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Chen D, Jiang J, Lu J, Wu P, Zhang H, Zuo C, Shi K. Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia. Front Neurol 2019; 10:369. [PMID: 31031697 PMCID: PMC6473028 DOI: 10.3389/fneur.2019.00369] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 03/25/2019] [Indexed: 12/17/2022] Open
Abstract
Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres.
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Affiliation(s)
- Danyan Chen
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Kuangyu Shi
- Department Nuclear Medicine, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
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2
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Crimi A, Giancardo L, Sambataro F, Gozzi A, Murino V, Sona D. MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis. Sci Rep 2019; 9:65. [PMID: 30635604 PMCID: PMC6329758 DOI: 10.1038/s41598-018-37300-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 11/23/2018] [Indexed: 01/09/2023] Open
Abstract
The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.
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Affiliation(s)
- Alessandro Crimi
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy. .,Institute of Neuropathology, University Hospital of Zürich, Zürich, Switzerland.
| | - Luca Giancardo
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy.,Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Fabio Sambataro
- Department of Experimental and Clinical Medical Sciences, University of Udine, Udine, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Vittorio Murino
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy.,Department of Computer Science, University of Verona, Verona, Italy
| | - Diego Sona
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy.,Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, Italy
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3
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Sudarov A, Zhang XJ, Braunstein L, Castro EL, Singh S, Taniguchi Y, Raj A, Shi SH, Moore H, Ross ME. Mature Hippocampal Neurons Require LIS1 for Synaptic Integrity: Implications for Cognition. Biol Psychiatry 2018; 83:518-529. [PMID: 29150182 PMCID: PMC5809292 DOI: 10.1016/j.biopsych.2017.09.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 08/23/2017] [Accepted: 09/07/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND Platelet-activating factor acetylhydrolase 1B1 (LIS1), a critical mediator of neuronal migration in developing brain, is expressed throughout life. However, relatively little is known about LIS1 function in the mature brain. We previously demonstrated that LIS1 involvement in the formation and turnover of synaptic protrusions and synapses of young brain after neuronal migration is complete. Here we examine the requirement for LIS1 to maintain hippocampal circuit function in adulthood. METHODS Effects of conditional Lis1 inactivation in excitatory pyramidal neurons, starting in juvenile mouse brain, were probed using high-resolution approaches combining mouse genetics, designer receptor exclusively activated by designer drug technology to specifically manipulate CA1 pyramidal neuron excitatory activity, electrophysiology, hippocampus-selective behavioral testing, and magnetic resonance imaging tractography to examine the connectivity of LIS1-deficient neurons. RESULTS We found progressive excitatory and inhibitory postsynaptic dysfunction as soon as 10 days after conditional inactivation of Lis1 targeting CA1 pyramidal neurons. Surprisingly, by postnatal day 60 it also caused CA1 histological disorganization, with a selective decline in parvalbumin-expressing interneurons and further reduction in inhibitory neurotransmission. Accompanying these changes were behavioral and cognitive deficits that could be rescued by either designer receptor exclusively activated by designer drug-directed specific increases in CA1 excitatory transmission or pharmacological enhancement of gamma-aminobutyric acid transmission. Lagging behind electrophysiological changes was a progressive, selective decline in neural connectivity, affecting hippocampal efferent pathways documented by magnetic resonance imaging tractography. CONCLUSIONS LIS1 supports synaptic function and plasticity of mature CA1 neurons. Postjuvenile loss of LIS1 disrupts the structure and cellular composition of the hippocampus, its connectivity with other brain regions, and cognition dependent on hippocampal circuits.
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Affiliation(s)
- Anamaria Sudarov
- Center for Neurogenetics, Weill Cornell Medical College,Feil Family Brain & Mind Research Institute, Weill Cornell Medical College
| | - Xin-Jun Zhang
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
| | - Leighton Braunstein
- Center for Neurogenetics, Weill Cornell Medical College,Feil Family Brain & Mind Research Institute, Weill Cornell Medical College
| | | | - Shawn Singh
- Center for Neurogenetics, Weill Cornell Medical College,Feil Family Brain & Mind Research Institute, Weill Cornell Medical College
| | - Yu Taniguchi
- Center for Neurogenetics, Weill Cornell Medical College,Feil Family Brain & Mind Research Institute, Weill Cornell Medical College
| | - Ashish Raj
- Feil Family Brain & Mind Research Institute, Weill Cornell Medical College;,Radiology, Weill Cornell Medical College
| | - Song-Hai Shi
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
| | - Holly Moore
- New York Psychiatric Institute,Department of Psychiatry, Columbia University Medical Center
| | - M. Elizabeth Ross
- Center for Neurogenetics, Weill Cornell Medical College,Feil Family Brain & Mind Research Institute, Weill Cornell Medical College
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4
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Machado C, Rodríguez R, Estévez M, Leisman G, Melillo R, Chinchilla M, Portela L. Anatomic and Functional Connectivity Relationship in Autistic Children During Three Different Experimental Conditions. Brain Connect 2015; 5:487-96. [PMID: 26050707 DOI: 10.1089/brain.2014.0335] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
A group of 21 autistic children were studied for determining the relationship between the anatomic (AC) versus functional (FC) connectivity, considering short-range and long-range brain networks. AC was assessed by the DW-MRI technique and FC by EEG coherence calculation, in three experimental conditions: basal, watching a popular cartoon with audio (V-A), and with muted audio track (VwA). For short-range connections, basal records, statistical significant correlations were found for all EEG bands in the left hemisphere, but no significant correlations were found for fast EEG frequencies in the right hemisphere. For the V-A condition, significant correlations were mainly diminished for the left hemisphere; for the right hemisphere, no significant correlations were found for the fast EEG frequency bands. For the VwA condition, significant correlations for the rapid EEG frequencies mainly disappeared for the right hemisphere. For long-range connections, basal records showed similar correlations for both hemispheres. For the right hemisphere, significant correlations incremented to all EEG bands for the V-A condition, but these significant correlations disappeared for the fast EEG frequencies in the VwA condition. It appears that in a resting-state condition, AC is better associated with functional connectivity for short-range connections in the left hemisphere. The V-A experimental condition enriches the AC and FC association for long-range connections in the right hemisphere. This might be related to an effective connectivity improvement due to full video stimulation (visual and auditory). An impaired audiovisual interaction in the right hemisphere might explain why significant correlations disappeared for the fast EEG frequencies in the VwA experimental condition.
