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Kim T, Kang DW, Salazar Fajardo JC, Jang H, Um YH, Kim S, Wang SM, Kim D, Lim HK. Safety and feasibility of optimized transcranial direct current stimulation in patients with mild cognitive impairment due to Alzheimer's disease: a multicenter study protocol for a randomized controlled trial. Front Neurol 2024; 15:1356073. [PMID: 38660096 PMCID: PMC11040101 DOI: 10.3389/fneur.2024.1356073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/20/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Transcranial direct current stimulation (tDCS) may effectively preserve and improve cognitive function in patients with mild cognitive impairment (MCI). Research has shown that Individual brain characteristics can influence the effects of tDCS. Computer three-dimensional brain modeling based on magnetic resonance imaging (MRI) has been suggested as an alternative for determining the most accurate tDCS electrode position based on the patients' individual brain characteristics to enhance tDCS effects. Therefore, this study aims to determine the feasibility and safety of applying tDCS treatment using optimized and personalized tDCS electrode positions in patients with Alzheimer's disease (AD)-induced MCI using computer modeling and compare the results with those of a sham group to improve cognitive function. Method A prospective active-sham group feasibility study was set to recruit 40 participants, who will be randomized into Optimized-tDCS and Sham-tDCS groups. The parameters for tDCS will be 2 mA (disk electrodes R = 1.5 cm) for 30 min during two sets of 15 sessions (2 weeks of resting period in between), using two electrodes in pairs. Using computer modeling, the tDCS electrode positions of each participant will be personalized. Outcome measurements are going to be obtained at three points: baseline, first post-test, and second post-test. The AD assessment scale-cognitive subscale (ADAS-Cog) and the Korean version of Mini-Mental State Examination (K-MMSE), together with other secondary outcomes and safety tests will be used. Discussion For the present study, we hypothesize that compared to a sham group, the optimized personalized tDCS application would be effective in improving the cognitive function of patients with AD-induced MCI and the participants would tolerate the tDCS intervention without any significant adverse effects.Clinical trial registration: https://cris.nih.go.kr, identifier [KCT0008918].
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Affiliation(s)
- TaeYeong Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | - Hanna Jang
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Mehta P, Soliman A, Rodriguez-Vera L, Schmidt S, Muniz P, Rodriguez M, Forcadell M, Gonzalez-Perez E, Vozmediano V. Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition. Pharmaceutics 2024; 16:226. [PMID: 38399280 PMCID: PMC10892872 DOI: 10.3390/pharmaceutics16020226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/30/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.
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Affiliation(s)
- Parsshava Mehta
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL 32827, USA; (P.M.); (A.S.); (S.S.)
| | - Amira Soliman
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL 32827, USA; (P.M.); (A.S.); (S.S.)
- Department of Pharmacy Practice, Faculty of Pharmacy, Helwan University, Helwan 11795, Egypt
| | - Leyanis Rodriguez-Vera
- Model Informed Development, CTI Laboratories, Covington, KY 41011, USA; (L.R.-V.); (P.M.); (M.R.)
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL 32827, USA; (P.M.); (A.S.); (S.S.)
| | - Paula Muniz
- Model Informed Development, CTI Laboratories, Covington, KY 41011, USA; (L.R.-V.); (P.M.); (M.R.)
| | - Monica Rodriguez
- Model Informed Development, CTI Laboratories, Covington, KY 41011, USA; (L.R.-V.); (P.M.); (M.R.)
| | - Marta Forcadell
- Neuraxpharm Pharmaceuticals SL, Clinical Research and Evidence-Generation Science, 08970 Barcelona, Spain; (M.F.); (E.G.-P.)
| | - Emili Gonzalez-Perez
- Neuraxpharm Pharmaceuticals SL, Clinical Research and Evidence-Generation Science, 08970 Barcelona, Spain; (M.F.); (E.G.-P.)
| | - Valvanera Vozmediano
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL 32827, USA; (P.M.); (A.S.); (S.S.)
- Model Informed Development, CTI Laboratories, Covington, KY 41011, USA; (L.R.-V.); (P.M.); (M.R.)
