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Kesler SR, Harrison RA, Schutz ADLT, Michener H, Bean P, Vallone V, Prinsloo S. Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival. Front Neurol 2024; 15:1345520. [PMID: 38601343 PMCID: PMC11004301 DOI: 10.3389/fneur.2024.1345520] [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: 11/29/2023] [Accepted: 03/14/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. Methods We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. Results We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Discussion Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.
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Affiliation(s)
- Shelli R. Kesler
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
| | - Rebecca A. Harrison
- Division of Neurology, BC Cancer, The University of British Columbia, Vancouver, BC, Canada
| | - Alexa De La Torre Schutz
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
| | - Hayley Michener
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States
| | - Paris Bean
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States
| | - Veronica Vallone
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States
| | - Sarah Prinsloo
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States
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Kesler SR, Harrison RA, Schultz ADLT, Michener H, Bean P, Vallone V, Prinsloo S. Strength of spatial correlation between structural brain network connectivity and brain-wide patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.27.23299085. [PMID: 38076940 PMCID: PMC10705651 DOI: 10.1101/2023.11.27.23299085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.
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Affiliation(s)
- Shelli R Kesler
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX USA
| | - Rebecca A Harrison
- BC Cancer, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | | | - Hayley Michener
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Paris Bean
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Veronica Vallone
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah Prinsloo
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, TX, USA
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Chen HJ, Ke J, Qiu J, Xu Q, Zhong Y, Lu GM, Wu Y, Qi R, Chen F. Altered whole-brain resting-state functional connectivity and brain network topology in typhoon-related post-traumatic stress disorder. Ther Adv Psychopharmacol 2023; 13:20451253231175302. [PMID: 37342156 PMCID: PMC10278414 DOI: 10.1177/20451253231175302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 04/24/2023] [Indexed: 06/22/2023] Open
Abstract
Background Altered resting-state functional connectivity has been found in patients with post-traumatic stress disorder (PTSD). However, the alteration of resting-state functional connectivity at whole-brain level in typhoon-traumatized individuals with PTSD remains largely unknown. Objectives To investigate changes in whole-brain resting-state functional connectivity and brain network topology in typhoon-traumatized subjects with and without PTSD. Design Cross-sectional study. Methods Twenty-seven patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC) underwent resting-state functional MRI scanning. The whole brain resting-state functional connectivity network was constructed based on the automated anatomical labeling atlas. The graph theory method was used to analyze the topological properties of the large-scale resting-state functional connectivity network. Whole-brain resting-state functional connectivity and the topological network property were compared by analyzing the variance. Results There was no significant difference in the area under the curve of γ, λ, σ, global efficiency, and local efficiency among the three groups. The PTSD group showed increased dorsal cingulate cortex (dACC) resting-state functional connectivity with the postcentral gyrus (PoCG) and paracentral lobe and increased nodal betweenness centrality in the precuneus relative to both control groups. Compared with the PTSD and HC groups, the TEC group showed increased resting-state functional connectivity between the hippocampus and PoCG and increased connectivity strength in the putamen. In addition, compared with the HC group, both the PTSD and TEC groups showed increased connectivity strength and nodal efficiency in the insula. Conclusion Aberrant resting-state functional connectivity and topology were found in all trauma-exposed individuals. These findings broaden our knowledge of the neuropathological mechanisms of PTSD.
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Affiliation(s)
- Hui Juan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jun Ke
- Department of Medical Imaging, Jinling Hospital, Medical School, Nanjing University, Nanjing, China
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jie Qiu
- Department of Ultrasound, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Medical School, Nanjing University, Nanjing, China
| | - Yuan Zhong
- Department of Medical Imaging, Jinling Hospital, Medical School, Nanjing University, Nanjing, China
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School, Nanjing University, Nanjing, China
| | - Yanglei Wu
- MR Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Rongfeng Qi
- Department of Medical Imaging, Jinling Hospital, Medical School, Nanjing University, Nanjing 210002, Jiangsu, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua Street, Xiuying District, Haikou 570311, Hainan, China
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Predicting overall survival in diffuse glioma from the presurgical connectome. Sci Rep 2022; 12:18783. [PMID: 36335224 PMCID: PMC9637134 DOI: 10.1038/s41598-022-22387-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 ± 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 ± 0.02), clinical + (AUC 0.79 ± 0.01) and radiomic models (AUC 0.75 ± 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making.
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Deng L, Liu H, Liu W, Liao Y, Liang Q, Wang W. Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:1375-1384. [PMID: 36411688 PMCID: PMC10930362 DOI: 10.11817/j.issn.1672-7347.2022.220085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. METHODS A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. RESULTS The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). CONCLUSIONS The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
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Affiliation(s)
- Lingling Deng
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Huasheng Liu
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Wen Liu
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Yunjie Liao
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Qi Liang
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Wei Wang
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
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Chen H, Dai L, Zhang Y, Feng L, Jiang Z, Wang X, Xie D, Guo J, Chen H, Wang J, Liu C. Network Reconfiguration Among Cerebellar Visual, and Motor Regions Affects Movement Function in Spinocerebellar Ataxia Type 3. Front Aging Neurosci 2022; 14:773119. [PMID: 35478700 PMCID: PMC9036064 DOI: 10.3389/fnagi.2022.773119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 03/14/2022] [Indexed: 12/01/2022] Open
Abstract
Background Spinocerebellar ataxia type 3 (SCA3) is a rare movement disorder characterized with ataxia. Previous studies on movement disorders show that the whole-brain functional network tends to be more regular, and these reconfigurations correlate with genetic and clinical variables. Methods To test whether the brain network in patients with SCA3 follows a similar reconfiguration course to other movement disorders, we recruited 41 patients with SCA3 (mean age = 40.51 ± 12.13 years; 23 male) and 41 age and sex-matched healthy individuals (age = 40.10 ± 11.56 years; 24 male). In both groups, the whole-brain network topology of resting-state functional magnetic resonance imaging (rs-fMRI) was conducted using graph theory, and the relationships among network topologies, cytosine-adenine-guanine (CAG) repeats, clinical symptoms, and functional connectivity were explored in SCA3 patients using partial correlation analysis, controlling for age and sex. Results The brain networks tended to be more regular with a higher clustering coefficient, local efficiency, and modularity in patients with SCA3. Hubs in SCA3 patients were reorganized as the number of hubs increased in motor-related areas and decreased in cognitive areas. At the global level, small-worldness and normalized clustering coefficients were significantly positively correlated with clinical motor symptoms. At the nodal level, the clustering coefficient and local efficiency increased significantly in the visual (bilateral cuneus) and sensorimotor (right cerebellar lobules IV, V, VI) networks and decreased in the cognitive areas (right middle frontal gyrus). The clustering coefficient and local efficiency in the bilateral cuneus gyrus were negatively correlated with clinical motor symptoms. The functional connectivity between right caudate nucleus and bilateral calcarine gyrus were negatively correlated with disease duration, while connectivity between right posterior cingulum gyrus and left cerebellar lobule III, left inferior occipital gyrus and right cerebellar lobule IX was positively correlated. Conclusion Our results demonstrate that a more regular brain network occurred in SCA3 patients, with motor and visual-related regions, such as, cerebellar lobules and cuneus gyrus, both forayed neighbor nodes as “resource predators” to compensate for normal function, with motor and visual function having the higher priority comparing with other high-order functions. This study provides new information about the neurological mechanisms underlying SCA3 network topology impairments in the resting state, which give a potential guideline for future clinical treatments. Clinical Trial Registration [www.ClinicalTrials.gov], identifier [ChiCTR1800019901].
