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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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Frieske J, Pareto D, García-Vidal A, Cuypers K, Meesen RL, Alonso J, Arévalo MJ, Galán I, Renom M, Vidal-Jordana Á, Auger C, Montalban X, Rovira À, Sastre-Garriga J. Can cognitive training reignite compensatory mechanisms in advanced multiple sclerosis patients? An explorative morphological network approach. Neuroscience 2022; 495:86-96. [DOI: 10.1016/j.neuroscience.2022.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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Hu H, Jiang Y, Xia M, Tang Y, Zhang T, Cui H, Wang J, Xu L, Curtin A, Sheng J, Cao X, Guo Q, Jia Y, Li C, Wang Z, Luo C, Wang J. Functional reconfiguration of cerebellum-cerebral neural loop in schizophrenia following electroconvulsive therapy. Psychiatry Res Neuroimaging 2022; 320:111441. [PMID: 35085957 DOI: 10.1016/j.pscychresns.2022.111441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 11/15/2021] [Accepted: 01/17/2022] [Indexed: 11/26/2022]
Abstract
Recent evidence highlights the role of the cerebellum-cerebral loop in the pathophysiology of schizophrenia (SZ). Electroconvulsive therapy (ECT) is clinically applied to augment the effect of antipsychotic drugs. The study aims to address whether the cerebellum-cerebral loop is involved in the mechanisms of ECT's augmentation effect. Forty-two SZ patients and 23 healthy controls (HC) were recruited and scanned using resting-state functional MRI (rs-fMRI). Twenty-one patients received modified ECT plus antipsychotics (MSZ group), and 21 patients took antipsychotics only (DSZ group). All patients were re-scanned four weeks later. Brain functional network was constructed according to the graph theory. The sub-network exhibited longitudinal changes after ECT or medications were constructed. For the MSZ group, a sub-network involving default-mode network and cerebellum showed significant longitudinal changes. For the DSZ group, a different sub-network involving the thalamus, frontal and occipital cortex was found to be altered in the follow-up scan. In addition, the changing FC of the left cerebellar crus2 region was correlated with the changing scores of the psychotic symptoms only in the MSZ group but not in the DSZ group. In conclusion, the cerebral-cerebellum loop is possibly involved in the antipsychotic mechanisms of ECT for schizophrenia.
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Affiliation(s)
- Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yuchao Jiang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Junjie Wang
- Institute of Mental Health, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu, 215137, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Adrian Curtin
- School of Biomedical Engineering & Health Sciences, Drexel University, Philadelphia, PA 19104, United States; Med-X Institute, Shanghai Jiao Tong University, Shanghai 200300, China
| | - Jianhua Sheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Xinyi Cao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Qian Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yuping Jia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China.
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.
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54
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Shang Y, Yang Y, Zheng G, Zhao Z, Wang Y, Yang L, Han L, Yao Z, Hu B. Aberrant functional network topology and effective connectivity in burnout syndrome. Clin Neurophysiol 2022; 138:163-172. [DOI: 10.1016/j.clinph.2022.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 12/11/2022]
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55
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Luo S, Zhu Y, Han S. Functional connectome fingerprint of holistic-analytic cultural style. Soc Cogn Affect Neurosci 2022; 17:172-186. [PMID: 34160613 PMCID: PMC8847908 DOI: 10.1093/scan/nsab080] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/25/2021] [Accepted: 06/23/2021] [Indexed: 11/14/2022] Open
Abstract
Although research in the field of cultural psychology and cultural neuroscience has revealed that culture is an important factor related to the human behaviors and neural activities in various tasks, it remains unclear how different brain regions organize together to construct a topological network for the representation of individual's cultural tendency. In this study, we examined the hypothesis that resting-state brain network properties can reflect individual's cultural background or tendency. By combining the methods of resting-state magnetic resonance imaging and graph theoretical analysis, significant cultural differences between participants from Eastern and Western cultures were found in the degree and global efficiency of regions mainly within the default mode network and subcortical network. Furthermore, the holistic-analytic thinking style, as a cultural value, provided a partial explanation for the cultural differences on various nodal metrics. Validation analyses further confirmed that these network properties effectively predicted the tendency of holistic-analytic cultural style within a group (r = 0.23) and accurately classified cultural groups (65%). The current study establishes a neural connectome representation of holistic-analytic cultural style including the topological brain network properties of regions in the default mode network, the basal ganglia and amygdala, which enable accurate cultural group membership classification.
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Affiliation(s)
- Siyang Luo
- Department of Psychology, Guangdong Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou 510006, China
| | - Yiyi Zhu
- Department of Psychology, Guangdong Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou 510006, China
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
| | - Shihui Han
- School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100080, China
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Abstract
OBJECTIVE Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. APPROACH Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain. MAIN RESULTS (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. SIGNIFICANCE EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.
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57
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Cherkasova MV, Fu JF, Jarrett M, Johnson P, Abel S, Tam R, Rauscher A, Sossi V, Kolind S, Li DKB, Sadovnick AD, Machan L, Girard JM, Emond F, Vosoughi R, Traboulsee A, Stoessl AJ. Cortical morphology predicts placebo response in multiple sclerosis. Sci Rep 2022; 12:732. [PMID: 35031632 PMCID: PMC8760243 DOI: 10.1038/s41598-021-04462-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/22/2021] [Indexed: 11/27/2022] Open
Abstract
Despite significant insights into the neural mechanisms of acute placebo responses, less is known about longer-term placebo responses, such as those seen in clinical trials, or their interactions with brain disease. We examined brain correlates of placebo responses in a randomized trial of a then controversial and now disproved endovascular treatment for multiple sclerosis. Patients received either balloon or sham extracranial venoplasty and were followed for 48 weeks. Venoplasty had no therapeutic effect, but a subset of both venoplasty- and sham-treated patients reported a transient improvement in health-related quality of life, suggesting a placebo response. Placebo responders did not differ from non-responders in total MRI T2 lesion load, count or location, nor were there differences in normalized brain volume, regional grey or white matter volume or cortical thickness (CT). However, responders had higher lesion activity. Graph theoretical analysis of CT covariance showed that non-responders had a more small-world-like CT architecture. In non-responders, lesion load was inversely associated with CT in somatosensory, motor and association areas, precuneus, and insula, primarily in the right hemisphere. In responders, lesion load was unrelated to CT. The neuropathological process in MS may produce in some a cortical configuration less capable of generating sustained placebo responses.
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Affiliation(s)
- Mariya V Cherkasova
- Department of Psychology, University of British Columbia, Vancouver, Canada. .,Department of Psychology, West Virginia University, 2128 Life Science Building, Morgantown, WV, 26506, USA.
| | - Jessie F Fu
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Michael Jarrett
- Population Data BC, University of British Columbia, Vancouver, BC, Canada
| | - Poljanka Johnson
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada.,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Depatment of Pediatrics (Division of Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Shannon Kolind
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - A Dessa Sadovnick
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Lindsay Machan
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - J Marc Girard
- Centre Hospitalier de L'Université de Montréal, Montréal, QC, Canada
| | - Francois Emond
- CHU de Québec-Université Laval, Hôpital de L'Enfant-Jésus, Québec, Canada
| | - Reza Vosoughi
- Department of Internal Medicine (Neurology), University of Manitoba, Winnipeg, Canada
| | - Anthony Traboulsee
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - A Jon Stoessl
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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Tur C, Grussu F, De Angelis F, Prados F, Kanber B, Calvi A, Eshaghi A, Charalambous T, Cortese R, Chard DT, Chataway J, Thompson AJ, Ciccarelli O, Gandini Wheeler-Kingshott CAM. Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique. Neuroimage Clin 2021; 33:102904. [PMID: 34875458 PMCID: PMC8654632 DOI: 10.1016/j.nicl.2021.102904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/20/2021] [Accepted: 11/29/2021] [Indexed: 11/20/2022]
Abstract
Predicting disability in progressive multiple sclerosis (MS) is extremely challenging. Although there is some evidence that the spatial distribution of white matter (WM) lesions may play a role in disability accumulation, the lack of well-established quantitative metrics that characterise these aspects of MS pathology makes it difficult to assess their relevance for clinical progression. This study introduces a novel approach, called SPACE-MS, to quantitatively characterise spatial distributional features of brain MS lesions, so that these can be assessed as predictors of disability accumulation. In SPACE-MS, the covariance matrix of the spatial positions of each patient's lesional voxels is computed and its eigenvalues extracted. These are combined to derive rotationally-invariant metrics known to be common and robust descriptors of ellipsoid shape such as anisotropy, planarity and sphericity. Additionally, SPACE-MS metrics include a neuraxis caudality index, which we defined for the whole-brain lesion mask as well as for the most caudal brain lesion. These indicate how distant from the supplementary motor cortex (along the neuraxis) the whole-brain mask or the most caudal brain lesions are. We applied SPACE-MS to data from 515 patients involved in three studies: the MS-SMART (NCT01910259) and MS-STAT1 (NCT00647348) secondary progressive MS trials, and an observational study of primary and secondary progressive MS. Patients were assessed on motor and cognitive disability scales and underwent structural brain MRI (1.5/3.0 T), at baseline and after 2 years. The MRI protocol included 3DT1-weighted (1x1x1mm3) and 2DT2-weighted (1x1x3mm3) anatomical imaging. WM lesions were semiautomatically segmented on the T2-weighted scans, deriving whole-brain lesion masks. After co-registering the masks to the T1 images, SPACE-MS metrics were calculated and analysed through a series of multiple linear regression models, which were built to assess the ability of spatial distributional metrics to explain concurrent and future disability after adjusting for confounders. Patients whose WM lesions laid more caudally along the neuraxis or were more isotropically distributed in the brain (i.e. with whole-brain lesion masks displaying a high sphericity index) at baseline had greater motor and/or cognitive disability at baseline and over time, independently of brain lesion load and atrophy measures. In conclusion, here we introduced the SPACE-MS approach, which we showed is able to capture clinically relevant spatial distributional features of MS lesions independently of the sheer amount of lesions and brain tissue loss. Location of lesions in lower parts of the brain, where neurite density is particularly high, such as in the cerebellum and brainstem, and greater spatial spreading of lesions (i.e. more isotropic whole-brain lesion masks), possibly reflecting a higher number of WM tracts involved, are associated with clinical deterioration in progressive MS. The usefulness of the SPACE-MS approach, here demonstrated in MS, may be explored in other conditions also characterised by the presence of brain WM lesions.