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Wang C, Zhang T, Chen X, He S, Li S, Wu S. BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife 2023; 12:e86365. [PMID: 38132087 PMCID: PMC10796146 DOI: 10.7554/elife.86365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 12/20/2023] [Indexed: 12/23/2023] Open
Abstract
Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs.
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Affiliation(s)
- Chaoming Wang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
| | - Tianqiu Zhang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Xiaoyu Chen
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Sichao He
- Beijing Jiaotong UniversityBeijingChina
| | - Shangyang Li
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Si Wu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
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Kim T, Salazar Fajardo JC, Jang H, Lee J, Kim Y, Kim G, Kim D. Effect of optimized transcranial direct current stimulation on motor cortex activation in patients with sub-acute or chronic stroke: a study protocol for a single-blinded cross-over randomized control trial. Front Neurosci 2023; 17:1328727. [PMID: 38192515 PMCID: PMC10773722 DOI: 10.3389/fnins.2023.1328727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction Transcranial direct current stimulation (tDCS) has shown positive but inconsistent results in stroke rehabilitation. This could be attributed to inter-individual variations in brain characteristics and stroke lesions, which limit the use of a single tDCS protocol for all post-stroke patients. Optimizing the electrode location in tDCS for each individual using magnetic resonance imaging (MRI) to generate three-dimensional computer models and calculate the electric field (E-field) induced by tDCS at a specific target point in the primary motor cortex may help reduce these inconsistencies. In stroke rehabilitation, locating the optimal position that generates a high E-field in a target area can influence motor recovery. Therefore, this study was designed to determine the effect of personalized tDCS electrode positions on hand-knob activation in post-stroke patients. Method This is a crossover study with a sample size of 50 participants, who will be randomly assigned to one of six groups and will receive one session of either optimized-active, conventional-active, or sham tDCS, with 24 h between sessions. The tDCS parameters will be 1 mA (5 × 5 cm electrodes) for 20 min. The motor-evoked potential (MEP) will be recorded before and after each session over the target area (motor cortex hand-knob) and the MEP hotspot. The MEP amplitude at the target location will be the primary outcome. Discussion We hypothesize that the optimized-active tDCS session would show a greater increase in MEP amplitude over the target area in patients with subacute and chronic stroke than conventional and sham tDCS sessions.Clinical trial registration: https://cris.nih.go.kr, identifier KCT0007536.
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Affiliation(s)
- TaeYeong Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | | | - Hanna Jang
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Juwon Lee
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Yeonkyung Kim
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Gowun Kim
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
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5
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Giotakos O. Editorial: From brain priorities to brain modeling. Front Psychiatry 2023; 14:1272054. [PMID: 37908597 PMCID: PMC10614046 DOI: 10.3389/fpsyt.2023.1272054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/04/2023] [Indexed: 11/02/2023] Open
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Chwał J, Kostka P, Tkacz E. Assessment of the Extent of Intracerebral Hemorrhage Using 3D Modeling Technology. Healthcare (Basel) 2023; 11:2441. [PMID: 37685475 PMCID: PMC10487057 DOI: 10.3390/healthcare11172441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/08/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
The second most common cause of stroke, accounting for 10% of hospital admissions, is intracerebral hemorrhage (ICH), and risk factors include diabetes, smoking, and hypertension. People with intracerebral bleeding experience symptoms that are related to the functions that are managed by the affected part of the brain. Having obtained 15 computed tomography (CT) scans from five patients with ICH, we decided to use three-dimensional (3D) modeling technology to estimate the bleeding volume. CT was performed on admission to hospital, and after one week and two weeks of treatment. We segmented the brain, ventricles, and hemorrhage using semi-automatic algorithms in Slicer 3D, then improved the obtained models in Blender. Moreover, the accuracy of the models was checked by comparing corresponding CT scans with 3D brain model cross-sections. The goal of the research was to examine the possibility of using 3D modeling technology to visualize intracerebral hemorrhage and assess its treatment.
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Affiliation(s)
- Joanna Chwał
- Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (P.K.); (E.T.)
- Joint Doctoral School, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Paweł Kostka
- Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (P.K.); (E.T.)
| | - Ewaryst Tkacz
- Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (P.K.); (E.T.)