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Affiliation(s)
- Hui Chen
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Limeng Dai
- Department of Medical Genetics, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yuhan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Liu Feng
- Department of Laboratory Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Zhenzhen Jiang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xingang Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Dongjing Xie
- Department of Neurology, Xinqiao Hospital and The Second Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jing Guo
- Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- *Correspondence: Huafu Chen,
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- Jian Wang,
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- Chen Liu,
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The disrupted topological properties of structural networks showed recovery in ischemic stroke patients: a longitudinal design study. BMC Neurosci 2021; 22:47. [PMID: 34340655 PMCID: PMC8330082 DOI: 10.1186/s12868-021-00652-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 07/22/2021] [Indexed: 12/12/2022] Open
Abstract
Introduction Stroke is one of the leading causes of substantial disability worldwide. Previous studies have shown brain functional and structural alterations in adults with stroke. However, few studies have examined the longitudinal reorganization in whole-brain structural networks in stroke. Methods Here, we applied graph theoretical analysis to investigate the longitudinal topological organization of white matter networks in 20 ischemic stroke patients with a one-month interval between two timepoints. Two sets of clinical scores, Fugl-Meyer motor assessment (FMA) and neurological deficit scores (NDS), were assessed for all patients on the day the image data were collected. Results The stroke patients exhibited significant increases in FMA scores and significant reductions in DNS between the two timepoints. All groups exhibited small-world organization (σ > 1) in the brain structural network, including a high clustering coefficient (γ > 1) and a low normalized characteristic path length (λ ≈ 1). However, compared to healthy controls, stroke patients showed significant decrease in nodal characteristics at the first timepoint, primarily in the right supplementary motor area, right middle temporal gyrus, right inferior parietal lobe, right postcentral gyrus and left posterior cingulate gyrus. Longitudinal results demonstrated that altered nodal characteristics were partially restored one month later. Additionally, significant correlations between the nodal characteristics of the right supplementary motor area and the clinical scale scores (FMA and NDS) were observed in stroke patients. Similar behavioral-neuroimaging correlations were found in the right inferior parietal lobe. Conclusion Altered topological properties may be an effect of stroke, which can be modulated during recovery. The longitudinal results and the neuroimaging-behavioral relationship may provide information for understanding brain recovery from stroke. Future studies should detect whether observed changes in structural topological properties can predict the recovery of daily cognitive function in stroke. Supplementary Information The online version contains supplementary material available at 10.1186/s12868-021-00652-1.
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Abstract
Prenatal alcohol exposure leads to alterations in cognition, behavior and underlying brain architecture. However, prior studies have not integrated structural and functional imaging data in children with prenatal alcohol exposure. The aim of this study was to characterize disruptions in both structural and functional brain network organization after prenatal alcohol exposure in very early life. A group of 11 neonates with prenatal alcohol exposure and 14 unexposed controls were investigated using diffusion weighted structural and resting state functional magnetic resonance imaging. Covariance networks were created using graph theoretical analyses for each data set, controlling for age and sex. Group differences in global hub arrangement and regional connectivity were determined using nonparametric permutation tests. Neonates with prenatal alcohol exposure and controls exhibited similar global structural network organization. However, global functional networks of neonates with prenatal alcohol exposure comprised of temporal and limbic hubs, while hubs were more distributed in controls representing an early default mode network. On a regional level, controls showed prominent structural and functional connectivity in parietal and occipital regions. Neonates with prenatal alcohol exposure showed regionally, predominant structural and functional connectivity in several subcortical regions and occipital regions. The findings suggest early functional disruption on a global and regional level after prenatal alcohol exposure and indicate suboptimal organization of functional networks. These differences likely underlie sensory dysregulation and behavioral difficulties in prenatal alcohol exposure.
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The association of genetic polymorphisms with neuroconnectivity in breast cancer patients. Sci Rep 2021; 11:6169. [PMID: 33731765 PMCID: PMC7971072 DOI: 10.1038/s41598-021-85768-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 03/02/2021] [Indexed: 11/21/2022] Open
Abstract
Genetic polymorphisms in select genes, including APOE (apolipoprotein E), COMT (Catechol-O-Methyltransferase), MDR1 (multi-drug resistance 1), BDNF (brain derived neurotrophic factor), and GST (glutathione-S-transferase), have been associated with vulnerability to cognitive impairment. In this study, we evaluated the relationship of these genetic variants to measures of brain health in patients with breast cancer, including neurocognitive testing and functional connectome analysis. Women with breast cancer (n = 83) and female healthy controls (n = 53) were evaluated. They underwent resting-state functional MRI scans and neurocognitive testing. Polymerase chain reaction (PCR) was performed on saliva samples to identify single nucleotide polymorphisms (SNPs) in candidate genes: APOE, COMT, MDR1, BDNF, and GST. Breast cancer patients treated with chemotherapy had slower processing speed (p = 0.04) and poorer reported executive function (p < 0.0001) than healthy controls. Those chemotherapy-treated patients that were APOE e4 carriers had significantly slower processing speed. A greater number of risk-related alleles was associated with poorer connectivity in the regions of the left cuneus and left calcarine. While breast cancer patients that are APOE e4 carriers may have a select vulnerability to processing speed impairments, other risk-related alleles were not found to influence cognitive test performance in this population. Conversely, regions of impaired functional connectivity appeared to be related to risk-related genetic polymorphisms in breast cancer patients. This suggests that a cancer patient’s SNPs in candidate genes may influence the risk of neurotoxicity. Further study evaluating the impact of genotype on biomarkers of brain health in cancer survivors is warranted.
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Li Y, Wang Y, Wang Y, Wang H, Li D, Chen Q, Huang W. Impaired Topological Properties of Gray Matter Structural Covariance Network in Epilepsy Children With Generalized Tonic-Clonic Seizures: A Graph Theoretical Analysis. Front Neurol 2020; 11:253. [PMID: 32373045 PMCID: PMC7176815 DOI: 10.3389/fneur.2020.00253] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 03/17/2020] [Indexed: 12/30/2022] Open
Abstract
Modern network science has provided exciting new opportunities for understanding the human brain as a complex network of interacting regions. The improved knowledge of human brain network architecture has made it possible for clinicians to detect the network changes in neurological diseases. Generalized tonic–clonic seizure (GTCS) is a subtype of epilepsy characterized by generalized spike-wave discharge involving the bilateral hemispheres during seizure. Network researches in adults with GTCS exhibited that GTCS can be conceptualized as a network disorder. However, the overall organization of the brain structural covariance network in children with GTCS remains largely unclear. Here, we used a graph theory method to assess the gray matter structural covariance network organization of 14 pediatric patients diagnosed with GTCS and 29 healthy control children. The group differences in regional and global topological properties were investigated. Results revealed significant changes in nodal betweenness locating in brain regions known to be abnormal in GTCS (the right thalamus, bilateral temporal pole, and some regions of default mode network). The network hub analysis results were in accordance with the regional betweenness, which presented a disrupted regional topology of structural covariance network in children with GTCS. To our knowledge, the present study is the first work reporting the changes of structural topological properties in children with GTCS. The findings contribute new insights into the understanding of the neural mechanisms underlying GTCS and highlight critical regions for future neuroimaging research in children with GTCS.
