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Hou W, Sours Rhodes C, Jiang L, Roys S, Zhuo J, JaJa J, Gullapalli RP. Dynamic Functional Network Analysis in Mild Traumatic Brain Injury. Brain Connect 2020; 9:475-487. [PMID: 30982332 DOI: 10.1089/brain.2018.0629] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is one of the most common neurological disorders for which a subset of patients develops persistent postconcussive symptoms. Previous studies discovered abnormalities and disruptions in the brain functional networks of mTBI patients principally using static functional connectivity measures which assume that neural communication across the brain is static during resting state conditions. In this study, we examine the differences in dynamic neural communication between mTBI and control participants through the application of a combination of dynamic functional analysis and graph theoretic algorithms. Resting state functional magnetic resonance imaging data was obtained on 47 mTBI patients at the acute stage of injury and 30 demographically matched healthy control participants. Results show unique alterations in both the static and dynamic functional connectivity at the acute stage in mTBI patients who suffer persistent symptoms (≥6 months after injury). In addition, mTBI patients with postconcussion syndrome demonstrated a unique allocation of time in various brain states compared to both control participants and mTBI patients with favorable outcomes. These findings suggest that global damage to the overall communication across the brain in the acute stage may contribute to chronic mTBI symptoms. Dynamic functional analysis is a powerful tool that provides insights into the brain states and the innovative analysis methodology utilized may hold the potential to delineate patients predisposed to poor outcomes upon early presentation following injury.
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Affiliation(s)
- Wenshuai Hou
- 1 Department of Electrical and Computer Engineering, University of Maryland Institute for Advanced Computer Services (UMIACS), College Park, Maryland
| | - Chandler Sours Rhodes
- 2 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Li Jiang
- 2 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Steven Roys
- 2 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Jiachen Zhuo
- 2 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joseph JaJa
- 1 Department of Electrical and Computer Engineering, University of Maryland Institute for Advanced Computer Services (UMIACS), College Park, Maryland
| | - Rao P Gullapalli
- 2 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
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Lusher JD, Ji JX, Orr JM. Implementation of High-Performance Correlation and Mapping Engine for Rapid Generation of Brain Connectivity Networks from Big fMRI Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1032-1036. [PMID: 30440567 DOI: 10.1109/embc.2018.8512413] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the emergence of the dynamic functional connectivity analysis, and the studies relying on real-time neurological feedback, the need for rapid processing methods becomes even more critical. Seed-based Correlation Analysis (SCA) of fMRI data has been used to create brain connectivity networks. With close to a million voxels in a fMRI dataset, the number of calculations involved in SCA becomes high. This work aims to demonstrate a new approach which produces high-resolution brain connectivity maps rapidly. The results show that HPCME with four FPGAs can improve the SCA processing speed by a factor of 40 or more over that of a PC workstation with a multicore CPU.
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Liu J, Li S, Liang J, Jiang Y, Wan Y, Zhou S, Cheng W. ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer. Cancer Manag Res 2019; 11:2379-2392. [PMID: 30988639 PMCID: PMC6438265 DOI: 10.2147/cmar.s189784] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable. Materials and methods Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples. Results We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA. Conclusion In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.
