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Li Q, Xu X, Qian Y, Cai H, Zhao W, Zhu J, Yu Y. Resting-state brain functional alterations and their genetic mechanisms in drug-naive first-episode psychosis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:13. [PMID: 36841861 PMCID: PMC9968350 DOI: 10.1038/s41537-023-00338-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/27/2023]
Abstract
Extensive research has established the presence of resting-state brain functional damage in psychosis. However, the genetic mechanisms of such disease phenotype are yet to be unveiled. We investigated resting-state brain functional alterations in patients with drug-naive first-episode psychosis (DFP) by performing a neuroimaging meta-analysis of 8 original studies comprising 500 patients and 469 controls. Combined with the Allen Human Brain Atlas, we further conducted transcriptome-neuroimaging spatial correlations to identify genes whose expression levels were linked to brain functional alterations in DFP, followed by a range of gene functional characteristic analyses. Meta-analysis revealed a mixture of increased and decreased brain function in widespread areas including the default-mode, visual, motor, striatal, and cerebellar systems in DFP. Moreover, these brain functional alterations were spatially associated with the expression of 1662 genes, which were enriched for molecular functions, cellular components, and biological processes of the cerebral cortex, as well as psychiatric disorders including schizophrenia. Specific expression analyses demonstrated that these genes were specifically expressed in the brain tissue, in cortical neurons and immune cells, and during nearly all developmental periods. Concurrently, the genes could construct a protein-protein interaction network supported by hub genes and were linked to multiple behavioral domains including emotion, attention, perception, and motor. Our findings provide empirical evidence for the notion that brain functional damage in DFP involves a complex interaction of polygenes with various functional characteristics.
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Affiliation(s)
- Qian Li
- grid.459419.4Department of Radiology, Chaohu Hospital of Anhui Medical University, 238000 Hefei, China ,grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Xiaotao Xu
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Yinfeng Qian
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Huanhuan Cai
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Wenming Zhao
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China. .,Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China. .,Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China. .,Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China. .,Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
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2
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Frequency-specific brain network architecture in resting-state fMRI. Sci Rep 2023; 13:2964. [PMID: 36806195 PMCID: PMC9941507 DOI: 10.1038/s41598-023-29321-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 02/02/2023] [Indexed: 02/22/2023] Open
Abstract
The analysis of brain function in resting-state network (RSN) models, ascertained through the functional connectivity pattern of resting-state functional magnetic resonance imaging (rs-fMRI), is sufficiently powerful for studying large-scale functional integration of the brain. However, in RSN-based research, the network architecture has been regarded as the same through different frequency bands. Thus, here, we aimed to examined whether the network architecture changes with frequency. The blood oxygen level-dependent (BOLD) signal was decomposed into four frequency bands-ranging from 0.007 to 0.438 Hz-and the clustering algorithm was applied to each of them. The best clustering number was selected for each frequency band based on the overlap ratio with task activation maps. The results demonstrated that resting-state BOLD signals exhibited frequency-specific network architecture; that is, the networks finely subdivided in the lower frequency bands were integrated into fewer networks in higher frequency bands rather than reconfigured, and the default mode network and networks related to perception had sufficiently strong architecture to survive in an environment with a lower signal-to-noise ratio. These findings provide a novel framework to enable improved understanding of brain function through the multiband frequency analysis of ultra-slow rs-fMRI data.