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Affiliation(s)
- Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; MS Centre of Catalonia (Cemcat), Vall d'Hebron Institute of Research, Vall d'Hebron Barcelona Hospital Campus, Spain.
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Floriana De Angelis
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; Centre for Medical Image Computing, Medical Physics and Biomedical Engineering Department, University College London, UK; e-Health Center, Universitat Oberta de Catalunya, Spain
| | - Baris Kanber
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering Department, University College London, UK
| | - Alberto Calvi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Arman Eshaghi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Thalis Charalambous
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Rosa Cortese
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK
| | - Jeremy Chataway
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK
| | - Alan J Thompson
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; Department of Brain and Behavioural Sciences, University of Pavia, Italy; Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy.
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Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure. Brain Imaging Behav 2021; 16:1049-1064. [PMID: 34724163 PMCID: PMC8558548 DOI: 10.1007/s11682-021-00574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 09/28/2021] [Indexed: 12/03/2022]
Abstract
Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.
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60
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Zhang D, Huang Y, Gao J, Lei Y, Ai K, Tang M, Yan X, Lei X, Yang Z, Shao Z, Zhang X. Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications. Front Neurosci 2021; 15:726350. [PMID: 34630014 PMCID: PMC8493598 DOI: 10.3389/fnins.2021.726350] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/31/2021] [Indexed: 01/19/2023] Open
Abstract
Microvascular complications can accelerate cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and have a high impact on their quality of life; however, the underlying mechanism is still unclear. The complex network in the human brain is the physiological basis for information processing and cognitive expression. Therefore, this study explored the relationship between the functional network topological properties and cognitive function in T2DM patients with and without microvascular complications (T2DM-C and T2DM-NC, respectively). Sixty-seven T2DM patients and 41 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological assessment. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. The T2DM-C group exhibited significantly higher local efficiency (Eloc), normalized cluster coefficient (γ), and small-world characteristics (σ) than the HCs. Patients with T2DM at different clinical stages (T2DM-C and T2DM-NC) showed varying degrees of abnormalities in node properties. In addition, compared with T2DM-NC patients, T2DM-C patients showed nodal properties disorders in the occipital visual network, cerebellum and middle temporal gyrus. The Eloc metrics were positively correlated with HbA1c level (P = 0.001, r = 0.515) and the NE values in the right paracentral lobule were negatively related with serum creatinine values (P = 0.001, r = −0.517) in T2DM-C patients. This study found that T2DM-C patients displayed more extensive changes at different network topology scales. The visual network and cerebellar may be the central vulnerable regions of T2DM-C patients.
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Affiliation(s)
- Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yang Huang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yumeng Lei
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi'an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xuejiao Yan
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhen Yang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhirong Shao
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
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61
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Tozlu C, Jamison K, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain Behav 2021; 11:e2353. [PMID: 34498432 PMCID: PMC8553317 DOI: 10.1002/brb3.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/28/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions. METHODS Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC). RESULTS The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. CONCLUSION rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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Zhu J, Zeng Q, Shi Q, Li J, Dong S, Lai C, Cheng G. Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective. Front Aging Neurosci 2021; 13:710735. [PMID: 34557085 PMCID: PMC8452898 DOI: 10.3389/fnagi.2021.710735] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency. Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed. Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV. Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.
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Affiliation(s)
- Junlan Zhu
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China.,Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Qiaoling Zeng
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Qiao Shi
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jiao Li
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Shuwen Dong
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Chao Lai
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Guanxun Cheng
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
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63
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Bosticardo S, Schiavi S, Schaedelin S, Lu PJ, Barakovic M, Weigel M, Kappos L, Kuhle J, Daducci A, Granziera C. Microstructure-Weighted Connectomics in Multiple Sclerosis. Brain Connect 2021; 12:6-17. [PMID: 34210167 PMCID: PMC8867108 DOI: 10.1089/brain.2021.0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Introduction: Graph theory has been applied to study the pathophysiology of multiple sclerosis (MS) since it provides global and focal measures of brain network properties that are affected by MS. Typically, the connection strength and, consequently, the network properties are computed by counting the number of streamlines (NOS) connecting couples of gray matter regions. However, recent studies have shown that this method is not quantitative. Methods: We evaluated diffusion-based microstructural measures extracted from three different models to assess the network properties in a group of 66 MS patients and 64 healthy subjects. Besides, we assessed their correlation with patients' disability and with a biological measure of neuroaxonal damage. Results: Graph metrics extracted from connectomes weighted by intra-axonal microstructural components were the most sensitive to MS pathology and the most related to clinical disability. In contrast, measures of network segregation extracted from the connectomes weighted by maps describing extracellular diffusivity were the most related to serum concentration of neurofilament light chain. Network properties assessed with NOS were neither sensitive to MS pathology nor correlated with clinical and pathological measures of disease impact in MS patients. Conclusion: Using tractometry-derived graph measures in MS patients, we identified a set of metrics based on microstructural components that are highly sensitive to the disease and that provide sensitive correlates of clinical and biological deterioration in MS patients.
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Affiliation(s)
- Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Sabine Schaedelin
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Address correspondence to: Cristina Granziera, Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Gewerbestrasse 16, 4123 Allschwil, BL, Switzerland
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Yang J, Lei D, Peng J, Suo X, Pinaya WHL, Li W, Li J, Kemp GJ, Peng R, Gong Q. Disrupted brain gray matter networks in drug-naïve participants with essential tremor. Neuroradiology 2021; 63:1501-1510. [PMID: 33782719 DOI: 10.1007/s00234-021-02653-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/20/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To use structural magnetic resonance imaging and graph theory approaches to investigate the topological organization of the brain morphological network based on gray matter in essential tremor, and its potential relation to disease severity. METHODS In this prospective study conducted from November 2018 to November 2019, 36 participants with essential tremor and 37 matched healthy controls underwent magnetic resonance imaging. Brain networks based on the morphological similarity of gray matter across regions were analyzed using graph theory. Nonparametric permutation testing was used to assess group differences in topological metrics. Support vector machine was applied to the gray matter morphological matrices to classify participants with essential tremor vs. healthy controls. RESULTS Compared with healthy controls, participants with essential tremor showed increased global efficiency (p < 0.01) and decreased path length (p < 0.01); abnormal nodal properties in frontal, parietal, and cerebellar lobes; and disconnectivity in cerebello-thalamo-cortical network. The abnormal brain nodal centralities (left superior cerebellum gyrus; right caudate nucleus) correlated with clinical measures, both motor (Fahn-Tolosa-Marìn tremor rating, p = 0.017, r = - 0.41) and nonmotor (Hamilton depression scale, p = 0.040, r = - 0.36; Hamilton anxiety scale, p = 0.008, r = - 0.436). Gray matter morphological matrices classified individuals with high accuracy of 80.0%. CONCLUSION Participants with essential tremor showed randomization in global properties and dysconnectivity in the cerebello-thalamo-cortical network. Participants with essential tremor could be distinguished from healthy controls by gray matter morphological matrices.
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Affiliation(s)
- Jing Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Jiaxin Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Junying Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Rong Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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65
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Barile B, Marzullo A, Stamile C, Durand-Dubief F, Sappey-Marinier D. Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity. Brain Connect 2021; 12:476-488. [PMID: 34269618 DOI: 10.1089/brain.2020.1003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS). METHOD An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High). RESULTS The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class. CONCLUSION The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.
