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Lu M, Guo Z, Gao Z. Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy. Front Hum Neurosci 2023; 17:1295326. [PMID: 38178992 PMCID: PMC10765510 DOI: 10.3389/fnhum.2023.1295326] [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: 09/16/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators. Methods The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests. Results DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset. Conclusion Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.
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Affiliation(s)
- Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
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2
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Sun Y, Shi Q, Ye M, Miao A. Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation. Front Neurosci 2023; 17:1282232. [PMID: 38075280 PMCID: PMC10701286 DOI: 10.3389/fnins.2023.1282232] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024] Open
Abstract
Objective Although intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers. Methods Our study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study. Results Compared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI. Conclusion The brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties.
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Affiliation(s)
- Yulei Sun
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qi Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, China
| | - Min Ye
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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3
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Finn ES, Poldrack RA, Shine JM. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 2023; 623:263-273. [PMID: 37938706 DOI: 10.1038/s41586-023-06670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023]
Abstract
Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA.
| | | | - James M Shine
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
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Bacon EJ, Jin C, He D, Hu S, Wang L, Li H, Qi S. Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis. Front Neurosci 2023; 17:1163111. [PMID: 37152592 PMCID: PMC10157077 DOI: 10.3389/fnins.2023.1163111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 02/10/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Objective Epilepsy is considered as a neural network disorder. Seizure activity in epilepsy may disturb brain networks and damage brain functions. We propose using resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize connectivity patterns in drug-resistant epilepsy. Methods This study enrolled 47 participants, including 28 with drug-resistant epilepsy and 19 healthy controls. Functional and effective connectivity was employed to assess drug-resistant epilepsy patients within resting state networks. The resting state functional connectivity (FC) analysis was performed to assess connectivity between each patient and healthy controls within the default mode network (DMN) and the dorsal attention network (DAN). In addition, dynamic causal modeling was used to compute effective connectivity (EC). Finally, a statistical analysis was performed to evaluate our findings. Results The FC analysis revealed significant connectivity changes in patients giving 64.3% (18/28) and 78.6% (22/28) for DMN and DAN, respectively. Statistical analysis of FC was significant between the medial prefrontal cortex, posterior cingulate cortex, and bilateral inferior parietal cortex for DMN. For DAN, it was significant between the left and the right intraparietal sulcus and the frontal eye field. For the DMN, the patient group showed significant EC connectivity in the right inferior parietal cortex and the medial prefrontal cortex for the DMN. There was also bilateral connectivity between the medial prefrontal cortex and the posterior cingulate cortex, as well as between the left and right inferior parietal cortex. For DAN, patients showed significant connectivity in the right frontal eye field and the right intraparietal sulcus. Bilateral connectivity was also found between the left frontal eye field and the left intraparietal sulcus, as well as between the right frontal eye field and the right intraparietal sulcus. The statistical analysis of the EC revealed a significant result in the medial prefrontal cortex and the right intraparietal cortex for the DMN. The DAN was found significant in the left frontal eye field, as well as the left and right intraparietal sulcus. Conclusion Our results provide preliminary evidence to support that the combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks.
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Affiliation(s)
- Eric Jacob Bacon
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Chaoyang Jin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuaishuai Hu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Han Li
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
- *Correspondence: Han Li,
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
- Shouliang Qi,
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Aerts H, Colenbier N, Almgren H, Dhollander T, Daparte JR, Clauw K, Johri A, Meier J, Palmer J, Schirner M, Ritter P, Marinazzo D. Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling. Sci Data 2022; 9:676. [PMID: 36335218 PMCID: PMC9637199 DOI: 10.1038/s41597-022-01806-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients’ caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed. Measurement(s) | BOLD signal • Diffusion Anisotropy | Technology Type(s) | Functional Magnetic Resonance Imaging • Diffusion Weighted Imaging | Factor Type(s) | Surgery | Sample Characteristic - Organism | Homo sapiens |
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Sawada M, Adolphs R, Dlouhy BJ, Jenison RL, Rhone AE, Kovach CK, Greenlee JDW, Howard Iii MA, Oya H. Mapping effective connectivity of human amygdala subdivisions with intracranial stimulation. Nat Commun 2022; 13:4909. [PMID: 35987994 PMCID: PMC9392722 DOI: 10.1038/s41467-022-32644-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 08/08/2022] [Indexed: 01/21/2023] Open
Abstract
The primate amygdala is a complex consisting of over a dozen nuclei that have been implicated in a host of cognitive functions, individual differences, and psychiatric illnesses. These functions are implemented through distinct connectivity profiles, which have been documented in animals but remain largely unknown in humans. Here we present results from 25 neurosurgical patients who had concurrent electrical stimulation of the amygdala with intracranial electroencephalography (electrical stimulation tract-tracing; es-TT), or fMRI (electrical stimulation fMRI; es-fMRI), methods providing strong inferences about effective connectivity of amygdala subdivisions with the rest of the brain. We quantified functional connectivity with medial and lateral amygdala, the temporal order of these connections on the timescale of milliseconds, and also detail second-order effective connectivity among the key nodes. These findings provide a uniquely detailed characterization of human amygdala functional connectivity that will inform functional neuroimaging studies in healthy and clinical populations.
