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Wu W, Hoffman P. Verbal semantic expertise is associated with reduced functional connectivity between left and right anterior temporal lobes. Cereb Cortex 2024; 34:bhae256. [PMID: 38897815 PMCID: PMC11186671 DOI: 10.1093/cercor/bhae256] [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: 04/11/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
The left and right anterior temporal lobes (ATLs) encode semantic representations. They show graded hemispheric specialization in function, with the left ATL contributing preferentially to verbal semantic processing. We investigated the cognitive correlates of this organization, using resting-state functional connectivity as a measure of functional segregation between ATLs. We analyzed two independent resting-state fMRI datasets (n = 86 and n = 642) in which participants' verbal semantic expertise was measured using vocabulary tests. In both datasets, people with more advanced verbal semantic knowledge showed weaker functional connectivity between left and right ventral ATLs. This effect was highly specific. It was not observed for within-hemisphere connections between semantic regions (ventral ATL and inferior frontal gyrus (IFG), though it was found for left-right IFG connectivity in one dataset). Effects were not found for tasks probing semantic control, nonsemantic cognition, or face recognition. Our results suggest that hemispheric specialization in the ATLs is not an innate property but rather emerges as people develop highly detailed verbal semantic representations. We speculate that this effect is a consequence of the left ATL's greater connectivity with left-lateralized written word recognition regions, which causes it to preferentially represent meaning for advanced vocabulary acquired primarily through reading.
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Affiliation(s)
- Wei Wu
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
- Department of Music, Durham University, Palace Green, Durham DH1 3RL, United Kingdom
| | - Paul Hoffman
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
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2
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Huang C, Li A, Pang Y, Yang J, Zhang J, Wu X, Mei L. How the intrinsic functional connectivity patterns of the semantic network support semantic processing. Brain Imaging Behav 2024; 18:539-554. [PMID: 38261218 DOI: 10.1007/s11682-024-00849-y] [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] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing.
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Affiliation(s)
- Chengmei Huang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Aqian Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Yingdan Pang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jiayi Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jingxian Zhang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Xiaoyan Wu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Leilei Mei
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China.
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Tetereva A, Knodt AR, Melzer TR, van der Vliet W, Gibson B, Hariri AR, Whitman ET, Li J, Deng J, Ireland D, Ramrakha S, Pat N. Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.589404. [PMID: 38746222 PMCID: PMC11092590 DOI: 10.1101/2024.05.03.589404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.
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Zhan X, Lang J, Yang LZ, Li H. Modeling the association between functional connectivity and lateralization with the activity flow framework. Brain Res 2024; 1830:148831. [PMID: 38412885 DOI: 10.1016/j.brainres.2024.148831] [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: 07/27/2023] [Revised: 02/21/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function. Recent years have seen growing interest in the association between lateralization and FC. However, FC metrics vary from spurious correlation to causal associations. If lateralization manifests local processing and causal network interactions, more causally valid FC metrics should predict lateralization index (LI) better than FC based on simple correlations. The present study directly investigates this hypothesis within the activity flow framework to compare the association between lateralization and four brain connectivity metrics: correlation-based FC, multiple-regression FC, partial-correlation FC, and combinedFC. We propose two modeling approaches: the one-step approach, which models the relationship between LI and FC directly, and the two-step approach, which predicts the brain activation and calculates the LI. Our results indicated that multiple-regression FC, partial-correlation FC, and combinedFC could significantly improve the model prediction compared to correlation-based FC, which was consistent in a spatial working memory task (typically right-lateralized) and a language task (typically left-lateralized). The one-step and two-step approach yielded similar conclusions. In addition, the finding was replicated in a clinical sample of schizophrenia (SZ), bipolar disorder (BP), and attention deficit hyperactivity disorder (ADHD). The present study suggests that the causal interactions among brain regions help shape the lateralization pattern.
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Affiliation(s)
- Xue Zhan
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; University of Science and Technology of China, Hefei 230026, PR China
| | - Jinwei Lang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; University of Science and Technology of China, Hefei 230026, PR China
| | - Li-Zhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, PR China.
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, PR China.
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5
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Luo J, Qin P, Bi Q, Wu K, Gong G. Individual variability in functional connectivity of human auditory cortex. Cereb Cortex 2024; 34:bhae007. [PMID: 38282455 DOI: 10.1093/cercor/bhae007] [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: 10/19/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/30/2024] Open
Abstract
Individual variability in functional connectivity underlies individual differences in cognition and behaviors, yet its association with functional specialization in the auditory cortex remains elusive. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, this study was designed to investigate the spatial distribution of auditory cortex individual variability in its whole-brain functional network architecture. An inherent hierarchical axis of the variability was discerned, which radiates from the medial to lateral orientation, with the left auditory cortex demonstrating more pronounced variations than the right. This variability exhibited a significant correlation with the variations in structural and functional metrics in the auditory cortex. Four auditory cortex subregions, which were identified from a clustering analysis based on this variability, exhibited unique connectional fingerprints and cognitive maps, with certain subregions showing specificity to speech perception functional activation. Moreover, the lateralization of the connectional fingerprint exhibited a U-shaped trajectory across the subregions. These findings emphasize the role of individual variability in functional connectivity in understanding cortical functional organization, as well as in revealing its association with functional specialization from the activation, connectome, and cognition perspectives.
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Affiliation(s)
- Junhao Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Peipei Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
| | - Ke Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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Liu X, Hu Y, Hao Y, Yang L. Individual differences in the neural architecture in semantic processing. Sci Rep 2024; 14:170. [PMID: 38168133 PMCID: PMC10761854 DOI: 10.1038/s41598-023-49538-8] [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: 05/24/2023] [Accepted: 12/09/2023] [Indexed: 01/05/2024] Open
Abstract
Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China.
| | - Yiwen Hu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Yaokun Hao
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
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Zhang S, Zhang L, Ma L, Wu H, Liu L, He X, Gao M, Li R. Neuropsychological, plasma marker, and functional connectivity changes in Alzheimer's disease patients infected with COVID-19. Front Aging Neurosci 2023; 15:1302281. [PMID: 38187359 PMCID: PMC10766841 DOI: 10.3389/fnagi.2023.1302281] [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: 09/26/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Patients with COVID-19 may experience various neurological conditions, including cognitive impairment, encephalitis, and stroke. This is particularly significant in individuals who already have Alzheimer's disease (AD), as the cognitive impairments can be more pronounced in these cases. However, the extent and underlying mechanisms of cognitive impairments in COVID-19-infected AD patients have yet to be fully investigated through clinical and neurophysiological approaches. Methods This study included a total of 77 AD patients. Cognitive functions were assessed using neuropsychiatric scales for all participants, and plasma biomarkers of amyloid protein and tau protein were measured in a subset of 25 participants. To investigate the changes in functional brain connectivity induced by COVID-19 infection, a cross-sectional neuroimaging design was conducted involving a subset of 37 AD patients, including a control group of 18 AD participants without COVID-19 infection and a COVID-19 group consisting of 19 AD participants. Results For the 77 AD patients between the stages of pre and post COVID-19 infection, there were significant differences in cognitive function and psychobehavioral symptoms on the Montreal Scale (MoCA), the neuropsychiatric inventory (NPI), the clinician's global impression of change (CIBIC-Plus), and the activity of daily living scale (ADL). The COVID-19 infection significantly decreased the plasma biomarker level of Aβ42 and increased the plasma p-tau181 level in AD patients. The COVID-19-infected AD patients show decreased local coherence (LCOR) in the anterior middle temporal gyrus and decreased global correlation (GCOR) in the precuneus and the medial prefrontal cortex. Conclusion The findings suggest clinical, cognitive, and neural alterations following COVID-19 infection in AD patients and emphasize the need for close monitoring of symptoms in AD patients who have had COVID-19 and further exploration of the underlying mechanisms.