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Affiliation(s)
- JinHui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
| | - SiYue Li
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
| | - JunYa Liang
- Hypertension Research Center, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu, China
| | - Yi Jiang
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
| | - YiCong Wan
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
| | - ShuLin Zhou
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
| | - WenJun Cheng
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China,
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Kerepesi C, Varga B, Szalkai B, Grolmusz V. The dorsal striatum and the dynamics of the consensus connectomes in the frontal lobe of the human brain. Neurosci Lett 2018; 673:51-55. [PMID: 29496609 DOI: 10.1016/j.neulet.2018.02.052] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 02/07/2018] [Accepted: 02/23/2018] [Indexed: 11/15/2022]
Abstract
In the applications of the graph theory, it is unusual that one considers numerous, pairwise different graphs on the very same set of vertices. In the case of human braingraphs or connectomes, however, this is the standard situation: the nodes correspond to anatomically identified cerebral regions, and two vertices are connected by an edge if a diffusion MRI-based workflow identifies a fiber of axons, running between the two regions, corresponding to the two vertices. Therefore, if we examine the braingraphs of n subjects, then we have n graphs on the very same, anatomically identified vertex set. It is a natural idea to describe the k-frequently appearing edges in these graphs: the edges that are present between the same two vertices in at least k out of the n graphs. Based on the NIH-funded large Human Connectome Project's public data release, we have reported the construction of the Budapest Reference Connectome Server http://www.connectome.pitgroup.org that generates and visualizes these k-frequently appearing edges. We call the graphs of the k-frequently appearing edges "k-consensus connectomes" since an edge could be included only if it is present in at least k graphs out of n. Considering the whole human brain, we have reported a surprising property of these consensus connectomes earlier. In the present work we are focusing on the frontal lobe of the brain, and we report here a similarly surprising dynamical property of the consensus connectomes when k is gradually changed from k = n to k = 1: the connections between the nodes of the frontal lobe are seemingly emanating from those nodes that were connected to sub-cortical structures of the dorsal striatum: the caudate nucleus, and the putamen. We hypothesize that this dynamic behavior copies the axonal fiber development of the frontal lobe. An animation of the phenomenon is presented at https://youtu.be/wBciB2eW6_8.
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Affiliation(s)
- Csaba Kerepesi
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; Institute for Computer Science and Control (MTA SZTAKI), Hungarian Academy of Sciences.
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Balázs Szalkai
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; Uratim Ltd., H-1118 Budapest, Hungary.
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Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology. Harv Rev Psychiatry 2017; 25:209-217. [PMID: 28816791 PMCID: PMC5644502 DOI: 10.1097/hrp.0000000000000166] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. This review focuses on the use of resting-state functional connectivity MRI to assess brain circuits, on the advances generated by the Human Connectome Project, and on how these advances potentially contribute to understanding neural circuit dysfunction in psychopathology. The review gives particular attention to the methods developed by the Human Connectome Project that may be especially relevant to studies of psychopathology; it outlines some of the key findings about what constitutes a brain region; and it highlights new information about the nature and stability of brain circuits. Some of the Human Connectome Project's new findings particularly relevant to psychopathology-about neural circuits and their relationships to behavior-are also presented. The review ends by discussing the extension of Human Connectome Project methods across the lifespan and into manifest illness. Potential treatment implications are also considered.
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Loewe K, Donohue SE, Schoenfeld MA, Kruse R, Borgelt C. Memory-Efficient Analysis of Dense Functional Connectomes. Front Neuroinform 2016; 10:50. [PMID: 27965565 PMCID: PMC5126118 DOI: 10.3389/fninf.2016.00050] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 10/31/2016] [Indexed: 12/22/2022] Open
Abstract
The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.
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Affiliation(s)
- Kristian Loewe
- Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Department of Computer Science, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany
| | - Sarah E Donohue
- Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany; Center for Cognitive Neuroscience, Duke UniversityDurham, NC, USA
| | - Mircea A Schoenfeld
- Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany; Kliniken SchmiederAllensbach, Germany
| | - Rudolf Kruse
- Department of Computer Science, Otto-von-Guericke University Magdeburg, Germany
| | - Christian Borgelt
- Department of Computer Science, Otto-von-Guericke University Magdeburg, Germany
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Minati L, Chiesa P, Tabarelli D, D'Incerti L, Jovicich J. Synchronization, non-linear dynamics and low-frequency fluctuations: analogy between spontaneous brain activity and networked single-transistor chaotic oscillators. CHAOS (WOODBURY, N.Y.) 2015; 25:033107. [PMID: 25833429 PMCID: PMC5848689 DOI: 10.1063/1.4914938] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 03/02/2015] [Indexed: 05/11/2023]
Abstract
In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.
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Affiliation(s)
- Ludovico Minati
- Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Pietro Chiesa
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Davide Tabarelli
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Ludovico D'Incerti
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
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