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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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2
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Zhang H, Giannakopoulos P, Haller S, Lee SW, Qiu S, Shen D. Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment. Neuroinformatics 2020; 17:547-561. [PMID: 30739281 DOI: 10.1007/s12021-018-9413-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer's disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed inter-network high-order FC (IN-HOFC), as a useful complement to the traditional inter-network FC methods, for characterizing more complex organizations among the large-scale brain networks. In the IN-HOFC, both network definition and inter-network FC are defined in a high-order manner. To test whether IN-HOFC is more sensitive to cognition decline due to brain diseases than traditional inter-network FC, 77 mild cognitive impairments (MCIs) and 89 controls are compared among the conventional methods and our IN-HOFC. The result shows that IN-HOFCs among three temporal lobe-related high-order networks are dampened in MCIs. The impairment of IN-HOFC is especially found between the anterior and posterior medial temporal lobe and could be a potential MCI biomarker at the network level. The competing network-level low-order FC methods, however, either revealing less or failing to detect any group difference. This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies.
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Affiliation(s)
- Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, CB#7513, 130 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | | | - Sven Haller
- Affidea CDRC - Centre Diagnostique Radiologique de Carouge, Carouge, Switzerland
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Department of Neuroradiology, University Hospital Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, 16 Jichang Road, Guangzhou, 510405, Guangdong, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, CB#7513, 130 Mason Farm Road, Chapel Hill, NC, 27599, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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Wang Y, Sibaii F, Custead R, Oh H, Barlow SM. Functional Connectivity Evoked by Orofacial Tactile Perception of Velocity. Front Neurosci 2020; 14:182. [PMID: 32210753 PMCID: PMC7068713 DOI: 10.3389/fnins.2020.00182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/19/2020] [Indexed: 11/13/2022] Open
Abstract
The cortical representations of orofacial pneumotactile stimulation involve complex neuronal networks, which are still unknown. This study aims to identify the characteristics of functional connectivity (FC) evoked by three different saltatory velocities over the perioral and buccal surface of the lower face using functional magnetic resonance imaging in twenty neurotypical adults. Our results showed a velocity of 25 cm/s evoked stronger connection strength between the right dorsolateral prefrontal cortex and the right thalamus than a velocity of 5 cm/s. The decreased FC between the right secondary somatosensory cortex and right posterior parietal cortex for 5-cm/s velocity versus all three velocities delivered simultaneously (“All ON”) and the increased FC between the right thalamus and bilateral secondary somatosensory cortex for 65 cm/s vs “All ON” indicated that the right secondary somatosensory cortex might play a role in the orofacial tactile perception of velocity. Our results have also shown different patterns of FC for each seed (bilateral primary and secondary somatosensory cortex) at various velocity contrasts (5 vs 25 cm/s, 5 vs 65 cm/s, and 25 vs 65 cm/s). The similarities and differences of FC among three velocities shed light on the neuronal networks encoding the orofacial tactile perception of velocity.
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Affiliation(s)
- Yingying Wang
- Neuroimaging for Language, Literacy and Learning Laboratory, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States.,Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States.,Nebraska Center for Research on Children, Youth, Families and schools, University of Nebraska-Lincoln, Lincoln, NE, United States.,Biomedical Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Fatima Sibaii
- Neuroimaging for Language, Literacy and Learning Laboratory, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States.,Biomedical Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Rebecca Custead
- Communication Neuroscience Laboratory, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Hyuntaek Oh
- Biomedical Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.,Communication Neuroscience Laboratory, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Steven M Barlow
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States.,Biomedical Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.,Communication Neuroscience Laboratory, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States
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4
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Puszta A, Pertich Á, Katona X, Bodosi B, Nyujtó D, Giricz Z, Eördegh G, Nagy A. Power-spectra and cross-frequency coupling changes in visual and Audio-visual acquired equivalence learning. Sci Rep 2019; 9:9444. [PMID: 31263168 PMCID: PMC6603188 DOI: 10.1038/s41598-019-45978-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 06/17/2019] [Indexed: 11/09/2022] Open
Abstract
The three phases of the applied acquired equivalence learning test, i.e. acquisition, retrieval and generalization, investigate the capabilities of humans in associative learning, working memory load and rule-transfer, respectively. Earlier findings denoted the role of different subcortical structures and cortical regions in the visual test. However, there is a lack of information about how multimodal cues modify the EEG-patterns during acquired equivalence learning. To test this we have recorded EEG from 18 healthy volunteers and analyzed the power spectra and the strength of cross-frequency coupling, comparing a unimodal visual-guided and a bimodal, audio-visual-guided paradigm. We found that the changes in the power of the different frequency band oscillations were more critical during the visual paradigm and they showed less synchronized activation compared to the audio-visual paradigm. These findings indicate that multimodal cues require less prominent, but more synchronized cortical contribution, which might be a possible biomarker of forming multimodal associations.
