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Li J, Segel A, Feng X, Tu JC, Eck A, King KT, Adeyemo B, Karcher NR, Chen L, Eggebrecht AT, Wheelock MD. Network-level enrichment provides a framework for biological interpretation of machine learning results. Netw Neurosci 2024; 8:762-790. [PMID: 39355443 PMCID: PMC11349033 DOI: 10.1162/netn_a_00383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/15/2024] [Indexed: 10/03/2024] Open
Abstract
Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.
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Affiliation(s)
- Jiaqi Li
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Ari Segel
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Xinyang Feng
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Andy Eck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Kelsey T King
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University in St. Louis, MO, USA
| | - Nicole R Karcher
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Likai Chen
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
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2
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Petersen SE, Seitzman BA, Nelson SM, Wig GS, Gordon EM. Principles of cortical areas and their implications for neuroimaging. Neuron 2024; 112:2837-2853. [PMID: 38834069 DOI: 10.1016/j.neuron.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.
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Affiliation(s)
- Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin A Seitzman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gagan S Wig
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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3
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Lei T, Liao X, Liang X, Sun L, Xia M, Xia Y, Zhao T, Chen X, Men W, Wang Y, Ma L, Liu N, Lu J, Zhao G, Ding Y, Deng Y, Wang J, Chen R, Zhang H, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Functional network modules overlap and are linked to interindividual connectome differences during human brain development. PLoS Biol 2024; 22:e3002653. [PMID: 39292711 PMCID: PMC11441662 DOI: 10.1371/journal.pbio.3002653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 09/30/2024] [Accepted: 08/29/2024] [Indexed: 09/20/2024] Open
Abstract
The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.
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Affiliation(s)
- Tianyuan Lei
- Department of Psychiatry, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal 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, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical College, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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4
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Luckett PH, Olufawo MO, Park KY, Lamichhane B, Dierker D, Verastegui GT, Lee JJ, Yang P, Kim A, Butt OH, Chheda MG, Snyder AZ, Shimony JS, Leuthardt EC. Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning. J Neurooncol 2024; 169:175-185. [PMID: 38789843 PMCID: PMC11269343 DOI: 10.1007/s11060-024-04715-1] [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: 04/15/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
PURPOSE High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care. METHODS Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70). RESULTS The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor. CONCLUSION The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.
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Affiliation(s)
- Patrick H Luckett
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
| | - Michael O Olufawo
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Ki Yun Park
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Bidhan Lamichhane
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Peter Yang
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Albert Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Omar H Butt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Milan G Chheda
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, USA
- Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, USA
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, USA
- National Center for Adaptive Neurotechnologies, Albany, NY, USA
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5
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Demeter DV, Greene DJ. The promise of precision functional mapping for neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01941-z. [PMID: 39085426 DOI: 10.1038/s41386-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.
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Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
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6
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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7
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Wang G, Datta A, Lindquist MA. Improved fMRI-based pain prediction using Bayesian group-wise functional registration. Biostatistics 2024; 25:885-903. [PMID: 37805937 DOI: 10.1093/biostatistics/kxad026] [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: 03/13/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 10/10/2023] Open
Abstract
In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.