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Affiliation(s)
- Berardino Barile
- Université Claude Bernard Lyon 1, 27098, 1CREATIS (UMR5220 & INSERM U1206), 43 Boulevard du 11 Novembre 1918, Villeurbanne, Villeurbanne, France, 69100;
| | - Aldo Marzullo
- University of Calabria, 18950, Mathematics and Computer Science, Arcavacata di Rende, Calabria, Italy;
| | | | - Francoise Durand-Dubief
- Hospices Civils de Lyon, 26900, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
| | - Dominique Sappey-Marinier
- Université de Lyon, 133614, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
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66
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Lipp I, Foster C, Stickland R, Sgarlata E, Tallantyre EC, Davidson AE, Robertson NP, Jones DK, Wise RG, Tomassini V. Predictors of training-related improvement in visuomotor performance in patients with multiple sclerosis: A behavioural and MRI study. Mult Scler 2021; 27:1088-1101. [PMID: 32749927 PMCID: PMC8151554 DOI: 10.1177/1352458520943788] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND The development of tailored recovery-oriented strategies in multiple sclerosis requires early identification of an individual's potential for functional recovery. OBJECTIVE To identify predictors of visuomotor performance improvements, a proxy of functional recovery, using a predictive statistical model that combines demographic, clinical and magnetic resonance imaging (MRI) data. METHODS Right-handed multiple sclerosis patients underwent baseline disability assessment and MRI of the brain structure, function and vascular health. They subsequently undertook 4 weeks of right upper limb visuomotor practice. Changes in performance with practice were our outcome measure. We identified predictors of improvement in a training set of patients using lasso regression; we calculated the best performing model in a validation set and applied this model to a test set. RESULTS Patients improved their visuomotor performance with practice. Younger age, better visuomotor abilities, less severe disease burden and concurrent use of preventive treatments predicted improvements. Neuroimaging localised outcome-relevant sensory motor regions, the microstructure and activity of which correlated with performance improvements. CONCLUSION Initial characteristics, including age, disease duration, visuo-spatial abilities, hand dexterity, self-evaluated disease impact and the presence of disease-modifying treatments, can predict functional recovery in individual patients, potentially improving their clinical management and stratification in clinical trials. MRI is a correlate of outcome, potentially supporting individual prognosis.
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Affiliation(s)
- Ilona Lipp
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK/Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Catherine Foster
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Rachael Stickland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Eleonora Sgarlata
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Emma C Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Alison E Davidson
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Neil P Robertson
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK/Institute for Advanced Biomedical Technologies (ITAB), Department of Neurosciences, Imaging and Clinical Sciences, University ‘G. d’Annunzio’ of Chieti-Pescara, Chieti, Italy
| | - Valentina Tomassini
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK/Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK/Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK/Institute for Advanced Biomedical Technologies (ITAB), Department of Neurosciences, Imaging and Clinical Sciences, University ‘G. d’Annunzio’ of Chieti-Pescara, Chieti, Italy
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67
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Lei D, Li W, Tallman MJ, Patino LR, McNamara RK, Strawn JR, Klein CC, Nery FG, Fleck DE, Qin K, Ai Y, Yang J, Zhang W, Lui S, Gong Q, Adler CM, Sweeney JA, DelBello MP. Changes in the brain structural connectome after a prospective randomized clinical trial of lithium and quetiapine treatment in youth with bipolar disorder. Neuropsychopharmacology 2021; 46:1315-1323. [PMID: 33753882 PMCID: PMC8134458 DOI: 10.1038/s41386-021-00989-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/02/2021] [Accepted: 02/16/2021] [Indexed: 02/06/2023]
Abstract
The goals of the current study were to determine whether topological organization of brain structural networks is altered in youth with bipolar disorder, whether such alterations predict treatment outcomes, and whether they are normalized by treatment. Youth with bipolar disorder were randomized to double-blind treatment with quetiapine or lithium and assessed weekly. High-resolution MRI images were collected from children and adolescents with bipolar disorder who were experiencing a mixed or manic episode (n = 100) and healthy youth (n = 63). Brain networks were constructed based on the similarity of morphological features across regions and analyzed using graph theory approaches. We tested for pretreatment anatomical differences between bipolar and healthy youth and for changes in neuroanatomic network metrics following treatment in the youth with bipolar disorder. Youth with bipolar disorder showed significantly increased clustering coefficient (Cp) (p = 0.009) and characteristic path length (Lp) (p = 0.04) at baseline, and altered nodal centralities in insula, inferior frontal gyrus, and supplementary motor area. Cp, Lp, and nodal centrality of the insula exhibited normalization in patients following treatment. Changes in these neuroanatomic parameters were correlated with improvement in manic symptoms but did not differ between the two drug therapies. Baseline structural network matrices significantly differentiated medication responders and non-responders with 80% accuracy. These findings demonstrate that both global and nodal structural network features are altered in early course bipolar disorder, and that pretreatment alterations in neuroanatomic features predicted treatment outcome and were reduced by treatment. Similar connectome normalization with lithium and quetiapine suggests that the connectome changes are a downstream effect of both therapies that is related to their clinical efficacy.
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Affiliation(s)
- Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Wenbin Li
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jeffrey R Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Christina C Klein
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Fabiano G Nery
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Yuan Ai
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Jing Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China.
| | - Caleb M Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, P. R. China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Liu Y, Hu A, Chen L, Li B, Zhang M, Xi P, Yang Q, Tang R, Huang Q, He J, Lang Y, Zhang Y. Association between cortical thickness and distinct vascular cognitive impairment and dementia in patients with white matter lesions. Exp Physiol 2021; 106:1612-1620. [PMID: 33866642 DOI: 10.1113/ep089419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/08/2021] [Indexed: 12/29/2022]
Abstract
NEW FINDINGS What is the central question of this study? White matter lesions (WMLs) are a brain disease characterized by altered brain structural and functional connectivity, but findings have shown an inconsistent pattern: are there distinct cortical thickness changes in patients with WMLs subtypes? What is the main finding and its importance? Patients with WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed distinct pathophysiology in cortical thickness. These neural correlates of WMLs should be considered in future treatment. ABSTRACT The effect of cortical thickness on white matter lesions (WMLs) in patients with distinct vascular cognitive impairments is relatively unknown. This study investigated the correlation between cortical thickness and vascular cognitive manifestations. WML patients and healthy controls from Beijing Tiantan Hospital between 2014 and 2018 were included. The patients were further divided into two subgroups, namely WMLs with non-dementia vascular cognitive impairment (WML-VCIND) and WMLs with vascular dementia (WML-VaD) according to the Clinical Dementia Rating (CDR) scale and the Beijing version of the Montreal Cognitive Assessment (MoCA). Changes in cortical thickness were calculated using FreeSurfer. Pearson's correlation analysis was performed to explore the relationship between cognitive manifestations and cortical thickness in WML patients. Forty-five WML patients and 23 healthy controls were recruited. The WML group exhibited significant difference in cortical thickness compared to the control group. Significantly decreased cortical thickness in the middle and superior frontal gyri, middle temporal gyrus, angular gyrus and insula was found in the WML-VaD versus WML-VCIND subgroup. Cortical thickness deficits of the left caudal middle frontal gyrus (r = 0.451, P = 0.002), left rostral middle frontal gyrus (r = 0.514, P < 0.001), left superior frontal gyrus (r = 0.410, P = 0.006), right middle temporal gyrus (r = 0.440, P = 0.003), right pars triangularis (r = 0.462, P = 0.002), right superior frontal gyrus (r = 0.434, P = 0.004) and right insula (r = 0.499, P = 0.001) were positively correlated with the MoCA score in WML patients. The specific pattern of cortical thickness deficits in the WML-VaD subgroup revealed the pathophysiology of WMLs, which should be considered in future treatment of WMLs.
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Affiliation(s)
- Yafei Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Anming Hu
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Luyao Chen
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Minjian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Pengcheng Xi
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Qinghu Yang
- College of Life Sciences & Research Center for Resource Peptide Drugs, Shaanxi Engineering & Technological Research Center for Conversation & Utilization of Regional Biological Resources, Yanan University, Yanan, China
| | - Rongyu Tang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Qiang Huang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.,Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Yiran Lang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Rocca MA, Valsasina P, Meani A, Pagani E, Cordani C, Cervellin C, Filippi M. Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2021; 8:8/4/e1006. [PMID: 34021055 PMCID: PMC8143700 DOI: 10.1212/nxi.0000000000001006] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/05/2021] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration. METHODS Baseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06-7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns. RESULTS At follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected p = range 0.01-0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01-0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening (p = 0.05) and SPMS conversion (p = 0.02) prediction. CONCLUSIONS Integration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.