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Affiliation(s)
- Shouzi Zhang
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Li Zhang
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Li Ma
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Haiyan Wu
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Lixin Liu
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Xuelin He
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Maolong Gao
- Department of Science and Technology, Beijing Geriatric Hospital, Beijing, China
| | - Rui Li
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Liu X, Yang L. Individual differences in the language task-evoked and resting-state functional networks. Front Hum Neurosci 2023; 17:1283069. [PMID: 38021226 PMCID: PMC10656779 DOI: 10.3389/fnhum.2023.1283069] [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: 08/25/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, Beijing, China
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Sánchez A, Carreiras M, Paz-Alonso PM. Word frequency and reading demands modulate brain activation in the inferior frontal gyrus. Sci Rep 2023; 13:17217. [PMID: 37821488 PMCID: PMC10567770 DOI: 10.1038/s41598-023-44420-z] [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: 05/18/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023] Open
Abstract
Processing efficiency differs between high- and low-frequency words, with less frequent words resulting in longer response latencies in several linguistic behavioral tasks. Nevertheless, studies using functional MRI to investigate the word frequency effect have employed diverse methodologies and produced heterogeneous results. In this study, we examine the effect of word frequency through complementary analytical approaches and functional connectivity analyses. Furthermore, we examine whether reading demands, which have been shown to influence reading-related activation, modulate the effects of word frequency. We conducted MRI scanning on 54 healthy participants who performed two versions of a single-word reading task involving high- and low-frequency words: a low-level perceptual reading task and a high-level semantic reading task. The results indicate that word frequency influenced the activation of the pars orbitalis and pars triangularis of the inferior frontal gyrus, but only in the semantic reading task. Additionally, the ventral occipitotemporal cortex exhibited stronger regional activation during the semantic reading task compared to the perceptual reading task, with no effects of word frequency. Functional connectivity analyses demonstrated significant coupling among regions within both the dorsal and ventral reading networks, without any observable effects of word frequency or task. These findings were consistent across group- and individual-level analytical approaches. Overall, our results provide further support for the involvement of the inferior frontal gyrus in semantic processing during reading, as indicated by the effect of word frequency and the influence of reading demands, highlighting the role of the ventral reading network. These findings are discussed in line with their implications for lexical and pre-lexical reading processing.
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Affiliation(s)
- Abraham Sánchez
- Basque Center On Cognition Brain and Language (BCBL), BCBL, Mikeletegi Pasealekua 69, 2, 20009, Donostia-San Sebastián, Spain.
- University of the Basque Country (EHU/UPV), Donostia-San Sebastián, Spain.
| | - Manuel Carreiras
- Basque Center On Cognition Brain and Language (BCBL), BCBL, Mikeletegi Pasealekua 69, 2, 20009, Donostia-San Sebastián, Spain
- University of the Basque Country (EHU/UPV), Donostia-San Sebastián, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Pedro M Paz-Alonso
- Basque Center On Cognition Brain and Language (BCBL), BCBL, Mikeletegi Pasealekua 69, 2, 20009, Donostia-San Sebastián, Spain.
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
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Cao Z, Guo M, Cao X, Liu T, Hu S, Xiao Y, Zhang M, Liu H. Progress in TLE treatment from 2003 to 2023: scientific measurement and visual analysis based on CiteSpace. Front Neurol 2023; 14:1223457. [PMID: 37854064 PMCID: PMC10580429 DOI: 10.3389/fneur.2023.1223457] [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: 05/16/2023] [Accepted: 08/30/2023] [Indexed: 10/20/2023] Open
Abstract
Objective Temporal lobe epilepsy (TLE) is the most common cause of drug-resistant epilepsy and can be treated surgically to control seizures. In this study, we analyzed the relevant research literature in the field of temporal lobe epilepsy (TLE) treatment to understand the background, hotspots, and trends in TLE treatment research. Methods We discussed the trend, frontier, and hotspot of scientific output in TLE treatment research in the world in the last 20 years by searching the core collection of the Web of Science database. Excel and CiteSpace software were used to analyze the basic data of the literature. Result We identified a total of 2,051 publications on TLE treatment from 75 countries between 2003 and 2023. We found that the publication rate was generally increasing. The United States was the most publishing country; among the research institutions on TLE treatment, the University of California system published the most relevant literature and collaborated the most with other institutions. The co-citation of literature, keyword co-occurrence, and its clustering analysis showed that the early studies focused on open surgical treatment, mainly by lobectomy. In recent years, the attention given to stereotactic, microsurgery, and other surgical techniques has gradually increased, and the burst analysis indicated that new research hotspots may appear in the future in the areas of improved surgical procedures and mechanism research.
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Affiliation(s)
- Zhan Cao
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingjie Guo
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Henan University, Kaifeng, China
| | - Xun Cao
- Medical College of Zhengzhou University, Zhengzhou, China
| | - Tiantian Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaowen Hu
- Department of Urinary Surgery, Huaihe Hospital of Henan University, Kaifeng, China
| | - Yafei Xiao
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
| | - Min Zhang
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfang Liu
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Deco G, Sanz Perl Y, de la Fuente L, Sitt JD, Yeo BTT, Tagliazucchi E, Kringelbach ML. The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network. Netw Neurosci 2023; 7:966-998. [PMID: 37781151 PMCID: PMC10473271 DOI: 10.1162/netn_a_00300] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 12/14/2022] [Indexed: 10/03/2023] Open
Abstract
A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Clayton VIC, Australia
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Laura de la Fuente
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Jacobo D. Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - B. T. Thomas Yeo
- Centre for Sleep & Cognition, Centre for Translational MR Research, Department of Electrical and Computer Engineering, N.1. Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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12
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Thomas G, McMahon KL, Finch E, Copland DA. Interindividual variability and consistency of language mapping paradigms for presurgical use. BRAIN AND LANGUAGE 2023; 243:105299. [PMID: 37413742 DOI: 10.1016/j.bandl.2023.105299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 04/08/2023] [Accepted: 06/21/2023] [Indexed: 07/08/2023]
Abstract
Most functional MRI studies of language processing have focussed on group-level inference, but for clinical use, the aim is to predict outcomes at an individual patient level. This requires being able to identify atypical activation and understand how differences relate to language outcomes. A language mapping paradigm that selectively activates left hemisphere language regions in healthy individuals allows atypical activation in a patient to be more easily identified. We investigated the interindividual variability and consistency of language activation in 12 healthy participants using three tasks-verb generation, responsive naming, and sentence comprehension-for future presurgical use. Responsive naming produced the most consistent left-lateralised activation across participants in frontal and temporal regions that postsurgical voxel-based lesion-symptom mapping studies suggest are most critical for language outcomes. Studies with a long-term clinical aim of predicting language outcomes in neurosurgical patients and stroke patients should first establish paradigm validity at an individual level in healthy participants.
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Affiliation(s)
- Georgia Thomas
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia; Queensland Aphasia Research Centre, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia; Herston Imaging Research Facility, The University of Queensland, Brisbane, Australia
| | - Emma Finch
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia; Research and Innovation, West Moreton Health, Ipswich, Australia; Speech Pathology Department, Princess Alexandra Hospital, Brisbane, Australia
| | - David A Copland
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia; Queensland Aphasia Research Centre, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia; Surgical Treatment and Rehabilitation Service (STARS) Education and Research Alliance, The University of Queensland and Metro North Health, Queensland, Australia
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13
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Duman AN, Tatar AE. Topological data analysis for revealing dynamic brain reconfiguration in MEG data. PeerJ 2023; 11:e15721. [PMID: 37489123 PMCID: PMC10363343 DOI: 10.7717/peerj.15721] [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: 01/13/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.