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Affiliation(s)
- Guoqing Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
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8
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function. J Neurophysiol 2024; 131:1014-1082. [PMID: 38489238 PMCID: PMC11383390 DOI: 10.1152/jn.00308.2023] [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: 08/16/2023] [Revised: 01/18/2024] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
The cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks from functional MRI (fMRI) data in intensively sampled participants. The procedure was developed in two participants (scanned 31 times) and then prospectively applied to 15 participants (scanned 8-11 times). Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). Within each SAAM, two candidate control regions were adjacent to three separate domain-specialized regions. Response properties were explored with task data. The somatomotor and visual networks responded to body movements and visual stimulation, respectively. Second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions dissociated across language, social, and spatial/episodic processing domains. These results suggest that progressively higher-order networks nest outward from primary sensory and motor cortices. Within the apex zones of association cortex, there is specialization that repeatedly divides domain-flexible from domain-specialized regions. We discuss implications of these findings, including how repeating organizational motifs may emerge during development.NEW & NOTEWORTHY The organization of cerebral networks was estimated within individuals with intensive, repeat sampling of fMRI data. A hierarchical organization emerged in each individual that delineated first-, second-, and third-order cortical networks. Regions of distinct third-order association networks consistently exhibited side-by-side juxtapositions that repeated across multiple cortical zones, with clear and robust functional specialization among the embedded regions.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Aihuiping Xue
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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9
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Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 PMCID: PMC11248096 DOI: 10.1038/s41593-024-01618-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
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Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
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Power LC, Mirro AE, Binkley MM, Wang J, Guilliams KP, Lewis JB, Ford AL, Shimony JS, An H, Lee JM, Fields ME. Reversibility of Cognitive Deficits and Functional Connectivity With Transfusion in Children With Sickle Cell Disease. Neurology 2024; 102:e209429. [PMID: 38710015 PMCID: PMC11177587 DOI: 10.1212/wnl.0000000000209429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/28/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND AND OBJECTIVES People with sickle cell disease (SCD) are at risk of cognitive dysfunction independent of stroke. Diminished functional connectivity in select large-scale networks and white matter integrity reflect the neurologic consequences of SCD. Because chronic transfusion therapy is neuroprotective in preventing stroke and strengthening executive function abilities in people with SCD, we hypothesized that red blood cell (RBC) transfusion facilitates the acute reversal of disruptions in functional connectivity while white matter integrity remains unaffected. METHODS Children with SCD receiving chronic transfusion therapy underwent a brain MRI measuring white matter integrity with diffusion tensor imaging and resting-state functional connectivity within 3 days before and after transfusion of RBCs. Cognitive assessments with the NIH Toolbox were acquired after transfusion and then immediately before the following transfusion cycle. RESULTS Sixteen children with a median age of 12.5 years were included. Global assessments of functional connectivity using homotopy (p = 0.234) or modularity (p = 0.796) did not differ with transfusion. Functional connectivity within the frontoparietal network significantly strengthened after transfusion (median intranetwork Z-score 0.21 [0.17-0.30] before transfusion, 0.29 [0.20-0.36] after transfusion, p < 0.001), while there was not a significant change seen within the sensory motor, visual, auditory, default mode, dorsal attention, or cingulo-opercular networks. Corresponding to the change within the frontoparietal network, there was a significant improvement in executive function abilities after transfusion (median executive function composite score 87.7 [81.3-90.7] before transfusion, 90.3 [84.3-93.7] after transfusion, p = 0.021). Participants with stronger connectivity in the frontoparietal network before transfusion had a significantly greater improvement in the executive function composite score with transfusion (r = 0.565, 95% CI 0.020-0.851, p = 0.044). While functional connectivity and executive abilities strengthened with transfusion, there was not a significant change in white matter integrity as assessed by fractional anisotropy and mean diffusivity within 16 white matter tracts or globally with tract-based spatial statistics. DISCUSSION Strengthening of functional connectivity with concomitant improvement in executive function abilities with transfusion suggests that functional connectivity MRI could be used as a biomarker for acutely reversible neurocognitive injury as novel therapeutics are developed for people with SCD.
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Affiliation(s)
- Landon C Power
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Amy E Mirro
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Micahel M Binkley
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jinli Wang
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Kristin P Guilliams
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Josiah B Lewis
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Andria L Ford
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Joshua S Shimony
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Hongyu An
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jin-Moo Lee
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Melanie E Fields
- From the Department of Pediatrics (L.C.P., A.E.M., M.M.B., K.P.G., M.E.F.), Center for Biostatistics and Data Science (J.W.), Department of Neurology (K.P.G., J.B.L., A.L.F., J.-M.L., M.E.F.), Mallinckrodt Institute of Radiology (K.P.G., A.L.F., J.S.S., H.A., J.-M.L.), and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
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11
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Hermosillo RJM, Moore LA, Feczko E, Miranda-Domínguez Ó, Pines A, Dworetsky A, Conan G, Mooney MA, Randolph A, Graham A, Adeyemo B, Earl E, Perrone A, Carrasco CM, Uriarte-Lopez J, Snider K, Doyle O, Cordova M, Koirala S, Grimsrud GJ, Byington N, Nelson SM, Gratton C, Petersen S, Feldstein Ewing SW, Nagel BJ, Dosenbach NUF, Satterthwaite TD, Fair DA. A precision functional atlas of personalized network topography and probabilities. Nat Neurosci 2024; 27:1000-1013. [PMID: 38532024 PMCID: PMC11089006 DOI: 10.1038/s41593-024-01596-5] [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: 02/14/2022] [Accepted: 02/08/2024] [Indexed: 03/28/2024]
Abstract
Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.
