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Lucas A, Mouchtaris S, Tranquille A, Sinha N, Gallagher R, Mojena M, Stein JM, Das S, Davis KA. Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study. Epilepsia 2024; 65:1092-1106. [PMID: 38345348 DOI: 10.1111/epi.17908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy. METHODS Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach. RESULTS In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. SIGNIFICANCE Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sofia Mouchtaris
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashley Tranquille
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ryan Gallagher
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marissa Mojena
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kathryn A Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Ji J, Zhang Z, Han L, Liu J. MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation. Comput Biol Med 2024; 170:107940. [PMID: 38232454 DOI: 10.1016/j.compbiomed.2024.107940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/18/2023] [Accepted: 01/01/2024] [Indexed: 01/19/2024]
Abstract
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder-decoder based methods can effectively extract the connections in fMRI time series, which have achieved promising performance. However, these methods generally use Granger causality model, which may identify false directions due to the non-stationary characteristic of fMRI data. Additionally, fMRI datasets have limited sample sizes, which significantly constrains the development of these methods. In this paper, we propose a novel brain effective connectivity estimation method based on causal autoencoder with meta-knowledge transfer, called MetaCAE. The proposed approach employs a causal autoencoder (CAE) to extract causal dependencies from non-stationary fMRI time series, and leverages meta-knowledge transfer to improve the estimation accuracy on small-sample data. More specifically, MetaCAE first employs a temporal convolutional encoder to extract non-stationary temporal information from fMRI time series. Then it uses a structural equation model-based decoder to decode causal relationships between brain regions. Finally, it utilizes a model-agnostic meta-learning method to learn the meta-knowledge of the shared brain effective connectivity among different subjects, and transfers the meta-knowledge to the CAE to enhance its estimation ability on small-sample fMRI data. Comprehensive experiments on both simulated and real-world data demonstrate the efficacy of MetaCAE in estimating brain effective connectivity.
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Affiliation(s)
- Junzhong Ji
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Zuozhen Zhang
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Lu Han
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jinduo Liu
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China.
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3
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Marks VS, Balzekas I, Grimm JA, Richner TJ, Sladky V, Mivalt F, Gregg NM, Lundstrom BN, Miller KJ, Joseph B, Van Gompel J, Brinkmann B, Croarkin P, Alden EC, Kremen V, Kucewicz M, Worrell GA. High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.14.24302765. [PMID: 38405801 PMCID: PMC10888989 DOI: 10.1101/2024.02.14.24302765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients' cognitive function, independently of its impact on seizure rates.
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Affiliation(s)
- Victoria S Marks
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
| | - Irena Balzekas
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
| | - Jessica A Grimm
- Department of Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Thomas J Richner
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Vladimir Sladky
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
- International Clinic Research Center, St. Anne's University Research Hospital, Brno, Czechia
| | - Filip Mivalt
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Nicholas M Gregg
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Brian N Lundstrom
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- International Clinic Research Center, St. Anne's University Research Hospital, Brno, Czechia
- Department of Biostatistics, Mayo Clinic, Rochester, MN, United States
- BioTechMed Center, Brain & Mind Electrophysiology Lab, Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Boney Joseph
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Jamie Van Gompel
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Benjamin Brinkmann
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Paul Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Eva C Alden
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Michal Kucewicz
- BioTechMed Center, Brain & Mind Electrophysiology Lab, Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Gregory A Worrell
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
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Kasai S, Watanabe K, Umemura Y, Ishimoto Y, Sasaki M, Nagaya H, Tatsuo S, Mikami T, Tamada Y, Ide S, Tomiyama M, Matsuzaka M, Kakeda S. Altered structural hippocampal intra-networks in a general elderly Japanese population with mild cognitive impairment. Sci Rep 2023; 13:13330. [PMID: 37587138 PMCID: PMC10432547 DOI: 10.1038/s41598-023-39569-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/27/2023] [Indexed: 08/18/2023] Open
Abstract
Although altered networks inside the hippocampus (hippocampal intra-networks) have been observed in dementia, the evaluation of hippocampal intra-networks using magnetic resonance imaging (MRI) is challenging. We employed conventional structural imaging and incident component analysis (ICA) to investigate the structural covariance of the hippocampal intra-networks. We aimed to assess altered hippocampal intra-networks in patients with mild cognitive impairment (MCI). A cross-sectional study of 2122 participants with 3T MRI (median age 69 years, 60.9% female) were divided into 218 patients with MCI and 1904 cognitively normal older adults (CNOA). By employing 3D T1-weighted imaging, voxels within the hippocampus were entered into the ICA analysis to extract the structural covariance intra-networks within the hippocampus. The ICA extracted 16 intra-networks from the hippocampal structural images, which were divided into two bilateral networks and 14 ipsilateral networks. Of the 16 intra-networks, two (one bilateral network and one ipsilateral networks) were significant predictors of MCI from the CNOA after adjusting for age, sex, education, disease history, and hippocampal volume/total intracranial volume ratio. In conclusion, we found that the relationship between hippocampal intra-networks and MCI was independent from the hippocampal volume. Our results suggest that altered hippocampal intra-networks may reflect a different pathology in MCI from that of brain atrophy.
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Affiliation(s)
- Sera Kasai
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Keita Watanabe
- Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajiimachi, Jokyo-ku, Kyoto-shi, Kyoto-fu, Japan.
| | - Yoshihito Umemura
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Yuka Ishimoto
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Miho Sasaki
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Haruka Nagaya
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Soichiro Tatsuo
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Tatsuya Mikami
- Innovation Center for Health Promotion, Hirosaki University, Hirosaki, Japan
| | - Yoshinori Tamada
- Innovation Center for Health Promotion, Hirosaki University, Hirosaki, Japan
| | - Satoru Ide
- Department of Radiology, University of Occupational and Environmental Health, School of Medicine, Kitakyushu, Japan
| | - Masahiko Tomiyama
- Department of Neurology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Masashi Matsuzaka
- Department of Medical Informatics, Hirosaki University Hospital, Hirosaki, Japan
| | - Shingo Kakeda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
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5
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Zhang Z, Zhang Z, Ji J, Liu J. Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data. Brain Sci 2023; 13:995. [PMID: 37508927 PMCID: PMC10376969 DOI: 10.3390/brainsci13070995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC. In detail, AT-EC first employs an amortization transformer to model the dynamics of fMRI time series and infer brain effective connectivity across different subjects, which can train an amortized model that leverages the shared knowledge from different subjects. Then, an assisted learning mechanism based on functional connectivity is designed to assist the estimation of the brain effective connectivity network. Experimental results on both simulated and real-world data demonstrate the efficacy of our method.
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Affiliation(s)
- Zuozhen Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Ziqi Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Junzhong Ji
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jinduo Liu
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Wang N, Liang C, Zhang X, Sui C, Gao Y, Guo L, Wen H. Brain structure-function coupling associated with cognitive impairment in cerebral small vessel disease. Front Neurosci 2023; 17:1163274. [PMID: 37346086 PMCID: PMC10279881 DOI: 10.3389/fnins.2023.1163274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is a common chronic and progressive disease that can lead to mental and cognitive impairment. Damage to brain structure and function may play an important role in the neuropsychiatric disorders of patients with CSVD. Increasing evidence suggests that functional changes are accompanied by structural changes in corresponding brain regions. Thus, normal structure-function coupling is essential for optimal brain performance, and disrupted structure-function coupling can be found in many neurological and psychiatric disorders. To date, most studies on patients with CSVD have focused on separate structures or functions, including reductions in white matter volume and blood flow, which lead to cognitive dysfunction. However, there are few studies on brain structure-function coupling in patients with CSVD. In recent years, with the rapid development of multilevel (voxel-wise, neurovascular, regional level, and network level) brain structure-functional coupling analysis methods based on multimodal magnetic resonance imaging (MRI), new evidence has been provided to reveal the correlation between brain function and structural abnormalities and cognitive impairment. Therefore, studying brain structure-function coupling has a potential significance in the exploration and elucidation of the neurobiological mechanism of cognitive impairment in patients with CSVD. This article mainly describes the currently popular brain structure-function coupling analysis technology based on multimodal MRI and the important research progress of these coupling technologies on CSVD and cognitive impairment to provide a perspective for the study of the pathogenesis and early diagnosis of CSVD.