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Guassi Moreira JF, Méndez Leal AS, Waizman YH, Saragosa-Harris N, Ninova E, Silvers JA. Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models. J Neurosci 2021; 41:6006-6017. [PMID: 34039658 PMCID: PMC8276740 DOI: 10.1523/jneurosci.3182-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 05/06/2021] [Accepted: 05/12/2021] [Indexed: 11/21/2022] Open
Abstract
Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity related to reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral PFC and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the NAcc predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior.SIGNIFICANCE STATEMENT Adolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly because of statistical approaches used by prior neuroimaging studies of system-based theories. We used brain modeling, an approach that predicts behavior from brain activity, of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.
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Affiliation(s)
- João F Guassi Moreira
- Department of Psychology, University of California, Los Angeles, California 90095-1563
| | - Adriana S Méndez Leal
- Department of Psychology, University of California, Los Angeles, California 90095-1563
| | - Yael H Waizman
- Department of Psychology, University of California, Los Angeles, California 90095-1563
| | | | - Emilia Ninova
- Department of Psychology, University of California, Los Angeles, California 90095-1563
| | - Jennifer A Silvers
- Department of Psychology, University of California, Los Angeles, California 90095-1563
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Abstract
The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an uncontrolled societal hype, the more amplified, the more "Blue Sky" the claim is. Will we ever be able to simulate a brain in silico? Will "it" (the digital avatar) be conscious? The Blue Brain Project (BBP) and the European flagship the Human Brain Project (HBP) have surfed on this wave for the past 10 years. Their already significant lifetimes now offer new case studies for neuroscience sociology and epistemology, as the projects mature. Their distinctive "Blue Sky" flavor has been a key feature in securing unprecedented funding (more than one billion Euros) mostly through supranational institutions. The longitudinal analysis of these ventures provides clues to how the neuromyth they propagate sells science, in a scientific world based on an economy of promises.
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Affiliation(s)
- Yves Frégnac
- UNIC-NeuroPSI, Institut des Neurosciences Paris-Saclay, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France
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Moreno-Blanco D, Solana-Sánchez J, Sánchez-González P, Oropesa I, Cáceres C, Cattaneo G, Tormos-Muñoz JM, Bartrés-Faz D, Pascual-Leone Á, Gómez EJ. Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review. Sensors (Basel) 2019; 19:s19194183. [PMID: 31561599 PMCID: PMC6806336 DOI: 10.3390/s19194183] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/19/2019] [Accepted: 09/24/2019] [Indexed: 01/10/2023]
Abstract
Brain health refers to the preservation of brain integrity and function optimized for an individual’s biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction.
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Affiliation(s)
- Diego Moreno-Blanco
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (I.O.); (C.C.); (E.J.G.)
- Correspondence:
| | - Javier Solana-Sánchez
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, 08916 Badalona, Spain; (J.S.-S.); (G.C.); (J.M.T.-M.); (D.B.-F.); (Á.P.-L.)
- Universitat Autònoma de Barcelona, 08193 Barcelona, Spain, and with Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, 08916 Badalona, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (I.O.); (C.C.); (E.J.G.)
- Centro de Investigación Biomédica en Red, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Ignacio Oropesa
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (I.O.); (C.C.); (E.J.G.)
| | - César Cáceres
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (I.O.); (C.C.); (E.J.G.)
- ETSI Informática, Universidad Rey Juan Carlos, 28933 Madrid, Spain
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, 08916 Badalona, Spain; (J.S.-S.); (G.C.); (J.M.T.-M.); (D.B.-F.); (Á.P.-L.)
- Institut d’Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
| | - Josep M. Tormos-Muñoz
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, 08916 Badalona, Spain; (J.S.-S.); (G.C.); (J.M.T.-M.); (D.B.-F.); (Á.P.-L.)
- Universitat Autònoma de Barcelona, 08193 Barcelona, Spain, and with Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, 08916 Badalona, Spain
| | - David Bartrés-Faz
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, 08916 Badalona, Spain; (J.S.-S.); (G.C.); (J.M.T.-M.); (D.B.-F.); (Á.P.-L.)