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Affiliation(s)
- Ali Nabi Duman
- Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Ahmet E. Tatar
- Center for Information Technology, University of Groningen, Groningen, Netherlands
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14
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Hwang J, Lustig N, Jung M, Lee JH. Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification. Heliyon 2023; 9:e18086. [PMID: 37519689 PMCID: PMC10372668 DOI: 10.1016/j.heliyon.2023.e18086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 05/18/2023] [Accepted: 07/06/2023] [Indexed: 08/01/2023] Open
Abstract
Deep neural networks (DNNs) have been adopted widely as classifiers for functional magnetic resonance imaging (fMRI) data, advancing beyond traditional machine learning models. Consequently, transfer learning of the pre-trained DNN becomes crucial to enhance DNN classification performance, specifically by alleviating an overfitting issue that occurs when a substantial number of DNN parameters are fitted to a relatively small number of fMRI samples. In this study, we first systematically compared the two most popularly used, unsupervised pretraining models for resting-state fMRI (rfMRI) volume data to pre-train the DNNs, namely autoencoder (AE) and restricted Boltzmann machine (RBM). The group in-brain mask used when training AE and RBM displayed a sizable overlap ratio with Yeo's seven functional brain networks (FNs). The parcellated FNs obtained from the RBM were fine-grained compared to those from the AE. The pre-trained AE and RBM served as the weight parameters of the first of the two hidden DNN layers, and the DNN fulfilled the task classifier role for fMRI (tfMRI) data in the Human Connectome Project (HCP). We tested two transfer learning schemes: (1) fixing and (2) fine-tuning the DNN's pre-trained AE or RBM weights. The DNN with transfer learning was compared to a baseline DNN, trained using random initial weights. Overall, DNN classification performance from the transfer learning proved superior when the pre-trained RBM weights were fixed and when the pre-trained AE weights were fine-tuned (average error rates: 14.8% for fixed RBM, 15.1% fine-tuned AE, and 15.5% for the baseline model) compared to the alternative scenarios of DNN transfer learning schemes. Moreover, the optimal transfer learning scheme between the fixed RBM and fine-tuned AE varied according to seven task conditions in the HCP. Nonetheless, the computational load reduced substantially for the fixed-weight-based transfer learning compared to the fine-tuning-based transfer learning (e.g., the number of weight parameters for the fixed-weight-based DNN model reduced to 1.9% compared with a baseline/fine-tuned DNN model). Our findings suggest that weight initialization at the DNN's first layer using RBM-based pre-trained weights provides the most promising approach when the whole-brain fMRI volume supports associated task classification. We believe that our proposed scheme could be applied to a variety of task conditions to improve their classification performance and to utilize computational resources efficiently using our AE/RBM-based pre-trained weights compared to random initial weights for DNN training.
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Affiliation(s)
| | | | | | - Jong-Hwan Lee
- Corresponding author. Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, South Korea.
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15
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Koyama Y, Yamamoto T, Hirayama JI, Jimura K, Sadato N, Chikazoe J. Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.12.543130. [PMID: 37425727 PMCID: PMC10326986 DOI: 10.1101/2023.06.12.543130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Functional MRI (fMRI) has been instrumental in understanding how cognitive processes are spatially mapped in the brain, yielding insights about brain regions and functions. However, in case the orthogonality of behavioral or stimulus timing is not guaranteed, the estimated brain maps fail to dissociate each cognitive process, and the resultant maps become unstable. Also, the brain mapping exercise can not provide temporal information on the cognitive process. Here we propose a qualitatively different approach to fMRI analysis, named Cognitive Dynamics Estimation (CDE), that estimates how multiple cognitive processes change over time even when behavior or stimulus logs are unavailable. This method transposes the conventional brain mapping; the brain activity pattern at each time point is subject to regression analysis with data-driven maps of cognitive processes as regressors, resulting in the time series of cognitive processes. The estimated time series captured the fluctuation of intensity and timing of cognitive processes on a trial-by-trial basis, which conventional analysis could not capture. Notably, the estimated time series predicted participants' cognitive ability to perform each psychological task. As an addition to our fMRI analytic toolkit, these results suggest the potential for CDE to elucidate underexplored cognitive phenomena, especially in the temporal domain.
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Affiliation(s)
- Yutaro Koyama
- Department of Psychiatry, University of Wisconsin-Madison, WI 53719, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Tetsuya Yamamoto
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Jun-Ichiro Hirayama
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Ibaraki, 305-8568, Japan
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi 371-8510
| | - Norihiro Sadato
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan
| | - Junichi Chikazoe
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Araya, Inc., Tokyo, 107-6024, Japan
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16
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Labache L, Ge T, Yeo BTT, Holmes AJ. Language network lateralization is reflected throughout the macroscale functional organization of cortex. Nat Commun 2023; 14:3405. [PMID: 37296118 PMCID: PMC10256741 DOI: 10.1038/s41467-023-39131-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.
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Affiliation(s)
- Loïc Labache
- Department of Psychology, Yale University, New Haven, CT, 06520, US.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, US
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, US
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02142, US
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore, SG, 119077, Singapore
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore, SG, 119077, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore, SG, 119077, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, US
- National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, SG, 119077, Singapore
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, US.
- Department of Psychiatry, Yale University, New Haven, CT, 06520, US.
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, US.
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, 08854, US.
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17
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Camargo A, Mauro GD, Wang Z. Task-induced changes in brain entropy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289255. [PMID: 37205436 PMCID: PMC10187354 DOI: 10.1101/2023.04.28.23289255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional entropy has been increasingly assessed using resting state fMRI. Response of regional entropy to task has been scarcely studied. The purpose of this study is to characterize task-induced regional brain entropy (BEN) alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Compared to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area including both the task activated regions and task non-specific regions such as the task negative area and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.
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Affiliation(s)
- Aldo Camargo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
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18
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Ji JL, Demšar J, Fonteneau C, Tamayo Z, Pan L, Kraljič A, Matkovič A, Purg N, Helmer M, Warrington S, Winkler A, Zerbi V, Coalson TS, Glasser MF, Harms MP, Sotiropoulos SN, Murray JD, Anticevic A, Repovš G. QuNex-An integrative platform for reproducible neuroimaging analytics. Front Neuroinform 2023; 17:1104508. [PMID: 37090033 PMCID: PMC10113546 DOI: 10.3389/fninf.2023.1104508] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Jure Demšar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Purg
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Anderson Winkler
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Valerio Zerbi
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
| | - Timothy S. Coalson
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham NIHR Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Physics, Yale University, New Haven, CT, United States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Psychology, Yale University School of Medicine, New Haven, CT, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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19
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Sugimoto H, Abe MS, Otake-Matsuura M. Word-producing brain: Contribution of the left anterior middle temporal gyrus to word production patterns in spoken language. BRAIN AND LANGUAGE 2023; 238:105233. [PMID: 36842390 DOI: 10.1016/j.bandl.2023.105233] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/27/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
Vocabulary is based on semantic knowledge. The anterior temporal lobe (ATL) has been considered an essential region for processing semantic knowledge; nonetheless, the association between word production patterns and the structural and functional characteristics of the ATL remains unclear. To examine this, we analyzed over one million words from group conversations among community-dwelling older adults and their multimodal magnetic resonance imaging data. A quantitative index for the word production patterns, namely the exponent β of Heaps' law, positively correlated with the left anterior middle temporal gyrus volume. Moreover, β negatively correlated with its resting-state functional connectivity with the precuneus. There was no significant correlation with the diffusion tensor imaging metrics in any fiber. These findings suggest that the vocabulary richness in spoken language depends on the brain status characterized by the semantic knowledge-related brain structure and its activation dissimilarity with the precuneus, a core region of the default mode network.
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Affiliation(s)
- Hikaru Sugimoto
- RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Masato S Abe
- RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; Faculty of Culture and Information Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto-fu 610-0394, Japan.
| | - Mihoko Otake-Matsuura
- RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
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20
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Vucurovic K, Raucher-Chéné D, Obert A, Gobin P, Henry A, Barrière S, Traykova M, Gierski F, Portefaix C, Caillies S, Kaladjian A. Activation of the left medial temporal gyrus and adjacent brain areas during affective theory of mind processing correlates with trait schizotypy in a nonclinical population. Soc Cogn Affect Neurosci 2023; 18:6701589. [PMID: 36107738 PMCID: PMC9949503 DOI: 10.1093/scan/nsac051] [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: 05/25/2021] [Revised: 07/31/2022] [Accepted: 09/13/2022] [Indexed: 11/12/2022] Open
Abstract
Schizophrenia, a severe psychiatric disorder, is associated with abnormal brain activation during theory of mind (ToM) processing. Researchers recently suggested that there is a continuum running from subclinical schizotypal personality traits to fully expressed schizophrenia symptoms. Nevertheless, it remains unclear whether schizotypal personality traits in a nonclinical population are associated with atypical brain activation during ToM tasks. Our aim was to investigate correlations between fMRI brain activation during affective ToM (ToMA) and cognitive ToM (ToMC) tasks and scores on the Schizotypal Personality Questionnaire (SPQ) and the Basic Empathy Scale in 39 healthy individuals. The total SPQ score positively correlated with brain activation during ToMA processing in clusters extending from the left medial temporal gyrus (MTG), lingual gyrus and fusiform gyrus to the parahippocampal gyrus (Brodmann area: 19). During ToMA processing, the right inferior occipital gyrus, right MTG, precuneus and posterior cingulate cortex negatively correlated with the emotional disconnection subscore and the total score of self-reported empathy. These posterior brain regions are known to be involved in memory and language, as well as in creative reasoning, in nonclinical individuals. Our findings highlight changes in brain processing associated with trait schizotypy in nonclinical individuals during ToMA but not ToMC processing.
