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Oltmer J, Mattern H, Beck J, Yakupov R, Greenberg SM, Zwanenburg JJ, Arts T, Düzel E, van Veluw SJ, Schreiber S, Perosa V. Enlarged perivascular spaces in the basal ganglia are associated with arteries not veins. J Cereb Blood Flow Metab 2024:271678X241260629. [PMID: 38863151 DOI: 10.1177/0271678x241260629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
Enlarged perivascular spaces (EPVS) are common in cerebral small vessel disease (CSVD) and have been identified as a marker of dysfunctional brain clearance. However, it remains unknown if the enlargement occurs predominantly around arteries or veins. We combined in vivo ultra-high-resolution MRI and histopathology to investigate the spatial relationship of veins and arteries with EPVS within the basal ganglia (BG). Furthermore, we assessed the relationship between the EPVS and measures of blood-flow (blood-flow velocity, pulsatility index) in the small arteries of the BG. Twenty-four healthy controls, twelve non-CAA CSVD patients, and five probable CAA patients underwent a 3 tesla [T] and 7T MRI-scan, and EPVS, arteries, and veins within the BG were manually segmented. Furthermore, the scans were co-registered. Six autopsy-cases were also assessed. In the BG, EPVS were significantly closer to and overlapped more frequently with arteries than with veins. Histological analysis showed a higher proportion of BG EPVS surrounding arteries than veins. Finally, the pulsatility index of BG arteries correlated with EPVS volume. Our results are in line with previous works and establish a pathophysiological relationship between arteries and EPVS, contributing to elucidating perivascular clearance routes in the human brain.
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Affiliation(s)
- Jan Oltmer
- Athinoula A. Martinos Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Digital Health & Innovation, Vivantes Netzwerk für Gesundheit GmbH, Berlin, Germany
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Biomedical Magnetic Resonance (BMMR), Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Julia Beck
- Department of Neurology, Otto-Von-Guericke University, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Steven M Greenberg
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaco Jm Zwanenburg
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tine Arts
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Susanne J van Veluw
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital, MassGeneral Institute for Neurodegenerative Disease, Charlestown, MA, USA
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Department of Neurology, Otto-Von-Guericke University, Magdeburg, Germany
- Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Valentina Perosa
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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2
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Biswas R, Sripada S. Causal functional connectivity in Alzheimer's disease computed from time series fMRI data. Front Comput Neurosci 2023; 17:1251301. [PMID: 38169714 PMCID: PMC10758424 DOI: 10.3389/fncom.2023.1251301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
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Affiliation(s)
- Rahul Biswas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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3
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Penalba-Sánchez L, Oliveira-Silva P, Sumich AL, Cifre I. Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study. Front Aging Neurosci 2023; 14:1037347. [PMID: 36698861 PMCID: PMC9869068 DOI: 10.3389/fnagi.2022.1037347] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. Methods In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. Results Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. Conclusion The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain,Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal,NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom,*Correspondence: Lucía Penalba-Sánchez,
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Alexander Luke Sumich
- NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom
| | - Ignacio Cifre
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
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4
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Hersche M, Lippuner S, Korb M, Benini L, Rahimi A. Near-channel classifier: symbiotic communication and classification in high-dimensional space. Brain Inform 2021; 8:16. [PMID: 34403011 PMCID: PMC8371050 DOI: 10.1186/s40708-021-00138-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 07/29/2021] [Indexed: 12/02/2022] Open
Abstract
Brain-inspired high-dimensional (HD) computing represents and manipulates data using very long, random vectors with dimensionality in the thousands. This representation provides great robustness for various classification tasks where classifiers operate at low signal-to-noise ratio (SNR) conditions. Similarly, hyperdimensional modulation (HDM) leverages the robustness of complex-valued HD representations to reliably transmit information over a wireless channel, achieving a similar SNR gain compared to state-of-the-art codes. Here, we first propose methods to improve HDM in two ways: (1) reducing the complexity of encoding and decoding operations by generating, manipulating, and transmitting bipolar or integer vectors instead of complex vectors; (2) increasing the SNR gain by 0.2 dB using a new soft-feedback decoder; it can also increase the additive superposition capacity of HD vectors up to 1.7\documentclass[12pt]{minimal}
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\begin{document}$$\times$$\end{document}× in noise-free cases. Secondly, we propose to combine encoding/decoding aspects of communication with classification into a single framework by relying on multifaceted HD representations. This leads to a near-channel classification (NCC) approach that avoids transformations between different representations and the overhead of multiple layers of encoding/decoding, hence reducing latency and complexity of a wireless smart distributed system while providing robustness against noise and interference from other nodes. We provide a use-case for wearable hand gesture recognition with 5 classes from 64 EMG sensors, where the encoded vectors are transmitted to a remote node for either performing NCC, or reconstruction of the encoded data. In NCC mode, the original classification accuracy of 94% is maintained, even in the channel at SNR of 0 dB, by transmitting 10,000-bit vectors. We remove the redundancy by reducing the vector dimensionality to 2048-bit that still exhibits a graceful degradation: less than 6% accuracy loss is occurred in the channel at − 5 dB, and with the interference from 6 nodes that simultaneously transmit their encoded vectors. In the reconstruction mode, it improves the mean-squared error by up to 20 dB, compared to standard decoding, when transmitting 2048-dimensional vectors.