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Affiliation(s)
- András Puszta
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary.
| | - Ákos Pertich
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary
| | - Xénia Katona
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary
| | - Balázs Bodosi
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary
| | - Diána Nyujtó
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary
| | - Zsófia Giricz
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary
| | - Gabriella Eördegh
- Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Tisza Lajos krt. 64, Szeged, Hungary
| | - Attila Nagy
- Department of Physiology, Faculty of Medicine, University of Szeged, Dóm tér 10, Szeged, Hungary.
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5
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Puszta A, Katona X, Bodosi B, Pertich Á, Nyujtó D, Braunitzer G, Nagy A. Cortical Power-Density Changes of Different Frequency Bands in Visually Guided Associative Learning: A Human EEG-Study. Front Hum Neurosci 2018; 12:188. [PMID: 29867412 PMCID: PMC5951962 DOI: 10.3389/fnhum.2018.00188] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 04/18/2018] [Indexed: 11/29/2022] Open
Abstract
The computer-based Rutgers Acquired Equivalence test (RAET) is a widely used paradigm to test the function of subcortical structures in visual associative learning. The test consists of an acquisition (pair learning) and a test (rule transfer) phase, associated with the function of the basal ganglia and the hippocampi, respectively. Obviously, such a complex task also requires cortical involvement. To investigate the activity of different cortical areas during this test, 64-channel EEG recordings were recorded in 24 healthy volunteers. Fast-Fourier and Morlet wavelet convolution analyses were performed on the recordings. The most robust power changes were observed in the theta (4–7 Hz) and gamma (>30 Hz) frequency bands, in which significant power elevation was observed in the vast majority of the subjects, over the parieto-occipital and temporo-parietal areas during the acquisition phase. The involvement of the frontal areas in the acquisition phase was remarkably weaker. No remarkable cortical power elevations were found in the test phase. In fact, the power of the alpha and beta bands was significantly decreased over the parietooccipital areas. We conclude that the initial acquisition of the image pairs requires strong cortical involvement, but once the pairs have been learned, neither retrieval nor generalization requires strong cortical contribution.
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Affiliation(s)
- András Puszta
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Xénia Katona
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Balázs Bodosi
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Ákos Pertich
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Diána Nyujtó
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Gábor Braunitzer
- Laboratory for Perception & Cognition and Clinical Neuroscience (LPCCN), National Institute of Psychiatry and Addictions at Nyírő Gyula Hospital, Budapest, Hungary
| | - Attila Nagy
- Sensorimotor Lab, Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
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6
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Di Liberto GM, Lalor EC, Millman RE. Causal cortical dynamics of a predictive enhancement of speech intelligibility. Neuroimage 2018; 166:247-258. [DOI: 10.1016/j.neuroimage.2017.10.066] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 10/04/2017] [Accepted: 10/30/2017] [Indexed: 11/28/2022] Open
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7
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Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates. PLoS One 2017; 12:e0183914. [PMID: 28877194 PMCID: PMC5587334 DOI: 10.1371/journal.pone.0183914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 08/14/2017] [Indexed: 11/19/2022] Open
Abstract
Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis.
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8
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Yoo K, Lee P, Chung MK, Sohn WS, Chung SJ, Na DL, Ju D, Jeong Y. Degree-based statistic and center persistency for brain connectivity analysis. Hum Brain Mapp 2016; 38:165-181. [PMID: 27593391 DOI: 10.1002/hbm.23352] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 05/18/2016] [Accepted: 08/08/2016] [Indexed: 12/16/2022] Open
Abstract
Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Kwangsun Yoo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Peter Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Moo K Chung
- Department of Biostatistics and Medical Informatics and Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin 53706
| | - William S Sohn
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Sun Ju Chung
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea
| | - Daheen Ju
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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