- Institut d’Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, i Institut de Neurociències, Universitat de Barcelona, 08036 Barcelona, Spain
| | - Álvaro Pascual-Leone
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, 08916 Badalona, Spain; (J.S.-S.); (G.C.); (J.M.T.-M.); (D.B.-F.); (Á.P.-L.)
- Hinda and Arthur Marcus Institute for Aging Research and the Center for Memory Health, Hebrew SeniorLife, Department of Neurology, Harvard Medical School, Boston, MA 02131, USA
| | - Enrique J. Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (I.O.); (C.C.); (E.J.G.)
- Centro de Investigación Biomédica en Red, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
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Li S, Wang Y, Li S, Lv Y, Zhang L, Zou J, Ma L. Research on Assisting Clinicians to Operate rTMS Precisely Based on the Coil Magnetic Field Spatial Distribution With Magnetic Resonance Imaging Navigation. Front Neurosci 2019; 13:858. [PMID: 31481867 PMCID: PMC6709653 DOI: 10.3389/fnins.2019.00858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/30/2019] [Indexed: 01/14/2023] Open
Abstract
Objective: To assist clinicians to operate repetitive Transcranial Magnetic Stimulation (rTMS) precisely based on the coil magnetic field spatial distribution with Magnetic Resonance Imaging (MRI) Navigation. Methods: A fast method for calculating electromagnetic fields in layered brain structures in frequency domain was proposed. By approaching Bessel function in different intervals, the integral with a highly oscillatory kernel was transformed into two parts: a definite integral and a weakened oscillatory one. The distribution of induced current density and magnetic field intensity of rTMS stimulation effect on brain was quantitatively calculated, so that clinicians could intuitively grasp the safe range of coil stimulation on the brain. Then, the crucial factor of the stimulation effect of rTMS was determined, and an accurate coil positioning of the rTMS efficiently was completed. Result: The maximal attenuation of induced electric field and magnetic induction intensity was 72.20 and 86.867% at 3 cm away from the skin in the brain layered model. The clinical examination results of electric field intensity distribution, magnetic field intensity distribution, current density distribution, layered brain modeling, and coil location speed in the brain model teaching group were significantly higher than those in the traditional teaching group (P < 0.001). Conclusion: It is suitable for clinicians to quickly complete the precise positioning of rTMS, master the adjustment of coil stimulation therapeutic parameters, and realize the precise positioning operation of rTMS with MRI navigation in intracranial. Clinical Trial registration: Chinese Clinical Trial Registry (ChiCTR1800018616); Registered on 30th September 2018
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Affiliation(s)
- Shijun Li
- Department of Medical Instruments, Chinese People's Liberation Army General Hospital, Beijing, China.,Department of Radiology, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yi Wang
- Department of Stomatology, Chinese People's Liberation Army General Hospital, Beijing, China
| | - ShengJie Li
- Department of Rehabilitation, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yanwei Lv
- Clinical Epidemiology and Biostatistics Research Office, Beijing Research Institute of Traumatology and Orthopaedics, Beijing, China
| | - Lei Zhang
- Department of Medical Information, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jun Zou
- Department of Electrical Engineering, Tsinghua University, Beijing, China
| | - Lin Ma
- Department of Radiology, Chinese People's Liberation Army General Hospital, Beijing, China
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Abstract
Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.