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Affiliation(s)
- Na Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Changhu Liang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xinyue Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chaofan Sui
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yian Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Lingfei Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing, China
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Plachti A, Latzman RD, Balajoo SM, Hoffstaedter F, Madsen KS, Baare W, Siebner HR, Eickhoff SB, Genon S. Hippocampal anterior- posterior shift in childhood and adolescence. Prog Neurobiol 2023; 225:102447. [PMID: 36967075 PMCID: PMC10185869 DOI: 10.1016/j.pneurobio.2023.102447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
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Affiliation(s)
- Anna Plachti
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Robert D Latzman
- Data Sciences Institute, Takeda Pharmaceutical, Cambridge, MA, USA
| | | | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Radiography, Department of Technology, University College Copenhagen, Copenhagen, Denmark
| | - William Baare
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium.
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Comino Garcia-Munoz A, Alemán-Gómez Y, Toledano R, Poch C, García-Morales I, Aledo-Serrano Á, Gil-Nagel A, Campo P. Morphometric and microstructural characteristics of hippocampal subfields in mesial temporal lobe epilepsy and their correlates with mnemonic discrimination. Front Neurol 2023; 14:1096873. [PMID: 36864916 PMCID: PMC9972498 DOI: 10.3389/fneur.2023.1096873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 01/18/2023] [Indexed: 02/16/2023] Open
Abstract
Introduction Pattern separation (PS) is a fundamental aspect of memory creation that defines the ability to transform similar memory representations into distinct ones, so they do not overlap when storing and retrieving them. Experimental evidence in animal models and the study of other human pathologies have demonstrated the role of the hippocampus in PS, in particular of the dentate gyrus (DG) and CA3. Patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HE) commonly report mnemonic deficits that have been associated with failures in PS. However, the link between these impairments and the integrity of the hippocampal subfields in these patients has not yet been determined. The aim of this work is to explore the association between the ability to perform mnemonic functions and the integrity of hippocampal CA1, CA3, and DG in patients with unilateral MTLE-HE. Method To reach this goal we evaluated the memory of patients with an improved object mnemonic similarity test. We then analyzed the hippocampal complex structural and microstructural integrity using diffusion weighted imaging. Results Our results indicate that patients with unilateral MTLE-HE present alterations in both volume and microstructural properties at the level of the hippocampal subfields DG, CA1, CA3, and the subiculum, that sometimes depend on the lateralization of their epileptic focus. However, none of the specific changes was found to be directly related to the performance of the patients in a pattern separation task, which might indicate a contribution of various alterations to the mnemonic deficits or the key contribution of other structures to the function. Discussion we established for the first time the alterations in both the volume and the microstructure at the level of the hippocampal subfields in a group of unilateral MTLE patients. We observed that these changes are greater in the DG and CA1 at the macrostructural level, and in CA3 and CA1 in the microstructural level. None of these changes had a direct relation to the performance of the patients in a pattern separation task, which suggests a contribution of various alterations to the loss of function.
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Affiliation(s)
- Alicia Comino Garcia-Munoz
- Centre de Résonance Magnétique Biologique et Médicale-Unité Mixte de Recherche 7339, Aix-Marseille Université, Marseille, France
| | - Yasser Alemán-Gómez
- Connectomics Lab, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Rafael Toledano
- Epilepsy Unit, Neurology Department, Hospital Ruber Internacional, Madrid, Spain,Epilepsy Unit, Neurology Department, University Hospital Ramón y Cajal, Madrid, Spain
| | - Claudia Poch
- Facultad de Lenguas y Educación, Universidad de Nebrija, Madrid, Spain
| | - Irene García-Morales
- Epilepsy Unit, Neurology Department, Hospital Ruber Internacional, Madrid, Spain,Epilepsy Unit, Neurology Department, University Hospital of San Carlos, Madrid, Spain
| | - Ángel Aledo-Serrano
- Epilepsy Unit, Neurology Department, Hospital Ruber Internacional, Madrid, Spain
| | - Antonio Gil-Nagel
- Epilepsy Unit, Neurology Department, Hospital Ruber Internacional, Madrid, Spain
| | - Pablo Campo
- Department of Basic Psychology, Autonoma University of Madrid, Madrid, Spain,*Correspondence: Pablo Campo ✉
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9
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Wang Y, Zhang Y, Zheng W, Liu X, Zhao Z, Li S, Chen N, Yang L, Fang L, Yao Z, Hu B. Age-Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals. J Magn Reson Imaging 2023; 57:434-443. [PMID: 35924281 DOI: 10.1002/jmri.28318] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Healthy aging is usually accompanied by alterations in brain network architecture, influencing information processing and cognitive performance. However, age-associated coordination patterns of morphological networks and cognitive variation are not well understood. PURPOSE To investigate the age-related differences of cortical topology in morphological brain networks from multiple perspectives. STUDY TYPE Prospective, observational multisite study. POPULATION A total of 1427 healthy participants (59.1% female, 51.75 ± 19.82 years old) from public datasets. FIELD STRENGTH/SEQUENCE 1.5 T/3 T, T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence. ASSESSMENT The multimodal parcellation atlas was used to define regions of interest (ROIs). The Jensen-Shannon divergence-based individual morphological networks were constructed by estimating the interregional similarity of cortical thickness distribution. Graph-theory based global network properties were then calculated, followed by ROI analysis (including global/nodal topological analysis and hub analysis) with statistical tests. STATISTICAL TESTS Chi-square test, Jensen-Shannon divergence-based similarity measurement, general linear model with false discovery rate correction. Significance was set at P < 0.05. RESULTS The clustering coefficient (q = 0.016), global efficiency (q = 0.007), and small-worldness (q = 0.006) were significantly negatively quadratic correlated with age. The group-level hubs of seven age groups were found mainly distributed in default mode network, visual network, salient network, and somatosensory motor network (the sum of these hubs' distribution in each group exceeds 55%). Further ROI-wise analysis showed significant nodal trajectories of intramodular connectivities. DATA CONCLUSION These results demonstrated the age-associated reconfiguration of morphological networks. Specifically, network segregation/integration had an inverted U-shaped relationship with age, which indicated age-related differences in transmission efficiency. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,Guangyuan Mental Health Center, Guangyuan, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xia Liu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lei Fang
- PET/CT Center, The 940th Hospital of Joint Logistic Support Force of PLA, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.,Engineering Research Center of Open Source Software and Real-Time System, Lanzhou University, Ministry of Education, Lanzhou, China
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10
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Peng X, Liu Q, Hubbard CS, Wang D, Zhu W, Fox MD, Liu H. Robust dynamic brain coactivation states estimated in individuals. SCIENCE ADVANCES 2023; 9:eabq8566. [PMID: 36652524 PMCID: PMC9848428 DOI: 10.1126/sciadv.abq8566] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/14/2022] [Indexed: 06/01/2023]
Abstract
A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.