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Affiliation(s)
- Maria A Rocca
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Claudio Cordani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiara Cervellin
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Brier MR, Snyder AZ, Tanenbaum A, Rudick RA, Fisher E, Jones S, Shimony JS, Cross AH, Benzinger TLS, Naismith RT. Quantitative signal properties from standardized MRIs correlate with multiple sclerosis disability. Ann Clin Transl Neurol 2021; 8:1096-1109. [PMID: 33943045 PMCID: PMC8108425 DOI: 10.1002/acn3.51354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice. METHODS We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures. RESULTS Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability. INTERPRETATION These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability.
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Affiliation(s)
- Matthew R. Brier
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Abraham Z. Snyder
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Aaron Tanenbaum
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | | | | | | | - Joshua S. Shimony
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Anne H. Cross
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Robert T. Naismith
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
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71
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Qin K, Lei D, Yang J, Li W, Tallman MJ, Duran LRP, Blom TJ, Bruns KM, Cotton S, Sweeney JA, Gong Q, DelBello MP. Network-level functional topological changes after mindfulness-based cognitive therapy in mood dysregulated adolescents at familial risk for bipolar disorder: a pilot study. BMC Psychiatry 2021; 21:213. [PMID: 33910549 PMCID: PMC8080341 DOI: 10.1186/s12888-021-03211-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/09/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Given that psychopharmacological approaches routinely used to treat mood-related problems may result in adverse outcomes in mood dysregulated adolescents at familial risk for bipolar disorder (BD), Mindfulness-Based Cognitive Therapy for Children (MBCT-C) provides an alternative effective and safe option. However, little is known about the brain mechanisms of beneficial outcomes from this intervention. Herein, we aimed to investigate the network-level neurofunctional effects of MBCT-C in mood dysregulated adolescents. METHODS Ten mood dysregulated adolescents at familial risk for BD underwent a 12-week MBCT-C intervention. Resting-state functional magnetic resonance imaging (fMRI) was performed prior to and following MBCT-C. Topological metrics of three intrinsic functional networks (default mode network (DMN), fronto-parietal network (FPN) and cingulo-opercular network (CON)) were investigated respectively using graph theory analysis. RESULTS Following MBCT-C, mood dysregulated adolescents showed increased global efficiency and decreased characteristic path length within both CON and FPN. Enhanced functional connectivity strength of frontal and limbic areas were identified within the DMN and CON. Moreover, change in characteristic path length within the CON was suggested to be significantly related to change in the Emotion Regulation Checklist score. CONCLUSIONS 12-week MBCT-C treatment in mood dysregulated adolescents at familial risk for BD yield network-level neurofunctional effects within the FPN and CON, suggesting enhanced functional integration of the dual-network. Decreased characteristic path length of the CON may be associated with the improvement of emotion regulation following mindfulness training. However, current findings derived from small sample size should be interpreted with caution. Future randomized controlled trials including larger samples are critical to validate our findings.
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Affiliation(s)
- Kun Qin
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Du Lei
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Jing Yang
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wenbin Li
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Maxwell J. Tallman
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Luis Rodrigo Patino Duran
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Thomas J. Blom
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Kaitlyn M. Bruns
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Sian Cotton
- grid.24827.3b0000 0001 2179 9593Department of Family and Community Medicine, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - John A. Sweeney
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. .,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China. .,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China.
| | - Melissa P. DelBello
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
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Alterations in functional connectivity are associated with white matter lesions and information processing efficiency in multiple sclerosis. Brain Imaging Behav 2021; 15:375-388. [PMID: 32114647 DOI: 10.1007/s11682-020-00264-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Functional connectivity (FC) is typically altered in individuals with Multiple Sclerosis (MS). However, in relapsing-remitting multiple sclerosis (RRMS) patients, the relationship between brain FC, tissue integrity and cognitive impairment is still unclear as contradictory findings have been documented. In this exploratory study we compared both the whole brain connectome and resting state networks (RSNs) FC of twenty-one RRMS and seventeen healthy controls (HCs), using combined network based statistics and independent component analyses. The total white matter (WM) lesion volume and information processing efficiency were also correlated with FC in the RRMS group. Both whole brain connectome and individual RSNs FC were diminished in patients with RRMS compared to HC. Additionally, the reduction in FC was found to be a function of the total WM lesion volume, with greatest impact in those harboring the largest lesion volume. Finally, a positive correlation between FC and information processing efficiency was observed in RRMS. This complimentary whole brain and RSNs FC approach can contribute to clarify literature inconsistencies regarding FC alterations and provide new insights on the white matter structural damage in explaining functional abnormalities in RRMS.
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73
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Jin M, Wang L, Wang H, Han X, Diao Z, Guo W, Yang Z, Ding H, Wang Z, Zhang P, Zhao P, Lv H, Liu W, Wang Z. Altered resting-state functional networks in patients with hemodialysis: a graph-theoretical based study. Brain Imaging Behav 2021; 15:833-845. [PMID: 32314197 DOI: 10.1007/s11682-020-00293-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Recent studies have demonstrated that hemodialysis patients exhibit disruptions in functional networks with invisible cerebral alterations. We explored the alterations of functional connectivity in hemodialysis patients using the graph-theory method. A total of 46 hemodialysis patients (53.11 ± 1.58 years, 28 males) and 47 healthy controls (55.57 ± 0.86 years, 22 males) were scanned by using resting-state functional magnetic resonance imaging. The brains of these patients were divided into 90 regions and functional connectivity was constructed with the automatic anatomical labeling atlas. In the defined threshold range, the graph-theory analysis was performed to compare the topological properties including global, regional and edge parameters between the hemodialysis and the healthy control groups. Both hemodialysis patients and healthy control subjects demonstrated common small-world property of the brain functional connections. At the global level, the parameters normalized clustering coefficients and small-worldness were significantly decreased in hemodialysis patients compared with those noted in healthy controls. At the regional level, abnormal nodal metrics (increased or decreased nodal degree, betweenness centrality and efficiency) were widely found in hemodialysis patients compared with those of healthy controls. The network-based statistical method was employed and two disrupted neural circuits with 18 nodes and 19 edges (P = 0.0139, corrected) and 10 nodes and 11 edges (P = 0.0399, corrected) were detected. Of note, the edge-increased functional connectivity was associated with the salience network and the frontal-temporal-basal ganglia connection, whereas the edge-decreased functional connectivity was associated with the frontoparietal network. The graph-theory method may be one of the potential tools to detect disruptions of cerebral functional connectivity and provide important evidence for understanding the neuropathology of hemodialysis patients from the disrupted network organization perspective.
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Affiliation(s)
- Mei Jin
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liyan Wang
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hao Wang
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xue Han
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zongli Diao
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wang Guo
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Heyu Ding
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zheng Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wenhu Liu
- Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Suo X, Lei D, Li N, Li W, Kemp GJ, Sweeney JA, Peng R, Gong Q. Disrupted morphological grey matter networks in early-stage Parkinson's disease. Brain Struct Funct 2021; 226:1389-1403. [PMID: 33825053 PMCID: PMC8096749 DOI: 10.1007/s00429-020-02200-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/16/2020] [Indexed: 02/05/2023]
Abstract
While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Nannan Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Rong Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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Jouzizadeh M, Ghaderi AH, Cheraghmakani H, Baghbanian SM, Khanbabaie R. Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis. Brain Connect 2021; 11:359-367. [PMID: 33780635 DOI: 10.1089/brain.2020.0857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.
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Affiliation(s)
- Mojtaba Jouzizadeh
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran
| | - Amir Hossein Ghaderi
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Hamed Cheraghmakani
- Department of Neurology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Khanbabaie
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran.,Department of Physics, I.K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
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Churchill NW, Hutchison MG, Graham SJ, Schweizer TA. Long-term changes in the small-world organization of brain networks after concussion. Sci Rep 2021; 11:6862. [PMID: 33767293 PMCID: PMC7994718 DOI: 10.1038/s41598-021-85811-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 03/04/2021] [Indexed: 11/09/2022] Open
Abstract
There is a growing body of literature using functional MRI to study the acute and long-term effects of concussion on functional brain networks. To date, studies have largely focused on changes in pairwise connectivity strength between brain regions. Less is known about how concussion affects whole-brain network topology, particularly the “small-world” organization which facilitates efficient communication at both local and global scales. The present study addressed this knowledge gap by measuring local and global efficiency of 26 concussed athletes at acute injury, return to play (RTP) and one year post-RTP, along with a cohort of 167 athletic controls. On average, concussed athletes showed no alterations in local efficiency but had elevated global efficiency at acute injury, which had resolved by RTP. Athletes with atypically long recovery, however, had reduced global efficiency at 1 year post-RTP, suggesting long-term functional abnormalities for this subgroup. Analyses of nodal efficiency further indicated that global network changes were driven by high-efficiency visual and sensorimotor regions and low-efficiency frontal and subcortical regions. This study provides evidence that concussion causes subtle acute and long-term changes in the small-world organization of the brain, with effects that are related to the clinical profile of recovery.
