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Chelladurai A, Narayan DL, Divakarachari PB, Loganathan U. fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network. Brain Sci 2023; 13:893. [PMID: 37371371 DOI: 10.3390/brainsci13060893] [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: 05/04/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.
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Affiliation(s)
- Aarthi Chelladurai
- Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode 637205, Tamil Nadu, India
| | - Dayanand Lal Narayan
- Department of Computer Science Engineering, GITAM School of Technology, GITAM University, Bengaluru 561203, Karnataka, India
| | | | - Umasankar Loganathan
- Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai 600077, Tamilnadu, India
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Zhang W, Duan Y, Qi L, Li Z, Ren J, Nangale N, Yang C. Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network. Brain Topogr 2023:10.1007/s10548-023-00962-z. [PMID: 37204610 DOI: 10.1007/s10548-023-00962-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 04/15/2023] [Indexed: 05/20/2023]
Abstract
Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.
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Affiliation(s)
- Wenxiu Zhang
- Department of Environment and Life Sciences, Beijing University of Technology, Beijing, China
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
| | - Lei Qi
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
| | - Zhimei Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiechuan Ren
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Chunlan Yang
- Department of Environment and Life Sciences, Beijing University of Technology, Beijing, China.
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Shen Y, Lu Q, Zhang T, Yan H, Mansouri N, Osipowicz K, Tanglay O, Young I, Doyen S, Lu X, Zhang X, Sughrue ME, Wang T. Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia. Front Aging Neurosci 2022; 14:962319. [PMID: 36118683 PMCID: PMC9475065 DOI: 10.3389/fnagi.2022.962319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveProgressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.ResultsSubjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.ConclusionOur methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.
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Affiliation(s)
- Ying Shen
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Qian Lu
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Tianjiao Zhang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hailang Yan
- Department of Radiology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xi Lu
- Department of Rehabilitation Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Xia Zhang
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Shenzhen Xijia Medical Technology Company, Shenzhen, China
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Michael E. Sughrue,
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Tong Wang,
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Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2535954. [PMID: 36035823 PMCID: PMC9417789 DOI: 10.1155/2022/2535954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/12/2022] [Accepted: 07/06/2022] [Indexed: 11/22/2022]
Abstract
The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.
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Dong N, Fu C, Li R, Zhang W, Liu M, Xiao W, Taylor HM, Nicholas PJ, Tanglay O, Young IM, Osipowicz KZ, Sughrue ME, Doyen SP, Li Y. Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:854733. [PMID: 35592700 PMCID: PMC9110794 DOI: 10.3389/fnagi.2022.854733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Alzheimer’s Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.
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Affiliation(s)
- Ningxin Dong
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Changyong Fu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weixin Xiao
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
| | | | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yunxia Li,
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Jitsuishi T, Yamaguchi A. Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data. Sci Rep 2022; 12:4284. [PMID: 35277565 PMCID: PMC8917197 DOI: 10.1038/s41598-022-08231-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/03/2022] [Indexed: 12/13/2022] Open
Abstract
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.
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Affiliation(s)
- Tatsuya Jitsuishi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Atsushi Yamaguchi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan.