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Affiliation(s)
- Xue Fan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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Latimer B, Bergin DA, Guntu V, Schulz DJ, Nair SS. Integrating Model-Based Approaches into a Neuroscience Curriculum-An Interdisciplinary Neuroscience Course in Engineering. IEEE Trans Ed 2019; 62:48-56. [PMID: 35573982 PMCID: PMC9107338 DOI: 10.1109/te.2018.2859411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
CONTRIBUTION This paper demonstrates curricular modules that incorporate engineering model-based approaches, including concepts related to circuits, systems, modeling, electrophysiology, programming, and software tutorials that enhance learning in undergraduate neuroscience courses. These modules can also be integrated into other neuroscience courses. BACKGROUND Educators in biological and physical sciences urge incorporation of computation and engineering approaches into biology. Model-based approaches can provide insights into neural function; prior studies show these are increasingly being used in research in biology. Reports about their integration in undergraduate neuroscience curricula, however, are scarce. There is also a lack of suitable courses to satisfy engineering students' interest in the challenges in the growing area of neural sciences. INTENDED OUTCOMES (1) Improved student learning in interdisciplinary neuroscience; (2) enhanced teaching by neuroscience faculty; (3) research preparation of undergraduates; and 4) increased interdisciplinary interactions. APPLICATION DESIGN An interdisciplinary undergraduate neuroscience course that incorporates computation and model-based approaches and has both software- and wet-lab components, was designed and co-taught by colleges of engineering and arts and science. FINDINGS Model-based content improved learning in neuroscience for three distinct groups: 1) undergraduates; 2) Ph.D. students; and 3) post-doctoral researchers and faculty. Moreover, the importance of the content and the utility of the software in enhancing student learning was rated highly by all these groups, suggesting a critical role for engineering in shaping the neuroscience curriculum. The model for cross-training also helped facilitate interdisciplinary research collaborations.
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Affiliation(s)
- Benjamin Latimer
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
| | - David A Bergin
- Educational, School and Counseling Psychology, University of Missouri, Columbia, MO 65211 USA
| | - Vinay Guntu
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
| | - David J Schulz
- Department of Biological Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Satish S Nair
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
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Farisco M, Kotaleski JH, Evers K. Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues. Front Psychol 2018; 9:585. [PMID: 29740372 PMCID: PMC5928391 DOI: 10.3389/fpsyg.2018.00585] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/06/2018] [Indexed: 11/15/2022] Open
Abstract
Modeling and simulations have gained a leading position in contemporary attempts to describe, explain, and quantitatively predict the human brain's operations. Computer models are highly sophisticated tools developed to achieve an integrated knowledge of the brain with the aim of overcoming the actual fragmentation resulting from different neuroscientific approaches. In this paper we investigate the plausibility of simulation technologies for emulation of consciousness and the potential clinical impact of large-scale brain simulation on the assessment and care of disorders of consciousness (DOCs), e.g., Coma, Vegetative State/Unresponsive Wakefulness Syndrome, Minimally Conscious State. Notwithstanding their technical limitations, we suggest that simulation technologies may offer new solutions to old practical problems, particularly in clinical contexts. We take DOCs as an illustrative case, arguing that the simulation of neural correlates of consciousness is potentially useful for improving treatments of patients with DOCs.
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Affiliation(s)
- Michele Farisco
- Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden
- Science and Society Unit, Biogem Genetic Research Centre, Ariano Irpino (AV), Italy
| | - Jeanette H. Kotaleski
- Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Neuroscience, Karolinska Institute, Solna, Sweden
| | - Kathinka Evers
- Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden
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Abstract
With 300,000,000 riders annually, roller coasters are a popular recreational activity. Although the number of roller coaster injuries is relatively low, the precise effect of roller coaster rides on our brains remains unknown. Here we present the quantitative characterization of brain displacements and deformations during roller coaster rides. For two healthy adult male subjects, we recorded head accelerations during three representative rides, and, for comparison, during running and soccer headers. From the recordings, we simulated brain displacements and deformations using rigid body dynamics and finite element analyses. Our findings show that despite having lower linear accelerations than sports head impacts, roller coasters may lead to brain displacements and strains comparable to mild soccer headers. The peak change in angular velocity on the rides was 9.9 rad/sec, which was higher than the 5.6 rad/sec in soccer headers with ball velocities reaching 7 m/sec. Maximum brain surface displacements of 4.0 mm and maximum principal strains of 7.6% were higher than in running and similar to soccer headers, but below the reported average concussion strain. Brain strain rates during roller coaster rides were similar to those in running, and lower than those in soccer headers. Strikingly, on the same ride and at a similar position, the two subjects experienced significantly different head kinematics and brain deformation. These results indicate that head motion and brain deformation during roller coaster rides are highly sensitive to individual subjects. Although our study suggests that roller coaster rides do not present an immediate risk of acute brain injury, their long-term effects require further longitudinal study.