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Affiliation(s)
- Calixto Machado
- 1 Department of Clinical Neurophysiology, Institute of Neurology and Neurosurgery , Havana, Cuba
| | - Rafael Rodríguez
- 2 International Center for Neurological Restoration , Havana, Cuba
| | - Mario Estévez
- 1 Department of Clinical Neurophysiology, Institute of Neurology and Neurosurgery , Havana, Cuba
| | - Gerry Leisman
- 3 The National Institute for Brain & Rehabilitation Sciences , Nazareth, Israel .,4 Biomechanics Laboratory, O.R.T.-Braude College of Engineering , Karmiel, Israel .,5 Facultad Manuel Fajardo, University of the Medical Sciences , Havana, Cuba
| | - Robert Melillo
- 6 Institute for Brain and Rehabilitation Science , Gilbert, Arizona
| | - Mauricio Chinchilla
- 1 Department of Clinical Neurophysiology, Institute of Neurology and Neurosurgery , Havana, Cuba
| | - Liana Portela
- 1 Department of Clinical Neurophysiology, Institute of Neurology and Neurosurgery , Havana, Cuba
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5
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Sanz-Leon P, Knock SA, Spiegler A, Jirsa VK. Mathematical framework for large-scale brain network modeling in The Virtual Brain. Neuroimage 2015; 111:385-430. [PMID: 25592995 DOI: 10.1016/j.neuroimage.2015.01.002] [Citation(s) in RCA: 157] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 12/29/2014] [Accepted: 01/01/2015] [Indexed: 12/19/2022] Open
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6
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Batalle D, Muñoz-Moreno E, Arbat-Plana A, Illa M, Figueras F, Eixarch E, Gratacos E. Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction. Neuroimage 2014; 100:24-38. [DOI: 10.1016/j.neuroimage.2014.05.065] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Revised: 05/14/2014] [Accepted: 05/25/2014] [Indexed: 10/25/2022] Open
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7
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Probabilistic diffusion tractography reveals improvement of structural network in musicians. PLoS One 2014; 9:e105508. [PMID: 25157896 PMCID: PMC4144874 DOI: 10.1371/journal.pone.0105508] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Accepted: 07/24/2014] [Indexed: 11/25/2022] Open
Abstract
Purpose Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. Methods Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. Results Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. Conclusions We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.
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8
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Pérez A, García-Pentón L, Canales-Rodríguez EJ, Lerma-Usabiaga G, Iturria-Medina Y, Román FJ, Davidson D, Alemán-Gómez Y, Acha J, Carreiras M. Brain morphometry of Dravet syndrome. Epilepsy Res 2014; 108:1326-34. [PMID: 25048308 DOI: 10.1016/j.eplepsyres.2014.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 06/06/2014] [Accepted: 06/28/2014] [Indexed: 01/12/2023]
Abstract
The aim of this study was to identify differential global and local brain structural patterns in Dravet Syndrome (DS) patients as compared with a control subject group, using brain morphometry techniques which provide a quantitative whole-brain structural analysis that allows for specific patterns to be generalized across series of individuals. Nine patients with the diagnosis of DS that tested positive for mutation in the SCN1A gene and nine well-matched healthy controls were investigated using voxel brain morphometry (VBM), cortical thickness and cortical gyrification measurements. Global volume reductions of gray matter (GM) and white matter (WM) were related to DS. Local volume reductions corresponding to several white matter regions in brainstem, cerebellum, corpus callosum, corticospinal tracts and association fibers (left inferior fronto-occipital fasciculus and left uncinate fasciculus) were also found. Furthermore, DS showed a reduced cortical folding in the right precentral gyrus. The present findings describe DS-related brain structure abnormalities probably linked to the expression of the SCN1A mutation.