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Affiliation(s)
- Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Óscar Miranda-Domínguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Adam Pines
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michael A Mooney
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Mental Health Innovation, Oregon Health and Science University, Portland, OR, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | | | - Kathy Snider
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA
- Joint Doctoral Program in Clinical Psychology, University of California San Diego, San Diego, CA, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Gracie J Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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12
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Zhu J, Tian KJ, Carrasco M, Denison RN. Temporal attention recruits fronto-cingulate cortex to amplify stimulus representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583738. [PMID: 38496610 PMCID: PMC10942468 DOI: 10.1101/2024.03.06.583738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The human brain receives a continuous stream of input, but it faces significant constraints in its ability to process every item in a sequence of stimuli. Voluntary temporal attention can alleviate these constraints by using information about upcoming stimulus timing to selectively prioritize a task-relevant item over others in a sequence. But the neural mechanisms underlying this ability remain unclear. Here, we manipulated temporal attention to successive stimuli in a two-target temporal cueing task, while controlling for temporal expectation by using fully predictable stimulus timing. We recorded magnetoencephalography (MEG) in human observers and measured the effects of temporal attention on orientation representations of each stimulus using time-resolved multivariate decoding in both sensor and source space. Voluntary temporal attention enhanced the orientation representation of the first target 235-300 milliseconds after target onset. Unlike previous studies that did not isolate temporal attention from temporal expectation, we found no evidence that temporal attention enhanced early visual evoked responses. Instead, and unexpectedly, the primary source of enhanced decoding for attended stimuli in the critical time window was a contiguous region spanning left frontal cortex and cingulate cortex. The results suggest that voluntary temporal attention recruits cortical regions beyond the ventral stream at an intermediate processing stage to amplify the representation of a target stimulus, which may serve to protect it from subsequent interference by a temporal competitor.
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13
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Harp NR, Nielsen AN, Schultz DH, Neta M. In the face of ambiguity: intrinsic brain organization in development predicts one's bias toward positivity or negativity. Cereb Cortex 2024; 34:bhae102. [PMID: 38494885 PMCID: PMC10945044 DOI: 10.1093/cercor/bhae102] [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: 05/31/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/19/2024] Open
Abstract
Exacerbated negativity bias, including in responses to ambiguity, represents a common phenotype of internalizing disorders. Individuals differ in their propensity toward positive or negative appraisals of ambiguity. This variability constitutes one's valence bias, a stable construct linked to mental health. Evidence suggests an initial negativity in response to ambiguity that updates via regulatory processes to support a more positive bias. Previous work implicates the amygdala and prefrontal cortex, and regions of the cingulo-opercular system, in this regulatory process. Nonetheless, the neurodevelopmental origins of valence bias remain unclear. The current study tests whether intrinsic brain organization predicts valence bias among 119 children and adolescents (6 to 17 years). Using whole-brain resting-state functional connectivity, a machine-learning model predicted valence bias (r = 0.20, P = 0.03), as did a model restricted to amygdala and cingulo-opercular system features (r = 0.19, P = 0.04). Disrupting connectivity revealed additional intra-system (e.g. fronto-parietal) and inter-system (e.g. amygdala to cingulo-opercular) connectivity important for prediction. The results highlight top-down control systems and bottom-up perceptual processes that influence valence bias in development. Thus, intrinsic brain organization informs the neurodevelopmental origins of valence bias, and directs future work aimed at explicating related internalizing symptomology.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, 300 George Street, New Haven, CT 06511, United States
| | - Ashley N Nielsen
- Department of Neurology, Washington University, 660 S. Euclid Ave., St. Louis, MO 63110, United States
| | - Douglas H Schultz
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
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14
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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15
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Church JA. The Brain's Control Networks in Reading: Insights From Cross-Task Studies of Youth. MIND, BRAIN AND EDUCATION : THE OFFICIAL JOURNAL OF THE INTERNATIONAL MIND, BRAIN, AND EDUCATION SOCIETY 2023; 17:257-266. [PMID: 38745918 PMCID: PMC11091959 DOI: 10.1111/mbe.12372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 06/04/2023] [Indexed: 05/16/2024]
Abstract
