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Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.
Literature Information
| PMID | 23747961 |
|---|---|
| Journal | NeuroImage |
| Impact Factor | 4.5 |
| JCR Quartile | Q1 |
| Publication Year | 2013 |
| Times Cited | 523 |
| Keywords | Functional MRI, Graph-theory-based parcellation, Network analysis, Resting-state connectivity, Whole-brain atlas |
| Literature Type | Journal Article, Research Support, N.I.H., Extramural |
| ISSN | 1053-8119 |
| Pages | 403-15 |
| Issue | 82() |
| Authors | X Shen, F Tokoglu, X Papademetris, R T Constable |
TL;DR
This study introduces a novel groupwise graph-theory-based parcellation method for defining nodes in network analysis, addressing limitations of existing atlases by ensuring functional homogeneity within each subunit. The results demonstrate high reproducibility across multiple healthy volunteer groups and provide three functional atlases with varying parcellation levels, which are made publicly available to enhance functional MRI analysis.
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Functional MRI · Graph-theory-based parcellation · Network analysis · Resting-state connectivity · Whole-brain atlas
Abstract
In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.
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Primary Questions Addressed
- How does the proposed groupwise parcellation method improve upon existing atlases based on cytoarchitecture or task-based fMRI?
- What specific challenges are associated with ensuring functional homogeneity within each subunit during the parcellation process?
- In what ways can the derived atlases at different parcellation levels (100, 200, 300) be utilized in clinical settings or research?
- How does the reproducibility of parcellation results across multiple groups of healthy volunteers affect the validity of network analyses?
- What criteria should researchers consider when selecting the appropriate number of nodes for their specific fMRI studies?
Key Findings
Research Background and Objectives
The paper addresses the need for a more precise method of defining nodes for network analysis in functional MRI (fMRI) studies. Traditional atlases, which rely on cytoarchitecture or anatomical features, often fail to accurately represent the functional characteristics of brain regions due to the averaging of diverse timecourses within a single node. The objective of this study is to propose a groupwise graph-theory-based parcellation approach that enhances the definition of nodes, ensuring functional homogeneity and consistency across groups.
Main Methods/Materials/Experimental Design
The proposed method employs a groupwise optimization technique that focuses on maintaining functional homogeneity within subunits while ensuring that the definitions are applicable at the group level. The following steps outline the technical approach:
- Groupwise Optimization: This step involves analyzing multiple groups of healthy volunteers to ensure that each node is defined based on consistent functional characteristics.
- Functional Homogeneity: Ensures that each subunit represents a cohesive functional area, minimizing the inclusion of disparate functional areas within a single node.
- Parcellation Results: The study generated parcellations for 100, 200, and 300 subunits, making it adaptable to different research needs.
- Atlas Availability: The derived atlases are made freely available online, along with tools for integration with existing analysis software such as SPM and BioImage Suite.
Key Results and Findings
- The proposed parcellation approach demonstrated high reproducibility across different groups of healthy volunteers.
- Parcellation results indicated that the selection of the number of nodes significantly affects the representation of functional areas in the brain.
- The atlases created (100, 200, and 300 subunits) provide a resource for researchers to utilize in fMRI studies, potentially improving the accuracy of functional connectivity analyses.
Main Conclusions/Significance/Innovation
The study presents a novel approach to brain parcellation that prioritizes functional homogeneity, thereby enhancing the accuracy of network analyses in fMRI research. The availability of multiple parcellation atlases offers a significant resource for the neuroscience community, allowing for improved interpretations of brain function and connectivity. This work represents a methodological advancement in the field of neuroimaging, addressing limitations associated with existing atlases.
Research Limitations and Future Directions
- Limitations: The study primarily focuses on healthy volunteers, which may limit the applicability of the atlases to clinical populations. Additionally, the choice of parameters for node selection can influence results, necessitating careful consideration in future applications.