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3
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Cognitive decline is associated with frequency-specific resting state functional changes in normal aging. Brain Imaging Behav 2022; 16:2120-2132. [PMID: 35864341 DOI: 10.1007/s11682-022-00682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2022] [Indexed: 11/02/2022]
Abstract
Resting state low-frequency brain activity may aid in our understanding of the mechanisms of aging-related cognitive decline. Our purpose was to explore the characteristics of the amplitude of low-frequency fluctuations (ALFF) in different frequency bands of fMRI to better understand cognitive aging. Thirty-seven cognitively normal older individuals underwent a battery of neuropsychological tests and MRI scans at baseline and four years later. ALFF from five different frequency bands (typical band, slow-5, slow-4, slow-3, and slow-2) were calculated and analyzed. A two-way ANOVA was used to explore the interaction effects in voxel-wise whole brain ALFF of the time and frequency bands. Paired-sample t-test was used to explore within-group changes over four years. Partial correlation analysis was performed to assess associations between the altered ALFF and cognitive function. Significant interaction effects of time × frequency were distributed over inferior frontal gyrus, superior frontal gyrus, right rolandic operculum, left thalamus, and right putamen. Significant ALFF reductions in all five frequency bands were mainly found in the right hemisphere and the posterior cerebellum; whereas localization of the significantly increased ALFF were mainly found in the cerebellum at typical band, slow-5 and slow-4 bands, and left hemisphere and the cerebellum at slow-3, slow-2 bands. In addition, ALFF changes showed frequency-specific correlations with changes in cognition. These results suggest that changes of local brain activity in cognitively normal aging should be investigated in multiple frequency bands. The association between ALFF changes and cognitive function can potentially aid better understanding of the mechanisms underlying normal cognitive aging.
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Li X, Li H, Cao L, Liu J, Xing H, Huang X, Gong Q. Application of graph theory across multiple frequency bands in drug-naïve obsessive-compulsive disorder with no comorbidity. J Psychiatr Res 2022; 150:272-278. [PMID: 35427825 DOI: 10.1016/j.jpsychires.2022.03.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 03/14/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
Abstract
Recently, graph theoretical analysis based on resting-state functional magnetic resonance imaging has provided a means of investigating the complex brain connectome in obsessive-compulsive disorder (OCD) patients. However, these studies have been restricted to spontaneous blood oxygen level-dependent (BOLD) signals with frequency bands between 0.01 and 0.08 Hz, and the parameters from graph theory across multiple frequency bands have seldom been studied. Here, we calculated global metrics (small-worldness, global efficiency and modularity) and nodal metrics (degree centrality, betweenness centrality, nodal clustering coefficient and shortest path) at four different frequency bands (slow-2 (0.199-0.25 Hz), slow-3 (0.074-0.198 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz), from 0.01 to 0.25 Hz) in seventy-three OCD patients and ninety healthy controls. The analyses were also calculated in traditional low-frequency bands (0.01-0.08 Hz) for reference. For the global metrics, the OCD patients showed increased small-worldness and modularity only in the slow-3 band. For the local metrics, we observed a frequency-dependent characteristic, with the main significant differences in regions including the right precentral gyrus, occipital region, right anterior cingulum cortex and fusiform cortex. Our results suggested frequency-specific abnormalities of the brain connectome in OCD and the future studies may need to consider different frequency bands when measuring spontaneous activity in the brain.
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Affiliation(s)
- Xue Li
- College of Physics, Sichuan University, Chengdu, PR China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China
| | - Hailong Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Lingxiao Cao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Jing Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Haoyang Xing
- College of Physics, Sichuan University, Chengdu, PR China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China.
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
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5
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Yang H, Zhang H, Meng C, Wohlschläger A, Brandl F, Di X, Wang S, Tian L, Biswal B. Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study. Hum Brain Mapp 2022; 43:3792-3808. [PMID: 35475569 PMCID: PMC9294298 DOI: 10.1002/hbm.25884] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 11/09/2022] Open
Abstract
The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Afra Wohlschläger
- Department of Neuroradiology, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Felix Brandl
- Department of Psychiatry, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Xin Di
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Shuai Wang
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Lin Tian
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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Ikeda S, Kawano K, Watanabe S, Yamashita O, Kawahara Y. Predicting behavior through dynamic modes in resting-state fMRI data. Neuroimage 2021; 247:118801. [PMID: 34896588 DOI: 10.1016/j.neuroimage.2021.118801] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 11/20/2022] Open
Abstract
Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0-0.1,...,0.6-0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.