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Affiliation(s)
- Xiaolong Peng
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Liu
- Changping Laboratory, Beijing, China
| | - Catherine S. Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
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11
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Watanabe K, Okamoto N, Ueda I, Tesen H, Fujii R, Ikenouchi A, Yoshimura R, Kakeda S. Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder. Brain Commun 2023; 5:fcac323. [PMID: 36601619 PMCID: PMC9798279 DOI: 10.1093/braincomms/fcac323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/27/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.
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Affiliation(s)
- Keita Watanabe
- Open Innovation Institute, Kyoto University, Kyoto 6068501, Japan
| | - Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Issei Ueda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
| | - Hirofumi Tesen
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Rintaro Fujii
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Shingo Kakeda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
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12
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Liu J, Ji J, Xun G, Zhang A. Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5993-6006. [PMID: 33886478 DOI: 10.1109/tnnls.2021.3072149] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.
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13
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Lucas A, Nanga RPR, Hadar P, Chen S, Gibson A, Oechsel K, Elliott MA, Stein JM, Das S, Reddy R, Detre JA, Davis KA. Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging. Hum Brain Mapp 2022; 44:549-558. [PMID: 36173151 PMCID: PMC9842879 DOI: 10.1002/hbm.26083] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/18/2022] [Accepted: 08/29/2022] [Indexed: 01/25/2023] Open
Abstract
Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.
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Affiliation(s)
- Alfredo Lucas
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,University of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ravi Prakash Reddy Nanga
- Center for Advanced Metabolic Imaging in Precision MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Peter Hadar
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA,Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Stephanie Chen
- Department of Neurology (work conducted while at the University of Pennsylvania)University of Maryland School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Adam Gibson
- Virginia Commonwealth University School of Medicine (work conducted while at the University of Pennsylvania)PhiladelphiaPennsylvaniaUSA
| | - Kelly Oechsel
- Wake Forest University School of Medicine (work conducted while at the University of Pennsylvania)PhiladelphiaPennsylvaniaUSA
| | - Mark A. Elliott
- Center for Advanced Metabolic Imaging in Precision MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Joel M. Stein
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Sandhitsu Das
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ravinder Reddy
- Center for Advanced Metabolic Imaging in Precision MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - John A. Detre
- Center for Advanced Metabolic Imaging in Precision MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA,Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA,Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Kathryn A. Davis
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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14
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Postnikova TY, Diespirov GP, Amakhin DV, Vylekzhanina EN, Soboleva EB, Zaitsev AV. Impairments of Long-Term Synaptic Plasticity in the Hippocampus of Young Rats during the Latent Phase of the Lithium-Pilocarpine Model of Temporal Lobe Epilepsy. Int J Mol Sci 2021; 22:ijms222413355. [PMID: 34948152 PMCID: PMC8705146 DOI: 10.3390/ijms222413355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Status epilepticus (SE) causes persistent abnormalities in the functioning of neuronal networks, often resulting in worsening epileptic seizures. Many details of cellular and molecular mechanisms of seizure-induced changes are still unknown. The lithium–pilocarpine model of epilepsy in rats reproduces many features of human temporal lobe epilepsy. In this work, using the lithium–pilocarpine model in three-week-old rats, we examined the morphological and electrophysiological changes in the hippocampus within a week following pilocarpine-induced seizures. We found that almost a third of the neurons in the hippocampus and dentate gyrus died on the first day, but this was not accompanied by impaired synaptic plasticity at that time. A diminished long-term potentiation (LTP) was observed following three days, and the negative effect of SE on plasticity increased one week later, being accompanied by astrogliosis. The attenuation of LTP was caused by the weakening of N-methyl-D-aspartate receptor (NMDAR)-dependent signaling. NMDAR-current was more than two-fold weaker during high-frequency stimulation in the post-SE rats than in the control group. Application of glial transmitter D-serine, a coagonist of NMDARs, allows the enhancement of the NMDAR-dependent current and the restoration of LTP. These results suggest that the disorder of neuron–astrocyte interactions plays a critical role in the impairment of synaptic plasticity.
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15
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Willems T, Henke K. Imaging human engrams using 7 Tesla magnetic resonance imaging. Hippocampus 2021; 31:1257-1270. [PMID: 34739173 PMCID: PMC9298259 DOI: 10.1002/hipo.23391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 12/15/2022]
Abstract
The investigation of the physical traces of memories (engrams) has made significant progress in the last decade due to optogenetics and fluorescent cell tagging applied in rodents. Engram cells were identified. The ablation of engram cells led to the loss of the associated memory, silent memories were reactivated, and artificial memories were implanted in the brain. Human engram research lags behind engram research in rodents due to methodological and ethical constraints. However, advances in multivariate analysis techniques of functional magnetic resonance imaging (fMRI) data and machine learning algorithms allowed the identification of stable engram patterns in humans. In addition, MRI scanners with an ultrahigh field strength of 7 Tesla (T) have left their prototype state and became more common around the world to assist human engram research. Although most engram research in humans is still being performed with a field strength of 3T, fMRI at 7T will push engram research. Here, we summarize the current state and findings of human engram research and discuss the advantages and disadvantages of applying 7 versus 3T fMRI to image human memory traces.
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Affiliation(s)
- Tom Willems
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Katharina Henke
- Institute of Psychology, University of Bern, Bern, Switzerland
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16
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Hadar PN, Kini LG, Nanga RPR, Shinohara RT, Chen SH, Shah P, Wisse LEM, Elliott MA, Hariharan H, Reddy R, Detre JA, Stein JM, Das S, Davis KA. Volumetric glutamate imaging (GluCEST) using 7T MRI can lateralize nonlesional temporal lobe epilepsy: A preliminary study. Brain Behav 2021; 11:e02134. [PMID: 34255437 PMCID: PMC8413808 DOI: 10.1002/brb3.2134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 03/18/2021] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION Drug-resistant epilepsy patients show worse outcomes after resection when standard neuroimaging is nonlesional, which occurs in one-third of patients. In prior work, we employed 2-D glutamate imaging, Glutamate Chemical Exchange Saturation Transfer (GluCEST), to lateralize seizure onset in nonlesional temporal lobe epilepsy (TLE) based on increased ipsilateral GluCEST signal in the total hippocampus and hippocampal head. We present a significant advancement to single-slice GluCEST imaging, allowing for three-dimensional analysis of brain glutamate networks. METHODS The study population consisted of four MRI-negative, nonlesional TLE patients (two male, two female) with electrographically identified left temporal onset seizures. Imaging was conducted on a Siemens 7T MRI scanner using the CEST method for glutamate, while the advanced normalization tools (ANTs) pipeline and the Automated Segmentation of the Hippocampal Subfields (ASHS) method were employed for image analysis. RESULTS Volumetric GluCEST imaging was validated in four nonlesional TLE patients showing increased glutamate lateralized to the hippocampus of seizure onset (p = .048, with a difference among ipsilateral to contralateral GluCEST signal percentage ranging from -0.05 to 1.37), as well as increased GluCEST signal in the ipsilateral subiculum (p = .034, with a difference among ipsilateral to contralateral GluCEST signal ranging from 0.13 to 1.57). CONCLUSIONS The ability of 3-D, volumetric GluCEST to localize seizure onset down to the hippocampal subfield in nonlesional TLE is an improvement upon our previous 2-D, single-slice GluCEST method. Eventually, we hope to expand volumetric GluCEST to whole-brain glutamate imaging, thus enabling noninvasive analysis of glutamate networks in epilepsy and potentially leading to improved clinical outcomes.
