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Chung J, Bridgeford EW, Powell M, Pisner D, Xu T, Vogelstein JT. Are human connectomes heritable? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.02.532875. [PMID: 37066291 PMCID: PMC10103997 DOI: 10.1101/2023.04.02.532875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A complete understanding of human behavior and disease depends upon our ability to parse genetic and environmental influences in the human brain. The heritability of a trait quantifies the degree of its variability due to genetic influences. Classical approach for quantifying heritability operate on simple traits, and sometimes do not properly model and control for other potential sources of variation, such as age or sex. We therefore develop Causal Heritability of Networks (CHaiN) to rigorously quantify heritability of human brain networks (i.e., connectomes). We applied CHaiN to 1024 anatomical connectomes derived from the Human Connectome Project. Connectomes appeared to be heritable, but heritability was insignificant once we addressed variability within networks. These results suggest that previous conclusions on connectome heritability may be driven by the shared network structures, and highlights the importance of modeling networks and other sources of variability when studying heritability of connectomes.
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Litwińczuk MC, Muhlert N, Trujillo‐Barreto N, Woollams A. Impact of brain parcellation on prediction performance in models of cognition and demographics. Hum Brain Mapp 2024; 45:e26592. [PMID: 38339892 PMCID: PMC10831203 DOI: 10.1002/hbm.26592] [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: 06/21/2023] [Revised: 12/18/2023] [Accepted: 12/31/2023] [Indexed: 02/12/2024] Open
Abstract
Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.
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Affiliation(s)
| | - Nils Muhlert
- School of Health SciencesUniversity of ManchesterManchesterUK
| | | | - Anna Woollams
- School of Health SciencesUniversity of ManchesterManchesterUK
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3
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Thirion B, Aggarwal H, Ponce AF, Pinho AL, Thual A. Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark. Brain Struct Funct 2024; 229:161-181. [PMID: 38012283 DOI: 10.1007/s00429-023-02723-x] [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: 08/08/2023] [Accepted: 10/11/2023] [Indexed: 11/29/2023]
Abstract
The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.
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Affiliation(s)
| | | | | | - Ana Luísa Pinho
- Department of Computer Science, Western University, London, ON, Canada
- Western Institute for Neuroscience, Western University, London, ON, Canada
| | - Alexis Thual
- Inria, CEA, Université Paris-Saclay, 91120, Palaiseau, France
- Inserm, Collège de France, Paris, France
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5
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Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [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: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
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Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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Ramli NZ, Yahaya MF, Mohd Fahami NA, Abdul Manan H, Singh M, Damanhuri HA. Brain volumetric changes in menopausal women and its association with cognitive function: a structured review. Front Aging Neurosci 2023; 15:1158001. [PMID: 37818479 PMCID: PMC10561270 DOI: 10.3389/fnagi.2023.1158001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
The menopausal transition has been proposed to put women at risk for undesirable neurological symptoms, including cognitive decline. Previous studies suggest that alterations in the hormonal milieu modulate brain structures associated with cognitive function. This structured review provides an overview of the relevant studies that have utilized MRI to report volumetric differences in the brain following menopause, and its correlations with the evaluated cognitive functions. We performed an electronic literature search using Medline (Ovid) and Scopus to identify studies that assessed the influence of menopause on brain structure with MRI. Fourteen studies met the inclusion criteria. Brain volumetric differences have been reported most frequently in the frontal and temporal cortices as well as the hippocampus. These regions are important for higher cognitive tasks and memory. Additionally, the deficit in verbal and visuospatial memory in postmenopausal women has been associated with smaller regional brain volumes. Nevertheless, the limited number of eligible studies and cross-sectional study designs warrant further research to draw more robust conclusions.
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Affiliation(s)
- Nur Zuliani Ramli
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Mohamad Fairuz Yahaya
- Department of Anatomy, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nur Azlina Mohd Fahami
- Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Meharvan Singh
- Department of Cell and Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States
| | - Hanafi Ahmad Damanhuri
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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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 DOI: 10.1186/s12967-023-04222-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Motzkin JC, Kanungo I, D’Esposito M, Shirvalkar P. Network targets for therapeutic brain stimulation: towards personalized therapy for pain. FRONTIERS IN PAIN RESEARCH 2023; 4:1156108. [PMID: 37363755 PMCID: PMC10286871 DOI: 10.3389/fpain.2023.1156108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.