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Affiliation(s)
- Shigeyuki Ikeda
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan.
| | - Koki Kawano
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Soichi Watanabe
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
| | - Yoshinobu Kawahara
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; Institute of Mathematics for Industry, Kyushu University, Fukuoka 819-0395, Japan
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7
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Yu Q, Cai Z, Li C, Xiong Y, Yang Y, He S, Tang H, Zhang B, Du S, Yan H, Chang C, Wang N. A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis. Front Hum Neurosci 2021; 15:739668. [PMID: 34566609 PMCID: PMC8455948 DOI: 10.3389/fnhum.2021.739668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/18/2021] [Indexed: 12/18/2022] Open
Abstract
Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.
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Affiliation(s)
- Qin Yu
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Zenglin Cai
- Department of Neurology, The Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, China
| | - Cunhua Li
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Yulong Xiong
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Yang Yang
- Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shuang He
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Haitong Tang
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Bo Zhang
- Department of Radiology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Shouyun Du
- Department of Neurology, Guanyun People's Hospital, Guanyun, China
| | - Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Chunqi Chang
- Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.,Pengcheng Laboratory, Shenzhen, China
| | - Nizhuan Wang
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
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8
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Cui L, Chen K, Huang L, Sun J, Lv Y, Jia X, Guo Q. Changes in local brain function in mild cognitive impairment due to semantic dementia. CNS Neurosci Ther 2021; 27:587-602. [PMID: 33650764 PMCID: PMC8025655 DOI: 10.1111/cns.13621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting-state functional MRI. METHODS We administered a battery of neuropsychological tests to twenty-two patients with mild cognitive impairment due to semantic dementia (MCI-SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting-state functional MRI with multiple sub-bands and indicators to evaluate functional activity. RESULTS Neuropsychological tests revealed a significant decline in semantic performance in the MCI-SD group, but no decline in other cognitive domains. Resting-state functional MRI revealed local functional changes in multiple brain regions in the MCI-SD group, distributed in different sub-bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub-bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI-SD group was positively correlated with general semantic tasks (oral sound naming, word-picture verification). CONCLUSION Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency-dependent sub-bands, especially the slow-2 and slow-3 sub-bands.
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Affiliation(s)
- Liang Cui
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Keliang Chen
- Department of NeurologyHuashan HospitalFudan UniversityShanghaiChina
| | - Lin Huang
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Jiawei Sun
- School of Information and Electronics TechnologyJiamusi UniversityJiamusiChina
| | - Yating Lv
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Xize Jia
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Qihao Guo
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
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9
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Yang J, Gohel S, Zhang Z, Hatzoglou V, Holodny AI, Vachha BA. Glioma-Induced Disruption of Resting-State Functional Connectivity and Amplitude of Low-Frequency Fluctuations in the Salience Network. AJNR Am J Neuroradiol 2021; 42:551-558. [PMID: 33384293 DOI: 10.3174/ajnr.a6929] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/02/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND PURPOSE Cognitive challenges are prevalent in survivors of glioma, but their neurobiology is incompletely understood. The purpose of this study was to investigate the effect of glioma presence and tumor characteristics on resting-state functional connectivity and amplitude of low-frequency fluctuations of the salience network, a key neural network associated with cognition. MATERIALS AND METHODS Sixty-nine patients with glioma (mean age, 48.74 [SD, 14.32] years) who underwent resting-state fMRI were compared with 31 healthy controls (mean age, 49.68 [SD, 15.54] years). We identified 4 salience network ROIs: left/right dorsal anterior cingulate cortex and left/right anterior insula. Average salience network resting-state functional connectivity and amplitude of low-frequency fluctuations within the 4 salience network ROIs were computed. RESULTS Patients with gliomas showed decreased overall salience network resting-state functional connectivity (P = .001) and increased amplitude of low-frequency fluctuations in all salience network ROIs (P < .01) except in the left dorsal anterior cingulate cortex. Compared with controls, patients with left-sided gliomas showed