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Affiliation(s)
- Peter N Hadar
- Penn Epilepsy Center, Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith G Kini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Prakash Reddy Nanga
- Center for Magnetic Resonance & Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie H Chen
- Penn Epilepsy Center, Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Penn Image Computing & Science Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark A Elliott
- Center for Magnetic Resonance & Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hari Hariharan
- Center for Magnetic Resonance & Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravinder Reddy
- Center for Magnetic Resonance & Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Center for Magnetic Resonance & Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Penn Image Computing & Science Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Penn Epilepsy Center, Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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17
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Zaitsev АV, Amakhin DV, Dyomina AV, Zakharova MV, Ergina JL, Postnikova TY, Diespirov GP, Magazanik LG. Synaptic Dysfunction in Epilepsy. J EVOL BIOCHEM PHYS+ 2021. [DOI: 10.1134/s002209302103008x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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18
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Chang WT, Langella SK, Tang Y, Ahmad S, Zhang H, Yap PT, Giovanello KS, Lin W. Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI. Sci Rep 2021; 11:10835. [PMID: 34035413 PMCID: PMC8149395 DOI: 10.1038/s41598-021-90364-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 05/05/2021] [Indexed: 02/04/2023] Open
Abstract
The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.
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Affiliation(s)
- Wei-Tang Chang
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Stephanie K. Langella
- grid.10698.360000000122483208Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yichuan Tang
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Sahar Ahmad
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Han Zhang
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Pew-Thian Yap
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Kelly S. Giovanello
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Weili Lin
- grid.10698.360000000122483208Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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19
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Davis KA, Jirsa VK, Schevon CA. Wheels Within Wheels: Theory and Practice of Epileptic Networks. Epilepsy Curr 2021; 21:15357597211015663. [PMID: 33988042 PMCID: PMC8512917 DOI: 10.1177/15357597211015663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Kathryn A. Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Viktor K. Jirsa
- Aix-Marseille Universite, Marseille, Provence-Alpes-Cote d’Azu, France
- INSERM, Paris, Ile-de-France, France
- Institute de Neurosciences des Systemes,
Marseille, Provence-Alpes-Cote d’Azu, France
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20
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Yang C, Ren J, Li W, Lu M, Wu S, Chu T. Individual-level morphological hippocampal networks in patients with Alzheimer's disease. Brain Cogn 2021; 151:105748. [PMID: 33971496 DOI: 10.1016/j.bandc.2021.105748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 11/15/2022]
Abstract
In patients with Alzheimer's Disease (AD), the hippocampal network has been extensively investigated in previous studies; however, little is known about the morphological network associated with the hippocampus in the AD patients. A total of 68 patients with AD and another 68 gender and age matched healthy subjects were studied. Individual-level morphological hippocampal networks were constructed based on volume and texture features extracted from MRI to study the connections between bilateral hippocampus and 11 other subcortical gray matter structures. The relationship between morphological connections and Mini-Mental State Examination (MMSE) scores was also studied. Connections between bilateral hippocampus and bilateral thalamus, bilateral putamen were significant differences between the AD patients and controls (p < 0.05). There were significantly different in bilateral hippocampal connectivity, and for the left hippocampus, the connection to the right caudate were found to be statistically significant. The morphological connections between left hippocampus and bilateral thalamus (left: R = 0.371, p < 0.001; right: R = 0.411, p < 0.001), bilateral putamen (left: R = 0.383, p < 0.001; right: R = 0.348, p < 0.001), right hippocampus and bilateral thalamus (left: R = 0.370, p < 0.001; right: R = 0.387, p < 0.001), left putamen (R = 0.377, p < 0.001) were significantly positively correlated with the MMSE scores. Similar patterns were observed for left and right hippocampal connectivity and the connections highly associated with MMSE scores were also within the abnormal connections in AD patients.
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Affiliation(s)
- Chunlan Yang
- Department of Environment and Life, Beijing University of Technology, Beijing 100020, China
| | - Jiechuan Ren
- Department of Epilepsy, Neurology Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Wan Li
- Department of Environment and Life, Beijing University of Technology, Beijing 100020, China
| | - Min Lu
- Department of Environment and Life, Beijing University of Technology, Beijing 100020, China
| | - Shuicai Wu
- Department of Environment and Life, Beijing University of Technology, Beijing 100020, China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong 264000, China.
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21
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Ezama L, Hernández-Cabrera JA, Seoane S, Pereda E, Janssen N. Functional connectivity of the hippocampus and its subfields in resting-state networks. Eur J Neurosci 2021; 53:3378-3393. [PMID: 33786931 PMCID: PMC8252772 DOI: 10.1111/ejn.15213] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/14/2021] [Accepted: 03/18/2021] [Indexed: 11/30/2022]
Abstract
Many neuroimaging studies have shown that the hippocampus participates in a resting‐state network called the default mode network. However, how the hippocampus connects to the default mode network, whether the hippocampus connects to other resting‐state networks and how the different hippocampal subfields take part in resting‐state networks remains poorly understood. Here, we examined these issues using the high spatial‐resolution 7T resting‐state fMRI dataset from the Human Connectome Project. We used data‐driven techniques that relied on spatially‐restricted Independent Component Analysis, Dual Regression and linear mixed‐effect group‐analyses based on participant‐specific brain morphology. The results revealed two main activity hotspots inside the hippocampus. The first hotspot was located in an anterior location and was correlated with the somatomotor network. This network was subserved by co‐activity in the CA1, CA3, CA4 and Dentate Gyrus fields. In addition, there was an activity hotspot that extended from middle to posterior locations along the hippocampal long‐axis and correlated with the default mode network. This network reflected activity in the Subiculum, CA4 and Dentate Gyrus fields. These results show how different sections of the hippocampus participate in two known resting‐state networks and how these two resting‐state networks depend on different configurations of hippocampal subfield co‐activity.
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Affiliation(s)
- Laura Ezama
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
| | - Juan A Hernández-Cabrera
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain.,Basque Center on Cognition Brain and Language, San Sebastián, Spain
| | - Sara Seoane
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
| | - Ernesto Pereda
- Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain.,Facultad de Ingeniería Industrial, Universidad de La Laguna, La Laguna, Spain
| | - Niels Janssen
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
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22
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Sheng X, Chen H, Shao P, Qin R, Zhao H, Xu Y, Bai F. Brain Structural Network Compensation Is Associated With Cognitive Impairment and Alzheimer's Disease Pathology. Front Neurosci 2021; 15:630278. [PMID: 33716654 PMCID: PMC7947929 DOI: 10.3389/fnins.2021.630278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/26/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Structural network alterations in Alzheimer's disease (AD) are related to worse cognitive impairment. The aim of this study was to quantify the alterations in gray matter associated with impaired cognition and their pathological biomarkers in AD-spectrum patients. METHODS We extracted gray matter networks from 3D-T1 magnetic resonance imaging scans, and a graph theory analysis was used to explore alterations in the network metrics in 34 healthy controls, 70 mild cognitive impairment (MCI) patients, and 40 AD patients. Spearman correlation analysis was computed to investigate the relationships among network properties, neuropsychological performance, and cerebrospinal fluid pathological biomarkers (i.e., Aβ, t-tau, and p-tau) in these subjects. RESULTS AD-spectrum individuals demonstrated higher nodal properties and edge properties associated with impaired memory function, and lower amyloid-β or higher tau levels than the controls. Furthermore, these compensations at the brain regional level in AD-spectrum patients were mainly in the medial temporal lobe; however, the compensation at the whole-brain network level gradually extended from the frontal lobe to become widely distributed throughout the cortex with the progression of AD. CONCLUSION The findings provide insight into the alterations in the gray matter network related to impaired cognition and pathological biomarkers in the progression of AD. The possibility of compensation was detected in the structural networks in AD-spectrum patients; the compensatory patterns at regional and whole-brain levels were different and the clinical significance was highlighted.