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Affiliation(s)
- Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yanfang Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huirong Wang
- Electromechanic Engineering College, Guangdong Engineering Polytechnic, Guangzhou, China
| | - Ding Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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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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Qi T, Schaadt G, Cafiero R, Brauer J, Skeide MA, Friederici AD. The emergence of long-range language network structural covariance and language abilities. Neuroimage 2019; 191:36-48. [DOI: 10.1016/j.neuroimage.2019.02.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/28/2019] [Accepted: 02/05/2019] [Indexed: 01/12/2023] Open
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Carey D, Nolan H, Kenny RA, Meaney J. Cortical covariance networks in ageing: Cross-sectional data from the Irish Longitudinal Study on Ageing (TILDA). Neuropsychologia 2019; 122:51-61. [DOI: 10.1016/j.neuropsychologia.2018.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 11/24/2018] [Accepted: 11/26/2018] [Indexed: 01/06/2023]
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15
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Nasal administration of mesenchymal stem cells restores cisplatin-induced cognitive impairment and brain damage in mice. Oncotarget 2018; 9:35581-35597. [PMID: 30473752 PMCID: PMC6238972 DOI: 10.18632/oncotarget.26272] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 10/06/2018] [Indexed: 12/13/2022] Open
Abstract
Cognitive impairments are a common side effect of chemotherapy that often persists long after treatment completion. There are no FDA-approved interventions to treat these cognitive deficits also called ‘chemobrain’. We hypothesized that nasal administration of mesenchymal stem cells (MSC) reverses chemobrain. To test this hypothesis, we used a mouse model of cognitive deficits induced by cisplatin that we recently developed. Mice were treated with two cycles of cisplatin followed by nasal administration of MSC. Cisplatin treatment induced deficits in the puzzle box, novel object/place recognition and Y-maze tests, indicating cognitive impairment. Nasal MSC treatment fully reversed these cognitive deficits in males and females. MSC also reversed the cisplatin-induced damage to cortical myelin. Resting state functional MRI and connectome analysis revealed a decrease in characteristic path length after cisplatin, while MSC treatment increased path length in cisplatin-treated mice. MSCs enter the brain but did not survive longer than 12-72 hrs, indicating that they do not replace damaged tissue. RNA-sequencing analysis identified mitochondrial oxidative phosphorylation as a top pathway activated by MSC administration to cisplatin-treated mice. Consistently, MSC treatment restored the cisplatin-induced mitochondrial dysfunction and structural abnormalities in brain synaptosomes. Nasal administration of MSC did not interfere with the peripheral anti-tumor effect of cisplatin. In conclusion, nasal administration of MSC may represent a powerful, non-invasive, and safe regenerative treatment for resolution of chemobrain.
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16
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Kranz MB, Voss MW, Cooke GE, Banducci SE, Burzynska AZ, Kramer AF. The cortical structure of functional networks associated with age-related cognitive abilities in older adults. PLoS One 2018; 13:e0204280. [PMID: 30240409 PMCID: PMC6150534 DOI: 10.1371/journal.pone.0204280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Accepted: 09/04/2018] [Indexed: 01/15/2023] Open
Abstract
Age and cortical structure are both associated with cognition, but characterizing this relationship remains a challenge. A popular approach is to use functional network organization of the cortex as an organizing principle for post-hoc interpretations of structural results. In the current study, we introduce two complimentary approaches to structural analyses that are guided by a-priori functional network maps. Specifically, we systematically investigated the relationship of cortical structure (thickness and surface area) of distinct functional networks to two cognitive domains sensitive to age-related decline thought to rely on both common and distinct processes (executive function and episodic memory) in older adults. We quantified the cortical structure of individual functional network's predictive ability and spatial extent (i.e., number of significant regions) with cognition and its mediating role in the age-cognition relationship. We found that cortical thickness, rather than surface area, predicted cognition across the majority of functional networks. The default mode and somatomotor network emerged as particularly important as they appeared to be the only two networks to mediate the age-cognition relationship for both cognitive domains. In contrast, thickness of the salience network predicted executive function and mediated the age-cognition relationship for executive function. These relationships remained significant even after accounting for global cortical thickness. Quantifying the number of regions related to cognition and mediating the age-cognition relationship yielded similar patterns of results. This study provides a potential approach to organize and describe the apparent widespread regional cortical structural relationships with cognition and age in older adults.
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Affiliation(s)
- Michael B. Kranz
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Michelle W. Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States of America
| | - Gillian E. Cooke
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Sarah E. Banducci
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Agnieszka Z. Burzynska
- Department of Human Development and Family Studies/ Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, United States of America
| | - Arthur F. Kramer
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Departments of Psychology and Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States of America
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17
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Kesler SR, Acton P, Rao V, Ray WJ. Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer's disease. Netw Neurosci 2018; 2:241-258. [PMID: 30215035 PMCID: PMC6130552 DOI: 10.1162/netn_a_00048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 02/14/2018] [Indexed: 12/19/2022] Open
Abstract
Neurodegeneration in Alzheimer's disease (AD) is associated with amyloid-beta peptide accumulation into insoluble amyloid plaques. The five-familial AD (5XFAD) transgenic mouse model exhibits accelerated amyloid-beta deposition, neuronal dysfunction, and cognitive impairment. We aimed to determine whether connectome properties of these mice parallel those observed in patients with AD. We obtained diffusion tensor imaging and resting-state functional magnetic resonance imaging data for four transgenic and four nontransgenic male mice. We constructed both structural and functional connectomes and measured their topological properties by applying graph theoretical analysis. We compared connectome properties between groups using both binarized and weighted networks. Transgenic mice showed higher characteristic path length in weighted structural connectomes and functional connectomes at minimum density. Normalized clustering and modularity were lower in transgenic mice across the upper densities of the structural connectome. Transgenic mice also showed lower small-worldness index in higher structural connectome densities and in weighted structural networks. Hyper-correlation of structural and functional connectivity was observed in transgenic mice compared with nontransgenic controls. These preliminary findings suggest that 5XFAD mouse connectomes may provide useful models for investigating the molecular mechanisms of AD pathogenesis and testing the effectiveness of potential treatments.
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Affiliation(s)
- Shelli R Kesler
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Acton
- Neurodegeneration Consortium, Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vikram Rao
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - William J Ray
- Neurodegeneration Consortium, Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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18
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Guo T, Guan X, Zeng Q, Xuan M, Gu Q, Huang P, Xu X, Zhang M. Alterations of Brain Structural Network in Parkinson's Disease With and Without Rapid Eye Movement Sleep Behavior Disorder. Front Neurol 2018; 9:334. [PMID: 29867741 PMCID: PMC5958180 DOI: 10.3389/fneur.2018.00334] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 04/26/2018] [Indexed: 11/29/2022] Open
Abstract
Background and objective Rapid eye movement sleep behavior disorder (RBD) has a strong association with alpha synucleinpathies such as Parkinson’s disease (PD) and PD patients with RBD tend to have a poorer prognosis. However, we still know little about the pathogenesis of RBD in PD. Therefore, we aim to detect the alterations of structural correlation network (SCN) in PD patients with and without RBD. Materials and methods A total of 191 PD patients, including 51 patients with possible RBD (pRBD) and 140 patients with non-possible RBD, and 76 normal controls were included in the present study. Structural brain networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches. Results There was no difference in global properties among the three groups. Significant enhanced regional nodal measures in limbic system, frontal-temporal regions, and occipital regions and decreased nodal measures in cerebellum were found in PD patients with pRBD (PD-pRBD) compared with PD patients without pRBD. Besides, nodes in frontal lobe, temporal lobe, and limbic system were served as hubs in both two PD groups, and PD-pRBD exhibited additionally recruited hubs in limbic regions. Conclusion Based on the SCN analysis, we found PD-pRBD exhibited a reorganization of nodal properties as well as the remapping of the hub distribution in whole brain especially in limbic system, which may shed light to the pathophysiology of PD with RBD.