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Affiliation(s)
- Ksenija Vucurovic
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Centre Rémois de Psychothérapie et Neuromodulation, 51100 Reims, France
| | - Delphine Raucher-Chéné
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France.,McGill University, Douglas Mental Health University Institute, 11290 Montreal, Canada
| | - Alexandre Obert
- Champollion National University Institute, Cognition Sciences, Technology & Ergonomics Laboratory, University of Toulouse, 81000 Albi, France
| | - Pamela Gobin
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France
| | - Audrey Henry
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France
| | - Sarah Barrière
- Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France
| | - Martina Traykova
- Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France
| | - Fabien Gierski
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France.,INSERM U1247 GRAP, Research Group on Alcohol and Drugs, Université de Picardie Jules Verne, 80000 Amiens, France
| | - Christophe Portefaix
- Radiology Department, Reims University Hospital, 51100 Reims, France.,University of Reims Champagne-Ardenne, CReSTIC Laboratory, 51100 Reims, France
| | - Stéphanie Caillies
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France
| | - Arthur Kaladjian
- Université de Reims Champagne Ardenne, Laboratoire Cognition, Santé, Société, EA 6291, 51100 Reims, France.,Pôle Universitaire de Psychiatrie, EPSM et CHU de Reims, 51100 Reims, France.,University of Reims Champagne-Ardenne Faculty of Medicine, 51100 Reims, France
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21
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Ge J, Yang G, Han M, Zhou S, Men W, Qin L, Lyu B, Li H, Wang H, Rao H, Cui Z, Liu H, Zuo XN, Gao JH. Increasing diversity in connectomics with the Chinese Human Connectome Project. Nat Neurosci 2023; 26:163-172. [PMID: 36536245 DOI: 10.1038/s41593-022-01215-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 10/25/2022] [Indexed: 12/24/2022]
Abstract
Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain-mind studies.
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Affiliation(s)
- Jianqiao Ge
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Meizhen Han
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sizhong Zhou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | | | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Haobo Wang
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | | | - Xi-Nian Zuo
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- McGovern Institute for Brain Research, Peking University, Beijing, China.
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
- Changping Laboratory, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
- National Biomedical Imaging Center, Peking University, Beijing, China.
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22
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Tomasi D, Volkow ND. Brain motion networks predict head motion during rest- and task-fMRI. Front Neurosci 2023; 17:1096232. [PMID: 37113158 PMCID: PMC10126373 DOI: 10.3389/fnins.2023.1096232] [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: 11/11/2022] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Introduction The capacity to stay still during scanning, which is necessary to avoid motion confounds while imaging, varies markedly between people. Methods Here we investigated the effect of head motion on functional connectivity using connectome-based predictive modeling (CPM) and publicly available brain functional magnetic resonance imaging (fMRI) data from 414 individuals with low frame-to-frame motion (Δd < 0.18 mm). Leave-one-out was used for internal cross-validation of head motion prediction in 207 participants, and twofold cross-validation was used in an independent sample (n = 207). Results and Discussion Parametric testing, as well as CPM-based permutations for null hypothesis testing, revealed strong linear associations between observed and predicted values of head motion. Motion prediction accuracy was higher for task- than for rest-fMRI, and for absolute head motion (d) than for Δd. Denoising attenuated the predictability of head motion, but stricter framewise displacement threshold (FD = 0.2 mm) for motion censoring did not alter the accuracy of the predictions obtained with lenient censoring (FD = 0.5 mm). For rest-fMRI, prediction accuracy was lower for individuals with low motion (mean Δd < 0.02 mm; n = 200) than for those with moderate motion (Δd < 0.04 mm; n = 414). The cerebellum and default-mode network (DMN) regions that forecasted individual differences in d and Δd during six different tasks- and two rest-fMRI sessions were consistently prone to the deleterious effect of head motion. However, these findings generalized to a novel group of 1,422 individuals but not to simulated datasets without neurobiological contributions, suggesting that cerebellar and DMN connectivity could partially reflect functional signals pertaining to inhibitory motor control during fMRI.
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Affiliation(s)
- Dardo Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- *Correspondence: Dardo Tomasi,
| | - Nora D. Volkow
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- National Institute on Drug Abuse, Bethesda, MD, United States
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23
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Youssofzadeh V, Conant L, Stout J, Ustine C, Humphries C, Gross WL, Shah-Basak P, Mathis J, Awe E, Allen L, DeYoe EA, Carlson C, Anderson CT, Maganti R, Hermann B, Nair VA, Prabhakaran V, Meyerand B, Binder JR, Raghavan M. Late dominance of the right hemisphere during narrative comprehension. Neuroimage 2022; 264:119749. [PMID: 36379420 PMCID: PMC9772156 DOI: 10.1016/j.neuroimage.2022.119749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/12/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.
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Affiliation(s)
- Vahab Youssofzadeh
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA,Corresponding author. (V. Youssofzadeh)
| | - Lisa Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jeffrey Stout
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Candida Ustine
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - William L. Gross
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA,Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Jed Mathis
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA,Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elizabeth Awe
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Linda Allen
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A. DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Chad Carlson
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A. Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivek Prabhakaran
- Radiology, University of Wisconsin-Madison, Madison, WI, USA,Medical Physics, University of Wisconsin-Madison, Madison, WI, USA,Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Beth Meyerand
- Radiology, University of Wisconsin-Madison, Madison, WI, USA,Medical Physics, University of Wisconsin-Madison, Madison, WI, USA,Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
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24
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Ding B, Dragonu I, Rua C, Carlin JD, Halai AD, Liebig P, Heidemann R, Correia MM, Rodgers CT. Parallel transmit (pTx) with online pulse design for task-based fMRI at 7 T. Magn Reson Imaging 2022; 93:163-174. [PMID: 35863691 DOI: 10.1016/j.mri.2022.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 06/17/2022] [Accepted: 07/11/2022] [Indexed: 10/31/2022]
Abstract
PURPOSE Parallel transmission (pTx) is an approach to improve image uniformity for ultra-high field imaging. In this study, we modified an echo planar imaging (EPI) sequence to design subject-specific pTx pulses online. We compared its performance against EPI with conventional circularly polarised (CP) pulses. METHODS We compared the pTx-EPI and CP-EPI sequences in a short EPI acquisition protocol and for two different functional paradigms in six healthy volunteers (2 female, aged 23-36 years, mean age 29.2 years). We chose two paradigms that are typically affected by signal dropout at 7 T: a visual objects localiser to determine face/scene selective brain regions and a semantic-processing task. RESULTS Across all subjects, pTx-EPI improved whole-brain mean temporal signal-to-noise ratio (tSNR) by 11.0% compared to CP-EPI. We also compared the ability of pTx-EPI and CP-EPI to detect functional activation for three contrasts over the two paradigms: face > object and scene > object for the visual objects localiser and semantic association > pattern matching for the semantic-processing paradigm. Across all three contrasts, pTx-EPI showed higher median z-scores and detected more active voxels in relevant areas, as determined from previous 3 T studies. CONCLUSION We have demonstrated a workflow for EPI acquisitions with online per-subject pulse calculations. We saw improved performance in both tSNR and functional acquisitions from pTx-EPI. Thus, we believe that online calculation pTx-EPI is robust enough for future fMRI studies, especially where activation is expected in brain areas liable to significant signal dropout.