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Affiliation(s)
- N W Churchill
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada. .,Neuroscience Research Program, St. Michael's Hospital, Toronto, ON, Canada.
| | - M G Hutchison
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.,Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON, Canada
| | - S J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Science Center, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - T A Schweizer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.,Neuroscience Research Program, St. Michael's Hospital, Toronto, ON, Canada.,Faculty of Medicine (Neurosurgery), University of Toronto, Toronto, ON, Canada.,The Institute of Biomaterials and Biomedical Engineering (IBBME), University of Toronto, Toronto, ON, Canada
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77
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Li C, Pang X, Shi K, Long Q, Liu J, Zheng J. The Insula Is a Hub for Functional Brain Network in Patients With Anti- N-Methyl-D-Aspartate Receptor Encephalitis. Front Neurosci 2021; 15:642390. [PMID: 33790737 PMCID: PMC8005702 DOI: 10.3389/fnins.2021.642390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/29/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND In recent years, imaging technologies have been rapidly evolving, with an emphasis on the characterization of brain structure changes and functional imaging in patients with autoimmune encephalitis. However, the neural basis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and its linked cognitive decline is unclear. Our research aimed to assess changes in the functional brain network in patients with anti-NMDAR encephalitis and whether these changes lead to cognitive impairment. METHODS Twenty-one anti-NMDAR encephalitis patients and 22 age-, gender-, and education status-matched healthy controls were assessed using resting functional magnetic resonance imaging (fMRI) scanning and neuropsychological tests, including the Hamilton Depression Scale (HAMD24), the Montreal Cognitive Assessment (MoCA), and the Hamilton Anxiety Scale (HAMA). A functional brain network was constructed using fMRI, and the topology of the network parameters was analyzed using graph theory. Next, we extracted the aberrant topological parameters of the functional network as seeds and compared causal connectivity with the whole brain. Lastly, we explored the correlation of aberrant topological structures with deficits in cognitive performance. RESULTS Relative to healthy controls, anti-NMDAR encephalitis patients exhibited decreased MoCA scores and increased HAMA and HAMD24 scores (p < 0.05). The nodal clustering coefficient and nodal local efficiency of the left insula (Insula_L) were significantly decreased in anti-NMDAR encephalitis patients (p < 0.05 following Bonferroni correction). Moreover, anti-NMDAR encephalitis patients showed a weakened causal connectivity from the left insula to the left inferior parietal lobe (Parietal_Inf_L) compared to healthy controls. Conversely, the left superior parietal lobe (Parietal_sup_L) exhibited an enhanced causal connectivity to the left insula in anti-NMDAR encephalitis patients compared to controls. Unexpectedly, these alterations were not correlated with any neuropsychological test scores. CONCLUSION This research describes topological abnormalities in the functional brain network in anti-NMDAR encephalitis. These results will be conducive to understand the structure and function of the brain network of patients with anti-NMDAR encephalitis and further explore the neuropathophysiological mechanisms.
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Affiliation(s)
- Chunyan Li
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaomin Pang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ke Shi
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qijia Long
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinping Liu
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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78
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Wang S, Gan S, Yang X, Li T, Xiong F, Jia X, Sun Y, Liu J, Zhang M, Bai L. Decoupling of structural and functional connectivity in hubs and cognitive impairment after mild traumatic brain injury. Brain Connect 2021; 11:745-758. [PMID: 33605188 DOI: 10.1089/brain.2020.0852] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION Mild traumatic brain injury (mild TBI) exhibited abnormal brain network topologies associated with cognitive dysfunction. However, it was still unclear which aspects of network organization were critical underlying the key pathology of mild TBI. Here, a multi-imaging strategy was applied to capture dynamic topological features of both structural and functional connectivity networks (SCN and FCN), to provide more sensitive detection of altered FCN from its anatomical backbone and identify novel biomarkers of mild TBI outcomes. METHODS 62 mild TBI patients (30 subjects as an original sample with 3-12 months follow-up, 32 subjects as independent replicated sample), and 37 healthy controls were recruited. Both diffusion tensor imaging (DTI) and resting-state fMRI were used to create global connectivity matrices in the same individuals. Global and regional network analyses were applied to identify group differences and correlations with clinical assessments. RESULTS Most global network properties were conserved in both SCNs and FCNs in subacute mild TBI, whereas SCNs presented decreased global efficiency and characteristic path length at follow-up. Specifically, some hubs in healthy brain networks typically became non-hubs in patients and vice versa, such as the medial prefrontal cortex, superior temporal gyrus, middle frontal gyrus. The relationship between structural and functional connectivity (SC and FC) in patients also showed salient decoupling as a function of time, primarily located in the hubs. CONCLUSIONS These results suggested mild TBI influences the relationship between SCN and FCN, and the SC-FC coupling strength may be used as a potential biomarker to predict long-term outcomes after injury.
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Affiliation(s)
- Shan Wang
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xianning Road, Xi'an, China, 710049;
| | - Shuoqiu Gan
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Xuefei Yang
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Tianhui Li
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Feng Xiong
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Xiaoyan Jia
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Yingxiang Sun
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Jun Liu
- Xiangya Hospital Central South University, 159374, Department of Radiology, Changsha, Hunan, China;
| | - Ming Zhang
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Lijun Bai
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
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79
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Li X, Lei D, Niu R, Li L, Suo X, Li W, Yang C, Yang T, Ren J, Pinaya WHL, Zhou D, Kemp GJ, Gong Q. Disruption of gray matter morphological networks in patients with paroxysmal kinesigenic dyskinesia. Hum Brain Mapp 2021; 42:398-411. [PMID: 33058379 PMCID: PMC7776009 DOI: 10.1002/hbm.25230] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/01/2020] [Accepted: 09/29/2020] [Indexed: 02/05/2023] Open
Abstract
This study explores the topological properties of brain gray matter (GM) networks in patients with paroxysmal kinesigenic dyskinesia (PKD) and asks whether GM network features have potential diagnostic value. We used 3D T1-weighted magnetic resonance imaging and graph theoretical approaches to investigate the topological organization of GM morphological networks in 87 PKD patients and 115 age- and sex-matched healthy controls. We applied a support vector machine to GM morphological network matrices to classify PKD patients versus healthy controls. Compared with the HC group, the GM morphological networks of PKD patients showed significant abnormalities at the global level, including an increase in characteristic path length (Lp) and decreases in local efficiency (Eloc ), clustering coefficient (Cp), normalized clustering coefficient (γ), and small-worldness (σ). The decrease in Cp was significantly correlated with disease duration and age of onset. The GM morphological networks of PKD patients also showed significant changes in nodal topological characteristics, mainly in the basal ganglia-thalamus circuitry, default-mode network and central executive network. Finally, we used the GM morphological network matrices to classify individuals as PKD patients versus healthy controls, achieving 87.8% accuracy. Overall, this study demonstrated disruption of GM morphological networks in PKD, which might extend our understanding of the pathophysiology of PKD; further, GM morphological network matrices might have the potential to serve as network neuroimaging biomarkers for the diagnosis of PKD.
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Affiliation(s)
- Xiuli Li
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
- Department of RadiologySichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of ChinaChengduChina
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
- Department of Psychiatry and Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Running Niu
- Department of RadiologySichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of ChinaChengduChina
| | - Lei Li
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Chen Yang
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Tianhua Yang
- Department of NeurologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Jiechuan Ren
- Department of NeurologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Walter H. L. Pinaya
- Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
- Center of Mathematics, Computing, and CognitionUniversidade Federal do ABCSanto AndréBrazil
| | - Dong Zhou
- Department of NeurologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of LiverpoolLiverpoolUK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan ProvinceChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan UniversityChengduChina
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80
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Luo L, Li Q, You W, Wang Y, Tang W, Li B, Yang Y, Sweeney JA, Li F, Gong Q. Altered brain functional network dynamics in obsessive-compulsive disorder. Hum Brain Mapp 2021; 42:2061-2076. [PMID: 33522660 PMCID: PMC8046074 DOI: 10.1002/hbm.25345] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/20/2020] [Accepted: 01/07/2021] [Indexed: 02/05/2023] Open
Abstract
Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanjie Tang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Bin Li
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yanchun Yang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
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81
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Wang E, Jia Y, Ya Y, Xu J, Mao C, Luo W, Fan G, Jiang Z. Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease. Front Aging Neurosci 2021; 12:575672. [PMID: 33519416 PMCID: PMC7843381 DOI: 10.3389/fnagi.2020.575672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/14/2020] [Indexed: 11/13/2022] Open
Abstract
Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.
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Affiliation(s)
- Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jin Xu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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82
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Cai S, Shi Z, Jiang C, Wang K, Chen L, Ai L, Zhang L. Hemisphere-Specific Functional Remodeling and Its Relevance to Tumor Malignancy of Cerebral Glioma Based on Resting-State Functional Network Analysis. Front Neurosci 2021; 14:611075. [PMID: 33519363 PMCID: PMC7838505 DOI: 10.3389/fnins.2020.611075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 12/11/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis. SUBJECTS AND METHODS One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation. RESULTS Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas. CONCLUSION Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.