Humans engage multiple brain systems to read successfully, including using regions important for vision, language, and control. Control refers to the set of executive processes in the brain that guide moment-to-moment behavior in service of our goals. There is a growing appreciation for the role of the brain's control system in reading comprehension, in reading skill change over time, and in those who have difficulty with the reading process. One way to understand the brain's control engagement in reading may be to study control engagement across multiple tasks in order to study consistencies, or cross-task similarities, relative to reading-specific variations. In this commentary, I briefly summarize some of our recent work studying the brain's control networks across different tasks (e.g., when reading, or doing different executive function tasks). I then review our findings of when control activation does or does not relate to measures of reading ability, and reading growth over time. The utility of cross-task comparisons in neuroimaging is noted, as well as the need to better understand multiple sources of heterogeneity in our developmental samples. I end by discussing a few of the many future directions for further study of the brain with regard to the brain's control processing and academic achievement.
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16
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Willbrand EH, Bunge SA, Weiner KS. Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci. J Cogn Neurosci 2023; 35:1846-1867. [PMID: 37677051 PMCID: PMC10586811 DOI: 10.1162/jocn_a_02049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.
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17
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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18
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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19
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Dowling AV, Seitzman BA, Mitchell TJ, Olufawo M, Dierker DL, Anandarajah H, Dworetsky A, McMichael A, Jiang C, Barbour DL, Schlaggar BL, Limbrick DD, Strahle JM, Rubin JB, Shimony JS, Perkins SM. Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy. Int J Part Ther 2023; 10:32-42. [PMID: 37823016 PMCID: PMC10563667 DOI: 10.14338/ijpt-22-00039.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/18/2023] [Indexed: 10/13/2023] Open
Abstract
Purpose Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry. Participants and Methods Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems. Results Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05). Conclusion Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.
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Affiliation(s)
- Anna V. Dowling
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Benjamin A. Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy J. Mitchell
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael Olufawo
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Donna L. Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hari Anandarajah
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ally Dworetsky
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alana McMichael
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Catherine Jiang
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Dennis L. Barbour
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - David D. Limbrick
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jennifer M. Strahle
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua B. Rubin
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Stephanie M. Perkins
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
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20
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Capobianco E, Dominietto M. Assessment of brain cancer atlas maps with multimodal imaging features. J Transl Med 2023; 21:385. [PMID: 37308956 PMCID: PMC10262565 DOI: 10.1186/s12967-023-04222-3] [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: 04/18/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.
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Affiliation(s)
- Enrico Capobianco
- The Jackson Laboratory, 10 Discovery Drive, Farmington, CT, 06032, USA.
| | - Marco Dominietto
- Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232, Villigen, Switzerland
- Gate To Brain SA, Via Livio 7, 6830, Chiasso, Switzerland
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21
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Hauptman M, Blank I, Fedorenko E. Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach. Cortex 2023; 162:96-114. [PMID: 37023480 PMCID: PMC10210011 DOI: 10.1016/j.cortex.2023.01.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/08/2022] [Accepted: 01/11/2023] [Indexed: 03/12/2023]
Abstract
Going beyond the literal meaning of language is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n = 1,430 participants) from 2001 to 2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. anatomical locations, as is typically done) for three candidate brain networks-the language-selective network (Fedorenko, Behr, & Kanwisher, 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated 'localizer' tasks that selectively target each network in question (n = 806 for language; n = 198 for ToM; n = 691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.
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Affiliation(s)
- Miriam Hauptman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Idan Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA; Department of Linguistics, UCLA, Los Angeles, CA 90095, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Program in Speech and Hearing in Bioscience and Technology, Harvard University, Boston, MA 02114, USA.