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Affiliation(s)
- Tao Guo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qiaoling Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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19
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Xu X, Guan X, Guo T, Zeng Q, Ye R, Wang J, Zhong J, Xuan M, Gu Q, Huang P, Pu J, Zhang B, Zhang M. Brain Atrophy and Reorganization of Structural Network in Parkinson's Disease With Hemiparkinsonism. Front Hum Neurosci 2018; 12:117. [PMID: 29636671 PMCID: PMC5881349 DOI: 10.3389/fnhum.2018.00117] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/12/2018] [Indexed: 11/13/2022] Open
Abstract
Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with hemiparkinsonism, which could potentially disclose the early biomarker for PD. Thirty-one PD patients with hemiparkinsonism and 37 age- and gender- matched normal controls were included in the present study. First of all, we normalized the left hemisphere of structural images as the contralateral side to the affected limbs. Deformation-based morphometry (DBM) was conducted to evaluate the brain atrophy and/or enlargement. structural networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches (e.g., small-worldness, global, and nodal measures). Significantly decreased deformation values were observed in the temporoparietal regions like bilateral middle temporal gyri, ipsilateral precuneus and contralateral Rolandic operculum extending to supramarginal and postcentral gyri. Lower deformation values in contralateral middle temporal gyrus were negatively correlated with higher motor impairment which was dominated by akinesia/rigidity. Moreover, nodal reorganization of structural network mainly located in frontal, temporal, subcortex and cerebellum was bilaterally explored in PD patients with hemiparkinsonism. Increased nodal properties could be commonly observed in frontal lobes. Disruption of subcortex including basal ganglia and amygdala was detected by nodal local efficiency and nodal clustering coefficient. Twelve hubs, mainly from paralimbic-limbic and heteromodal networks, were disrupted and, alternatively, 14 hubs, most of which were located in frontal lobes, were additionally detected in PD patients with hemiparkinsonism. In conclusion, during hemiparkinsonism period, mild brain atrophy in the temporoparietal regions and widespread reorganization of structural network, e.g., enhanced frontal function and disruption of basal ganglia nodes, occurred in both hemispheres. With our data, we can also argue that MTG contralateral to the affected limbs (expressing clinically verified brain atrophy) might be a potential living biomarker to monitor disease progression. Therefore, the combination of DBM and structural network analyses can provide a comprehensive and sensitive evaluation for potential pathogenesis of early PD patients with hemiparkinsonism.
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Affiliation(s)
- Xiaojun Xu
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qiaoling Zeng
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Rong Ye
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqiu Wang
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jianguo Zhong
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Min Xuan
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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20
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Hosseini SMH, Mazaika P, Mauras N, Buckingham B, Weinzimer SA, Tsalikian E, White NH, Reiss AL. Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes. Hum Brain Mapp 2018; 37:4034-4046. [PMID: 27339089 DOI: 10.1002/hbm.23293] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 05/24/2016] [Accepted: 06/12/2016] [Indexed: 02/05/2023] Open
Abstract
Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California.
| | - Paul Mazaika
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California
| | - Nelly Mauras
- Division of Endocrinology, Nemours Children's Health System, Jacksonville, Florida
| | - Bruce Buckingham
- Division of Pediatric Endocrinology, Stanford University, Stanford, California
| | - Stuart A Weinzimer
- Division of Pediatric Endocrinology, Yale University, New Haven, Connecticut
| | - Eva Tsalikian
- Division of Pediatric Endocrinology, University of Iowa, Iowa City, Iowa
| | - Neil H White
- Department of Pediatrics, Washington University, St. Louis, Missouri
| | - Allan L Reiss
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California
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21
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22
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Alterations in the expression of a neurodevelopmental gene exert long-lasting effects on cognitive-emotional phenotypes and functional brain networks: translational evidence from the stress-resilient Ahi1 knockout mouse. Mol Psychiatry 2017; 22:884-899. [PMID: 27021817 PMCID: PMC5444025 DOI: 10.1038/mp.2016.29] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 12/29/2015] [Accepted: 02/03/2016] [Indexed: 12/19/2022]
Abstract
Many psychiatric disorders are highly heritable and may represent the clinical outcome of early aberrations in the formation of neural networks. The placement of brain connectivity as an 'intermediate phenotype' renders it an attractive target for exploring its interaction with genomics and behavior. Given the complexity of genetic make up and phenotypic heterogeneity in humans, translational studies are indicated. Recently, we demonstrated that a mouse model with heterozygous knockout of the key neurodevelopmental gene Ahi1 displays a consistent stress-resilient phenotype. Extending these data, the current research describes our multi-faceted effort to link early variations in Ahi1 expression with long-term consequences for functional brain networks and cognitive-emotional phenotypes. By combining behavioral paradigms with graph-based analysis of whole-brain functional networks, and then cross-validating the data with robust neuroinformatic data sets, our research suggests that physiological variation in gene expression during neurodevelopment is eventually translated into a continuum of global network metrics that serve as intermediate phenotypes. Within this framework, we suggest that organization of functional brain networks may result, in part, from an adaptive trade-off between efficiency and resilience, ultimately culminating in a phenotypic diversity that encompasses dimensions such as emotional regulation and cognitive function.
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23
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Bruno JL, Hosseini SMH, Saggar M, Quintin EM, Raman MM, Reiss AL. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics. Cereb Cortex 2017; 27:2249-2259. [PMID: 27009247 DOI: 10.1093/cercor/bhw055] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.
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Affiliation(s)
- Jennifer Lynn Bruno
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - S M Hadi Hosseini
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Manish Saggar
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Eve-Marie Quintin
- School and Applied Child Psychology Program, McGill University, Montreal, QC, CanadaH3A 1Y2
| | - Mira Michelle Raman
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Allan L Reiss
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA.,Department of Radiology.,Department of Pediatrics, Stanford University, Stanford, CA 94305, USA
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24
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Liao X, Vasilakos AV, He Y. Small-world human brain networks: Perspectives and challenges. Neurosci Biobehav Rev 2017; 77:286-300. [PMID: 28389343 DOI: 10.1016/j.neubiorev.2017.03.018] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/19/2017] [Accepted: 03/31/2017] [Indexed: 12/15/2022]
Abstract
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
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Affiliation(s)
- Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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25
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Kesler SR, Adams M, Packer M, Rao V, Henneghan AM, Blayney DW, Palesh O. Disrupted brain network functional dynamics and hyper-correlation of structural and functional connectome topology in patients with breast cancer prior to treatment. Brain Behav 2017; 7:e00643. [PMID: 28293478 PMCID: PMC5346525 DOI: 10.1002/brb3.643] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 12/12/2016] [Accepted: 12/20/2016] [Indexed: 01/09/2023] Open
Abstract
INTRODUCTION Several previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non-CNS cancers. METHODS Using a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29-65 and 50 frequency-matched healthy female controls who underwent anatomic and resting-state functional MRI as well as cognitive testing. RESULTS Compared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics (p = .046) and cognitive functioning (p < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering (p < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls (p = .03). This hyper-correlation between structural and functional topologies was significantly associated with cognitive dysfunction (p = .005). CONCLUSIONS Our findings could not be accounted for by psychological distress and suggest that non-CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor-induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.