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Affiliation(s)
- Siqi Cai
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Chunxiang Jiang
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Kai Wang
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Lin Ai
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lijuan Zhang
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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83
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Sanabria-Diaz G, Melie-Garcia L, Draganski B, Demonet JF, Kherif F. Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment. Sci Rep 2021; 11:845. [PMID: 33436948 PMCID: PMC7804004 DOI: 10.1038/s41598-020-80909-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/30/2020] [Indexed: 01/29/2023] Open
Abstract
The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.
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Affiliation(s)
- Gretel Sanabria-Diaz
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland.
| | - Lester Melie-Garcia
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
| | | | - Ferath Kherif
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
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Yang F, Qu M, Zhang Y, Zhao L, Xing W, Zhou G, Tang J, Wu J, Zhang Y, Liao W. Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics. Front Neurosci 2020; 14:585588. [PMID: 33343281 PMCID: PMC7746555 DOI: 10.3389/fnins.2020.585588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/16/2020] [Indexed: 11/13/2022] Open
Abstract
Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.
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Affiliation(s)
- Fangxue Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Minli Qu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Linmei Zhao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Wu Xing
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Gaofeng Zhou
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jingyi Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Wu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuanchao Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,Molecular Imaging Research Center of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders (XiangYa), Changsha, China
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Effects of Rivastigmine on Brain Functional Networks in Patients With Alzheimer Disease Based on the Graph Theory. Clin Neuropharmacol 2020; 44:9-16. [PMID: 33337622 PMCID: PMC7813447 DOI: 10.1097/wnf.0000000000000427] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to explore the effect of rivastigmine on brain function in Alzheimer disease (AD) by analyzing brain functional network based on the graph theory. METHODS We enrolled 9 patients with mild to moderate AD who received rivastigmine treatment and 9 healthy controls (HC). Subsequently, we used resting-state functional magnetic resonance imaging data to establish the whole-brain functional network using a graph theory-based analysis. Furthermore, we compared systemic and local network indicators between pre- and posttreatment. RESULTS Patients with AD exhibited a posttreatment increase in the Mini-Mental State Examination scores and a decrease in the Alzheimer's Disease Assessment Scale cognitive subscale scores and activities of daily living. The systemic network for HC and patients with AD had good pre- and posttreatment clustering coefficients. There was no change in the Cp, Lp, Gamma, Lambda, and Sigma in patients with AD. There were no significant between-group differences in the pre- and posttreatment systemic network measures. Regarding the regional network, patients with AD showed increased betweenness centrality in the bilateral caudate nucleus and right superior temporal pole after treatment with rivastigmine. However, there was no between-group difference in the pre- and posttreatment betweenness centrality of these regions. There were no significant correlations between regional network measure changes and clinical score alterations in patients with AD. CONCLUSIONS There are similar systemic network properties between patients with AD and HC. Rivastigmine cannot alter systemic network attributes in patients with AD. However, it improves the topological properties of regional networks and between-node information transmission in patients with AD.
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Huang X, Chen J, Liu S, Gong Q, Liu T, Lu C, Qin Z, Cui H, Chen Y, Zhu Y. Impaired frontal‐parietal control network in chronic prostatitis/chronic pelvic pain syndrome revealed by graph theoretical analysis: A DTI study. Eur J Neurosci 2020; 53:1060-1071. [PMID: 32896914 DOI: 10.1111/ejn.14962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/28/2020] [Accepted: 08/29/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Xinfei Huang
- Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Jianhuai Chen
- Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Shaowei Liu
- Department of Radiology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Qingkuo Gong
- Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Tao Liu
- Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Chao Lu
- Department of Radiology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Zhan Qin
- Department of Andrology Guangdong Provincial Hospital of Chinese Medicine Zhuhai China
| | - Hongliang Cui
- Department of Urology Nantong Hospital of Traditional Chinese Medicine Nantong China
| | - Yun Chen
- Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Yongkang Zhu
- Department of General Surgery Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
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87
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Qiu YH, Huang ZH, Gao YY, Feng SJ, Huang B, Wang WY, Xu QH, Zhao JH, Zhang YH, Wang LM, Nie K, Wang LJ. Alterations in intrinsic functional networks in Parkinson's disease patients with depression: A resting-state functional magnetic resonance imaging study. CNS Neurosci Ther 2020; 27:289-298. [PMID: 33085178 PMCID: PMC7871794 DOI: 10.1111/cns.13467] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/07/2020] [Accepted: 09/26/2020] [Indexed: 12/11/2022] Open
Abstract
Aims The aim of this research was to investigate the alterations in functional brain networks and to assess the relationship between depressive impairment and topological network changes in Parkinson's disease (PD) patients with depression (DPD). Methods Twenty‐two DPD patients, 23 PD patients without depression (NDPD), and 25 matched healthy controls (HCs) were enrolled. All participants were examined by resting‐state functional magnetic resonance imaging scans. Graph theoretical analysis and network‐based statistic methods were used to analyze brain network topological properties and abnormal subnetworks, respectively. Results The DPD group showed significantly decreased local efficiency compared with the HC group (P = .008, FDR corrected). In nodal metrics analyses, the degree of the right inferior occipital gyrus (P = .0001, FDR corrected) was positively correlated with the Hamilton Depression Rating Scale scores in the DPD group. Meanwhile, the temporal visual cortex, including the bilateral middle temporal gyri and right inferior temporal gyrus in the HC and NDPD groups and the left posterior cingulate gyrus in the NDPD group, was defined as hub region, but not in the DPD group. Compared with the HC group, the DPD group had extensive weakening of connections between the temporal‐occipital visual cortex and the prefrontal‐limbic network. Conclusions These results suggest that PD depression is associated with disruptions in the topological organization of functional brain networks, mainly involved the temporal‐occipital visual cortex and the posterior cingulate gyrus and may advance our current understanding of the pathophysiological mechanisms underlying DPD.
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Affiliation(s)
- Yi-Hui Qiu
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Zhi-Heng Huang
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Yu-Yuan Gao
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Shu-Jun Feng
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wan-Yi Wang
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Qi-Huan Xu
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Jie-Hao Zhao
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Yu-Hu Zhang
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Li-Min Wang
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Kun Nie
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
| | - Li-Juan Wang
- Department of Neurology, Guangdong Provincial Peoples' Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, Guangzhou, China
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Welton T, Constantinescu CS, Auer DP, Dineen RA. Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship with Cognition. Brain Connect 2020; 10:95-104. [PMID: 32079409 PMCID: PMC7196369 DOI: 10.1089/brain.2019.0717] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships with cognitive performance (linear regression against the paced auditory serial addition test-3 seconds [PASAT-3], symbol digit modalities test [SDMT], and attention network test) and 1-month reliability (using the intraclass correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion magnetic resonance imaging data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small worldness, modularity, characteristic path length, R2 = 0.55; prediction of SDMT by small worldness, global efficiency, and characteristic path length, R2 = 0.60). Reliability of the measures for 1 month in a subset of nine participants was mostly rated as good (ICC >0.6) for both controls and MS patients in both functional and diffusion data, but was highly dependent on the chosen parcellation and graph density, with the 0.2–0.5 density range being the most reliable. This suggests that disrupted network organization predicts cognitive impairment in MS and its measurement is reliable for a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment.
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Affiliation(s)
- Thomas Welton
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,Sydney Translational Imaging Laboratory, Heart Research Institute, University of Sydney, Camperdown, Australia
| | - Cris S Constantinescu
- Clinical Neurology, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P Auer
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Rob A Dineen
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
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Li X, Li X, Chen S, Zhu J, Wang H, Tian Y, Yu Y. Effect of emotional enhancement of memory on recollection process in young adults: the influence factors and neural mechanisms. Brain Imaging Behav 2020; 14:119-129. [PMID: 30361944 PMCID: PMC7007901 DOI: 10.1007/s11682-018-9975-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Emotional enhancement of memory (EEM) is thought to modulate memory recollection rather than familiarity. However, the contributing factors and neural mechanisms are not well understood. To address these issues, we investigated how valence, arousal, and the amount of devoted attention influence the EEM effect on recollection. We also compared the topological properties among hippocampus- and perirhinal and entorhinal cortex-mediated emotional memory processing networks. Finally, we evaluated the correlations between emotional memory/EEM and inherent properties (i.e., amplitude of low-frequency fluctuation and node degree, efficiency, and betweenness) of the hippocampus and perirhinal and entorhinal cortices in 59 healthy young adults by resting-state functional magnetic resonance imaging. EEM was elicited by incidental encoding, negative images, and positive high-arousal images. The hippocampus was correlated with recollection sensitivity and EEMnegative-high-arousal. The emotional memory processing network mediated by the hippocampus had higher clustering coefficient, local efficiency, and normalized characteristic path length but lower normalized global efficiency than those mediated by the perirhinal and entorhinal cortices. The entorhinal cortex was associated with both recollection and familiarity sensitivity, but showed different correlation patterns. The perirhinal cortex was highly correlated with familiarity sensitivity of negative low-arousal stimuli. These results demonstrate that the EEM effect on memory recollection is influenced by valence, stimulus arousal, and amount of attention involved during encoding. Moreover, the hippocampus and perirhinal and entorhinal cortices play distinct roles in the recollection and familiarity of emotional memory and the EEM effect.