- Future Directions: Future research should explore the application of these atlases in clinical populations, such as individuals with neurological disorders. Further studies could also investigate the integration of this parcellation approach with machine learning techniques to enhance predictive modeling in brain function.
| Section | Summary |
|---|---|
| Research Background | Need for improved node definition in fMRI network analysis. |
| Methods | Groupwise optimization for functional homogeneity; generation of atlases for 100, 200, and 300 subunits. |
| Key Results | High reproducibility; significant impact of node selection on functional representation. |
| Conclusions | Novel parcellation method enhances fMRI analysis; atlases are a valuable resource for researchers. |
| Limitations & Future Work | Focus on healthy subjects; future studies should include clinical populations and machine learning applications. |
References
- Functional connectivity and brain networks in schizophrenia. - Mary-Ellen Lynall;Danielle S Bassett;Robert Kerwin;Peter J McKenna;Manfred Kitzbichler;Ulrich Muller;Ed Bullmore - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
- MR connectomics: Principles and challenges. - Patric Hagmann;Leila Cammoun;Xavier Gigandet;Stephan Gerhard;P Ellen Grant;Van Wedeen;Reto Meuli;Jean-Philippe Thiran;Christopher J Honey;Olaf Sporns - Journal of neuroscience methods (2010)
- fMRI resting state networks define distinct modes of long-distance interactions in the human brain. - M De Luca;C F Beckmann;N De Stefano;P M Matthews;S M Smith - NeuroImage (2006)
- Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. - N Tzourio-Mazoyer;B Landeau;D Papathanassiou;F Crivello;O Etard;N Delcroix;B Mazoyer;M Joliot - NeuroImage (2002)
- Large-scale brain networks in cognition: emerging methods and principles. - Steven L Bressler;Vinod Menon - Trends in cognitive sciences (2010)
- A common, high-dimensional model of the representational space in human ventral temporal cortex. - James V Haxby;J Swaroop Guntupalli;Andrew C Connolly;Yaroslav O Halchenko;Bryan R Conroy;M Ida Gobbini;Michael Hanke;Peter J Ramadge - Neuron (2011)
- Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. - Martijn P van den Heuvel;René C W Mandl;Cornelis J Stam;René S Kahn;Hilleke E Hulshoff Pol - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
- Intrinsic functional architecture in the anaesthetized monkey brain. - J L Vincent;G H Patel;M D Fox;A Z Snyder;J T Baker;D C Van Essen;J M Zempel;L H Snyder;M Corbetta;M E Raichle - Nature (2007)
- The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. - Aaron Alexander-Bloch;Renaud Lambiotte;Ben Roberts;Jay Giedd;Nitin Gogtay;Ed Bullmore - NeuroImage (2012)
- Development of distinct control networks through segregation and integration. - Damien A Fair;Nico U F Dosenbach;Jessica A Church;Alexander L Cohen;Shefali Brahmbhatt;Francis M Miezin;Deanna M Barch;Marcus E Raichle;Steven E Petersen;Bradley L Schlaggar - Proceedings of the National Academy of Sciences of the United States of America (2007)
Literatures Citing This Work
- Potential use and challenges of functional connectivity mapping in intractable epilepsy. - Robert Todd Constable;Dustin Scheinost;Emily S Finn;Xilin Shen;Michelle Hampson;F Scott Winstanley;Dennis D Spencer;Xenophon Papademetris - Frontiers in neurology (2013)
- Disruption of functional networks in dyslexia: a whole-brain, data-driven analysis of connectivity. - Emily S Finn;Xilin Shen;John M Holahan;Dustin Scheinost;Cheryl Lacadie;Xenophon Papademetris;Sally E Shaywitz;Bennett A Shaywitz;R Todd Constable - Biological psychiatry (2014)
- The impact of image smoothness on intrinsic functional connectivity and head motion confounds. - Dustin Scheinost;Xenophon Papademetris;R Todd Constable - NeuroImage (2014)
- Coupled Intrinsic Connectivity Distribution analysis: a method for exploratory connectivity analysis of paired FMRI data. - Dustin Scheinost;Xilin Shen;Emily Finn;Rajita Sinha;R Todd Constable;Xenophon Papademetris - PloS one (2014)
- Graph theory findings in the pathophysiology of temporal lobe epilepsy. - Sharon Chiang;Zulfi Haneef - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2014)
- Default mode network as a potential biomarker of chemotherapy-related brain injury. - Shelli R Kesler - Neurobiology of aging (2014)
- Connectomic profiles for individualized resting state networks and regions of interest. - Kaiming Li;Jason Langley;Zhihao Li;Xiaoping P Hu - Brain connectivity (2015)
- Direct imaging of functional networks. - Eric C Wong - Brain connectivity (2014)
- Spatial and temporal functional connectivity changes between resting and attentive states. - Signe Bray;Aiden E G F Arnold;Richard M Levy;Giuseppe Iaria - Human brain mapping (2015)
- Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. - Evan M Gordon;Timothy O Laumann;Babatunde Adeyemo;Jeremy F Huckins;William M Kelley;Steven E Petersen - Cerebral cortex (New York, N.Y. : 1991) (2016)
... (513 more literatures)
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