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Affiliation(s)
- Shelli R Kesler
- Department of Neuro-oncology University of Texas MD Anderson Cancer Center Houston TX USA
| | - Marjorie Adams
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA
| | - Melissa Packer
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA
| | - Vikram Rao
- Department of Neuro-oncology University of Texas MD Anderson Cancer Center Houston TX USA
| | | | - Douglas W Blayney
- Division of Medical Oncology Stanford University School of Medicine Stanford CA USA
| | - Oxana Palesh
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA
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26
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Lee H, Kang H, Chung MK, Lim S, Kim BN, Lee DS. Integrated multimodal network approach to PET and MRI based on multidimensional persistent homology. Hum Brain Mapp 2016; 38:1387-1402. [PMID: 27859919 DOI: 10.1002/hbm.23461] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Revised: 10/17/2016] [Accepted: 11/02/2016] [Indexed: 12/13/2022] Open
Abstract
Finding underlying relationships among multiple imaging modalities in a coherent fashion is one of the challenging problems in multimodal analysis. In this study, we propose a novel approach based on multidimensional persistence. In the extension of the previous threshold-free method of persistent homology, we visualize and discriminate the topological change of integrated brain networks by varying not only threshold but also mixing ratio between two different imaging modalities. The multidimensional persistence is implemented by a new bimodal integration method called 1D projection. When the mixing ratio is predefined, it constructs an integrated edge weight matrix by projecting two different connectivity information onto the one dimensional shared space. We applied the proposed methods to PET and MRI data from 23 attention deficit hyperactivity disorder (ADHD) children, 21 autism spectrum disorder (ASD), and 10 pediatric control subjects. From the results, we found that the brain networks of ASD, ADHD children and controls differ, with ASD and ADHD showing asymmetrical changes of connected structures between metabolic and morphological connectivities. The difference of connected structure between ASD and the controls was mainly observed in the metabolic connectivity. However, ADHD showed the maximum difference when two connectivity information were integrated with the ratio 0.6. These results provide a multidimensional homological understanding of disease-related PET and MRI networks that disclose the network association with ASD and ADHD. Hum Brain Mapp 38:1387-1402, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Hyekyoung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea
| | - Hyejin Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.,Data Science and Knowledge Creation Research Center, Seoul National University, Seoul, Korea
| | - Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.,Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin
| | - Seonhee Lim
- Department of Mathematical Sciences, Seoul National University College of Natural Sciences, Seoul, Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea
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27
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The effect of IDH1 mutation on the structural connectome in malignant astrocytoma. J Neurooncol 2016; 131:565-574. [PMID: 27848136 DOI: 10.1007/s11060-016-2328-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 11/08/2016] [Indexed: 12/11/2022]
Abstract
Mutation of the IDH1 gene is associated with differences in malignant astrocytoma growth characteristics that impact phenotypic severity, including cognitive impairment. We previously demonstrated greater cognitive impairment in patients with IDH1 wild type tumor compared to those with IDH1 mutant, and therefore we hypothesized that brain network organization would be lower in patients with wild type tumors. Volumetric, T1-weighted MRI scans were obtained retrospectively from 35 patients with IDH1 mutant and 32 patients with wild type malignant astrocytoma (mean age = 45 ± 14 years) and used to extract individual level, gray matter connectomes. Graph theoretical analysis was then applied to measure efficiency and other connectome properties for each patient. Cognitive performance was categorized as impaired or not and random forest classification was used to explore factors associated with cognitive impairment. Patients with wild type tumor demonstrated significantly lower network efficiency in several medial frontal, posterior parietal and subcortical regions (p < 0.05, corrected for multiple comparisons). Patients with wild type tumor also demonstrated significantly higher incidence of cognitive impairment (p = 0.03). Random forest analysis indicated that network efficiency was inversely, though nonlinearly associated with cognitive impairment in both groups (p < 0.0001). Cognitive reserve appeared to mediate this relationship in patients with mutant tumor suggesting greater neuroplasticity and/or benefit from neuroprotective factors. Tumor volume was the greatest contributor to cognitive impairment in patients with wild type tumor, supporting our hypothesis that greater lesion momentum between grades may cause more disconnection of core neurocircuitry and consequently lower efficiency of information processing.
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Sarubbo S, De Benedictis A, Merler S, Mandonnet E, Barbareschi M, Dallabona M, Chioffi F, Duffau H. Structural and functional integration between dorsal and ventral language streams as revealed by blunt dissection and direct electrical stimulation. Hum Brain Mapp 2016; 37:3858-3872. [PMID: 27258125 PMCID: PMC6867442 DOI: 10.1002/hbm.23281] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 05/07/2016] [Accepted: 05/24/2016] [Indexed: 01/24/2023] Open
Abstract
The most accepted framework of language processing includes a dorsal phonological and a ventral semantic pathway, connecting a wide network of distributed cortical hubs. However, the cortico-subcortical connectivity and the reciprocal anatomical relationships of this dual-stream system are not completely clarified. We performed an original blunt microdissection of 10 hemispheres with the exposition of locoregional short fibers and six long-range fascicles involved in language elaboration. Special attention was addressed to the analysis of termination sites and anatomical relationships between long- and short-range fascicles. We correlated these anatomical findings with a topographical analysis of 93 functional responses located at the terminal sites of the language bundles, collected by direct electrical stimulation in 108 right-handers. The locations of phonological and semantic paraphasias, verbal apraxia, speech arrest, pure anomia, and alexia were statistically analyzed, and the respective barycenters were computed in the MNI space. We found that terminations of main language bundles and functional responses have a wider distribution in respect to the classical definition of language territories. Our analysis showed that dorsal and ventral streams have a similar anatomical layer organization. These pathways are parallel and relatively segregated over their subcortical course while their terminal fibers are strictly overlapped at the cortical level. Finally, the anatomical features of the U-fibers suggested a role of locoregional integration between the phonological, semantic, and executive subnetworks of language, in particular within the inferoventral frontal lobe and the temporoparietal junction, which revealed to be the main criss-cross regions between the dorsal and ventral pathways. Hum Brain Mapp 37:3858-3872, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Silvio Sarubbo
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.
- Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.
| | - Alessandro De Benedictis
- Department of Neuroscience and Neurorehabilitation, Neurosurgery Unit, Bambino Gesù Children's Hospital - IRCCS, 4 Piazza Sant'Onofrio, Roma, 00165, Italy
| | - Stefano Merler
- Bruno Kessler Foundation (FBK), 18 via Sommarive, Trento, 38123, Italy
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisiere Hospital, 2 Rue Ambroise Pare, Paris, 75010, France
| | - Mattia Barbareschi
- Department of Histopathology, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Monica Dallabona
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Franco Chioffi
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
- Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui De Chauliac, Montpellier University Medical Center, 80 Av Augustin Fliche, Montpellier, 34295, France
- Institute for Neuroscience of Montpellier, INSERM U1051, Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors," Saint Eloi Hospital, Montpellier, France
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Zhou ZC, Salzwedel AP, Radtke-Schuller S, Li Y, Sellers KK, Gilmore JH, Shih YYI, Fröhlich F, Gao W. Resting state network topology of the ferret brain. Neuroimage 2016; 143:70-81. [PMID: 27596024 DOI: 10.1016/j.neuroimage.2016.09.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 08/17/2016] [Accepted: 09/01/2016] [Indexed: 12/22/2022] Open
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function.
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Affiliation(s)
- Zhe Charles Zhou
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Andrew P Salzwedel
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Susanne Radtke-Schuller
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Yuhui Li
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Kristin K Sellers
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Yen-Yu Ian Shih
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Small Animal Imaging Facility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Flavio Fröhlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
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Matsuda H. MRI morphometry in Alzheimer's disease. Ageing Res Rev 2016; 30:17-24. [PMID: 26812213 DOI: 10.1016/j.arr.2016.01.003] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 01/18/2016] [Accepted: 01/20/2016] [Indexed: 12/12/2022]
Abstract
MRI based evaluation of brain atrophy is regarded as a valid method to stage the disease and to assess progression in Alzheimer's disease (AD). Volumetric software programs have made it possible to quantify gray matter in the human brain in an automated fashion. At present, voxel based morphometry (VBM) is easily applicable to the routine clinical procedure with a short execution time. The importance of the VBM approach is that it is not biased to one particular structure and is able to assess anatomical differences throughout the brain. Stand-alone VBM software running on Windows, Voxel-based Specific Regional analysis system for AD (VSRAD), has been widely used in the clinical diagnosis of AD in Japan. On the other hand, recent application of graph theory to MRI has made it possible to analyze changes in structural connectivity in AD.
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Petersen A, Zhao J, Carmichael O, Müller HG. Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks. Brain Connect 2016; 6:540-7. [PMID: 27267074 DOI: 10.1089/brain.2016.0420] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.
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Affiliation(s)
- Alexander Petersen
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
| | - Jianyang Zhao
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
| | - Owen Carmichael
- 2 Pennington Biomedical Research Center, Louisiana State University , Baton Rouge, Louisiana
| | - Hans-Georg Müller
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
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Abstract
OBJECTIVES Blast explosions are the most frequent mechanism of traumatic brain injury (TBI) in recent wars, but little is known about their long-term effects. METHODS Functional connectivity (FC) was measured in 17 veterans an average of 5.46 years after their most serious blast related TBI, and in 15 demographically similar veterans without TBI or blast exposure. Subcortical FC was measured in bilateral caudate, putamen, and globus pallidus. The default mode and fronto-parietal networks were also investigated. RESULTS In subcortical regions, between-groups t tests revealed altered FC from the right putamen and right globus pallidus. However, following analysis of covariance (ANCOVA) with age, depression (Center for Epidemiologic Studies Depression Scale), and posttraumatic stress disorder symptom (PTSD Checklist - Civilian version) measures, significant findings remained only for the right globus pallidus with anticorrelation in bilateral temporal occipital fusiform cortex, occipital fusiform gyrus, lingual gyrus, and cerebellum, as well as the right occipital pole. No group differences were found for the default mode network. Although reduced FC was found in the fronto-parietal network in the TBI group, between-group differences were nonsignificant after the ANCOVA. CONCLUSIONS FC of the globus pallidus is altered years after exposure to blast related TBI. Future studies are necessary to explore the trajectory of changes in FC in subcortical regions after blast TBI, the effects of isolated versus repetitive blast-related TBI, and the relation to long-term outcomes in veterans. (JINS, 2016, 22, 631-642).
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Gaiteri C, Mostafavi S, Honey CJ, De Jager PL, Bennett DA. Genetic variants in Alzheimer disease - molecular and brain network approaches. Nat Rev Neurol 2016; 12:413-27. [PMID: 27282653 PMCID: PMC5017598 DOI: 10.1038/nrneurol.2016.84] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.
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Affiliation(s)
- Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
| | - Sara Mostafavi
- Department of Statistics, and Medical Genetics; Centre for Molecular and Medicine and Therapeutics, University of British Columbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z 4H4, Canada
| | - Christopher J Honey
- Department of Psychology, University of Toronto, 100 St. George Street, 4th Floor Sidney Smith Hall, Toronto, Ontario M5S 3G3, Canada
| | - Philip L De Jager
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
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Bernhardt BC, Bernasconi N, Hong SJ, Dery S, Bernasconi A. Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy. Cereb Cortex 2016; 26:3237-48. [PMID: 26223262 PMCID: PMC4898674 DOI: 10.1093/cercor/bhv166] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Temporal lobe epilepsy (TLE) is the most frequent drug-resistant epilepsy in adults and commonly associated with variable degrees of mesiotemporal atrophy on magnetic resonance imaging (MRI). Analyses of inter-regional connectivity have unveiled disruptions in large-scale cortico-cortical networks; little is known about the topological organization of the mesiotemporal lobe, the limbic subnetwork central to the disorder. We generated covariance networks based on high-resolution MRI surface-shape descriptors of the hippocampus, entorhinal cortex, and amygdala in 134 TLE patients and 45 age- and sex-matched controls. Graph-theoretical analysis revealed increased path length and clustering in patients, suggesting a shift toward a more regularized arrangement; findings were reproducible after split-half assessment and across 2 parcellation schemes. Analysis of inter-regional correlations and module participation showed increased within-structure covariance, but decreases between structures, particularly with regards to the hippocampus and amygdala. While higher clustering possibly reflects topological consequences of axonal sprouting, decreases in interstructure covariance may be a consequence of disconnection within limbic circuitry. Preoperative network parameters, specifically the segregation of the ipsilateral hippocampus, predicted long-term seizure freedom after surgery.
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Affiliation(s)
- Boris C. Bernhardt
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
- Deparment of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Sebastian Dery
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
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Kesler SR, Blayney DW. Neurotoxic Effects of Anthracycline- vs Nonanthracycline-Based Chemotherapy on Cognition in Breast Cancer Survivors. JAMA Oncol 2016; 2:185-92. [PMID: 26633037 DOI: 10.1001/jamaoncol.2015.4333] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Chemotherapy exposure is a known risk factor for cancer-related cognitive impairments. Anthracycline-based regimens are commonly used chemotherapies that have been shown to be associated with cognitive impairment and brain changes in clinical studies. OBJECTIVE To directly compare the effects of anthracycline and nonanthracycline regimens on cognitive status and functional brain connectivity. DESIGN, SETTING, AND PARTICIPANTS In this observational study, we retrospectively examined cognitive and resting state functional magnetic resonance imaging data acquired from 62 primary breast cancer survivors (mean [SD] age, 54.7 [8.5] years) who were more than 2 years off-therapy, on average. Twenty of these women received anthracycline-based chemotherapy as part of their primary treatment, 19 received nonanthracycline regimens, and 23 did not receive any chemotherapy. Participants were enrolled at a single academic institution (Stanford University) from 2008 to 2014, and the study analyses were performed at this time. MAIN OUTCOMES AND MEASURES Cognitive status was measured using standardized neuropsychological tests, and functional brain connectivity was evaluated using resting state functional magnetic resonance imaging with a focus on the brain's default mode network. RESULTS The anthracycline group demonstrated significantly lower verbal memory performance including immediate recall (F = 3.73; P = .03) and delayed recall (F = 11.11; P < .001) as well as lower left precuneus connectivity (F = 7.48; P = .001) compared with the other 2 groups. Patient-reported outcomes related to cognitive dysfunction (F = 7.27; P = .002) and psychological distress (F = 5.64; P = .006) were similarly elevated in both chemotherapy groups compared with the non-chemotherapy-treated controls. CONCLUSIONS AND RELEVANCE These results suggest that anthracyclines may have greater negative effects than nonanthracycline regimens on particular cognitive domains and brain network connections. Both anthracycline and nonanthracycline regimens may have nonspecific effects on other cognitive domains as well as certain patient reported outcomes. Further research is needed to identify potential methods for protecting the brain against the effects of various chemotherapeutic agents.