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Hao S, Yang C, Li Z, Ren J. Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of resting-state fMRI. Seizure 2022; 96:25-33. [DOI: 10.1016/j.seizure.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 12/30/2022] Open
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Liu W, Liu L, Cheng X, Ge H, Hu G, Xue C, Qi W, Xu W, Chen S, Gao R, Rao J, Chen J. Functional Integrity of Executive Control Network Contributed to Retained Executive Abilities in Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:710172. [PMID: 34899264 PMCID: PMC8664557 DOI: 10.3389/fnagi.2021.710172] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 10/19/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Mild cognitive impairment (MCI) is considered to be a transitional state between normal aging and Alzheimer's dementia (AD). Recent studies have indicated that executive function (EF) declines during MCI. However, only a limited number of studies have investigated the neural basis of EF deficits in MCI. Herein, we investigate the changes of regional brain spontaneous activity and functional connectivity (FC) of the executive control network (ECN) between high EF and low EF groups. Methods: According to EF composite score (ADNI-EF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we divided MCI into two groups, including the MCI-highEF group and MCI-lowEF group. Resting-state functional MRI was utilized to investigate the fractional amplitude of low-frequency fluctuation (fALFF) and ECN functional connectivity across 23 healthy controls (HC), 11 MCI-highEF, and 14 MCI-lowEF participants. Moreover, a partial correlation analysis was carried out to examine the relationship between altered fALFF or connectivity of the ECN and the ADNI-EF. Results: Compared to HC, the MCI-highEF participants demonstrated increased fALFF in the left superior temporal gyrus (STG), as well as decreased fALFF in the right precentral gyrus, right postcentral gyrus, and left middle frontal gyrus (MFG). The MCI-lowEF participants demonstrated increased fALFF in the cerebellar vermis and decreased fALFF in the left MFG. Additionally, compared to HC, the MCI-highEF participants indicated no significant difference in connectivity of the ECN. Furthermore, the MCI-lowEF participants showed increased ECN FC in the left cuneus and left MFG, as well as decreased ECN functional connectivity in the right parahippocampal gyrus (PHG). Notably, the altered fALFF in the left MFG was positively correlated to ADNI-EF, while the altered fALFF in cerebellar vermis is negatively correlated with ADNI-EF across the two MCI groups and the HC group. Altered ECN functional connectivity in the right PHG is negatively correlated to ADNI-EF, while altered ECN functional connectivity in the left cuneus is negatively correlated to ADNI-EF across the three groups. Conclusions: Our current study demonstrates the presence of different patterns of regional brain spontaneous activity and ECN FC in the MCI-highEF group and MCI-lowEF group. Furthermore, the ECN FC of the MCI-highEF group was not disrupted, which may contribute to retained EF in MCI.
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Affiliation(s)
- Wan Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Li Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinxin Cheng
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Run Gao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Zhang YM, Kang YF, Zeng JJ, Li L, Gao JM, Liu LZ, Shi LR, Liao WH. Surface-Based Falff: A Potential Novel Biomarker for Prediction of Radiation Encephalopathy in Patients With Nasopharyngeal Carcinoma. Front Neurosci 2021; 15:692575. [PMID: 34349618 PMCID: PMC8326829 DOI: 10.3389/fnins.2021.692575] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/21/2021] [Indexed: 11/13/2022] Open
Abstract
Radiation encephalopathy (RE) is an important potential complication in patients with nasopharyngeal carcinoma (NPC) who undergo radiotherapy (RT) that can affect the quality of life. However, a functional imaging biomarker of pre-symptomatic RE has not yet been established. This study aimed to assess radiation-induced gray matter functional alterations and explore fractional amplitude of low-frequency fluctuation (fALFF) as an imaging biomarker for predicting or diagnosing RE in patients with NPC. A total of 60 patients with NPC were examined, 21 in the pre-RT cohort and 39 in the post-RT cohort. Patients in the post-RT cohort were further divided into two subgroups according to the occurrence of RE in follow-up: post-RT non-RE (n = 21) and post-RT REproved infollow-up (n = 18). Surface-based and volume-based fALFF were used to detect radiation-induced functional alterations. Functional derived features were then adopted to construct a predictive model for the diagnosis of RE. We observed that surface-based fALFF could sensitively detect radiation-induced functional alterations in the intratemporal brain regions (such as the hippocampus and superior temporal gyrus), as well as the extratemporal regions (such as the insula and prefrontal lobe); however, no significant intergroup differences were observed using volume-based fALFF. No significant correlation between fALFF and radiation dose to the ipsilateral temporal lobe was observed. Support vector machine (SVM) analysis revealed that surface-based fALFF in the bilateral superior temporal gyri and left insula exhibited impressive performance (accuracy = 80.49%) in identifying patients likely to develop RE. We conclude that surface-based fALFF may serve as a sensitive imaging biomarker in the prediction of RE.
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Affiliation(s)
- You-Ming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Ya-Fei Kang
- School of Psychology, Shaanxi Normal University, Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Jun-Jie Zeng
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Li Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jian-Ming Gao
- Department of Radiation Oncology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-Zhi Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Liang-Rong Shi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
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