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Affiliation(s)
- Masahiro Sawada
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Department of Neurosurgery, Tazuke Kofukai Medical Research Institute and Kitano Hospital, Osaka, Japan
| | - Ralph Adolphs
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Brian J Dlouhy
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Rick L Jenison
- Department of Neuroscience, University of Wisconsin - Madison, Madison, WI, USA
| | - Ariane E Rhone
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Christopher K Kovach
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Jeremy D W Greenlee
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Matthew A Howard Iii
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
- Pappajohn Biomedical Institute, University of Iowa, Iowa City, IA, USA
| | - Hiroyuki Oya
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA.
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Thilakavathy P, Diwan B. An Adaboost Support Vector Machine Based Harris Hawks Optimization Algorithm for Intelligent Quotient Estimation from MRI Images. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10895-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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8
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Hearne LJ, Mill RD, Keane BP, Repovš G, Anticevic A, Cole MW. Activity flow underlying abnormalities in brain activations and cognition in schizophrenia. Sci Adv 2021; 7:7/29/eabf2513. [PMID: 34261649 PMCID: PMC8279516 DOI: 10.1126/sciadv.abf2513] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 05/28/2021] [Indexed: 05/03/2023]
Abstract
Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.
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Affiliation(s)
- Luke J Hearne
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA.
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Brian P Keane
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers University, Piscataway, NJ, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Aškerčeva 2, Ljubljana SI-1000, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
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Pedersen M, Zalesky A. Intracranial brain stimulation modulates fMRI-based network switching. Neurobiol Dis 2021; 156:105401. [PMID: 34023395 DOI: 10.1016/j.nbd.2021.105401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 02/04/2021] [Revised: 04/26/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022] Open
Abstract
The extent to which functional MRI (fMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous electrical stimulation (es-fMRI) and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced after intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is likely increased in epilepsy, we hypothesised that intracranial stimulation would reduce the brain's switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved es-fMRI connectivity. Network switching and synchrony was decreased after the first brain stimulation, followed by a more consistent pattern of network switching over time. This change was commonly observed in cortical networks and adjacent to the electrode targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in epilepsy.
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Affiliation(s)
- Mangor Pedersen
- Department of Psychology and Neuroscience, Auckland University of Technology (AUT), Auckland, New Zealand.
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, VIC, Australia; Melbourne School of Engineering, The University of Melbourne, VIC, Australia
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Rocchi F, Oya H, Balezeau F, Billig AJ, Kocsis Z, Jenison RL, Nourski KV, Kovach CK, Steinschneider M, Kikuchi Y, Rhone AE, Dlouhy BJ, Kawasaki H, Adolphs R, Greenlee JDW, Griffiths TD, Howard MA, Petkov CI. Common fronto-temporal effective connectivity in humans and monkeys. Neuron 2021; 109:852-868.e8. [PMID: 33482086 PMCID: PMC7927917 DOI: 10.1016/j.neuron.2020.12.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/02/2020] [Accepted: 12/30/2020] [Indexed: 01/24/2023]
Abstract
Human brain pathways supporting language and declarative memory are thought to have differentiated substantially during evolution. However, cross-species comparisons are missing on site-specific effective connectivity between regions important for cognition. We harnessed functional imaging to visualize the effects of direct electrical brain stimulation in macaque monkeys and human neurosurgery patients. We discovered comparable effective connectivity between caudal auditory cortex and both ventro-lateral prefrontal cortex (VLPFC, including area 44) and parahippocampal cortex in both species. Human-specific differences were clearest in the form of stronger hemispheric lateralization effects. In humans, electrical tractography revealed remarkably rapid evoked potentials in VLPFC following auditory cortex stimulation and speech sounds drove VLPFC, consistent with prior evidence in monkeys of direct auditory cortex projections to homologous vocalization-responsive regions. The results identify a common effective connectivity signature in human and nonhuman primates, which from auditory cortex appears equally direct to VLPFC and indirect to the hippocampus. VIDEO ABSTRACT.
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Affiliation(s)
- Francesca Rocchi
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK.
| | - Hiroyuki Oya
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA.
| | - Fabien Balezeau
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | | | - Zsuzsanna Kocsis
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK; Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA
| | - Rick L Jenison
- Department of Neuroscience, University of Wisconsin - Madison, Madison, WI, USA
| | - Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | | | - Mitchell Steinschneider
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Yukiko Kikuchi
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Ariane E Rhone
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA
| | - Brian J Dlouhy
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Hiroto Kawasaki
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA
| | - Ralph Adolphs
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Jeremy D W Greenlee
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK; Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Matthew A Howard
- Department of Neurosurgery, The University of Iowa, Iowa City, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA; Pappajohn Biomedical Institute, The University of Iowa, Iowa City, IA, USA
| | - Christopher I Petkov
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK.
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