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22
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Wood JL, Nee DE. Cingulo-Opercular Subnetworks Motivate Frontoparietal Subnetworks during Distinct Cognitive Control Demands. J Neurosci 2023; 43:1225-1237. [PMID: 36609452 PMCID: PMC9962782 DOI: 10.1523/jneurosci.1314-22.2022] [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: 07/04/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 01/09/2023] Open
Abstract
Cognitive control is the ability to flexibly adapt behavior in a goal-directed manner when habit will not suffice. Control can be separated into distinct forms based on the timescale (present-future) and/or medium (external-internal) over which it operates. Both the frontoparietal network (FPN) and cingulo-opercular network (CON) are engaged during control, but their respective functions and interactions remain unclear. Here, we examined activations in the FPN and CON with fMRI in humans (male and female) during a task that manipulated control across timescales/mediums. The findings show that the CON can be distinguished into the following two separable subnetworks mirroring the FPN: a rostral/ventral subnetwork sensitive to future-oriented control involving internal representations, and a caudal/dorsal subnetwork sensitive to present-oriented control involving external representations. Relative to the FPN, activation in the CON was particularly pronounced during transitions into and out of particular control demands. Moreover, the relationship of each CON subnetwork to behavior was mediated by a respective FPN subnetwork. Such data are consistent with the idea that the CON motivates the FPN, which, in turn, drives behavior. Within the CON, the dorsomedial prefrontal cortex (dmPFC) mediated the relationship between the anterior insula and FPN, suggesting that the dmPFC acts as the crux that links the CON to the FPN. Collectively, these data indicate that parallel CON-FPN subnetworks mediate controlled behaviors at distinct timescales/mediums.SIGNIFICANCE STATEMENT The cingulo-opercular network (CON) and frontoparietal network (FPN) are engaged in diverse, demanding tasks. A functional model describing how areas within these networks can be distinguished, and also interact, would facilitate understanding of how the brain adapts to demanding situations. During a comprehensive control task, fMRI data revealed that the FPN and CON can be fractionated into subnetworks based on control demands that are either externally oriented for use in the present, or control demands that operate internally to guide future behavior. Moreover, we found evidence for a chain of relationships from the CON to FPN to behavior consistent with the idea that the CON drives the FPN to adapt behavior.
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Affiliation(s)
- Jessica L Wood
- Department of Psychology, Florida State University, Tallahassee, Florida 32306-4301
| | - Derek Evan Nee
- Department of Psychology, Florida State University, Tallahassee, Florida 32306-4301
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23
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Perez DC, Dworetsky A, Braga RM, Beeman M, Gratton C. Hemispheric Asymmetries of Individual Differences in Functional Connectivity. J Cogn Neurosci 2023; 35:200-225. [PMID: 36378901 PMCID: PMC10029817 DOI: 10.1162/jocn_a_01945] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Resting-state fMRI studies have revealed that individuals exhibit stable, functionally meaningful divergences in large-scale network organization. The locations with strongest deviations (called network "variants") have a characteristic spatial distribution, with qualitative evidence from prior reports suggesting that this distribution differs across hemispheres. Hemispheric asymmetries can inform us on constraints guiding the development of these idiosyncratic regions. Here, we used data from the Human Connectome Project to systematically investigate hemispheric differences in network variants. Variants were significantly larger in the right hemisphere, particularly along the frontal operculum and medial frontal cortex. Variants in the left hemisphere appeared most commonly around the TPJ. We investigated how variant asymmetries vary by functional network and how they compare with typical network distributions. For some networks, variants seemingly increase group-average network asymmetries (e.g., the group-average language network is slightly bigger in the left hemisphere and variants also appeared more frequently in that hemisphere). For other networks, variants counter the group-average network asymmetries (e.g., the default mode network is slightly bigger in the left hemisphere, but variants were more frequent in the right hemisphere). Intriguingly, left- and right-handers differed in their network variant asymmetries for the cingulo-opercular and frontoparietal networks, suggesting that variant asymmetries are connected to lateralized traits. These findings demonstrate that idiosyncratic aspects of brain organization differ systematically across the hemispheres. We discuss how these asymmetries in brain organization may inform us on developmental constraints of network variants and how they may relate to functions differentially linked to the two hemispheres.