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Affiliation(s)
- Shelli R Kesler
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston
| | - Douglas W Blayney
- Division of Medical Oncology, Stanford University School of Medicine, Stanford, California
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Wang T, Wang K, Qu H, Zhou J, Li Q, Deng Z, Du X, Lv F, Ren G, Guo J, Qiu J, Xie P. Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks. Sci Rep 2016; 6:27964. [PMID: 27302485 PMCID: PMC4908416 DOI: 10.1038/srep27964] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 05/26/2016] [Indexed: 01/13/2023] Open
Abstract
Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression.
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Affiliation(s)
- Tao Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China
| | - Kangcheng Wang
- School of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Hang Qu
- Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.,Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jingjing Zhou
- Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhou Deng
- School of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xue Du
- School of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Gaoping Ren
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China
| | - Jing Guo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Peng Xie
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China.,Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China
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Caeyenberghs K, Taymans T, Wilson PH, Vanderstraeten G, Hosseini H, van Waelvelde H. Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism. Dev Sci 2016; 19:599-612. [DOI: 10.1111/desc.12424] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 01/29/2016] [Indexed: 01/18/2023]
Affiliation(s)
- Karen Caeyenberghs
- School of Psychology; Faculty of Health Sciences; Australian Catholic University; Australia
- School of Psychological Sciences; Monash Biomedical Imaging lab; Monash University; Australia
| | - Tom Taymans
- Department of Physical Therapy and Motor Rehabilitation; Faculty of Medicine and Health Sciences; University of Ghent; Belgium
| | - Peter H. Wilson
- School of Psychology; Faculty of Health Sciences; Australian Catholic University; Australia
| | - Guy Vanderstraeten
- Department of Physical Therapy and Motor Rehabilitation; Faculty of Medicine and Health Sciences; University of Ghent; Belgium
| | - Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences; School of Medicine; Stanford University; USA
| | - Hilde van Waelvelde
- Department of Physical Therapy and Motor Rehabilitation; Faculty of Medicine and Health Sciences; University of Ghent; Belgium
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Hasson U, Andric M, Atilgan H, Collignon O. Congenital blindness is associated with large-scale reorganization of anatomical networks. Neuroimage 2016; 128:362-372. [PMID: 26767944 PMCID: PMC4767220 DOI: 10.1016/j.neuroimage.2015.12.048] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 12/29/2015] [Accepted: 12/30/2015] [Indexed: 11/15/2022] Open
Abstract
Blindness is a unique model for understanding the role of experience in the development of the brain's functional and anatomical architecture. Documenting changes in the structure of anatomical networks for this population would substantiate the notion that the brain's core network-level organization may undergo neuroplasticity as a result of life-long experience. To examine this issue, we compared whole-brain networks of regional cortical-thickness covariance in early blind and matched sighted individuals. This covariance is thought to reflect signatures of integration between systems involved in similar perceptual/cognitive functions. Using graph-theoretic metrics, we identified a unique mode of anatomical reorganization in the blind that differed from that found for sighted. This was seen in that network partition structures derived from subgroups of blind were more similar to each other than they were to partitions derived from sighted. Notably, after deriving network partitions, we found that language and visual regions tended to reside within separate modules in sighted but showed a pattern of merging into shared modules in the blind. Our study demonstrates that early visual deprivation triggers a systematic large-scale reorganization of whole-brain cortical-thickness networks, suggesting changes in how occipital regions interface with other functional networks in the congenitally blind.
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Affiliation(s)
- Uri Hasson
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
| | - Michael Andric
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | - Hicret Atilgan
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | - Olivier Collignon
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy; CERNEC, Département de Psychologie, Université de Montréal, Montreal, QC, Canada
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Estimating individual contribution from group-based structural correlation networks. Neuroimage 2015; 120:274-84. [PMID: 26162553 DOI: 10.1016/j.neuroimage.2015.07.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Revised: 06/16/2015] [Accepted: 07/03/2015] [Indexed: 12/22/2022] Open
Abstract
Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.
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Magalhães R, Marques P, Soares J, Alves V, Sousa N. The Impact of Normalization and Segmentation on Resting-State Brain Networks. Brain Connect 2015; 5:166-76. [DOI: 10.1089/brain.2014.0292] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
- Department of Informatics, University of Minho, Braga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
| | - José Soares
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
| | - Victor Alves
- Department of Informatics, University of Minho, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
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41
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Gong Q, He Y. Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry 2015; 77:223-235. [PMID: 25444171 DOI: 10.1016/j.biopsych.2014.08.009] [Citation(s) in RCA: 321] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Revised: 07/27/2014] [Accepted: 08/16/2014] [Indexed: 12/31/2022]
Abstract
Depression is a multifactorial disorder with clinically heterogeneous features involving disturbances of mood and cognitive function. Noninvasive neuroimaging studies have provided rich evidence that these behavioral deficits in depression are associated with structural and functional abnormalities in specific regions and connections. Recent advances in brain connectomics through the use of graph theory highlight disrupted topological organization of large-scale functional and structural brain networks in depression, involving global topology (e.g., local clustering, shortest-path lengths, and global and local efficiencies), modular structure, and network hubs. These system-level disruptions show important correlates with genetic and environmental factors, which provide an integrative perspective on mood and cognitive deficits in depressive syndrome. Moreover, research suggests that the pathologic networks associated with depression represent potentially valuable biomarkers for early detection of this disorder and they are likely to be regulated and recalibrated by using pharmacologic, psychological, and brain stimulation therapies. These connectome-based imaging studies present new opportunities to reconceptualize the pathogenesis of depression, improve our knowledge of the biological mechanisms of therapeutic effects, and identify appropriate stimulation targets to optimize the clinical response in depression treatment. Here, we summarize the current findings and historical understanding of structural and functional connectomes in depression, focusing on graph analyses of depressive brain networks. We also consider methodological factors such as sample heterogeneity and poor test-retest reliability of recordings due to physiological, head motion, and imaging artifacts to discuss result inconsistencies among studies. We conclude with suggestions for future research directions on the emerging field of imaging connectomics in depression.
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Affiliation(s)
- Qiyong Gong
- Huaxi Magnetic Resonance Research Center, Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry , Yale University School of Medicine, New Haven, Connecticut; Department of Psychiatry, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group/McGovern Institute for Brain Research; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China..