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Affiliation(s)
- Xiaoning Sheng
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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23
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Plachti A, Kharabian S, Eickhoff SB, Maleki Balajoo S, Hoffstaedter F, Varikuti DP, Jockwitz C, Caspers S, Amunts K, Genon S. Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan. Brain 2021; 143:2788-2802. [PMID: 32851402 DOI: 10.1093/brain/awaa222] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/29/2020] [Accepted: 05/21/2020] [Indexed: 12/22/2022] Open
Abstract
The hippocampus is a plastic region and highly susceptible to ageing and dementia. Previous studies explicitly imposed a priori models of hippocampus when investigating ageing and dementia-specific atrophy but led to inconsistent results. Consequently, the basic question of whether macrostructural changes follow a cytoarchitectonic or functional organization across the adult lifespan and in age-related neurodegenerative disease remained open. The aim of this cross-sectional study was to identify the spatial pattern of hippocampus differentiation based on structural covariance with a data-driven approach across structural MRI data of large cohorts (n = 2594). We examined the pattern of structural covariance of hippocampus voxels in young, middle-aged, elderly, mild cognitive impairment and dementia disease samples by applying a clustering algorithm revealing differentiation in structural covariance within the hippocampus. In all the healthy and in the mild cognitive impaired participants, the hippocampus was robustly divided into anterior, lateral and medial subregions reminiscent of cytoarchitectonic division. In contrast, in dementia patients, the pattern of subdivision was closer to known functional differentiation into an anterior, body and tail subregions. These results not only contribute to a better understanding of co-plasticity and co-atrophy in the hippocampus across the lifespan and in dementia, but also provide robust data-driven spatial representations (i.e. maps) for structural studies.
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Affiliation(s)
- Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Shahrzad Kharabian
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Somayeh Maleki Balajoo
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Deepthi P Varikuti
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium
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24
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Ruotsalainen I, Glerean E, Karvanen J, Gorbach T, Renvall V, Syväoja HJ, Tammelin TH, Parviainen T. Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains. Brain Behav 2021; 11:e01941. [PMID: 33369275 PMCID: PMC7882164 DOI: 10.1002/brb3.1941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION Adolescents have experienced decreased aerobic fitness levels and insufficient physical activity levels over the past decades. While both physical activity and aerobic fitness are related to physical and mental health, little is known concerning how they manifest in the brain during this stage of development, characterized by significant physical and psychosocial changes. The aim of the study is to examine the associations between both physical activity and aerobic fitness with brains' functional connectivity. METHODS Here, we examined how physical activity and aerobic fitness are associated with local and interhemispheric functional connectivity of the adolescent brain (n = 59), as measured with resting-state functional magnetic resonance imaging. Physical activity was measured by hip-worn accelerometers, and aerobic fitness by a maximal 20-m shuttle run test. RESULTS We found that higher levels of moderate-to-vigorous intensity physical activity, but not aerobic fitness, were linked to increased local functional connectivity as measured by regional homogeneity in 13-16-year-old participants. However, we did not find evidence for significant associations between adolescents' physical activity or aerobic fitness and interhemispheric connectivity, as indicated by homotopic connectivity. CONCLUSIONS These results suggest that physical activity, but not aerobic fitness, is related to local functional connectivity in adolescents. Moreover, physical activity shows an association with a specific brain area involved in motor functions but did not display any widespread associations with other brain regions. These results can advance our understanding of the behavior-brain associations in adolescents.
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Affiliation(s)
- Ilona Ruotsalainen
- Department of Psychology, Centre for Interdisciplinary Brain Research, University of Jyväskylä, Jyväskylä, Finland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.,International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Juha Karvanen
- Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland
| | - Tetiana Gorbach
- Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Ville Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.,AMI Centre, Aalto University School of Science, Espoo, Finland
| | - Heidi J Syväoja
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Tuija H Tammelin
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Tiina Parviainen
- Department of Psychology, Centre for Interdisciplinary Brain Research, University of Jyväskylä, Jyväskylä, Finland
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25
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Stark SM, Frithsen A, Stark CE. Age-related alterations in functional connectivity along the longitudinal axis of the hippocampus and its subfields. Hippocampus 2021; 31:11-27. [PMID: 32918772 PMCID: PMC8354549 DOI: 10.1002/hipo.23259] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 07/31/2020] [Accepted: 08/07/2020] [Indexed: 12/13/2022]
Abstract
Hippocampal circuit alterations that differentially affect hippocampal subfields are associated with age-related memory decline. Additionally, functional organization along the longitudinal axis of the hippocampus has revealed distinctions between anterior and posterior (A-P) connectivity. Here, we examined the functional connectivity (FC) differences between young and older adults at high-resolution within the medial temporal lobe network (entorhinal, perirhinal, and parahippocampal cortices), allowing us to explore how hippocampal subfield connectivity across the longitudinal axis of the hippocampus changes with age. Overall, we found reliably greater connectivity for younger adults than older adults between the hippocampus and parahippocampal cortex (PHC) and perirhinal cortex (PRC). This drop in functional connectivity was more pronounced in the anterior regions of the hippocampus than the posterior ones, consistent for each of the hippocampal subfields. Further, intra-hippocampal connectivity also reflected an age-related decrease in functional connectivity within the anterior hippocampus in older adults that was offset by an increase in posterior hippocampal functional connectivity. Interestingly, the anterior-posterior dysfunction in older adults between hippocampus and PHC was predictive of lure discrimination performance on the Mnemonic similarity task (MST), suggesting a role in memory performance. While age-related dysfunction within the hippocampal subfields has been well-documented, these results suggest that the age-related dysfunction in hippocampal connectivity across the longitudinal axis may also contribute significantly to memory decline in older adults.
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Affiliation(s)
- Shauna M. Stark
- Department of Neurobiology and Behavior, University of California Irvine
| | - Amy Frithsen
- Department of Neurobiology and Behavior, University of California Irvine
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine
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26
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Davis KA, Morgan VL. Network Analyses in Epilepsy: Are Nodes and Edges Ready for Clinical Translation? Neurology 2020; 96:195-196. [PMID: 33361264 DOI: 10.1212/wnl.0000000000011316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Kathryn A Davis
- From the Department of Neurology (K.A.D.) and Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia; Vanderbilt University Institute of Imaging Science (V.L.M.), Department of Radiology and Radiological Sciences, Department of Neurological Surgery (V.L.M.), and Department of Neurology, Vanderbilt University Medical Center; and Department of Biomedical Engineering, Vanderbilt University, Nashville.
