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Abu Mhanna HY, Omar AF, Radzi YM, Oglat AA, Akhdar HF, Ewaidat HA, Almahmoud A, Badarneh LA, Malkawi AA, Malkawi A. Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery. J Multidiscip Healthc 2024; 17:2409-2424. [PMID: 38784380 PMCID: PMC11111578 DOI: 10.2147/jmdh.s470809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.
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Affiliation(s)
| | - Ahmad Fairuz Omar
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | - Yasmin Md Radzi
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | - Ammar A Oglat
- Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, 13133, Jordan
| | - Hanan Fawaz Akhdar
- Physics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia
| | - Haytham Al Ewaidat
- Department of Allied Medical Sciences-Radiologic Technology, Jordan University of Science and Technology (J.U.S.T), Irbid, 22110, Jordan
| | - Abdallah Almahmoud
- Department of Allied Medical Sciences-Radiologic Technology, Jordan University of Science and Technology (J.U.S.T), Irbid, 22110, Jordan
| | - Laith Al Badarneh
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | | | - Ahmed Malkawi
- Business Department, Al-Zaytoonah University, Amman, 594, Jordan
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Xu Q, Zhou LL, Xing C, Xu X, Feng Y, Lv H, Zhao F, Chen YC, Cai Y. Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture. Neuroimage 2024; 290:120566. [PMID: 38467345 DOI: 10.1016/j.neuroimage.2024.120566] [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: 10/05/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. METHODS A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. RESULTS Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. CONCLUSION Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.
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Affiliation(s)
- Qianhui Xu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fei Zhao
- Department of Speech and Language Therapy and Hearing Science, Cardiff Metropolitan University, Cardiff, UK
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China.
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China.
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Mellema CJ, Nguyen KP, Treacher A, Andrade AX, Pouratian N, Sharma VD, O'Suileabhain P, Montillo AA. Longitudinal prognosis of Parkinson's outcomes using causal connectivity. Neuroimage Clin 2024; 42:103571. [PMID: 38471435 PMCID: PMC10944096 DOI: 10.1016/j.nicl.2024.103571] [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: 08/25/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 03/14/2024]
Abstract
Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.
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Affiliation(s)
- Cooper J Mellema
- Lyda Hill Department of Bioinformatics, United States; Biomedical Engineering Department, United States; University of Texas Southwestern Medical Center, United States
| | - Kevin P Nguyen
- Lyda Hill Department of Bioinformatics, United States; Biomedical Engineering Department, United States; University of Texas Southwestern Medical Center, United States
| | - Alex Treacher
- Lyda Hill Department of Bioinformatics, United States; Biophysics Department, United States; University of Texas Southwestern Medical Center, United States
| | - Aixa X Andrade
- Lyda Hill Department of Bioinformatics, United States; Biomedical Engineering Department, United States; University of Texas Southwestern Medical Center, United States
| | - Nader Pouratian
- Neurosurgery Department, United States; University of Texas Southwestern Medical Center, United States
| | - Vibhash D Sharma
- Neurology Department, United States; University of Texas Southwestern Medical Center, United States
| | - Padraig O'Suileabhain
- Neurology Department, United States; University of Texas Southwestern Medical Center, United States
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, United States; Biomedical Engineering Department, United States; Advanced Imaging Research Center, United States; Radiology Department, United States; University of Texas Southwestern Medical Center, United States.
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Ren X, Dong B, Luan Y, Wu Y, Huang Y. Alterations via inter-regional connective relationships in Alzheimer's disease. Front Hum Neurosci 2023; 17:1276994. [PMID: 38021241 PMCID: PMC10672243 DOI: 10.3389/fnhum.2023.1276994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.