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Phillips DJ, McGlaughlin A, Ruth D, Jager LR, Soldan A. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods. NEUROIMAGE-CLINICAL 2015; 7:377-90. [PMID: 25984446 PMCID: PMC4429220 DOI: 10.1016/j.nicl.2015.01.007] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 12/03/2014] [Accepted: 01/09/2015] [Indexed: 11/30/2022]
Abstract
Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD.
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Affiliation(s)
- David J Phillips
- Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA
| | - Alec McGlaughlin
- Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA
| | - David Ruth
- Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA
| | - Leah R Jager
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Pineda-Pardo JÁ, Martínez K, Solana AB, Hernández-Tamames JA, Colom R, del Pozo F. Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered. Brain Topogr 2014; 28:187-96. [PMID: 25194331 DOI: 10.1007/s10548-014-0393-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 08/25/2014] [Indexed: 11/28/2022]
Abstract
Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very-large-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal-ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.
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Affiliation(s)
- José Ángel Pineda-Pardo
- Laboratory of Neuroimaging, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo De Alarcón, Spain,
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Kesler SR. Default mode network as a potential biomarker of chemotherapy-related brain injury. Neurobiol Aging 2014; 35 Suppl 2:S11-9. [PMID: 24913897 PMCID: PMC4120757 DOI: 10.1016/j.neurobiolaging.2014.03.036] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 03/11/2014] [Accepted: 03/14/2014] [Indexed: 01/01/2023]
Abstract
Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these "chemobrain" effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiological effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline.
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Affiliation(s)
- Shelli R Kesler
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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Hosseini SMH, Kramer JH, Kesler SR. Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease. Front Aging Neurosci 2014; 6:231. [PMID: 25206335 PMCID: PMC4143724 DOI: 10.3389/fnagi.2014.00231] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 08/11/2014] [Indexed: 01/18/2023] Open
Abstract
Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.
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Affiliation(s)
- S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine Stanford, CA, USA
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Shelli R Kesler
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine Stanford, CA, USA ; Stanford Cancer Institute Palo Alto, CA, USA
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Eddin AS, Wang J, Wu W, Sargolzaei S, Bjornson B, Jones RA, Gaillard WD, Adjouadi M. The effects of pediatric epilepsy on a language connectome. Hum Brain Mapp 2014; 35:5996-6010. [PMID: 25082062 DOI: 10.1002/hbm.22600] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Revised: 06/23/2014] [Accepted: 07/22/2014] [Indexed: 01/03/2023] Open
Abstract
This study introduces a new approach for assessing the effects of pediatric epilepsy on a language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI. An auditory word definition decision task paradigm was used to activate the language network for 29 patients and 30 controls. Evaluations illustrated that pediatric epilepsy is associated with a network efficiency reduction. Patients showed a propensity to inefficiently use the whole brain network to perform the language task; whereas, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was performed. The analysis revealed substantial global network feature differences between the patients and controls for the extent of activation network. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency toward randomness. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. We finally showed that a clustering scheme was able to fairly separate the subjects into their respective patient or control groups. The clustering was initiated using local and global nodal measurements. Compared to the intensity of activation network, the extent of activation network clustering demonstrated better precision. This ascertained that the network differences presented by the networks were associated with pediatric epilepsy.
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Affiliation(s)
- Anas Salah Eddin
- Department of Computer Science and Information Technology, Florida Polytechnic University, Lakeland, Florida; Department of Electrical and Computer Engineering, Florida International University, Miami, Florida
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Kesler SR, Gugel M, Pritchard-Berman M, Lee C, Kutner E, Hosseini SH, Dahl G, Lacayo N. Altered resting state functional connectivity in young survivors of acute lymphoblastic leukemia. Pediatr Blood Cancer 2014; 61:1295-9. [PMID: 24619953 PMCID: PMC4028071 DOI: 10.1002/pbc.25022] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Accepted: 02/14/2014] [Indexed: 12/21/2022]
Abstract
BACKGROUND Chemotherapy treatment for pediatric acute lymphoblastic leukemia (ALL) has been associated with long-term cognitive impairments in some patients. However, the neurobiologic mechanisms underlying these impairments, particularly in young survivors, are not well understood. This study aimed to examine intrinsic functional brain connectivity in pediatric ALL and its relationship with cognitive status. PROCEDURE We obtained resting state functional magnetic resonance imaging (rsfMRI) and cognitive testing data from 15 ALL survivors age 8-15 years and 14 matched healthy children. The ALL group had a history of intrathecal chemotherapy treatment but were off-therapy for at least 6 months at the time of enrollment. We used seed-based analyses to compare intrinsic functional brain network connectivity between the groups. We also explored correlations between connectivity and cognitive performance, demographic, medical, and treatment variables. RESULTS We demonstrated significantly reduced connectivity between bilateral hippocampus, left inferior occipital, left lingual gyrus, bilateral calcarine sulcus, and right amygdala in the ALL group compared to controls. The ALL group also showed regions of functional hyperconnectivity including right lingual gyrus, precuneus, bilateral superior occipital lobe, and right inferior occipital lobe. Functional hypoconnectivity was associated with reduced cognitive function as well as younger age at diagnosis in the ALL group. CONCLUSIONS This is the first study to demonstrate that intrinsic functional brain connectivity is disrupted in pediatric ALL following chemotherapy treatment. These results help explain cognitive dysfunction even when objective test performance is seemingly normal. Children diagnosed at a younger age may show increased vulnerability to altered functional brain connectivity.
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Affiliation(s)
- Shelli R. Kesler
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
,Correspondence to: Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, MC5795, Stanford, CA 94305-5795.
| | - Meike Gugel
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Mika Pritchard-Berman
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Clement Lee
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Emily Kutner
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - S.M. Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Gary Dahl
- Department of Pediatrics—Hematology/Oncology, Lucile Packard Children’s Hospital, Palo Alto, California
| | - Norman Lacayo
- Department of Pediatrics—Hematology/Oncology, Lucile Packard Children’s Hospital, Palo Alto, California
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Linking DMN connectivity to episodic memory capacity: what can we learn from patients with medial temporal lobe damage? NEUROIMAGE-CLINICAL 2014; 5:188-96. [PMID: 25068108 PMCID: PMC4110351 DOI: 10.1016/j.nicl.2014.05.008] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 05/14/2014] [Accepted: 05/14/2014] [Indexed: 11/24/2022]
Abstract
Computational models predict that focal damage to the Default Mode Network (DMN) causes widespread decreases and increases of functional DMN connectivity. How such alterations impact functioning in a specific cognitive domain such as episodic memory remains relatively unexplored. Here, we show in patients with unilateral medial temporal lobe epilepsy (mTLE) that focal structural damage leads indeed to specific patterns of DMN functional connectivity alterations, specifically decreased connectivity between both medial temporal lobes (MTLs) and the posterior part of the DMN and increased intrahemispheric anterior–posterior connectivity. Importantly, these patterns were associated with better and worse episodic memory capacity, respectively. These distinct patterns, shown here for the first time, suggest that a close dialogue between both MTLs and the posterior components of the DMN is required to fully express the extensive repertoire of episodic memory abilities. Focal structural damage correlates with widespread functional change in DMN in mTLE. Greater DMN connectivity alterations reflect worse clinical memory measures. Structural integrity moderates influence of functional connectivity on memory. Interhemispheric integration of MTL into posterior DMN may be key to better memory.
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