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Affiliation(s)
- Xiaoshu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Shujuan Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Haibao Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China.
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90
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Kuang C, Zha Y, Liu C, Chen J. Altered Topological Properties of Brain Structural Covariance Networks in Patients With Cervical Spondylotic Myelopathy. Front Hum Neurosci 2020; 14:364. [PMID: 33100992 PMCID: PMC7500316 DOI: 10.3389/fnhum.2020.00364] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/10/2020] [Indexed: 01/24/2023] Open
Abstract
Background Brain structural alterations play an important role in patients with cervical spondylotic myelopathy (CSM). However, while there have been studies on regional brain structural alterations, only few studies have focused on the topological organization of the brain structural covariance network. This work aimed to describe the structural covariance network architecture alterations that are possibly linked to cortex reorganization in patients with CSM. Methods High-resolution anatomical images of 31 CSM patients and 31 healthy controls (HCs) were included in the study. The images were acquired using a sagittal three-dimensional T1-weighted BRAVO sequence. Firstly, the gray matter volume of 90 brain regions of automated anatomical labeling atlas were computed using a VBM toolbox based on the DARTEL algorithm. Then, the brain structural covariance network was constructed by thresholding the gray matter volume correlation matrices. Subsequently, the network measures and nodal property were calculated based on graph theory. Finally, the differences in the network metrics and nodal property between groups were compared using a non-parametric test. Results Patients with CSM showed larger global efficiency and smaller local efficiency, clustering coefficient, characteristic path length, and sigma values than HCs. Patients with CSM had greater betweenness in the left superior parietal gyrus (SPG.L) and the left supplementary motor area (SMA.L) than HCs. Besides, patients with CSM had smaller betweenness in right middle occipital gyrus. The brain structural covariance networks of CSM patients exhibited equal resilience to random failure as those of HCs. However, the maximum relative size of giant connected components was approximately 10% larger in HCs than in CSM patients, upon removal of 44 nodes in targeted attack. Conclusion These observed alternations in global network measures in CSM patients reflect that the brain structural covariance network in CSM exhibits the less optimal small-world model compared to that in HCs. Increased betweenness in SPG.L and SMA.L seems to be related to cortex reorganization to recover multiple sensory functions after spinal cord injury in CSM patients. The network resilience of patients with CSM exhibiting a relative mild vulnerability, compared to HCs, is probably attributable to the balance and interplay between cortex reorganization and ongoing degeneration.
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Affiliation(s)
- Cuili Kuang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Changsheng Liu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
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Holla B, Biswal J, Ramesh V, Shivakumar V, Bharath RD, Benegal V, Venkatasubramanian G, Chand PK, Murthy P. Effect of prefrontal tDCS on resting brain fMRI graph measures in Alcohol Use Disorders: A randomized, double-blind, sham-controlled study. Prog Neuropsychopharmacol Biol Psychiatry 2020; 102:109950. [PMID: 32339664 DOI: 10.1016/j.pnpbp.2020.109950] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 03/31/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Transcranial Direct Current Stimulation (tDCS) is a promising new adjuvant approach in the treatment of Alcohol Use Disorders (AUDs) that has the potential to ameliorate the aberrations secondary to chronic alcohol use. In this study, using a randomized, double-blind, sham-controlled, parallel-arm design, we examined the effects of prefrontal tDCS on resting-state functional magnetic resonance imaging (rsfMRI) and its correlates with impulsivity and time to first lapse in subjects with AUDs. METHODS Patients with AUD as per DSM-5 criteria were randomly allocated to receive a five-day course of either verum-tDCS (n = 12) or sham-tDCS (n = 12). Of them, 21 patients (verum/sham = 11/10) participated in both baseline and post-intervention 10-min rsfMRI sessions. Outside the scanner, subjects also performed the Stop-Signal Task at two time-points (baseline and post-intervention), which provided a measure of changes in impulsivity following tDCS. After completion of the post-intervention scan, all subjects were discharged and were followed-up for 90 days post-discharge or until lapse to first alcohol use. RESULTS Graph theoretical analysis of rsfMRI data revealed that verum-tDCS (but not sham) resulted in a significant increase in the global efficiency of brain networks with a concurrent significant reduction in global clustering; network-based statistical analysis identified a significant increase in the functional connectivity of a specific sub-network involving prefrontal regions. Furthermore, increased global efficiency of brain networks following verum tDCS predicted a significantly reduced likelihood of relapse. In addition, a reduction in the global clustering had a significant positive correlation with a reduction in the measure of impulsivity. CONCLUSIONS The present study adds further support to the increasing evidence base for the clinical utility of tDCS in AUDs. Importantly, we observed improvement in both whole-brain network efficiency as well as inter-regional connectivity within a specific local prefrontal sub-network that is relevant to the neurobiology of AUDs. Replication and extension of these promising leads from the present study can facilitate clinical translation of tDCS, given its advantages (i.e. safety, cost-effectiveness, administration ease with potential for remotely-supervised / home-based application) for treating patients with AUDs.
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Affiliation(s)
- Bharath Holla
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Jitendriya Biswal
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Vinutha Ramesh
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Venkataram Shivakumar
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Rose Dawn Bharath
- Neuroimaging and Interventional Radiology, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Vivek Benegal
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Ganesan Venkatasubramanian
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India.
| | - Prabhat Kumar Chand
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Pratima Murthy
- Departments of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
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92
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Acupuncture Modulates Disrupted Whole-Brain Network after Ischemic Stroke: Evidence Based on Graph Theory Analysis. Neural Plast 2020; 2020:8838498. [PMID: 32922447 PMCID: PMC7453235 DOI: 10.1155/2020/8838498] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/14/2020] [Accepted: 07/22/2020] [Indexed: 11/18/2022] Open
Abstract
Background Stroke can lead to disruption of the whole-brain network in patients. Acupuncture can modulate the functional network on a large-scale level in healthy individuals. However, whether and how acupuncture can make a potential impact on the disrupted whole-brain network after ischemic stroke remains elusive. Methods 26 stroke patients with a right hemispheric subcortical infarct were recruited. We gathered the functional magnetic resonance imaging (fMRI) from patients with stroke and healthy controls in the resting state and after acupuncture intervention, to investigate the instant alterations of the large-scale functional networks. The graph theory analysis was applied using the GRETNA and SPM12 software to construct the whole-brain network and yield the small-world parameters and network efficiency. Results Compared with the healthy subjects, the stroke patients had a decreased normalized small-worldness (σ), global efficiency (E g), and the mean local efficiency (E loc) of the whole-brain network in the resting state. There was a correlation between the duration after stroke onset and E loc. Acupuncture improved the patients' clustering coefficient (C p) and E loc but did not make a significant impact on the σ and E g. The postacupuncture variables of the whole-brain network had no association with the time of onset. Conclusion The poststroke whole-brain network tended to a random network with reduced network efficiency. Acupuncture was able to modulate the disrupted patterns of the whole-brain network following the subcortical ischemic stroke. Our findings shed light on the potential mechanisms of the functional reorganization on poststroke brain networks involving acupuncture intervention from a large-scale perspective.
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93
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Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging. ENTROPY 2020; 22:e22080826. [PMID: 33286597 PMCID: PMC7517412 DOI: 10.3390/e22080826] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 01/18/2023]
Abstract
The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.
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94
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Racz FS, Stylianou O, Mukli P, Eke A. Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia. Front Syst Neurosci 2020; 14:49. [PMID: 32792917 PMCID: PMC7394222 DOI: 10.3389/fnsys.2020.00049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022] Open
Abstract
Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.
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Affiliation(s)
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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95
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Schiavi S, Petracca M, Battocchio M, El Mendili MM, Paduri S, Fleysher L, Inglese M, Daducci A. Sensory-motor network topology in multiple sclerosis: Structural connectivity analysis accounting for intrinsic density discrepancy. Hum Brain Mapp 2020; 41:2951-2963. [PMID: 32412678 PMCID: PMC7336144 DOI: 10.1002/hbm.24989] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 03/04/2020] [Accepted: 03/06/2020] [Indexed: 12/11/2022] Open
Abstract
Graph theory and network modelling have been previously applied to characterize motor network structural topology in multiple sclerosis (MS). However, between‐group differences disclosed by graph analysis might be primarily driven by discrepancy in density, which is likely to be reduced in pathologic conditions as a consequence of macroscopic damage and fibre loss that may result in less streamlines properly traced. In this work, we employed the convex optimization modelling for microstructure informed tractography (COMMIT) framework, which, given a tractogram, estimates the actual contribution (or weight) of each streamline in order to optimally explain the diffusion magnetic resonance imaging signal, filtering out those that are implausible or not necessary. Then, we analysed the topology of this ‘COMMIT‐weighted sensory‐motor network’ in MS accounting for network density. By comparing with standard connectivity analysis, we also tested if abnormalities in network topology are still identifiable when focusing on more ‘quantitative’ network properties. We found that topology differences identified with standard tractography in MS seem to be mainly driven by density, which, in turn, is strongly influenced by the presence of lesions. We were able to identify a significant difference in density but also in network global and local properties when accounting for density discrepancy. Therefore, we believe that COMMIT may help characterize the structural organization in pathological conditions, allowing a fair comparison of connectomes which considers discrepancies in network density. Moreover, discrepancy‐corrected network properties are clinically meaningful and may help guide prognosis assessment and treatment choice.
