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Peng L, Su J, Hu D, Yu Y, Wei H, Li M. Measuring functional connectivity in frequency-domain helps to better characterize brain function. Hum Brain Mapp 2024; 45:e26726. [PMID: 38949487 PMCID: PMC11215841 DOI: 10.1002/hbm.26726] [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: 08/19/2023] [Revised: 03/25/2024] [Accepted: 05/09/2024] [Indexed: 07/02/2024] Open
Abstract
Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Jianpo Su
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Dewen Hu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Yang Yu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Huilin Wei
- Systems Engineering InstituteAcademy of Military SciencesBeijingChina
| | - Ming Li
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
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Samantaray T, Saini J, Pal PK, Gupta CN. Brain connectivity for subtypes of parkinson's disease using structural MRI. Biomed Phys Eng Express 2024; 10:025012. [PMID: 38224618 DOI: 10.1088/2057-1976/ad1e77] [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: 09/20/2023] [Accepted: 01/15/2024] [Indexed: 01/17/2024]
Abstract
Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.
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Affiliation(s)
- Tanmayee Samantaray
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, 560029, India
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health & Neuro Sciences, Bengaluru, 560029, India
| | - Cota Navin Gupta
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
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Zhang J, Wang Q, Wang X, Qiao L, Liu M. Preserving specificity in federated graph learning for fMRI-based neurological disorder identification. Neural Netw 2024; 169:584-596. [PMID: 37956575 DOI: 10.1016/j.neunet.2023.11.004] [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: 08/14/2023] [Revised: 10/22/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1218 subjects suggest that SFGL outperforms several state-of-the-art approaches.
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Affiliation(s)
- Junhao Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China
| | - Qianqian Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Xiaochuan Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China; School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong, 250101, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Jiang X, Fan J, Zhu Z, Wang Z, Guo Y, Liu X, Jia F, Dai C. Cybersecurity in neural interfaces: Survey and future trends. Comput Biol Med 2023; 167:107604. [PMID: 37883851 DOI: 10.1016/j.compbiomed.2023.107604] [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: 06/01/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.
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Affiliation(s)
- Xinyu Jiang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Jiahao Fan
- The Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ziyue Zhu
- The Department of Bioengineering, Imperial College London, SW7 2AZ London, UK
| | - Zihao Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yao Guo
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangyu Liu
- The College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Fumin Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Chenyun Dai
- School of Information Science and Technology, Fudan University, Shanghai, China.
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Sandhu SS, Gorji HT, Tavakolian P, Tavakolian K, Akhbardeh A. Medical Imaging Applications of Federated Learning. Diagnostics (Basel) 2023; 13:3140. [PMID: 37835883 PMCID: PMC10572559 DOI: 10.3390/diagnostics13193140] [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] [Received: 09/16/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.
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Affiliation(s)
- Sukhveer Singh Sandhu
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Hamed Taheri Gorji
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
- SafetySpect Inc., 4200 James Ray Dr., Grand Forks, ND 58202, USA
| | - Pantea Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
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Nazir S, Kaleem M. Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13091532. [PMID: 37174925 PMCID: PMC10177193 DOI: 10.3390/diagnostics13091532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow G4 0BA, UK
| | - Mohammad Kaleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
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