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Affiliation(s)
- Calvin Kuo
- Department of Mechanical Engineering, Stanford University, Stanford, California
| | - Lyndia C. Wu
- Department of Bioengineering, Stanford University, Stanford, California
| | - Patrick P. Ye
- Department of Bioengineering, Stanford University, Stanford, California
| | - Kaveh Laksari
- Department of Bioengineering, Stanford University, Stanford, California
| | - David B. Camarillo
- Department of Mechanical Engineering, Stanford University, Stanford, California
- Department of Bioengineering, Stanford University, Stanford, California
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California
- Department of Bioengineering, Stanford University, Stanford, California
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Alcocer-Sosa M, Gutiérrez D. Third-order harmonic expansion of the magnetoencephalography forward and inverse problems in an ellipsoidal brain model. Int J Numer Method Biomed Eng 2017; 33:e2810. [PMID: 27343200 DOI: 10.1002/cnm.2810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 05/10/2016] [Accepted: 06/13/2016] [Indexed: 06/06/2023]
Abstract
We present a forward modeling solution in the form of an array response kernel for magnetoencephalography. We consider the case when the brain's anatomy is approximated by an ellipsoid and an equivalent current dipole model is used to approximate brain sources. The proposed solution includes the contributions up to the third-order ellipsoidal harmonic terms; hence, we compare this new approximation against the previously available one that only considered up to second-order harmonics. We evaluated the proposed solution when used in the inverse problem of estimating physiologically feasible visual evoked responses from magnetoencephalography data. Our results showed that the contribution of the third-order harmonic terms provides a more realistic representation of the magnetic fields (closer to those generated with a numerical approximation based on the boundary element method) and, subsecuently, the estimated equivalent current dipoles are a better fit to those observed in practice (e.g., in visual evoked potentials). Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Mauricio Alcocer-Sosa
- Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey, Mexico
| | - David Gutiérrez
- Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey, Mexico
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Matzke H, Schirner M, Vollbrecht D, Rothmeier S, Llarena A, Rojas R, Triebkorn P, Domide L, Mersmann J, Solodkin A, Jirsa VK, McIntosh AR, Ritter P. TVB-EduPack-An Interactive Learning and Scripting Platform for The Virtual Brain. Front Neuroinform 2015; 9:27. [PMID: 26635597 PMCID: PMC4658631 DOI: 10.3389/fninf.2015.00027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 11/03/2015] [Indexed: 11/22/2022] Open
Abstract
The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org.
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Affiliation(s)
- Henrik Matzke
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany
| | - Michael Schirner
- Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany
| | - Daniel Vollbrecht
- Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany
| | - Simon Rothmeier
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany
| | - Adalberto Llarena
- Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany ; Intelligent Systems and Robotics Lab, Department of Mathematics and Computer Science, Free University Berlin, Germany
| | - Raúl Rojas
- Intelligent Systems and Robotics Lab, Department of Mathematics and Computer Science, Free University Berlin, Germany
| | - Paul Triebkorn
- Department of Neurology, Charité - University Medicine Berlin, Germany
| | | | | | - Ana Solodkin
- Departments of Anatomy & Neurobiology and Neurology, School of Medicine, University of California, Irvine Irvine, CA, USA
| | - Viktor K Jirsa
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes UMR 1106, Université d'Aix-Marseille Marseille, France
| | | | - Petra Ritter
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany ; Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University Berlin, Germany
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Eguchi A, Neymotin SA, Stringer SM. Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity. Front Neural Circuits 2014; 8:16. [PMID: 24659956 PMCID: PMC3950416 DOI: 10.3389/fncir.2014.00016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 02/17/2014] [Indexed: 01/13/2023] Open
Abstract
Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red-green and blue-yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell).