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Affiliation(s)
- Belinda Ding
- Wolfson Brain Imaging Centre, University of Cambridge, UK.
| | | | - Catarina Rua
- Wolfson Brain Imaging Centre, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, UK; Invicro, Invicro London, UK
| | | | - Ajay D Halai
- MRC Cognition and Brain Science Unit, Cambridge, UK
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25
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Complementary hemispheric lateralization of language and social processing in the human brain. Cell Rep 2022; 41:111617. [DOI: 10.1016/j.celrep.2022.111617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 06/10/2022] [Accepted: 10/16/2022] [Indexed: 11/09/2022] Open
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26
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Zahnert F, Kräling G, Melms L, Belke M, Kleinholdermann U, Timmermann L, Hirsch M, Jansen A, Mross P, Menzler K, Habermehl L, Knake S. Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes. Hum Brain Mapp 2022; 44:496-508. [PMID: 36098483 PMCID: PMC9842893 DOI: 10.1002/hbm.26074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/22/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI] = 0.68[0.64-0.73], accuracy[CI] = 68.34%[63-73.2%]; RF: AUC-ROC[CI] = 0.7[0.66-0.73], accuracy[CI] = 64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI] = 0.69[0.53-0.84], accuracy[CI] = 68.09[53.2-80.9]; RF: AUC-ROC[CI] = 0.68[0.53-0.84], accuracy[CI] = 68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.
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Affiliation(s)
- Felix Zahnert
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Gunter Kräling
- Department of Medical TechnologyUniversity Hospital MarburgMarburgGermany
| | - Leander Melms
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Marcus Belke
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany
| | - Urs Kleinholdermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Lars Timmermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Martin Hirsch
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Andreas Jansen
- Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany,Department for Psychiatry and PsychotherapyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Peter Mross
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Katja Menzler
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Lena Habermehl
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Susanne Knake
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
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27
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Wang X, Krieger-Redwood K, Zhang M, Cui Z, Wang X, Karapanagiotidis T, Du Y, Leech R, Bernhardt BC, Margulies DS, Smallwood J, Jefferies E. Physical distance to sensory-motor landmarks predicts language function. Cereb Cortex 2022; 33:4305-4318. [PMID: 36066439 PMCID: PMC10110440 DOI: 10.1093/cercor/bhac344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 11/14/2022] Open
Abstract
Auditory language comprehension recruits cortical regions that are both close to sensory-motor landmarks (supporting auditory and motor features) and far from these landmarks (supporting word meaning). We investigated whether the responsiveness of these regions in task-based functional MRI is related to individual differences in their physical distance to primary sensorimotor landmarks. Parcels in the auditory network, that were equally responsive across story and math tasks, showed stronger activation in individuals who had less distance between these parcels and transverse temporal sulcus, in line with the predictions of the "tethering hypothesis," which suggests that greater proximity to input regions might increase the fidelity of sensory processing. Conversely, language and default mode parcels, which were more active for the story task, showed positive correlations between individual differences in activation and sensory-motor distance from primary sensory-motor landmarks, consistent with the view that physical separation from sensory-motor inputs supports aspects of cognition that draw on semantic memory. These results demonstrate that distance from sensorimotor regions provides an organizing principle of functional differentiation within the cortex. The relationship between activation and geodesic distance to sensory-motor landmarks is in opposite directions for cortical regions that are proximal to the heteromodal (DMN and language network) and unimodal ends of the principal gradient of intrinsic connectivity.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Department of Psychology, University of York, Heslington, York YO10 5DD, UK
| | | | - Meichao Zhang
- Department of Psychology, University of York, Heslington, York YO10 5DD, UK
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA
| | | | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Chinese Institute for Brain Research, Beijing 102206, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Robert Leech
- Centre for Neuroimaging Science, Kings College London, London, UK
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France.,Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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28
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Capouskova K, Kringelbach ML, Deco G. Modes of cognition: Evidence from metastable brain dynamics. Neuroimage 2022; 260:119489. [PMID: 35882268 DOI: 10.1016/j.neuroimage.2022.119489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 01/31/2023] Open
Abstract
Managing cognitive load depends on adequate resource allocation by the human brain through the engagement of metastable substates, which are large-scale functional networks that change over time. We employed a novel analysis method, deep autoencoder dynamical analysis (DADA), with 100 healthy adults selected from the Human Connectome Project (HCP) data set in rest and six cognitive tasks. The deep autoencoder of DADA described seven recurrent stochastic metastable substates from the functional connectome of BOLD phase coherence matrices. These substates were significantly differentiated in terms of their probability of appearance, time duration, and spatial attributes. We found that during different cognitive tasks, there was a higher probability of having more connected substates dominated by a high degree of connectivity in the thalamus. In addition, compared with those during tasks, resting brain dynamics have a lower level of predictability, indicating a more uniform distribution of metastability between substates, quantified by higher entropy. These novel findings provide empirical evidence for the philosophically motivated cognitive theory, suggesting on-line and off-line as two fundamentally distinct modes of cognition. On-line cognition refers to task-dependent engagement with the sensory input, while off-line cognition is a slower, environmentally detached mode engaged with decision and planning. Overall, the DADA framework provides a bridge between neuroscience and cognitive theory that can be further explored in the future.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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29
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Diaz MT, Zhang H, Cosgrove AL, Gertel VH, Troutman SBW, Karimi H. Neural sensitivity to semantic neighbors is stable across the adult lifespan. Neuropsychologia 2022; 171:108237. [PMID: 35413304 PMCID: PMC10022434 DOI: 10.1016/j.neuropsychologia.2022.108237] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 11/30/2022]
Abstract
As we age, language reflects patterns of both stability and change. On the one hand, vocabulary and semantic abilities are largely stable across the adult lifespan, yet lexical retrieval is often slower and less successful (i.e., slower picture naming times, increased tip of the tongue incidents). Although the behavioral bases of these effects have been well established, less is known about the brain regions that support these age-related differences. We used functional Magnetic Resonance Imaging (fMRI) to examine the neural basis of picture naming. Specifically, we were interested in whether older adults would be equally sensitive to semantic characteristics, specifically the number of semantic near neighbors. Near neighbors, defined here as items with a high degree of semantic feature overlap, were of interest as these are thought to elicit competition among potential candidates and increase naming difficulty. Consistent with prior reports, pictures with more semantic near neighbors were named more slowly and less accurately for all adults. Additionally, this interference for naming times was larger as age increased, starting around 30 years old. In contrast to the age-related behavioral slowing, the neural basis of these effects was stable across adulthood. Across all adults, a number of language-relevant regions including left posterior middle temporal gyrus and left inferior frontal gyrus, pars triangularis were sensitive to the number of near neighbors. Our results suggest that although middle-aged and older adults' picture naming is more slowed by increased semantic competition, the brain regions supporting semantic processes remain stable across the adult lifespan.
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Affiliation(s)
- Michele T Diaz
- Department of Psychology, The Pennsylvania State University, USA; Social, Life, and Engineering Sciences Imaging Center, The Pennsylvania State University, USA.
| | - Haoyun Zhang
- Social, Life, and Engineering Sciences Imaging Center, The Pennsylvania State University, USA
| | | | | | | | - Hossein Karimi
- Department of Psychology, The Pennsylvania State University, USA
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30
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Gross WL, Helfand AI, Swanson SJ, Conant LL, Humphries CJ, Raghavan M, Mueller WM, Busch RM, Allen L, Anderson CT, Carlson CE, Lowe MJ, Langfitt JT, Tivarus ME, Drane DL, Loring DW, Jacobs M, Morgan VL, Allendorfer JB, Szaflarski JP, Bonilha L, Bookheimer S, Grabowski T, Vannest J, Binder JR. Prediction of Naming Outcome With fMRI Language Lateralization in Left Temporal Epilepsy Surgery. Neurology 2022; 98:e2337-e2346. [PMID: 35410903 PMCID: PMC9202528 DOI: 10.1212/wnl.0000000000200552] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/02/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Naming decline after left temporal lobe epilepsy (TLE) surgery is common and difficult to predict. Preoperative language fMRI may predict naming decline, but this application is still lacking evidence. We performed a large multicenter cohort study of the effectiveness of fMRI in predicting naming deficits after left TLE surgery. METHODS At 10 US epilepsy centers, 81 patients with left TLE were prospectively recruited and given the Boston Naming Test (BNT) before and ≈7 months after anterior temporal lobectomy. An fMRI language laterality index (LI) was measured with an auditory semantic decision-tone decision task contrast. Correlations and a multiple regression model were built with a priori chosen predictors. RESULTS Naming decline occurred in 56% of patients and correlated with fMRI LI (r = -0.41, p < 0.001), age at epilepsy onset (r = -0.30, p = 0.006), age at surgery (r = -0.23, p = 0.039), and years of education (r = 0.24, p = 0.032). Preoperative BNT score and duration of epilepsy were not correlated with naming decline. The regression model explained 31% of the variance, with fMRI contributing 14%, with a 96% sensitivity and 44% specificity for predicting meaningful naming decline. Cross-validation resulted in an average prediction error of 6 points. DISCUSSION An fMRI-based regression model predicted naming outcome after left TLE surgery in a large, prospective multicenter sample, with fMRI as the strongest predictor. These results provide evidence supporting the use of preoperative language fMRI to predict language outcome in patients undergoing left TLE surgery. CLASSIFICATION OF EVIDENCE This study provides Class I evidence that fMRI language lateralization can help in predicting naming decline after left TLE surgery.