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Affiliation(s)
- Michael Hersche
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland. .,IBM Research-Zurich, Zurich, Switzerland.
| | - Stefan Lippuner
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Matthias Korb
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland.,Institute of Microelectronics and Integrated Circuits, Bundeswehr University, Munich, Germany
| | - Luca Benini
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland.,Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy
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5
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [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: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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6
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Lei B, Wu F, Zhou J, Xiong D, Wang K, Kong L, Ke P, Chen J, Ning Y, Li X, Xiang Z, Wu K. NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data. Neuroinformatics 2021; 19:79-91. [PMID: 32524429 DOI: 10.1007/s12021-020-09468-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.
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Affiliation(s)
- Bingye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China
| | - Fengchun Wu
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Kaixi Wang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, China
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China
| | - Xiaobo Li
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhiming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
- Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, 511400, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China.
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China.
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China.
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, China.
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China.
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575, Japan.
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7
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Kumar K, Toews M, Chauvin L, Colliot O, Desrosiers C. Multi-modal brain fingerprinting: A manifold approximation based framework. Neuroimage 2018; 183:212-226. [PMID: 30099077 DOI: 10.1016/j.neuroimage.2018.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 06/22/2018] [Accepted: 08/02/2018] [Indexed: 12/01/2022] Open
Abstract
This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting-state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis.
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Affiliation(s)
- Kuldeep Kumar
- Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, 1100 Notre-Dame W., Montreal, QC, H3C1K3, Canada; Inria Paris, Aramis Project-Team, 75013, Paris, France.
| | - Matthew Toews
- Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, 1100 Notre-Dame W., Montreal, QC, H3C1K3, Canada
| | - Laurent Chauvin
- Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, 1100 Notre-Dame W., Montreal, QC, H3C1K3, Canada
| | - Olivier Colliot
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM) - Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France; Inria Paris, Aramis Project-Team, 75013, Paris, France; AP-HP, Departments of Neurology and Neuroradiology, Hôpital Pitié-Salpêtrière, 75013, Paris, France
| | - Christian Desrosiers
- Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, 1100 Notre-Dame W., Montreal, QC, H3C1K3, Canada
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8
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Soares JM, Magalhães R, Moreira PS, Sousa A, Ganz E, Sampaio A, Alves V, Marques P, Sousa N. A Hitchhiker's Guide to Functional Magnetic Resonance Imaging. Front Neurosci 2016; 10:515. [PMID: 27891073 PMCID: PMC5102908 DOI: 10.3389/fnins.2016.00515] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 10/25/2016] [Indexed: 12/12/2022] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.
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Affiliation(s)
- José M. Soares
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Pedro S. Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Alexandre Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
- Department of Informatics, University of MinhoBraga, Portugal
| | - Edward Ganz
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Adriana Sampaio
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of MinhoBraga, Portugal
| | - Victor Alves
- Department of Informatics, University of MinhoBraga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
- Clinical Academic Center – BragaBraga, Portugal
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9
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Yang Y, Zhong N, Imamura K, Lu S, Li M, Zhou H, Li H, Yang X, Wan Z, Wang G, Hu B, Li K. Task and Resting-State fMRI Reveal Altered Salience Responses to Positive Stimuli in Patients with Major Depressive Disorder. PLoS One 2016; 11:e0155092. [PMID: 27192082 PMCID: PMC4871416 DOI: 10.1371/journal.pone.0155092] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 04/25/2016] [Indexed: 02/06/2023] Open
Abstract
Altered brain function in patients with major depressive disorder (MDD) has been repeatedly demonstrated by task-based and resting-state studies, respectively. However, less is known concerning whether overlapped abnormalities in functional activities across modalities exist in MDD patients. To find out the answer, we implemented an fMRI experiment and collected both task and resting-state data from 19 MDD patients and 19 matched, healthy, controls. A distraction paradigm involving emotionally valenced pictures was applied to induce affective responses in subjects. As a result, concurrent deficits were found in arousing activation during a positive task in both the reward circuit and salience network (SN) that is composed of the dorsal part of anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in only the MDD group. Subsequent amplitude of low frequency fluctuations (ALFF) and functional connectivity analyses based on resting-state data exhibited consistent alterations in the bilateral AI of MDD patients, and indicated patients' difficulties in regulating the balance between central executive network (CEN) and default mode network (DMN) due to altered connectivity among the CEN, DMN, and SN. Our findings provide new evidence demonstrating impaired salience processing and resulting alterations in responses to positive stimuli in MDD patients. Furthermore, brain abnormalities synchronized across functional states in MDD patients can be evidenced by a combination of task and resting-state fMRI analyses.
