1
|
You P, Li X, Zhang F, Li Q. Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution. BME FRONTIERS 2022; 2022:9814824. [PMID: 37850179 PMCID: PMC10521716 DOI: 10.34133/2022/9814824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/08/2022] [Indexed: 10/19/2023] Open
Abstract
Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. Introduction. The concept of "connectional fingerprint" has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies on multiple brain regions have been conducted with promising results. However, performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data. Methods. We propose the Spatial-graph Convolution Parcellation (SGCP) framework, a two-stage deep learning-based modeling for the graph representation brain imaging. In the first stage, SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network. In the second stage, SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region. Results. SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset. Performance comparisons between SGCP, traditional parcellation methods, and other deep learning-based methods show that SGCP can achieve superior performance in all the cases. Conclusion. Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.
Collapse
Affiliation(s)
- Peiting You
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing, China
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| |
Collapse
|
2
|
Pospelov N, Tetereva A, Martynova O, Anokhin K. The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
3
|
Wang P, Zhang N. Decision tree classification algorithm for non-equilibrium data set based on random forests. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In order to overcome the problems of poor accuracy and high complexity of current classification algorithm for non-equilibrium data set, this paper proposes a decision tree classification algorithm for non-equilibrium data set based on random forest. Wavelet packet decomposition is used to denoise non-equilibrium data, and SNM algorithm and RFID are combined to remove redundant data from data sets. Based on the results of data processing, the non-equilibrium data sets are classified by random forest method. According to Bootstrap resampling method with certain constraints, the majority and minority samples of each sample subset are sampled, CART is used to train the data set, and a decision tree is constructed. Obtain the final classification results by voting on the CART decision tree classification. Experimental results show that the proposed algorithm has the characteristics of high classification accuracy and low complexity, and it is a feasible classification algorithm for non-equilibrium data set.
Collapse
Affiliation(s)
- Peng Wang
- Department of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
| | - Ningchao Zhang
- Department of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
| |
Collapse
|
4
|
|
5
|
Costumero V, d'Oleire Uquillas F, Diez I, Andorrà M, Basaia S, Bueichekú E, Ortiz-Terán L, Belloch V, Escudero J, Ávila C, Sepulcre J. Distance disintegration delineates the brain connectivity failure of Alzheimer's disease. Neurobiol Aging 2020; 88:51-60. [PMID: 31941578 PMCID: PMC7085436 DOI: 10.1016/j.neurobiolaging.2019.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 01/03/2023]
Abstract
Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects.
Collapse
Affiliation(s)
- Víctor Costumero
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Catalonia, Spain; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | | | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neurotechnology Laboratory, Tecnalia Health Department, Basque Country, Spain
| | - Magi Andorrà
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center of Neuroimmunology, Department of Neurology, Hospital Clinic of Barcelona, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Catalonia, Spain
| | - Silvia Basaia
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuroimaging Research Unit Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Laura Ortiz-Terán
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Joaquin Escudero
- Department of Neurology, General Hospital of Valencia, Valencia, Valencian Community, Spain
| | - César Ávila
- Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
| |
Collapse
|
6
|
Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
|
7
|
Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:4097561. [PMID: 30534150 PMCID: PMC6252210 DOI: 10.1155/2018/4097561] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/03/2018] [Accepted: 09/16/2018] [Indexed: 12/17/2022]
Abstract
To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.
Collapse
|
8
|
Yang J, Hu C, Guo N, Dutta J, Vaina LM, Johnson KA, Sepulcre J, Fakhri GE, Li Q. Partial volume correction for PET quantification and its impact on brain network in Alzheimer's disease. Sci Rep 2017; 7:13035. [PMID: 29026139 PMCID: PMC5638902 DOI: 10.1038/s41598-017-13339-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 08/21/2017] [Indexed: 12/28/2022] Open
Abstract
Amyloid positron emission tomography (PET) imaging is a valuable tool for research and diagnosis in Alzheimer’s disease (AD). Partial volume effects caused by the limited spatial resolution of PET scanners degrades the quantitative accuracy of PET image. In this study, we have applied a method to evaluate the impact of a joint-entropy based partial volume correction (PVC) technique on brain networks learned from a clinical dataset of AV-45 PET image and compare network properties of both uncorrected and corrected image-based brain networks. We also analyzed the region-wise SUVRs of both uncorrected and corrected images. We further performed classification tests on different groups using the same set of algorithms with same parameter settings. PVC has sometimes been avoided due to increased noise sensitivity in image registration and segmentation, however, our results indicate that appropriate PVC may enhance the brain network structure analysis for AD progression and improve classification performance.
Collapse
Affiliation(s)
- Jiarui Yang
- Boston University, Department of Biomedical Engineering, Boston, 02215, USA.,Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA
| | - Chenhui Hu
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA
| | - Ning Guo
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA
| | - Joyita Dutta
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA.,University of Massachusetts Lowell, Department of Electrical and Computer Engineering, Lowell, 01854, USA
| | - Lucia M Vaina
- Boston University, Department of Biomedical Engineering, Boston, 02215, USA.,Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA.,Harvard Medical School, Department of Radiology, Boston, 02115, USA
| | - Jorge Sepulcre
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA.,Harvard Medical School, Department of Radiology, Boston, 02115, USA
| | - Georges El Fakhri
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA.,Harvard Medical School, Department of Radiology, Boston, 02115, USA
| | - Quanzheng Li
- Massachusetts General Hospital, Department of Radiology, Boston, 02114, USA. .,Harvard Medical School, Department of Radiology, Boston, 02115, USA.
| |
Collapse
|
9
|
Wang Z, Zhu X, Adeli E, Zhu Y, Nie F, Munsell B, Wu G. Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning. Med Image Anal 2017; 39:218-230. [PMID: 28551556 PMCID: PMC5901767 DOI: 10.1016/j.media.2017.05.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 01/27/2017] [Accepted: 05/09/2017] [Indexed: 01/12/2023]
Abstract
Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer's disease and Parkinson's disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets.
Collapse
Affiliation(s)
- Zhengxia Wang
- Department of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, PR China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Automation, Chongqing University, Chongqing, 400044, PR China.
| | - Xiaofeng Zhu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department Computer Science and Information Engineering, Guangxi Normal University, Guilin, 541004, PR China
| | - Ehsan Adeli
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yingying Zhu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Feiping Nie
- School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China
| | - Brent Munsell
- Department of Computer Science, College of Charleston, Charleston, SC 29424, USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
| |
Collapse
|