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Affiliation(s)
- Xiaomei Ren
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Bowen Dong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Ying Luan
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yunzhi Huang
- Institute for AI in Medicine, School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China
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Wang A, Dong T, Wei T, Wu H, Yang Y, Ding Y, Li C, Yang W. Large-scale networks changes in Wilson's disease associated with neuropsychiatric impairments: a resting-state functional magnetic resonance imaging study. BMC Psychiatry 2023; 23:805. [PMID: 37924073 PMCID: PMC10623710 DOI: 10.1186/s12888-023-05236-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/29/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND In Wilson's disease (WD) patients, network connections across the brain are disrupted, affecting multidomain function. However, the details of this neuropathophysiological mechanism remain unclear due to the rarity of WD. In this study, we aimed to investigate alterations in brain network connectivity at the whole-brain level (both intra- and inter-network) in WD patients through independent component analysis (ICA) and the relationship between alterations in these brain network functional connections (FCs) and clinical neuropsychiatric features to understand the underlying pathophysiological and central compensatory mechanisms. METHODS Eighty-five patients with WD and age- and sex-matched 85 healthy control (HC) were recruited for resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We extracted the resting-state networks (RSNs) using the ICA method, analyzed the changes of FC in these networks and the correlation between alterations in FCs and clinical neuropsychiatric features. RESULTS Compared with HC, WD showed widespread lower connectivity within RSNs, involving default mode network (DMN), frontoparietal network (FPN), somatomotor network (SMN), dorsal attention network (DAN), especially in patients with abnormal UWDRS scores. Furthermore, the decreased FCs in the left medial prefrontal cortex (L_ MPFC), left anterior cingulate gyrus (L_ACC), precuneus (PCUN)within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-neurological characteristic examination (UWDRS-N), and the decreased FCs in the L_MPFC, PCUN within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-psychiatric symptoms examination (UWDRS-P). We additionally discovered that the patients with WD exhibited significantly stronger FC between the FPN and DMN, between the DAN and DMN, and between the FPN and DAN compared to HC. CONCLUSIONS We have provided evidence that WD is a disease with widespread dysfunctional connectivity in resting networks in brain, leading to neurological features and psychiatric symptoms (e.g. higher-order cognitive control and motor control impairments). The alter intra- and inter-network in the brain may be the neural underpinnings for the neuropathological symptoms and the process of injury compensation in WD patients.
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Affiliation(s)
- Anqin Wang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
| | - Ting Dong
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
| | - Taohua Wei
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
| | - Hongli Wu
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Yulong Yang
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Yufeng Ding
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Chuanfu Li
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China.
| | - Wenming Yang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China.
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China.
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China.
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Zhang YN, Xing XX, Chen L, Dong X, Pan HT, Hua XY, Wang K. Modification of the resting-state network involved at different stages of neuropathic pain. Neurosci Lett 2022; 789:136866. [PMID: 36075318 DOI: 10.1016/j.neulet.2022.136866] [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: 06/22/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 10/14/2022]
Abstract
Neuropathic pain (NeuP) is shown to be associated with abnormal changes in several specific brain regions. However, the large-scale interactivity of neuronal networks underlying the sensory and emotional abnormalities during NeuP remains unexplored. The present study aimed to explore the alterations in the relevant functional resting-state networks (RSNs) and their intra-networks at the different stages of NeuP based on resting-state functional magnetic resonance imaging (rs-fMRI). A NeuP rat model was established by chronic constriction injury (CCI). Three RSNs were identified to be associated with the NeuP, including the default mode network (DMN), sensorimotor network (SMN), and interoceptive network (IN). The functional connectivity (FC) of the left caudate putamen (CPu) within the DMN and the right piriform cortex within the IN were significantly reduced at the early stage of NeuP, when the maximum allodynia was apparent early, which reflected the suppressed function of the DMN and IN. At 4 weeks post-CCI, when negative emotions were present, the FC of the right insular cortex in the SMN and left visual cortex in the IN were significantly elevated, representing the increased excitability of both SMN and IN. Our study revealed the characteristic functional organization at the network level induced by NeuP and emphasized the role of SMN, DMN, and IN in the pathological mechanisms of NeuP.
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Affiliation(s)
- Ya-Nan Zhang
- Acupuncture Anesthesia Clinical Research Institute, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China
| | - Liu Chen
- Acupuncture Anesthesia Clinical Research Institute, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Xin Dong
- Acupuncture Anesthesia Clinical Research Institute, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Hao-Tian Pan
- Acupuncture Anesthesia Clinical Research Institute, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China.
| | - Ke Wang
- Acupuncture Anesthesia Clinical Research Institute, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
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Field A, Birn R. Two for the Price of One? Analyzing Task-based fMRI Data with Resting-State fMRI Methods. Radiology 2021; 301:185-186. [PMID: 34282973 DOI: 10.1148/radiol.2021211239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Aaron Field
- From the Departments of Radiology (A.F.) and Psychiatry (R.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, M/C 3252, Madison, WI 53792
| | - Rasmus Birn
- From the Departments of Radiology (A.F.) and Psychiatry (R.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, M/C 3252, Madison, WI 53792
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