| | - Victoria L Morgan
- From the Department of Neurology (K.A.D.) and Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia; Vanderbilt University Institute of Imaging Science (V.L.M.), Department of Radiology and Radiological Sciences, Department of Neurological Surgery (V.L.M.), and Department of Neurology, Vanderbilt University Medical Center; and Department of Biomedical Engineering, Vanderbilt University, Nashville
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27
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Paquola C, Benkarim O, DeKraker J, Larivière S, Frässle S, Royer J, Tavakol S, Valk S, Bernasconi A, Bernasconi N, Khan A, Evans AC, Razi A, Smallwood J, Bernhardt BC. Convergence of cortical types and functional motifs in the human mesiotemporal lobe. eLife 2020; 9:e60673. [PMID: 33146610 PMCID: PMC7671688 DOI: 10.7554/elife.60673] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 11/03/2020] [Indexed: 01/24/2023] Open
Abstract
The mesiotemporal lobe (MTL) is implicated in many cognitive processes, is compromised in numerous brain disorders, and exhibits a gradual cytoarchitectural transition from six-layered parahippocampal isocortex to three-layered hippocampal allocortex. Leveraging an ultra-high-resolution histological reconstruction of a human brain, our study showed that the dominant axis of MTL cytoarchitectural differentiation follows the iso-to-allocortical transition and depth-specific variations in neuronal density. Projecting the histology-derived MTL model to in-vivo functional MRI, we furthermore determined how its cytoarchitecture underpins its intrinsic effective connectivity and association to large-scale networks. Here, the cytoarchitectural gradient was found to underpin intrinsic effective connectivity of the MTL, but patterns differed along the anterior-posterior axis. Moreover, while the iso-to-allocortical gradient parametrically represented the multiple-demand relative to task-negative networks, anterior-posterior gradients represented transmodal versus unimodal networks. Our findings establish that the combination of micro- and macrostructural features allow the MTL to represent dominant motifs of whole-brain functional organisation.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Jordan DeKraker
- Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH ZurichZurichSwitzerland
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Sofie Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Andrea Bernasconi
- Neuroimaging Of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Neda Bernasconi
- Neuroimaging Of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Ali Khan
- Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
- McGill Centre for Integrative Neuroscience, McGill UniversityMontrealCanada
| | | | | | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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28
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Ly M, Foley L, Manivannan A, Hitchens TK, Richardson RM, Modo M. Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus. Hum Brain Mapp 2020; 41:4200-4218. [PMID: 32621364 PMCID: PMC7502840 DOI: 10.1002/hbm.25119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/01/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022] Open
Abstract
Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.
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Affiliation(s)
- Maria Ly
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Lesley Foley
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - T. Kevin Hitchens
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - R. Mark Richardson
- Department of Neurological SurgeryUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Brain InstituteUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Michel Modo
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
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29
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Canjels LP, Backes WH, van Veenendaal TM, Vlooswijk MC, Hofman PA, Aldenkamp AP, Rouhl RP, Jansen JF. Volumetric and Functional Activity Lateralization in Healthy Subjects and Patients with Focal Epilepsy: Initial Findings in a 7T MRI Study. J Neuroimaging 2020; 30:666-673. [PMID: 32472965 PMCID: PMC7586826 DOI: 10.1111/jon.12739] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/20/2020] [Accepted: 05/20/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE In 30% of the patients with focal epilepsy, an epileptogenic lesion cannot be visually detected with structural MRI. Ultra-high field MRI may be able to identify subtle pathology related to the epileptic focus. We set out to assess 7T MRI-derived volumetric and functional activity lateralization of the hippocampus, hippocampal subfields, temporal and frontal lobe in healthy subjects and MRI-negative patients with focal epilepsy. METHODS Twenty controls and 10 patients with MRI-negative temporal or frontal lobe epilepsy (TLE and FLE, respectively) underwent a 7T MRI exam. T1 -weigthed imaging and resting-state fMRI was performed. T1 -weighted images were segmented to yield volumes, while from fMRI data, the fractional amplitude of low frequency fluctuations was calculated. Subsequently, volumetric and functional lateralization was calculated from left-right asymmetry. RESULTS In controls, volumetric lateralization was symmetric, with a slight asymmetry of the hippocampus and subiculum, while functional lateralization consistently showed symmetry. Contrarily, in epilepsy patients, regions were less symmetric. In TLE patients with known focus, volumetric lateralization in the hippocampus and hippocampal subfields was indicative of smaller ipsilateral volumes. These patients also showed clear functional lateralization, though not consistently ipsilateral or contralateral to the epileptic focus. TLE patients with unknown focus showed an obvious volumetric lateralization, facilitating the localization of the epileptic focus. Lateralization results in the FLE patients were less consistent with the epileptic focus. CONCLUSION MRI-derived volume and fluctuation amplitude are highly symmetric in controls, whereas in TLE, volumetric and functional lateralization effects were observed. This highlights the potential of the technique.
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Affiliation(s)
- Lisanne P.W. Canjels
- Departments of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Walter H. Backes
- Departments of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- School for Cardiovascular DisordersMaastricht UniversityMaastrichtThe Netherlands
| | - Tamar M. van Veenendaal
- Departments of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Marielle C.G. Vlooswijk
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Academic Center for Epileptology Kempenhaeghe/Maastricht UMC+MaastrichtThe Netherlands
| | - Paul A.M. Hofman
- Departments of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Albert P. Aldenkamp
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Academic Center for Epileptology Kempenhaeghe/Maastricht UMC+MaastrichtThe Netherlands
| | - Rob P.W. Rouhl
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Academic Center for Epileptology Kempenhaeghe/Maastricht UMC+MaastrichtThe Netherlands
| | - Jacobus F.A. Jansen
- Departments of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
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30
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Ke J, Foley LM, Hitchens TK, Richardson RM, Modo M. Ex vivo mesoscopic diffusion MRI correlates with seizure frequency in patients with uncontrolled mesial temporal lobe epilepsy. Hum Brain Mapp 2020; 41:4529-4548. [PMID: 32691978 PMCID: PMC7555080 DOI: 10.1002/hbm.25139] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/08/2020] [Accepted: 07/05/2020] [Indexed: 12/28/2022] Open
Abstract
The role of hippocampal connectivity in mesial temporal lobe epilepsy (mTLE) remains poorly understood. The use of ex vivo hippocampal samples excised from patients with mTLE affords mesoscale diffusion magnetic resonance imaging (MRI) to identify individual cell layers, such as the pyramidal (PCL) and granule cell layers (GCL), which are thought to be impacted by seizure activity. Diffusion tensor imaging (DTI) of control (n = 3) and mTLE (n = 7) hippocampi on an 11.7 T MRI scanner allowed us to reveal intra‐hippocampal connectivity and evaluate how epilepsy affected mean (MD), axial (AD), and radial diffusivity (RD), as well as fractional anisotropy (FA). Regional measurements indicated a volume loss in the PCL of the cornu ammonis (CA) 1 subfield in mTLE patients compared to controls, which provided anatomical context. Diffusion measurements, as well as streamline density, were generally higher in mTLE patients compared to controls, potentially reflecting differences due to tissue fixation. mTLE measurements were more variable than controls. This variability was associated with disease severity, as indicated by a strong correlation (r = 0.87) between FA in the stratum radiatum and the frequency of seizures in patients. MD and RD of the PCL in subfields CA3 and CA4 also correlated strongly with disease severity. No correlation of MR measures with disease duration was evident. These results reveal the potential of mesoscale diffusion MRI to examine layer‐specific diffusion changes and connectivity to determine how these relate to clinical measures. Improving the visualization of intra‐hippocampal connectivity will advance the development of novel hypotheses about seizure networks.