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Affiliation(s)
- William Louis Gross
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH.
| | - Alexander I Helfand
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Sara J Swanson
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Lisa L Conant
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Colin J Humphries
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Manoj Raghavan
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Wade M Mueller
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Robyn M Busch
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Linda Allen
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Christopher Todd Anderson
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Chad E Carlson
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Mark J Lowe
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - John T Langfitt
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Madalina E Tivarus
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Daniel L Drane
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - David W Loring
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Monica Jacobs
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Victoria L Morgan
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Jane B Allendorfer
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Jerzy P Szaflarski
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Leonardo Bonilha
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Susan Bookheimer
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Thomas Grabowski
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Jennifer Vannest
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
| | - Jeffrey R Binder
- From the Departments of Neurology (W.L.G., A.H., S.J.S., L.L.C., C.H., M.R., L.A., C.T.A., C.E.C., J.R.B.), Anesthesiology (W.L.G.), and Neurosurgery (W.M.M.), Medical College of Wisconsin, Milwaukee; Departments of Neurology (R.M.B.) and Radiology (M.J.L.), Cleveland Clinic Foundation, OH; Departments of Neurology (J.T.L.) and Imaging Sciences (M.E.T.), University of Rochester, NY; Departments of Neurology (D.L.D., D.W.L.) and Pediatrics (D.L.D.), Emory University, Atlanta, GA; Department of Neurology (D.L.D., T.G.), University of Washington, Seattle; Departments of Psychology (M.J.) and Radiology (V.L.M.), Vanderbilt University Medical Center, Nashville, TN; Department of Neurology (J.B.A., J.P.S.), University of Alabama at Birmingham; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; Department of Neurology (S.B.), University of California, Los Angeles; and Department of Neurology (J.V.), University of Cincinnati, OH
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Di Plinio S. Testing the Magnitude of Correlations Across Experimental Conditions. Front Psychol 2022; 13:860213. [PMID: 35693490 PMCID: PMC9177411 DOI: 10.3389/fpsyg.2022.860213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Correlation coefficients are often compared to investigate data across multiple research fields, as they allow investigators to determine different degrees of correlation to independent variables. Even with adequate sample size, such differences may be minor but still scientifically relevant. To date, although much effort has gone into developing methods for estimating differences across correlation coefficients, adequate tools for variable sample sizes and correlational strengths have yet to be tested. The present study evaluated four different methods for detecting the difference between two correlations and tested the adequacy of each method using simulations with multiple data structures. The methods tested were Cohen's q, Fisher's method, linear mixed-effects models (LMEM), and an ad hoc developed procedure that integrates bootstrap and effect size estimation. Correlation strengths and sample size was varied across a wide range of simulations to test the power of the methods to reject the null hypothesis (i.e., the two correlations are equal). Results showed that Fisher's method and the LMEM failed to reject the null hypothesis even in the presence of relevant differences between correlations and that Cohen's method was not sensitive to the data structure. Bootstrap followed by effect size estimation resulted in a fair, unbiased compromise for estimating quantitative differences between statistical associations and producing outputs that could be easily compared across studies. This unbiased method is easily implementable in MatLab through the bootes function, which was made available online by the author at MathWorks.
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Affiliation(s)
- Simone Di Plinio
- Department of Neuroscience Imaging and Clinical Sciences, “G. D’Annunzio” University of Chieti-Pescara, Chieti, Italy
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Zhang D, Zhou L, Yang A, Li S, Chang C, Liu J, Zhou K. A connectome-based neuromarker of nonverbal number acuity and arithmetic skills. Cereb Cortex 2022; 33:881-894. [PMID: 35254408 PMCID: PMC9890459 DOI: 10.1093/cercor/bhac108] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 02/04/2023] Open
Abstract
The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual's ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.
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Affiliation(s)
- Dai Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Anmin Yang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Shanshan Li
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Chunqi Chang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, No. 30, Shuangqing Street, Haidian District, Beijing 100084, China
| | - Ke Zhou
- Corresponding author: Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China.
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Wu X, Kong X, Vatansever D, Liu Z, Zhang K, Sahakian BJ, Robbins TW, Feng J, Thompson P, Zhang J. Dynamic changes in brain lateralization correlate with human cognitive performance. PLoS Biol 2022; 20:e3001560. [PMID: 35298460 PMCID: PMC8929635 DOI: 10.1371/journal.pbio.3001560] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders. Hemispheric lateralization constitutes a core architectural principle of human brain organization, often argued to represent a stable, trait-like feature, but how does this fit with our increasing appreciation of the inherently dynamic nature of brain networks? This neuroimaging study reveals the dynamic nature of functional brain lateralization at resting-state and its relationship with language function and cognitive flexibility.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhaowen Liu
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Barbara J. Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W. Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Paul Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- * E-mail:
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35
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Gupta S, Lim M, Rajapakse JC. Decoding task specific and task general functional architectures of the brain. Hum Brain Mapp 2022; 43:2801-2816. [PMID: 35224817 PMCID: PMC9120557 DOI: 10.1002/hbm.25817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/01/2022] [Accepted: 02/13/2022] [Indexed: 11/06/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference‐based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Marcus Lim
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Jagath C. Rajapakse
- School of Computer Science and Engineering Nanyang Technological University Singapore
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36
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Metoki A, Wang Y, Olson IR. The Social Cerebellum: A Large-Scale Investigation of Functional and Structural Specificity and Connectivity. Cereb Cortex 2022; 32:987-1003. [PMID: 34428293 PMCID: PMC8890001 DOI: 10.1093/cercor/bhab260] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/30/2021] [Accepted: 07/02/2021] [Indexed: 12/15/2022] Open
Abstract
The cerebellum has been traditionally disregarded in relation to nonmotor functions, but recent findings indicate it may be involved in language, affective processing, and social functions. Mentalizing, or Theory of Mind (ToM), is the ability to infer mental states of others and this skill relies on a distributed network of brain regions. Here, we leveraged large-scale multimodal neuroimaging data to elucidate the structural and functional role of the cerebellum in mentalizing. We used functional activations to determine whether the cerebellum has a domain-general or domain-specific functional role, and effective connectivity and probabilistic tractography to map the cerebello-cerebral mentalizing network. We found that the cerebellum is organized in a domain-specific way and that there is a left cerebellar effective and structural lateralization, with more and stronger effective connections from the left cerebellar hemisphere to the right cerebral mentalizing areas, and greater cerebello-thalamo-cortical and cortico-ponto-cerebellar streamline counts from and to the left cerebellum. Our study provides novel insights to the network organization of the cerebellum, an overlooked brain structure, and mentalizing, one of humans' most essential abilities to navigate the social world.
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Affiliation(s)
- Athanasia Metoki
- Department of Psychology, Temple University, Philadelphia, PA 19122, USA
- Department of Neurology,Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA 19122, USA
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37
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Bi XA, Xing Z, Zhou W, Li L, Xu L. Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest with Brain Imaging and Genetic Data. IEEE J Biomed Health Inform 2022; 26:3068-3079. [PMID: 35157601 DOI: 10.1109/jbhi.2022.3151084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.