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Affiliation(s)
- Akihiro Eguchi
- Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, University of OxfordOxford, UK
| | - Samuel A. Neymotin
- Department of Physiology and Pharmacology, Downstate Medical Center, State University of New YorkNew York, NY, USA
- Department of Neurobiology, Yale University School of MedicineNew Haven, CT, USA
| | - Simon M. Stringer
- Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, University of OxfordOxford, UK
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Alnajjar F, Yamashita Y, Tani J. The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory. Front Neurorobot 2013; 7:2. [PMID: 23423881 PMCID: PMC3575058 DOI: 10.3389/fnbot.2013.00002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2012] [Accepted: 01/29/2013] [Indexed: 11/29/2022] Open
Abstract
Higher-order cognitive mechanisms (HOCM), such as planning, cognitive branching, switching, etc., are known to be the outcomes of a unique neural organizations and dynamics between various regions of the frontal lobe. Although some recent anatomical and neuroimaging studies have shed light on the architecture underlying the formation of such mechanisms, the neural dynamics and the pathways in and between the frontal lobe to form and/or to tune the stability level of its working memory remain controversial. A model to clarify this aspect is therefore required. In this study, we propose a simple neurocomputational model that suggests the basic concept of how HOCM, including the cognitive branching and switching in particular, may mechanistically emerge from time-based neural interactions. The proposed model is constructed such that its functional and structural hierarchy mimics, to a certain degree, the biological hierarchy that is believed to exist between local regions in the frontal lobe. Thus, the hierarchy is attained not only by the force of the layout architecture of the neural connections but also through distinct types of neurons, each with different time properties. To validate the model, cognitive branching and switching tasks were simulated in a physical humanoid robot driven by the model. Results reveal that separation between the lower and the higher-level neurons in such a model is an essential factor to form an appropriate working memory to handle cognitive branching and switching. The analyses of the obtained result also illustrates that the breadth of this separation is important to determine the characteristics of the resulting memory, either static memory or dynamic memory. This work can be considered as a joint research between synthetic and empirical studies, which can open an alternative research area for better understanding of brain mechanisms.
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DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Volumetric intraoperative brain deformation compensation: model development and phantom validation. IEEE Trans Med Imaging 2012; 31:1607-19. [PMID: 22562728 PMCID: PMC3600363 DOI: 10.1109/tmi.2012.2197407] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.
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Venkataraman A, Rathi Y, Kubicki M, Westin CF, Golland P. Joint modeling of anatomical and functional connectivity for population studies. IEEE Trans Med Imaging 2012; 31:164-82. [PMID: 21878411 PMCID: PMC4395500 DOI: 10.1109/tmi.2011.2166083] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation.
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Affiliation(s)
- Archana Venkataraman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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21
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Jespersen SN, Leigland LA, Cornea A, Kroenke CD. Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans Med Imaging 2012; 31:16-32. [PMID: 21768045 PMCID: PMC3271123 DOI: 10.1109/tmi.2011.2162099] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
As neurons of the developing brain form functional circuits, they undergo morphological differentiation. In immature cerebral cortex, radially-oriented cellular processes of undifferentiated neurons impede water diffusion parallel, but not perpendicular, to the pial surface, as measured via diffusion-weighted magnetic resonance imaging, and give rise to water diffusion anisotropy. As the cerebral cortex matures, the loss of water diffusion anisotropy accompanies cellular morphological differentiation. A quantitative relationship is proposed here to relate water diffusion anisotropy measurements directly to characteristics of neuronal morphology. This expression incorporates the effects of local diffusion anisotropy within cellular processes, as well as the effects of anisotropy in the orientations of cellular processes. To obtain experimental support for the proposed relationship, tissue from 13 and 31 day-old ferrets was stained using the rapid Golgi technique, and the 3-D orientation distribution of neuronal processes was characterized using confocal microscopic examination of reflected visible light images. Coregistration of the MRI and Golgi data enables a quantitative evaluation of the proposed theory, and excellent agreement with the theoretical results, as well as agreement with previously published values for locally-induced water diffusion anisotropy and volume fraction of the neuropil, is observed.
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Affiliation(s)
- Sune Nørhøj Jespersen
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, 8000 Aarhus, Denmark ()
| | - Lindsey A. Leigland
- Department of Behavioral Neuroscience and Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239 USA ()
| | - Anda Cornea
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR 97006 USA ()
| | - Christopher D. Kroenke
- Division of Neuroscience, Oregon National Primate Research Center, and the Department of Behavioral Neuroscience and Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239 USA ()
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Abstract
This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.
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Affiliation(s)
- Koen Van Leemput
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
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