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Affiliation(s)
| | | | | | | | - Caterina Gratton
- Northwestern University, Evanston, IL
- Florida State University, Tallahassee
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24
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Luckett PH, Lee JJ, Park KY, Raut RV, Meeker KL, Gordon EM, Snyder AZ, Ances BM, Leuthardt EC, Shimony JS. Resting state network mapping in individuals using deep learning. Front Neurol 2023; 13:1055437. [PMID: 36712434 PMCID: PMC9878609 DOI: 10.3389/fneur.2022.1055437] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/28/2022] [Indexed: 01/14/2023] Open
Abstract
Introduction Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings. Methods In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in n = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings. Results Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD. Discussion The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.
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Affiliation(s)
- Patrick H. Luckett
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - John J. Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ki Yun Park
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Ryan V. Raut
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Karin L. Meeker
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Evan M. Gordon
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric C. Leuthardt
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States
- Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO, United States
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, United States
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
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25
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Stoliker D, Egan GF, Friston KJ, Razi A. Neural Mechanisms and Psychology of Psychedelic Ego Dissolution. Pharmacol Rev 2022; 74:876-917. [PMID: 36786290 DOI: 10.1124/pharmrev.121.000508] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 06/26/2022] [Accepted: 06/29/2022] [Indexed: 11/22/2022] Open
Abstract
Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries-known as ego dissolution-surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A receptors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. SIGNIFICANCE STATEMENT: Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging findings by bridging the role between the 5-HT2A receptors and large-scale brain networks.
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Affiliation(s)
- Devon Stoliker
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Gary F Egan
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Karl J Friston
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Adeel Razi
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
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26
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Dehghan Nayyeri M, Missler M, Ritterbach R, Sundermann B, Wulms N, Süer A, Dugas M, Pereira MP, Ständer S, Schmelz M, Pfleiderer B. Altered resting-state functional connectivity of default mode network in brachioradial pruritus. J Eur Acad Dermatol Venereol 2022; 36:2214-2223. [PMID: 35796634 DOI: 10.1111/jdv.18411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/15/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Patients with chronic pruritus (CP) have a low quality of life, thus it is important to gain a better understanding of the underlying processes. Previous functional magnetic resonance imaging studies at rest (rs-fMRI) have shown that mainly areas associated with the default mode network (DMN), sensorimotor (SMN), frontoparietal (FPN) and salience networks (SN) are involved in the processing of itch in patients with chronic pruritus (CP), as well as the cortico-striatal circuit, which is involved in the motoric preparation of scratching. rs-fMRI studies on functional connectivity (FC) patterns of resting-state networks (RSNs) in patients with inflammatory atopic dermatitis (AD) or with neuropathic brachioradial pruritus (BRP) compared to healthy controls (HC) are lacking. OBJECTIVES The main goals of this study were to investigate whether functional connectivity within networks and areas associated with itch detection and processing are altered in patients with AD and BRP compared to matched healthy controls by rsfMRI, respectively. METHODS Patients with AD (n = 28) and with BRP (n = 28) were compared to corresponding matched healthy controls by rsfMRI. Group specific RSNs were identified by independent component analysis (ICA) and between-group differences in the RSNs were analyzed by dual regression technique. Seed-based functional connectivity was analyzed in several itch-related brain regions belonging to the DMN, SN and FPN, respectively. RESULTS ICA and seed-based analyses revealed decreased functional connectivity in BRP compared to HC specially within the DMN including the precuneus and cingulate cortex. For AD patients in comparison to HC, as well as when BRP and AD patients when compared directly, no significant FC differences at rest were seen. CONCLUSIONS Our findings point towards decreased FC particularly in the DMN at rest in patients with BRP. These results seem to indicate that central connectivity patterns at rest differentially encode itch in BRP and AD.