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38
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Banjac S, Roger E, Cousin E, Mosca C, Minotti L, Krainik A, Kahane P, Baciu M. Mapping of Language-and-Memory Networks in Patients With Temporal Lobe Epilepsy by Using the GE2REC Protocol. Front Hum Neurosci 2022; 15:752138. [PMID: 35069148 PMCID: PMC8772037 DOI: 10.3389/fnhum.2021.752138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/13/2021] [Indexed: 11/17/2022] Open
Abstract
Preoperative mapping of language and declarative memory functions in temporal lobe epilepsy (TLE) patients is essential since they frequently encounter deterioration of these functions and show variable degrees of cerebral reorganization. Due to growing evidence on language and declarative memory interdependence at a neural and neuropsychological level, we propose the GE2REC protocol for interactive language-and-memory network (LMN) mapping. GE2REC consists of three inter-related tasks, sentence generation with implicit encoding (GE) and two recollection (2REC) memory tasks: recognition and recall. This protocol has previously been validated in healthy participants, and in this study, we showed that it also maps the LMN in the left TLE (N = 18). Compared to healthy controls (N = 19), left TLE (LTLE) showed widespread inter- and intra-hemispheric reorganization of the LMN through reduced activity of regions engaged in the integration and the coordination of this meta-network. We also illustrated how this protocol could be implemented in clinical practice individually by presenting two case studies of LTLE patients who underwent efficient surgery and became seizure-free but showed different cognitive outcomes. This protocol can be advantageous for clinical practice because it (a) is short and easy to perform; (b) allows brain mapping of essential cognitive functions, even at an individual level; (c) engages language-and-memory interaction allowing to evaluate the integrative processes within the LMN; (d) provides a more comprehensive assessment by including both verbal and visual modalities, as well as various language and memory processes. Based on the available postsurgical data, we presented preliminary results obtained with this protocol in LTLE patients that could potentially inform the clinical practice. This implies the necessity to further validate the potential of GE2REC for neurosurgical planning, along with two directions, guiding resection and describing LMN neuroplasticity at an individual level.
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Affiliation(s)
- Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105, Grenoble, France
| | - Elise Roger
- Université Grenoble Alpes, CNRS LPNC UMR 5105, Grenoble, France
| | - Emilie Cousin
- Université Grenoble Alpes, CNRS LPNC UMR 5105, Grenoble, France
- Université Grenoble Alpes, UMS IRMaGe CHU Grenoble, Grenoble, France
| | - Chrystèle Mosca
- Université Grenoble Alpes, Grenoble Institute of Neuroscience ‘Synchronisation et modulation des réseaux neuronaux dans l’épilepsie’ & Neurology Department, Grenoble, France
| | - Lorella Minotti
- Université Grenoble Alpes, Grenoble Institute of Neuroscience ‘Synchronisation et modulation des réseaux neuronaux dans l’épilepsie’ & Neurology Department, Grenoble, France
| | - Alexandre Krainik
- Université Grenoble Alpes, UMS IRMaGe CHU Grenoble, Grenoble, France
| | - Philippe Kahane
- Université Grenoble Alpes, Grenoble Institute of Neuroscience ‘Synchronisation et modulation des réseaux neuronaux dans l’épilepsie’ & Neurology Department, Grenoble, France
| | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105, Grenoble, France
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39
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Lower resting brain entropy is associated with stronger task activation and deactivation. Neuroimage 2022; 249:118875. [PMID: 34998971 DOI: 10.1016/j.neuroimage.2022.118875] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 01/21/2023] Open
Abstract
Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.
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40
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Zhao W, Li H, Hao Y, Hu G, Zhang Y, Frederick BDB, Cong F. An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm. Hum Brain Mapp 2021; 43:1561-1576. [PMID: 34890077 PMCID: PMC8886658 DOI: 10.1002/hbm.25742] [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] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/03/2022] Open
Abstract
High dimensionality data have become common in neuroimaging fields, especially group‐level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data‐driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE‐based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group‐level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task‐evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.
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Affiliation(s)
- Wei Zhao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Huanjie Li
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yuxing Hao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Guoqiang Hu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yunge Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Blaise de B Frederick
- Brain Imaging Center, McLean Hospital, Belmont, Massachusetts, USA.,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province, Dalian University of Technology, Dalian, China.,Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland
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41
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Berro DH, Lemée JM, Leiber LM, Emery E, Menei P, Ter Minassian A. Overt speech critically changes lateralization index and did not allow determination of hemispheric dominance for language: an fMRI study. BMC Neurosci 2021; 22:74. [PMID: 34852787 PMCID: PMC8638205 DOI: 10.1186/s12868-021-00671-y] [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: 03/28/2021] [Accepted: 09/09/2021] [Indexed: 11/25/2022] Open
Abstract
Background Pre-surgical mapping of language using functional MRI aimed principally to determine the dominant hemisphere. This mapping is currently performed using covert linguistic task in way to avoid motion artefacts potentially biasing the results. However, overt task is closer to natural speaking, allows a control on the performance of the task, and may be easier to perform for stressed patients and children. However, overt task, by activating phonological areas on both hemispheres and areas involved in pitch prosody control in the non-dominant hemisphere, is expected to modify the determination of the dominant hemisphere by the calculation of the lateralization index (LI). Objective Here, we analyzed the modifications in the LI and the interactions between cognitive networks during covert and overt speech task. Methods Thirty-three volunteers participated in this study, all but four were right-handed. They performed three functional sessions consisting of (1) covert and (2) overt generation of a short sentence semantically linked with an audibly presented word, from which we estimated the “Covert” and “Overt” contrasts, and a (3) resting-state session. The resting-state session was submitted to spatial independent component analysis to identify language network at rest (LANG), cingulo-opercular network (CO), and ventral attention network (VAN). The LI was calculated using the bootstrapping method. Results The LI of the LANG was the most left-lateralized (0.66 ± 0.38). The LI shifted from a moderate leftward lateralization for the Covert contrast (0.32 ± 0.38) to a right lateralization for the Overt contrast (− 0.13 ± 0.30). The LI significantly differed from each other. This rightward shift was due to the recruitment of right hemispheric temporal areas together with the nodes of the CO. Conclusion Analyzing the overt speech by fMRI allowed improvement in the physiological knowledge regarding the coordinated activity of the intrinsic connectivity networks. However, the rightward shift of the LI in this condition did not provide the basic information on the hemispheric language dominance. Overt linguistic task cannot be recommended for clinical purpose when determining hemispheric dominance for language. Supplementary Information The online version contains supplementary material available at 10.1186/s12868-021-00671-y.
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Affiliation(s)
- David Hassanein Berro
- Department of Neurosurgery, University Hospital of Caen Normandy, Avenue de la Côte de Nacre, 14000, Caen, France. .,Normandie Univ, UNICAEN, CEA, CNRS, ISTCT/CERVOxy group, GIP Cyceron, Caen, France. .,INSERM, CRCINA, Team 17, IRIS building, Angers, France.
| | - Jean-Michel Lemée
- INSERM, CRCINA, Team 17, IRIS building, Angers, France.,Department of Neurosurgery, University Hospital of Angers, Angers, France
| | | | - Evelyne Emery
- Department of Neurosurgery, University Hospital of Caen Normandy, Avenue de la Côte de Nacre, 14000, Caen, France.,INSERM, UMR-S U1237, PhIND group, GIP Cyceron, Caen, France
| | - Philippe Menei
- INSERM, CRCINA, Team 17, IRIS building, Angers, France.,Department of Neurosurgery, University Hospital of Angers, Angers, France
| | - Aram Ter Minassian
- Department of Anesthesiology, University Hospital of Angers, Angers, France.,LARIS, ISISV team, University of Angers, Angers, France
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42
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Parcellation-Free prediction of task fMRI activations from dMRI tractography. Med Image Anal 2021; 76:102317. [PMID: 34871930 DOI: 10.1016/j.media.2021.102317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/22/2022]
Abstract
The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.