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Affiliation(s)
- Alejandro Pérez
- Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain.
| | - Lorna García-Pentón
- Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain
| | - Erick J Canales-Rodríguez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSam), 28007 Madrid, Spain; FIDMAG Germanes Hospitalàries, 08830, Sant Boi de Llobregat, Barcelona, Spain
| | | | | | - Francisco J Román
- Facultad de Psicología, Departamento de Psicología Biológica y de la Salud, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Doug Davidson
- Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain
| | - Yasser Alemán-Gómez
- Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, HGUGM, CIBERSAM, Madrid, Spain
| | - Joana Acha
- Euskal Herriko Unibertsitatea/Universidad del País Vasco EHU/UPV, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain; Euskal Herriko Unibertsitatea/Universidad del País Vasco EHU/UPV, Bilbao, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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9
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Xue K, Luo C, Zhang D, Yang T, Li J, Gong D, Chen L, Medina YI, Gotman J, Zhou D, Yao D. Diffusion tensor tractography reveals disrupted structural connectivity in childhood absence epilepsy. Epilepsy Res 2014; 108:125-38. [PMID: 24246142 DOI: 10.1016/j.eplepsyres.2013.10.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2012] [Revised: 09/01/2013] [Accepted: 10/13/2013] [Indexed: 10/26/2022]
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10
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Iturria-Medina Y. Anatomical brain networks on the prediction of abnormal brain states. Brain Connect 2013; 3:1-21. [PMID: 23249224 DOI: 10.1089/brain.2012.0122] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Graph-based brain anatomical network analysis models the brain as a graph whose nodes represent structural/functional regions, whereas the links between them represent nervous fiber connections. Initial studies of brain anatomical networks using this approach were devoted to describe the key organizational principles of the normal brain, while current trends seem to be more focused on detecting network alterations associated to specific brain disorders. Anatomical networks reconstructed using diffusion-weighed magnetic resonance-imaging techniques can be particularly useful in predicting abnormal brain states in which the white matter structure and, subsequently, the interconnections between gray matter regions are altered (e.g., due to the presence of diseases such as schizophrenia, stroke, multiple sclerosis, and dementia). This article offers an overview from early gross connectional anatomy explorations until more recent advances on anatomical brain network reconstruction approaches, with a specific focus on how the latter move toward the prediction of abnormal brain states. While anatomical graph-based predictor approaches are still at an early stage, they bear promising implications for individualized clinical diagnosis of neurological and psychiatric disorders, as well as for neurodevelopmental evaluations and subsequent assisted creation of educational strategies related to specific cognitive disorders.
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11
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Networks of anatomical covariance. Neuroimage 2013; 80:489-504. [PMID: 23711536 DOI: 10.1016/j.neuroimage.2013.05.054] [Citation(s) in RCA: 296] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2013] [Revised: 05/08/2013] [Accepted: 05/09/2013] [Indexed: 01/18/2023] Open
Abstract
Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.
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12
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Fekete T, Wilf M, Rubin D, Edelman S, Malach R, Mujica-Parodi LR. Combining classification with fMRI-derived complex network measures for potential neurodiagnostics. PLoS One 2013; 8:e62867. [PMID: 23671641 PMCID: PMC3646016 DOI: 10.1371/journal.pone.0062867] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Accepted: 03/28/2013] [Indexed: 11/21/2022] Open
Abstract
Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method’s applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.
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Affiliation(s)
- Tomer Fekete
- Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York, United States of America
| | - Meytal Wilf
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Denis Rubin
- Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York, United States of America
| | - Shimon Edelman
- Department of Psychology, Cornell University, Ithaca, New York, United States of America
| | - Rafael Malach
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Lilianne R. Mujica-Parodi
- Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York, United States of America
- * E-mail:
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Melie-García L, Sanabria-Diaz G, Sánchez-Catasús C. Studying the topological organization of the cerebral blood flow fluctuations in resting state. Neuroimage 2012; 64:173-84. [PMID: 22975159 DOI: 10.1016/j.neuroimage.2012.08.082] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Revised: 06/25/2012] [Accepted: 08/29/2012] [Indexed: 12/21/2022] Open
Abstract
In this paper the cerebral blood flow (CBF) in resting state obtained from SPECT imaging is employed as a hemodynamics descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in CBF between pairs of regions was measured by computing the Pearson correlation coefficient across 31 normal subjects. We demonstrated the CBF connectivity matrices follow 'small-world' attributes similar to previous studies using different modalities of neuroimaging data (MRI, fMRI, EEG, MEG). The highest concurrent fluctuations in CBF were detected between homologous cortical regions (homologous callosal connections). It was found that the existence of structural core regions or hubs positioned on a high proportion of shortest paths within the CBF network. These were anatomically distributed in frontal, limbic, occipital and parietal regions that suggest its important role in functional integration. Our findings point to a new possibility of using CBF variable to investigate the brain networks based on graph theory in normal and pathological states. Likewise, it opens a window to future studies to link covariation between morphometric descriptors, axonal connectivity and CBF processes with a potential diagnosis applications.
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