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Affiliation(s)
- Mahboobeh Dehghan Nayyeri
- Clinic of Radiology, University Hospital Muenster, Medical Faculty, University of Muenster, Muenster, Germany.,Department of Psychosomatic Medicine and Psychotherapy, LVR Clinic, Medical Faculty of the Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Miriam Missler
- Clinic of Radiology, University Hospital Muenster, Medical Faculty, University of Muenster, Muenster, Germany
| | - Rebecca Ritterbach
- Clinic of Radiology, University Hospital Muenster, Medical Faculty, University of Muenster, Muenster, Germany
| | - Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus, University of Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Niklas Wulms
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Aysenur Süer
- Institute of Medical Informatics, University of Muenster, Muenster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University Hospital Heidelberg, Heidelberg, Germany
| | - Manuel P Pereira
- Center for Chronic Pruritus, Department of Dermatology, University Hospital Muenster, Germany
| | - Sonja Ständer
- Center for Chronic Pruritus, Department of Dermatology, University Hospital Muenster, Germany
| | - Martin Schmelz
- Department of Experimental Pain Research, MCTN, Medical Faculty Mannheim, Heidelberg of University, Mannheim, Germany
| | - Bettina Pfleiderer
- Clinic of Radiology, University Hospital Muenster, Medical Faculty, University of Muenster, Muenster, Germany.,Otto Creutzfeld Center for Cognitive and Behavioral Neuroscience, Muenster, Germany
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27
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Cope TE, Hughes LE, Phillips HN, Adams NE, Jafarian A, Nesbitt D, Assem M, Woolgar A, Duncan J, Rowe JB. Causal Evidence for the Multiple Demand Network in Change Detection: Auditory Mismatch Magnetoencephalography across Focal Neurodegenerative Diseases. J Neurosci 2022; 42:3197-3215. [PMID: 35260433 PMCID: PMC8994545 DOI: 10.1523/jneurosci.1622-21.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 02/02/2023] Open
Abstract
The multiple demand (MD) system is a network of fronto-parietal brain regions active during the organization and control of diverse cognitive operations. It has been argued that this activation may be a nonspecific signal of task difficulty. However, here we provide convergent evidence for a causal role for the MD network in the "simple task" of automatic auditory change detection, through the impairment of top-down control mechanisms. We employ independent structure-function mapping, dynamic causal modeling (DCM), and frequency-resolved functional connectivity analyses of MRI and magnetoencephalography (MEG) from 75 mixed-sex human patients across four neurodegenerative syndromes [behavioral variant fronto-temporal dementia (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), posterior cortical atrophy (PCA), and Alzheimer's disease mild cognitive impairment with positive amyloid imaging (ADMCI)] and 48 age-matched controls. We show that atrophy of any MD node is sufficient to impair auditory neurophysiological response to change in frequency, location, intensity, continuity, or duration. There was no similar association with atrophy of the cingulo-opercular, salience or language networks, or with global atrophy. MD regions displayed increased functional but decreased effective connectivity as a function of neurodegeneration, suggesting partially effective compensation. Overall, we show that damage to any of the nodes of the MD network is sufficient to impair top-down control of sensation, providing a common mechanism for impaired change detection across dementia syndromes.SIGNIFICANCE STATEMENT Previous evidence for fronto-parietal networks controlling perception is largely associative and may be confounded by task difficulty. Here, we use a preattentive measure of automatic auditory change detection [mismatch negativity (MMN) magnetoencephalography (MEG)] to show that neurodegeneration in any frontal or parietal multiple demand (MD) node impairs primary auditory cortex (A1) neurophysiological response to change through top-down mechanisms. This explains why the impaired ability to respond to change is a core feature across dementias, and other conditions driven by brain network dysfunction, such as schizophrenia. It validates theoretical frameworks in which neurodegenerating networks upregulate connectivity as partially effective compensation. The significance extends beyond network science and dementia, in its construct validation of dynamic causal modeling (DCM), and human confirmation of frequency-resolved analyses of animal neurodegeneration models.
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Affiliation(s)
- Thomas E Cope
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
- Cambridge University Hospitals NHS Trust, Cambridge CB2 0SZ, United Kingdom
| | - Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - Holly N Phillips
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - Natalie E Adams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
| | - Amirhossein Jafarian
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
| | - David Nesbitt
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - Moataz Assem
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - Alexandra Woolgar
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - John Duncan
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge, Cambridge CB2 7EF, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge, Cambridge CB2 7EF, United Kingdom
- Cambridge University Hospitals NHS Trust, Cambridge CB2 0SZ, United Kingdom
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