increased amplitude of low-frequency fluctuations in the right dorsal anterior cingulate cortex (P = .002) and right anterior insula (P < .001), and patients with right-sided gliomas showed increased amplitude of low-frequency fluctuations in the left anterior insula (P = .002). Anterior tumors were associated with decreased salience network resting-state functional connectivity (P < .001) and increased amplitude of low-frequency fluctuations in the right anterior insula, left anterior insula, and right dorsal anterior cingulate cortex. Patients with high-grade gliomas had decreased salience network resting-state functional connectivity compared with healthy controls (P < .05). The right anterior insula showed increased amplitude of low-frequency fluctuations in patients with grade II and IV gliomas compared with controls (P < .01). CONCLUSIONS By demonstrating decreased resting-state functional connectivity and an increased amplitude of low-frequency fluctuations related to the salience network in patients with glioma, this study adds to our understanding of the neurobiology underpinning observable cognitive deficits in these patients. In addition to more conventional functional connectivity, amplitude of low-frequency fluctuations is a promising functional-imaging biomarker of tumor-induced vascular and neural pathology.
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Affiliation(s)
- J Yang
- From the Departments of Radiology (J.Y., V.H., A.I.H., B.A.V.)
- New York University Grossman School of Medicine (J.Y.), New York University, New York, New York
| | - S Gohel
- Department of Health Informatics (S.G.), Rutgers University School of Health Professions, Newark, New Jersey
| | - Z Zhang
- Epidemiology and Biostatistics (Z.Z.)
| | - V Hatzoglou
- From the Departments of Radiology (J.Y., V.H., A.I.H., B.A.V.)
- Brain Tumor Center (V.H., A.I.H., B.A.V.), Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiology (V.H., A.I.H., B.A.V.), Weill Medical College of Cornell University, New York, New York
| | - A I Holodny
- From the Departments of Radiology (J.Y., V.H., A.I.H., B.A.V.)
- Brain Tumor Center (V.H., A.I.H., B.A.V.), Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiology (V.H., A.I.H., B.A.V.), Weill Medical College of Cornell University, New York, New York
- Department of Neuroscience (A.I.H.), Weill-Cornell Graduate School of the Medical Sciences, New York, New York
| | - B A Vachha
- From the Departments of Radiology (J.Y., V.H., A.I.H., B.A.V.)
- Brain Tumor Center (V.H., A.I.H., B.A.V.), Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiology (V.H., A.I.H., B.A.V.), Weill Medical College of Cornell University, New York, New York
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10
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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11
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Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging 2020; 65:47-53. [PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/24/2020] [Accepted: 04/08/2020] [Indexed: 12/12/2022]
Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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12
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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13
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Szeszko PR, Yehuda R. Magnetic resonance imaging predictors of psychotherapy treatment response in post-traumatic stress disorder: A role for the salience network. Psychiatry Res 2019; 277:52-57. [PMID: 30755338 DOI: 10.1016/j.psychres.2019.02.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/01/2019] [Accepted: 02/02/2019] [Indexed: 01/21/2023]
Abstract
The earliest neuroimaging studies in post-traumatic stress disorder (PTSD) utilized positron emission tomography (PET) to examine the brain's response to glucocorticoid administration given predominant neurobiological models of the stress response focusing on that neuroendocrine system. This work revealed that the anterior cingulate cortex and amygdala, which is now considered part of the salience network, play a role in treatment response, and set the stage for subsequent magnetic resonance (MR) imaging studies focused on understanding the role of the salience network in the neurobiology of treatment response in PTSD. This selective review discusses magnetic resonance (MR) imaging studies that have been used to predict treatment response to cognitive-behavioral therapy (CBT) or prolonged exposure (PE) in PTSD, which have demonstrated abnormalities in processing involving the salience network, including the amygdala, anterior cingulate cortex and insula. Increased attention to environmental cues may signal alarm resulting in hypervigilance and overactive action-monitoring for the detection of threatening stimuli and an inability to integrate concomitant emotional and sensory functions in PTSD. Successful psychotherapy treatment response in PTSD appears to involve the ability to downregulate amygdala activity to trauma-related stimuli through improved regulation of attention by the anterior cingulate cortex and concomitant internal emotional states mediated by the insula. In addition, the ability to better modulate (normalize) the salience network following psychotherapy in PTSD may be associated with better crosstalk between untargeted inner thought (i.e., task-negative network) and the ability to focus attention on stimulus-dependent demands (i.e., task positive network).