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Affiliation(s)
- Julian C. Motzkin
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
| | - Ishan Kanungo
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Mark D’Esposito
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Prasad Shirvalkar
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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9
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Yoo JH, Chong B, Barber PA, Stinear C, Wang A. Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review. Neurorehabil Neural Repair 2023:15459683231173668. [PMID: 37191349 DOI: 10.1177/15459683231173668] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
BACKGROUND Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small. OBJECTIVES The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction. METHODS A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted. RESULTS Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction. CONCLUSION There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.
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Affiliation(s)
- Ji-Hun Yoo
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Peter Alan Barber
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Cathy Stinear
- Department of Medicine, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, The University of Auckland, Auckland, New Zealand
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10
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Saberi M, Khosrowabadi R, Khatibi A, Misic B, Jafari G. Pattern of frustration formation in the functional brain network. Netw Neurosci 2022; 6:1334-1356. [PMID: 38800463 PMCID: PMC11117102 DOI: 10.1162/netn_a_00268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/05/2022] [Indexed: 05/29/2024] Open
Abstract
The brain is a frustrated system that contains conflictual link arrangements named frustration. The frustration as a source of disorder prevents the system from settling into low-energy states and provides flexibility for brain network organization. In this research, we tried to identify the pattern of frustration formation in the brain at the levels of region, connection, canonical network, and hemisphere. We found that frustration formation has no uniform pattern. Some subcortical elements have an active role in frustration formation, despite low contributions from many cortical elements. Frustrating connections are mostly between-network connections, and triadic frustrations are mainly formed between three regions from three distinct canonical networks. We did not find any significant differences between brain hemispheres or any robust differences between the frustration formation patterns of various life-span stages. Our results may be interesting for those who study the organization of brain links and promising for those who want to manipulate brain networks.
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Affiliation(s)
- Majid Saberi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C. Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C. Tehran, Iran
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Gholamreza Jafari
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C. Tehran, Iran
- Physics Department, Shahid Beheshti University, Tehran, Iran
- Institute of Information Technology and Data Science, Irkutsk National Research Technical University, Irkutsk, Russia
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Revell AY, Silva AB, Arnold TC, Stein JM, Das SR, Shinohara RT, Bassett DS, Litt B, Davis KA. A framework For brain atlases: Lessons from seizure dynamics. Neuroimage 2022; 254:118986. [PMID: 35339683 PMCID: PMC9342687 DOI: 10.1016/j.neuroimage.2022.118986] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 01/03/2023] Open
Abstract
Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.
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Affiliation(s)
- Andrew Y Revell
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander B Silva
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Endeavor, Perelman school of Medicine, University of Pennsylvania, PA 19104, USA
| | - Dani S Bassett
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Luo W, Constable RT. Inside information: Systematic within-node functional connectivity changes observed across tasks or groups. Neuroimage 2021; 247:118792. [PMID: 34896289 PMCID: PMC8840325 DOI: 10.1016/j.neuroimage.2021.118792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/16/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
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Affiliation(s)
- Wenjing Luo
- Department of Biomedical Engineering, Yale University School of Medicine USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
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13
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Fouladivanda M, Kazemi K, Makki M, Khalilian M, Danyali H, Gervain J, Aarabi A. Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study. J Neural Eng 2021; 18. [PMID: 33930878 DOI: 10.1088/1741-2552/abfd46] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/30/2021] [Indexed: 12/11/2022]
Abstract
Objective.Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data.Approach.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Main results.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Significance.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.
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Affiliation(s)
- Mahshid Fouladivanda
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Malek Makki
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France
| | - Maedeh Khalilian
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France
| | - Habibollah Danyali
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Judit Gervain
- Integrative Neuroscience and Cognition Center, CNRS & Université de Paris, Paris, France.,Department of Developmental Psychology and Socialization, University of Padua, Padua, Italy
| | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France.,Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
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