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Affiliation(s)
- Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genova, Italy
| | - Maria Petracca
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Mohamed M El Mendili
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Swetha Paduri
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lazar Fleysher
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genova, Italy.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Ospedale Policlinico San Martino IRCCS, Genoa, Italy
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96
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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97
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Tur C, Kanber B, Eshaghi A, Altmann DR, Khaleeli Z, Prados F, Ourselin S, Thompson AJ, Gandini Wheeler-Kingshott CA, Toosy AT, Ciccarelli O. Clinical relevance of cortical network dynamics in early primary progressive MS. Mult Scler 2020; 26:442-456. [PMID: 30799709 DOI: 10.1177/1352458519831400] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Structural cortical networks (SCNs) reflect the covariance between the cortical thickness of different brain regions, which may share common functions and a common developmental evolution. SCNs appear abnormal in neurodegenerative conditions such as Alzheimer's and Parkinson's diseases, but have never been assessed in primary progressive multiple sclerosis (PPMS). OBJECTIVE The aim of this study was to test whether SCNs are abnormal in early PPMS and change over 5 years, and correlate with disability worsening. METHODS A total of 29 PPMS patients and 13 healthy controls underwent clinical and brain magnetic resonance imaging (MRI) assessments for 5 years. Baseline and 5-year follow-up cortical thickness values were obtained and used to build correlation matrices, considered as weighted graphs to obtain network metrics. Bootstrap-based statistics assessed SCN differences between patients and controls and between patients with fast and slow progression. RESULTS At baseline, patients showed features of lower connectivity (p = 0.02) and efficiency (p < 0.001) than controls. Over 5 years, patients, especially those with fastest clinical progression, showed significant changes suggesting an increase in network connectivity (p < 0.001) and efficiency (p < 0.02), not observed in controls. CONCLUSION SCNs are abnormal in early PPMS. Longitudinal SCN changes demonstrated a switch from low- to high-efficiency networks especially among fast progressors, indicating their clinical relevance.
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Affiliation(s)
- Carmen Tur
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Baris Kanber
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Arman Eshaghi
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Computer Science, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Dan R Altmann
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Statistics, London School of Hygiene & Tropical Medicine, University of London, London, UK
| | - Zhaleh Khaleeli
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Sebastian Ourselin
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK/School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London and St Thomas' Hospital, London, UK
| | - Alan J Thompson
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Claudia Am Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy/Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Ahmed T Toosy
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
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98
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A preliminary study of disrupted functional network in individuals with Internet gaming disorder: Evidence from the comparison with recreational game users. Addict Behav 2020; 102:106202. [PMID: 31801105 DOI: 10.1016/j.addbeh.2019.106202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 08/27/2019] [Accepted: 11/03/2019] [Indexed: 12/13/2022]
Abstract
Although online gaming may lead to Internet gaming disorder (IGD), most players are recreational game users (RGU) who do not develop IGD. So far, the topological organization of whole-brain functional networks in IGD remains poorly understood. The inclusion of RGU as a control group could minimize the potential effects of gaming experience and gaming-related cue familiarity on the neural characteristics of IGD subjects. In the present study, we applied graph theoretical analysis to preliminarily explore the topological organization of intrinsic functional brain networks in IGD. 61 IGD participants and 61 matched RGU participants were recruited to undergo a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and graph-based approaches were applied to analysis their topological attributes, including small-world, efficiency, and nodal centralities. Both of IGD and RGU groups showed efficient and economic small-world topology in brain functional networks. Although there was no significant group difference in global properties, subjects with IGD as compared to those with RGU showed increased nodal centralities in the reward, craving, emotional memory and sensory-motor processing regions. These results suggest that the functional network dysfunction, characterizing by heightened incentive motivation and sensory-motor coordination, may provide a new perspective for understanding the neural characteristics underlying IGD.
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Hawkins R, Shatil AS, Lee L, Sengupta A, Zhang L, Morrow S, Aviv RI. Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment. AJNR Am J Neuroradiol 2020; 41:449-455. [PMID: 32079601 DOI: 10.3174/ajnr.a6435] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 01/11/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment. MATERIALS AND METHODS We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores. RESULTS Greater path length (r = -0.28, P < .0057) and lower normalized path length (r = 0.36, P < .0004) demonstrated a correlation with average cognition when comparing healthy controls with patients with MS. Similarly, MS with cognitive impairment demonstrated a correlation between lower normalized path length (r = 0.40, P < .001) and reduced average cognition. Increased normalized path length was associated with better performance for processing (P < .001), learning (P < .001), and executive domain function (P = .0235), while reduced path length was associated with better executive (P = .0031) and visual domains. Normalized path length improved prediction for processing (R 2 = 43.6%, G2 = 20.9; P < .0001) and learning (R 2 = 40.4%, G2 = 26.1; P < .0001) over a null model comprising confounders. Similarly, higher normalized path length improved prediction of average z scores (G2 = 21.3; P < .0001) and, combined with WM volume, explained 52% of average cognition variance. CONCLUSIONS Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive z score prediction, explaining 52% of variance.
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Affiliation(s)
- R Hawkins
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - A S Shatil
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - L Lee
- Division of Neurology (L.L.), Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - A Sengupta
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - L Zhang
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - S Morrow
- Division of Neurology (S.M.), Lawson Health Research Institute, London Health Sciences Centre, University Hospital, London, Ontario, Canada
| | - R I Aviv
- Institute of Biomaterials and Biomedical Engineering (R.I.A.), University of Toronto, Toronto, Ontario, Canada .,Department of Radiology (R.I.A.), University of Ottawa, and Division of Neuroradiology, The Ottawa Hospital, Ottawa, Ontario, Canada
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100
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Xu SY, Lu FM, Wang MY, Hu ZS, Zhang J, Chen ZY, Armada-da-Silva PAS, Yuan Z. Altered Functional Connectivity in the Motor and Prefrontal Cortex for Children With Down's Syndrome: An fNIRS Study. Front Hum Neurosci 2020; 14:6. [PMID: 32116599 PMCID: PMC7034312 DOI: 10.3389/fnhum.2020.00006] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 01/09/2020] [Indexed: 11/14/2022] Open
Abstract
Children with Down's syndrome (DS) might exhibit disrupted brain functional connectivity in the motor and prefrontal cortex. To inspect the alterations in brain activation and functional connectivity for children with DS, the functional near-infrared spectroscopy (fNIRS) method was applied to examine the brain activation difference in the motor and prefrontal cortex between the DS and typically developing (TD) groups during a fine motor task. In addition, small-world analysis based on graph theory was also carried out to characterize the topological organization of functional brain networks. Interestingly, behavior data demonstrated that the DS group showed significantly long reaction time and low accuracy as compared to the TD group (p < 0.05). More importantly, significantly reduced brain activations in the frontopolar area, the pre-motor, and the supplementary motor cortex (p < 0.05) were identified in the DS group compared with the TD group. Meanwhile, significantly high global efficiency (E g ) and short average path length (L p ) were also detected for the DS group. This pilot study illustrated that the disrupted connectivity of frontopolar area, pre-motor, and supplementary motor cortex might be one of the core mechanisms associated with motor and cognitive impairments for children with DS. Therefore, the combination of the fNIRS technique with functional network analysis may pave a new avenue for improving our understanding of the neural mechanisms of DS.
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Affiliation(s)
- Shi-Yang Xu
- Faculty of Health Sciences, University of Macau, Macau, China
- Centre for Cognitive and Brain Sciences, University of Macau, Macau, China
| | - Feng-Mei Lu
- Faculty of Health Sciences, University of Macau, Macau, China
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Meng-Yun Wang
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Zhi-Shan Hu
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Juan Zhang
- Faculty of Education, University of Macau, Macau, China
| | - Zhi-Yi Chen
- Department of Ultrasound Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Paulo A. S. Armada-da-Silva
- Faculty of Human Kinetics, University of Lisbon, Cruz Quebrada, Portugal
- Neuromechanics of Human Movement, Faculty of Human Kinetics, CIPER, University of Lisbon, Lisbon, Portugal
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Macau, China
- Centre for Cognitive and Brain Sciences, University of Macau, Macau, China
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