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Affiliation(s)
- Philip R Szeszko
- James J. Peters VA Medical Center, Bronx, NY, United States; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
| | - Rachel Yehuda
- James J. Peters VA Medical Center, Bronx, NY, United States; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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14
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Eisenberg DP, Berman KF. Connections With Connections: Dopaminergic Correlates of Neural Network Properties. Biol Psychiatry 2019; 85:366-367. [PMID: 30732679 DOI: 10.1016/j.biopsych.2019.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 01/03/2019] [Indexed: 12/01/2022]
Affiliation(s)
- Daniel P Eisenberg
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland.
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15
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Gohel S, Gallego JA, Robinson DG, DeRosse P, Biswal B, Szeszko PR. Frequency specific resting state functional abnormalities in psychosis. Hum Brain Mapp 2018; 39:4509-4518. [PMID: 30160325 DOI: 10.1002/hbm.24302] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 06/13/2018] [Accepted: 06/20/2018] [Indexed: 12/18/2022] Open
Abstract
Resting state functional magnetic resonance imaging studies of psychosis have focused primarily on the amplitude of low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal ranging from .01 to 0.1 Hz. Few studies, however, have investigated the amplitude of frequency fluctuations within discrete frequency bands and higher than 0.1 Hz in patients with psychosis at different illness stages. We investigated BOLD signal within three frequency ranges including slow-4 (.027-.073 Hz), slow-3 (.074-0.198 Hz) and slow-2 (0.199-0.25 Hz) in 89 patients with either first-episode or chronic psychosis and 119 healthy volunteers. We investigated the amplitude of frequency fluctuations within three frequency bands using 47 regions-of-interest placed within 14 known resting state networks derived using group independent component analysis. There were significant group x frequency interactions for the visual and motor cortex networks, with the largest significant group differences (patients < healthy volunteers) evident in slow-4 and slow-3, respectively. Also, healthy volunteers had an overall higher amplitude of frequency fluctuations compared to patients across the three frequency ranges in the visual cortex, dorsal attention and motor cortex networks with the opposite effect (patients > healthy volunteers) evident within the salience and frontal gyrus networks. Subsequent analyses indicated that these effects were evident in both first-episode and chronic patients. Our study provides new data regarding the importance of BOLD signal fluctuations within different frequency bands in the neurobiology of psychosis.
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Affiliation(s)
- Suril Gohel
- Department of Health Informatics, School of Health Professions, Rutgers Biomedical and Health Sciences, Rutgers University, Newark, New Jersey
| | - Juan A Gallego
- Department of Psychiatry, Weill Cornell Medical College, New York, New York.,New York-Presbyterian Hospital - Westchester Division, White Plains, New York
| | - Delbert G Robinson
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York.,Psychiatry Research, Zucker Hillside Hospital, Northwell Health System, New York.,Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Pamela DeRosse
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York.,Psychiatry Research, Zucker Hillside Hospital, Northwell Health System, New York.,Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
| | - Philip R Szeszko
- Mental Illness Research Education Clinical Center and Mental Health Patient Care Center, James J. Peters Veterans Affairs Medical Center, Bronx, New York.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
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