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Affiliation(s)
- Justin Ke
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lesley M Foley
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - T Kevin Hitchens
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - R Mark Richardson
- Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurological Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Michel Modo
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, USA
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31
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Watanabe K, Kakeda S, Katsuki A, Ueda I, Ikenouchi A, Yoshimura R, Korogi Y. Whole-brain structural covariance network abnormality in first-episode and drug-naïve major depressive disorder. Psychiatry Res Neuroimaging 2020; 300:111083. [PMID: 32298948 DOI: 10.1016/j.pscychresns.2020.111083] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 03/31/2020] [Accepted: 03/31/2020] [Indexed: 12/18/2022]
Abstract
There has been a growing interest in the abnormality of networks across the brain in major depressive disorder (MDD). We aimed to investigate the structural covariance networks in patients with first-episode and drug-naïve MDD using structural imaging. A total of 77 patients with first-episode and drug-naïve MDD and 79 healthy subjects (HS) were recruited, from whom high-resolution T1-weighted images were analysed. Incident component analysis was used to calculate the brain networks based on grey matter volume covariance. There were significant differences in salience network, medial temporal lobe network, default mode network and central executive network between MDD and HS (p < 0.05). Further, the disturbance of medial temporal lobe network was significantly correlated with the severity of depressive symptoms (p < 0.05). In conclusion, we found a novel abnormality in the brain network in the medial temporal lobe primarily involving the hippocampus and parahippocampal gyrus in patients with first-episode and treatment-naïve MDD.
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Affiliation(s)
- Keita Watanabe
- Department of Radiology, University of Occupational and Environmental Health, Fukuoka, Japan.
| | - Shingo Kakeda
- Department of Diagnostic Radiology, Hirosaki University Graduate School of Medicine Radiology, Aomori, Japan
| | - Asuka Katsuki
- Department of Psychiatry, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Issei Ueda
- Department of Radiology, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Yukunori Korogi
- Department of Radiology, University of Occupational and Environmental Health, Fukuoka, Japan
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32
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Tanaka M, Osada T, Ogawa A, Kamagata K, Aoki S, Konishi S. Dissociable Networks of the Lateral/Medial Mammillary Body in the Human Brain. Front Hum Neurosci 2020; 14:228. [PMID: 32625073 PMCID: PMC7316159 DOI: 10.3389/fnhum.2020.00228] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 05/22/2020] [Indexed: 12/20/2022] Open
Abstract
The mammillary body (MB) has been thought to implement mnemonic functions. Although recent animal studies have revealed dissociable roles of the lateral and medial parts of the MB, the dissociable roles of the lateral/medial MB in the human brain is still unclear. Functional connectivity using resting-state functional magnetic resonance imaging (fMRI) provides a unique opportunity to noninvasively inspect the intricate functional organization of the human MB with a high degree of spatial resolution. The present study divided the human MB into lateral and medial parts and examined their functional connectivity with the hippocampal formation, tegmental nuclei, and anterior thalamus. The subiculum of the hippocampal formation was more strongly connected with the medial part than with the lateral part of the MB, whereas the pre/parasubiculum was more strongly connected with the lateral part than with the medial part of the MB. The dorsal tegmental nucleus was connected more strongly with the lateral part of the MB, whereas the ventral tegmental nucleus showed an opposite pattern. The anterior thalamus was connected more strongly with the medial part of the MB. These results confirm the extant animal literature on the lateral/medial MB and provide evidence on the parallel but dissociable systems involving the MB that ascribe mnemonic and spatial-navigation functions to the medial and lateral MBs, respectively.
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Affiliation(s)
- Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan.,Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan.,Sportology Center, Juntendo University School of Medicine, Tokyo, Japan.,Advanced Research Institute for Health Science, Juntendo University School of Medicine, Tokyo, Japan
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33
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Conrad EC, Bernabei JM, Kini LG, Shah P, Mikhail F, Kheder A, Shinohara RT, Davis KA, Bassett DS, Litt B. The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG. Netw Neurosci 2020; 4:484-506. [PMID: 32537538 PMCID: PMC7286312 DOI: 10.1162/netn_a_00131] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/10/2020] [Indexed: 12/12/2022] Open
Abstract
Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John M. Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith G. Kini
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Preya Shah
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Fadi Mikhail
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ammar Kheder
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A. Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S. Bassett
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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34
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Dalton MA, McCormick C, Maguire EA. Differences in functional connectivity along the anterior-posterior axis of human hippocampal subfields. Neuroimage 2019; 192:38-51. [PMID: 30840906 PMCID: PMC6503073 DOI: 10.1016/j.neuroimage.2019.02.066] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 02/21/2019] [Accepted: 02/27/2019] [Indexed: 12/24/2022] Open
Abstract
There is a paucity of information about how human hippocampal subfields are functionally connected to each other and to neighbouring extra-hippocampal cortices. In particular, little is known about whether patterns of functional connectivity (FC) differ down the anterior-posterior axis of each subfield. Here, using high resolution structural MRI we delineated the hippocampal subfields in healthy young adults. This included the CA fields, separating DG/CA4 from CA3, separating the pre/parasubiculum from the subiculum, and also segmenting the uncus. We then used high resolution resting state functional MRI to interrogate FC. We first analysed the FC of each hippocampal subfield in its entirety, in terms of FC with other subfields and with the neighbouring regions, namely entorhinal, perirhinal, posterior parahippocampal and retrosplenial cortices. Next, we analysed FC for different portions of each hippocampal subfield along its anterior-posterior axis, in terms of FC between different parts of a subfield, FC with other subfield portions, and FC of each subfield portion with the neighbouring cortical regions of interest. We found that intrinsic functional connectivity between the subfields aligned generally with the tri-synaptic circuit but also extended beyond it. Our findings also revealed that patterns of functional connectivity between the subfields and neighbouring cortical areas differed markedly along the anterior-posterior axis of each hippocampal subfield. Overall, these results contribute to ongoing efforts to characterise human hippocampal subfield connectivity, with implications for understanding hippocampal function.
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Affiliation(s)
- Marshall A Dalton
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Cornelia McCormick
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK.
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35
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Hahn A, Lanzenberger R, Kasper S. Making Sense of Connectivity. Int J Neuropsychopharmacol 2019; 22:194-207. [PMID: 30544240 PMCID: PMC6403091 DOI: 10.1093/ijnp/pyy100] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 11/07/2018] [Accepted: 12/11/2018] [Indexed: 02/07/2023] Open
Abstract
In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
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36
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Sanchez-Romero R, Ramsey JD, Zhang K, Glymour MRK, Huang B, Glymour C. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods. Netw Neurosci 2019; 3:274-306. [PMID: 30793083 PMCID: PMC6370458 DOI: 10.1162/netn_a_00061] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 05/18/2018] [Indexed: 12/27/2022] Open
Abstract
We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).
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Affiliation(s)
| | - Joseph D. Ramsey
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Biwei Huang
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Clark Glymour
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
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37
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Shah P, Bassett DS, Wisse LEM, Detre JA, Stein JM, Yushkevich PA, Shinohara RT, Elliott MA, Das SR, Davis KA. Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study. Hum Brain Mapp 2019; 40:2390-2398. [PMID: 30666753 DOI: 10.1002/hbm.24530] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/11/2019] [Indexed: 12/24/2022] Open
Abstract
Mesial temporal lobe epilepsy (TLE) is a common neurological disorder affecting the hippocampus and surrounding medial temporal lobe (MTL). Although prior studies have analyzed whole-brain network distortions in TLE patients, the functional network architecture of the MTL at the subregion level has not been examined. In this study, we utilized high-resolution 7T T2-weighted magnetic resonance imaging (MRI) and resting-state BOLD-fMRI to characterize volumetric asymmetry and functional network asymmetry of MTL subregions in unilateral medically refractory TLE patients and healthy controls. We subdivided the TLE group into mesial temporal sclerosis patients (TLE-MTS) and MRI-negative nonlesional patients (TLE-NL). Using an automated multi-atlas segmentation pipeline, we delineated 10 MTL subregions per hemisphere for each subject. We found significantly different patterns of volumetric asymmetry between the two groups, with TLE-MTS exhibiting volumetric asymmetry corresponding to decreased volumes ipsilaterally in all hippocampal subfields, and TLE-NL exhibiting no significant volumetric asymmetries other than a mild decrease in whole-hippocampal volume ipsilaterally. We also found significantly different patterns of functional network asymmetry in the CA1 subfield and whole hippocampus, with TLE-NL patients exhibiting asymmetry corresponding to increased connectivity ipsilaterally and TLE-MTS patients exhibiting asymmetry corresponding to decreased connectivity ipsilaterally. Our findings provide initial evidence that functional neuroimaging-based network properties within the MTL can distinguish between TLE subtypes. High-resolution MRI has potential to improve localization of underlying brain network disruptions in TLE patients who are candidates for surgical resection.