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43
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Karatepe HM, Safi D, Martineau L, Boucher O, Nguyen DK, Bouthillier A. Safety of an operculoinsulectomy in the language-dominant hemisphere for refractory epilepsy. Clin Neurol Neurosurg 2021; 211:107014. [PMID: 34794058 DOI: 10.1016/j.clineuro.2021.107014] [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: 07/13/2021] [Revised: 10/24/2021] [Accepted: 10/27/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Operculoinsular cortectomy is increasingly recognized as a therapeutic avenue for perisylvian refractory epilepsy. However, most neurosurgeons are reluctant to perform this type of procedure because of feared neurological complications, especially in the language-dominant hemisphere, as the insula is involved in speech and language processes. The goal of this retrospective study is to quantify the incidence and types of speech and language deficits associated with operculoinsulectomies in the dominant hemisphere for language, and to identify factors associated with these complications. METHODS Clinical, imaging, and surgical data of all patients who had an operculoinsulectomy for refractory epilepsy at our center between 1998 and 2018 were reviewed. Language lateralization was determined by functional magnetic resonance imaging (fMRI) and/or Wada test. Speech and language assessments were carried out by neurosurgeons, neurologists, neuropsychologists and/or speech language pathologists, before surgery, during the first week after surgery, and at least 6 months after surgery. RESULTS Amongst 44 operculoinsulectomies, 13 were performed in the language-dominant hemisphere. 46% of these patients presented with transient aphasia post-surgery. However, a few months later, the patients' performances on language assessments were not statistically different from before surgery, thus suggesting a complete recovery of speech and language functions. CONCLUSION Temporary aphasias after operculoinsulectomy for refractory epilepsy in the language-dominant hemisphere are frequent, but eventually subside. Potential mechanisms underlying this recovery are discussed.
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Affiliation(s)
- Hazal Melek Karatepe
- Division of Neurosurgery, University of Montreal Hospital Center (CHUM), Canada.
| | - Dima Safi
- Department of Speech Language Pathology, Université du Québec à Trois-Rivières, Canada
| | | | - Olivier Boucher
- Psychology, University of Montreal Hospital Center (CHUM), Canada
| | - Dang Khoa Nguyen
- Neurology, University of Montreal Hospital Center (CHUM), Canada
| | - Alain Bouthillier
- Division of Neurosurgery, University of Montreal Hospital Center (CHUM), Canada
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44
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Wu A, Nastase SA, Baldassano CA, Turk-Browne NB, Norman KA, Engelhardt BE, Pillow JW. Brain kernel: A new spatial covariance function for fMRI data. Neuroimage 2021; 245:118580. [PMID: 34740792 DOI: 10.1016/j.neuroimage.2021.118580] [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: 03/22/2021] [Revised: 07/30/2021] [Accepted: 09/14/2021] [Indexed: 11/20/2022] Open
Abstract
A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel's usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.
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Affiliation(s)
- Anqi Wu
- Center for Theoretical Neuroscience, Columbia University, New York City, NY, USA.
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
| | | | | | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
| | | | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
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45
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Gale MK, Nezafati M, Keilholz SD. Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2968-2971. [PMID: 34891868 DOI: 10.1109/embc46164.2021.9630607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.
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Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks. Med Image Anal 2021; 74:102203. [PMID: 34474216 DOI: 10.1016/j.media.2021.102203] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 06/04/2021] [Accepted: 06/08/2021] [Indexed: 11/20/2022]
Abstract
Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on data from the publicly available Human Connectome Project and on a brain tumor dataset acquired at the Johns Hopkins Hospital. We compare our method against feature-based machine learning approaches and a fully-connected deep learning model that does not account for the shared network organization of the data. Our model achieves significantly better performance than competing baselines. We also assess the generalizability and robustness of our method. Our results clearly demonstrate the advantages of our graph convolution architecture combined with multi-task learning and highlight the promise of using rs-fMRI as a presurgical mapping tool.
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Neuropsychosocial markers of binge drinking in young adults. Mol Psychiatry 2021; 26:4931-4943. [PMID: 32398720 PMCID: PMC7658012 DOI: 10.1038/s41380-020-0771-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/22/2020] [Accepted: 04/29/2020] [Indexed: 01/26/2023]
Abstract
Binge drinking is associated with disease and death, and developing tools to identify risky drinkers could mitigate its damage. Brain processes underlie risky drinking, so we examined whether neural and psychosocial markers could identify binge drinkers. Reward is the most widely studied neural process in addiction, but processes such as emotion, social cognition, and self-regulation are also involved. Here we examined whether neural processes apart from reward contribute to predicting risky drinking behaviors. From the Human Connectome Project, we identified 177 young adults who binged weekly and 309 nonbingers. We divided the sample into a training and a testing set and used machine-learning algorithms to classify participants based on psychosocial, neural, or both (neuropsychosocial) data. We also developed separate models for each of the seven fMRI tasks used in the study. An ensemble model developed in the training dataset was then applied to the testing dataset. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and differences between models were assessed using DeLong's test. The three models performed better than chance in the test sample with the neuropsychosocial (AUC = 0.86) and psychosocial (AUC = 0.84) performing better than the neural model (AUC = 0.64). Two fMRI-based models predicted binge drinking status better than chance, corresponding to the social and language tasks. Models developed with psychosocial and neural variables could contribute as diagnostic tools to help classify risky drinkers. Since social and language fMRI tasks performed best among the neural discriminators (including those from gambling and emotion tasks), it suggests the involvement of a broader range of brain processes than those traditionally associated with binge drinking in young adults.
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Li Q, Zhang W, Zhao L, Wu X, Liu T. Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition. IEEE Trans Biomed Eng 2021; 69:624-634. [PMID: 34357861 DOI: 10.1109/tbme.2021.3102466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Using deep neural networks (DNNs) to explore spatial patterns and temporal dynamics of human brain activities has been an important yet challenging problem because the artificial neural networks are hard to be designed manually. There have been several promising deep learning methods, e.g., deep belief network (DBN), convolutional neural network (CNN), and deep sparse recurrent auto-encoder (DSRAE), that can decompose neuroscientific and meaningful spatiotemporal patterns from 4D functional Magnetic Resonance Imaging (fMRI) data. However, those previous studies still depend on hand-crafted neural network architectures and hyperparameters, which are not optimal in various senses. In this paper, we employ the evolutionary algorithms (EA) to optimize the deep neural architecture of DSRAE by minimizing the expected loss of initialized models, named eNAS-DSRAE (evolutionary Neural Architecture Search on Deep Sparse Recurrent Auto-Encoder). Also, validation experiments are designed and performed on the publicly available human connectome project (HCP) 900 datasets, and the results achieved by the optimized eNAS-DSRAE suggested that our framework can successfully identify the spatiotemporal features and perform better than the hand-crafted neural network models. To our best knowledge, the proposed eNAS-DSRAE is not only among the earliest NAS models that can extract connectome-scale meaningful spatiotemporal brain networks from 4D fMRI data, but also is an effective framework to optimize the RNN-based models.
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Wang Z. The neurocognitive correlates of brain entropy estimated by resting state fMRI. Neuroimage 2021; 232:117893. [PMID: 33621695 PMCID: PMC8138544 DOI: 10.1016/j.neuroimage.2021.117893] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/02/2021] [Accepted: 02/16/2021] [Indexed: 11/17/2022] Open
Abstract
Resting state brain activity consumes most of brain energy, likely creating and maintaining a reserve of general brain functionality. The latent reserve if it exists may be reflected by the profound long-range fluctuations of resting brain activity. The long-range temporal coherence (LRTC) can be characterized by resting state fMRI (rsfMRI)-based brain entropy (BEN) mapping. While BEN mapping results have shown sensitivity to neuromodulations or disease conditions, the underlying neuromechanisms especially the associations of BEN or LRTC to neurocognition still remain unclear. To address this standing question and to test a novel hypothesis that resting BEN reflects a latent functional reserve through the link to general functionality, we mapped resting BEN of 862 young adults and comprehensively examined its associations to neurocognitions using data from the Human Connectome Project (HCP). Our results unanimously highlighted two brain circuits: the default mode network (DMN) and executive control network (ECN) through their negative associations of BEN to general functionality, which is independent of age and sex. While BEN in DMN/ECN increases with age, it decreases with education years. These results demonstrated the neurocognitive correlates of resting BEN in DMN/ECN and suggest resting BEN in DMN/ECN as a potential proxy of the latent functional reserve that facilitates general brain functionality and may be enhanced by education.
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Affiliation(s)
- Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670W. Baltimore St, Baltimore, MD 20201, United States.
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Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains. Neuroimage 2021; 235:118035. [PMID: 33838264 DOI: 10.1016/j.neuroimage.2021.118035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 11/15/2022] Open
Abstract
The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.
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