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Affiliation(s)
- Yang Yang
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Ning Zhong
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
- * E-mail:
| | - Kazuyuki Imamura
- Department of Systems Life Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Shengfu Lu
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Mi Li
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Haiyan Zhou
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Huaizhou Li
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Xiaojing Yang
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Zhijiang Wan
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Gang Wang
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Mood Disorders Center, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Bin Hu
- Ubiquitous Awareness and Intelligent Solutions Lab, Lanzhou University, Lanzhou, Gansu, China
| | - Kuncheng Li
- International WIC Institute, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
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10
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Liu S, Cai W, Pujol S, Kikinis R, Feng DD. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci 2016; 8:23. [PMID: 26941639 PMCID: PMC4763344 DOI: 10.3389/fnagi.2016.00023] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 02/01/2016] [Indexed: 01/24/2023] Open
Abstract
The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed nine types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI, and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.
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Affiliation(s)
- Sidong Liu
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
| | - Weidong Cai
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
| | - Sonia Pujol
- The Surgical Planning Laboratory, Harvard Medical School, Brigham and Women's HospitalBoston, MA, USA
| | - Ron Kikinis
- The Surgical Planning Laboratory, Harvard Medical School, Brigham and Women's HospitalBoston, MA, USA
| | - Dagan D. Feng
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
- The Med-X Research Institute, Shanghai Jiao Tong UniversityShanghai, China
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11
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Zheng C, Xia Y, Pan Y, Chen J. Automated identification of dementia using medical imaging: a survey from a pattern classification perspective. Brain Inform 2015; 3:17-27. [PMID: 27747596 PMCID: PMC4883162 DOI: 10.1007/s40708-015-0027-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Accepted: 11/26/2015] [Indexed: 11/28/2022] Open
Abstract
In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer’s disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge.
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Affiliation(s)
- Chuanchuan Zheng
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Yong Xia
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
| | - Yongsheng Pan
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Jinhu Chen
- Department of Radiation Oncology, Shandong Tumor Hospital, Jinan, 250117, People's Republic of China.
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12
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Wong KP, Bergsneider M, Glenn TC, Kepe V, Barrio JR, Hovda DA, Vespa PM, Huang SC. A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury. Brain Inform 2015; 3:1-15. [PMID: 27034916 PMCID: PMC4769312 DOI: 10.1007/s40708-015-0026-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 11/16/2015] [Indexed: 11/24/2022] Open
Abstract
Traumatic brain injury (TBI) is a major cause of mortality and morbidity, placing a significant financial burden on the healthcare system worldwide. Non-invasive neuroimaging technologies have been playing a pivotal role in the study of TBI, providing important information for surgical planning and patient management. Advances in understanding the basic mechanisms and pathophysiology of the brain following TBI are hindered by a lack of reliable image analysis methods for accurate quantitative assessment of TBI-induced structural and pathophysiological changes seen on anatomical and functional images obtained from multiple imaging modalities. Conventional region-of-interest (ROI) analysis based on manual labeling of brain regions is time-consuming and the results could be inconsistent within and among investigators. In this study, we propose a workflow solution framework that combined the use of non-linear spatial normalization of structural brain images and template-based anatomical labeling to automate the ROI analysis process. The proposed workflow solution is applied to dynamic PET scanning with 15O-water (0–10 min) and 18F-FDDNP (0–6 min) for measuring cerebral blood flow in patients with TBI.
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Affiliation(s)
- Koon-Pong Wong
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Marvin Bergsneider
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Thomas C Glenn
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Vladimir Kepe
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Jorge R Barrio
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - David A Hovda
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA ; Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Paul M Vespa
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA USA ; Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA USA ; Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
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13
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Abstract
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
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14
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Zhang F, Song Y, Cai W, Liu S, Liu S, Pujol S, Kikinis R, Xia Y, Fulham MJ, Feng DD, Alzheimers Disease Neuroimaging Initiative. Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging. IEEE Trans Biomed Eng 2015; 63:1058-1069. [PMID: 26372117 DOI: 10.1109/tbme.2015.2478028] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.
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Affiliation(s)
- Fan Zhang
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, N.S.W., Australia
| | - Yang Song
- Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney
| | - Weidong Cai
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney
| | - Sidong Liu
- Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney
| | - Siqi Liu
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney
| | - Sonia Pujol
- Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School
| | - Ron Kikinis
- Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School
| | - Yong Xia
- Shaanxi Key Lab of Speech and Image Information Processing, School of Computer Science and Technology, Northwestern Polytechnical University
| | - Michael J Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital
| | - David Dagan Feng
- BMIT Research Group, School of Information Technologies, University of Sydney
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15
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform 2015; 2:167-180. [PMID: 27747507 PMCID: PMC4737664 DOI: 10.1007/s40708-015-0019-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 08/08/2015] [Indexed: 12/20/2022] Open
Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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