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Affiliation(s)
- Preya Shah
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John A Detre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Center for Functional Neuroimaging, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mark A Elliott
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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38
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Li H, Jia X, Qi Z, Fan X, Ma T, Pang R, Ni H, Li CSR, Lu J, Li K. Disrupted Functional Connectivity of Cornu Ammonis Subregions in Amnestic Mild Cognitive Impairment: A Longitudinal Resting-State fMRI Study. Front Hum Neurosci 2018; 12:413. [PMID: 30420801 PMCID: PMC6216144 DOI: 10.3389/fnhum.2018.00413] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 09/24/2018] [Indexed: 12/16/2022] Open
Abstract
Background: The cornu ammonis (CA), as part of the hippocampal formation, represents a primary target region of neural degeneration in amnestic mild cognitive impairment (aMCI). Previous studies have revealed subtle structural deficits of the CA subregions (CA1-CA3, bilateral) in aMCI; however, it is not clear how the network function is impacted by aMCI. The present study examined longitudinal changes in resting state functional connectivity (FC) of each CA subregion and how these changes relate to neuropsychological profiles in aMCI. Methods: Twenty aMCI and 20 healthy control (HC) participants underwent longitudinal cognitive assessment and resting state functional MRI scans at baseline and 15 months afterward. Imaging data were processed with published routines in SPM8 and CONN software. Two-way analysis of covariance was performed with covariates of age, gender, education level, follow up interval, gray matter volume, mean FD, as well as global correlation (GCOR). Pearson’s correlation was conducted to evaluate the relationship between the longitudinal changes in CA subregional FC and neuropsychological performance in aMCI subjects. Results: Resting state FC between the right CA1 and right middle temporal gyrus (MTG) as well as between the left CA2 and bilateral cuneal cortex (CC) were decreased in aMCI subjects as compared to HC. Longitudinal decrease in FC between the right CA1 and right MTG was correlated with reduced capacity of episodic memory in aMCI subjects. Conclusion: The current findings suggest functional alterations in the CA subregions. CA1 connectivity with the middle temporal cortex may represent an important neural marker of memory dysfunction in aMCI.
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Affiliation(s)
- Hui Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Xiuqin Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.,Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Zhigang Qi
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Xiang Fan
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tian Ma
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ran Pang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hong Ni
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States.,Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, United States.,Beijing Huilongguan Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
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39
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Hadar PN, Kini LG, Coto C, Piskin V, Callans LE, Chen SH, Stein JM, Das SR, Yushkevich PA, Davis KA. Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy. Neuroimage Clin 2018; 20:1139-1147. [PMID: 30380521 PMCID: PMC6205355 DOI: 10.1016/j.nicl.2018.09.032] [Citation(s) in RCA: 6] [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: 03/01/2018] [Revised: 09/16/2018] [Accepted: 09/29/2018] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome. METHODS We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization. RESULTS The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients. SIGNIFICANCE While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.
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Affiliation(s)
- Peter N Hadar
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Lohith G Kini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Carlos Coto
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Virginie Piskin
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States; Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Lauren E Callans
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Stephanie H Chen
- Department of Neurology, University of Maryland, Baltimore, MD 21201, United States
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Sandhitsu R Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States; Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Paul A Yushkevich
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States; Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States.
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40
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Aslaksen PM, Bystad MK, Ørbo MC, Vangberg TR. The relation of hippocampal subfield volumes to verbal episodic memory measured by the California Verbal Learning Test II in healthy adults. Behav Brain Res 2018; 351:131-137. [DOI: 10.1016/j.bbr.2018.06.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 06/04/2018] [Accepted: 06/07/2018] [Indexed: 01/25/2023]
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41
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Abstract
Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Perry Zurn
- Department of Philosophy, American University, Washington, DC, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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Jarero-Basulto JJ, Gasca-Martínez Y, Rivera-Cervantes MC, Ureña-Guerrero ME, Feria-Velasco AI, Beas-Zarate C. Interactions Between Epilepsy and Plasticity. Pharmaceuticals (Basel) 2018; 11:ph11010017. [PMID: 29414852 PMCID: PMC5874713 DOI: 10.3390/ph11010017] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 02/01/2018] [Accepted: 02/06/2018] [Indexed: 02/06/2023] Open
Abstract
Undoubtedly, one of the most interesting topics in the field of neuroscience is the ability of the central nervous system to respond to different stimuli (normal or pathological) by modifying its structure and function, either transiently or permanently, by generating neural cells and new connections in a process known as neuroplasticity. According to the large amount of evidence reported in the literature, many stimuli, such as environmental pressures, changes in the internal dynamic steady state of the organism and even injuries or illnesses (e.g., epilepsy) may induce neuroplasticity. Epilepsy and neuroplasticity seem to be closely related, as the two processes could positively affect one another. Thus, in this review, we analysed some neuroplastic changes triggered in the hippocampus in response to seizure-induced neuronal damage and how these changes could lead to the establishment of temporal lobe epilepsy, the most common type of focal human epilepsy.
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Affiliation(s)
- José J Jarero-Basulto
- Cellular Neurobiology Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
| | - Yadira Gasca-Martínez
- Cellular Neurobiology Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
| | - Martha C Rivera-Cervantes
- Cellular Neurobiology Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
| | - Mónica E Ureña-Guerrero
- Neurotransmission Biology Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
| | - Alfredo I Feria-Velasco
- Cellular Neurobiology Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
| | - Carlos Beas-Zarate
- Development and Neural Regeneration Laboratory, Cell and Molecular Biology Department, CUCBA, University of Guadalajara, 45220 Zapopan, Jalisco, Mexico.
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Shah P, Bassett DS, Wisse LE, Detre JA, Stein JM, Yushkevich PA, Shinohara RT, Pluta JB, Valenciano E, Daffner M, Wolk DA, Elliott MA, Litt B, Davis KA, Das SR. Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI. Hum Brain Mapp 2018; 39:851-865. [PMID: 29159960 PMCID: PMC5764800 DOI: 10.1002/hbm.23887] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/31/2017] [Accepted: 11/06/2017] [Indexed: 12/13/2022] Open
Abstract
Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.
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Affiliation(s)
- Preya Shah
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of Electrical & Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Laura E.M. Wisse
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - John A. Detre
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Functional Neuroimaging, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Joel M. Stein
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Paul A. Yushkevich
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Russell T. Shinohara
- Department of BiostatisticsEpidemiology and Informatics, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - John B. Pluta
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Elijah Valenciano
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Molly Daffner
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Penn Memory Center, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Penn Memory Center, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Mark A. Elliott
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Brian Litt
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Kathryn A. Davis
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Sandhitsu R. Das
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
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