Sbaihat
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
- Department of Medical Imaging Arab‐American University Palestine Jenin Palestine
| | - Markus Lang
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 5, INM‐5 Jülich Germany
| | - Christine Wyss
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department for Psychiatry, Psychotherapy and Psychosomatics Social Psychiatry University Hospital of Psychiatry Zurich Zurich Switzerland
| | - Elena Rota Kops
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
| | - Jürgen Scheins
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
| | - Bernd Neumaier
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 5, INM‐5 Jülich Germany
| | - Johannes Ermert
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 5, INM‐5 Jülich Germany
| | - Hans Herzog
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
| | - Karl‐Joseph Langen
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- JARA BRAIN Translational Medicine Aachen Germany
- Department of Nuclear Medicine RWTH Aachen University Aachen Germany
| | - Christoph Lerche
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
| | - N. Jon Shah
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- JARA BRAIN Translational Medicine Aachen Germany
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 11, INM‐11 Jülich Germany
- Department of Neurology RWTH Aachen University Aachen Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
| | - Irene Neuner
- Forschungszentrum Jülich Institute of Neuroscience and Medicine 4, INM‐4 Jülich Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics RWTH Aachen University Aachen Germany
- JARA BRAIN Translational Medicine Aachen Germany
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Masturzo L, Carra P, Erba PA, Morrocchi M, Pilleri A, Sportelli G, Belcari N. Monte Carlo Characterization of the Trimage Brain PET System. J Imaging 2022; 8:jimaging8020021. [PMID: 35200724 PMCID: PMC8878795 DOI: 10.3390/jimaging8020021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/13/2022] [Accepted: 01/20/2022] [Indexed: 11/16/2022] Open
Abstract
The TRIMAGE project aims to develop a brain-dedicated PET/MR/EEG (Positron Emission Tomography/Magnetic Resonance/Electroencephalogram) system that is able to perform simultaneous PET, MR and EEG acquisitions. The PET component consists of a full ring with 18 sectors. Each sector includes three square detector modules based on dual sstaggered LYSO:Ce matrices read out by SiPMs. Using Monte Carlo simulations and following NEMA (National Electrical Manufacturers Association) guidelines, image quality procedures have been applied to evaluate the performance of the PET component of the system. The performance are reported in terms of spatial resolution, uniformity, recovery coefficient, spill over ratio, noise equivalent count rate (NECR) and scatter fraction. The results show that the TRIMAGE system is at the top of the current brain PET technologies.
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Affiliation(s)
- Luigi Masturzo
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
| | - Pietro Carra
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
- National Institute of Nuclear Physics (INFN), Pisa Section, 56127 Pisa, Italy
| | - Paola Anna Erba
- Department of Translational Research and New Technology in Medicine and Surgery, Regional Center of Nuclear Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, 56126 Pisa, Italy;
| | - Matteo Morrocchi
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
- National Institute of Nuclear Physics (INFN), Pisa Section, 56127 Pisa, Italy
| | - Alessandro Pilleri
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
| | - Giancarlo Sportelli
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
- National Institute of Nuclear Physics (INFN), Pisa Section, 56127 Pisa, Italy
- Correspondence:
| | - Nicola Belcari
- Department of Physics “E. Fermi”, University of Pisa, 56127 Pisa, Italy; (L.M.); (P.C.); (M.M.); (A.P.); (N.B.)
- National Institute of Nuclear Physics (INFN), Pisa Section, 56127 Pisa, Italy
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mGluR5 binding changes during a mismatch negativity task in a multimodal protocol with [ 11C]ABP688 PET/MR-EEG. Transl Psychiatry 2022; 12:6. [PMID: 35013095 PMCID: PMC8748790 DOI: 10.1038/s41398-021-01763-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/22/2021] [Accepted: 11/30/2021] [Indexed: 02/08/2023] Open
Abstract
Currently, the metabotropic glutamate receptor 5 (mGluR5) is the subject of several lines of research in the context of neurology and is of high interest as a target for positron-emission tomography (PET). Here, we assessed the feasibility of using [11C]ABP688, a specific antagonist radiotracer for an allosteric site on the mGluR5, to evaluate changes in glutamatergic neurotransmission through a mismatch-negativity (MMN) task as a part of a simultaneous and synchronized multimodal PET/MR-EEG study. We analyzed the effect of MMN by comparing the changes in nondisplaceable binding potential (BPND) prior to (baseline) and during the task in 17 healthy subjects by applying a bolus/infusion protocol. Anatomical and functional regions were analyzed. A small change in BPND was observed in anatomical regions (posterior cingulate cortex and thalamus) and in a functional network (precuneus) after the start of the task. The effect size was quantified using Kendall's W value and was 0.3. The motor cortex was used as a control region for the task and did not show any significant BPND changes. There was a significant ΔBPND between acquisition conditions. On average, the reductions in binding across the regions were - 8.6 ± 3.2% in anatomical and - 6.4 ± 0.5% in the functional network (p ≤ 0.001). Correlations between ΔBPND and EEG latency for both anatomical (p = 0.008) and functional (p = 0.022) regions were found. Exploratory analyses suggest that the MMN task played a role in the glutamatergic neurotransmission, and mGluR5 may be indirectly modulated by these changes.
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Mele G, Cavaliere C, Alfano V, Orsini M, Salvatore M, Aiello M. Simultaneous EEG-fMRI for Functional Neurological Assessment. Front Neurol 2019; 10:848. [PMID: 31456735 PMCID: PMC6700249 DOI: 10.3389/fneur.2019.00848] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 07/22/2019] [Indexed: 01/05/2023] Open
Abstract
The increasing incidence of neurodegenerative and psychiatric diseases requires increasingly sophisticated tools for their diagnosis and monitoring. Clinical assessment takes advantage of objective parameters extracted by electroencephalogram and magnetic resonance imaging (MRI) among others, to support clinical management of neurological diseases. The complementarity of these two tools can be now emphasized by the possibility of integrating the two technologies in a hybrid solution, allowing simultaneous acquisition of the two signals by the novel EEG-fMRI technology. This review will focus on simultaneous EEG-fMRI technology and related early studies, dealing about issues related to the acquisition and processing of simultaneous signals, and including critical discussion about clinical and technological perspectives.
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Belcari N, Bisogni MG, Camarlinghi N, Carra P, Cerello P, Morrocchi M, Patera A, Sportelli G, Del Guerra A. Design and Detector Performance of the PET Component of the TRIMAGE PET/MR/EEG Scanner. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2019.2906407] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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