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Ayyash S, Davis AD, Alders GL, MacQueen G, Strother SC, Hassel S, Zamyadi M, Arnott SR, Harris JK, Lam RW, Milev R, Müller DJ, Kennedy SH, Rotzinger S, Frey BN, Minuzzi L, Hall GB. Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study. IBRO Neurosci Rep 2024; 16:135-146. [PMID: 38293679 PMCID: PMC10826332 DOI: 10.1016/j.ibneur.2023.12.011] [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: 09/24/2023] [Accepted: 12/30/2023] [Indexed: 02/01/2024] Open
Abstract
Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.
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Affiliation(s)
- Sondos Ayyash
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Andrew D Davis
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Glenda MacQueen
- Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Stefanie Hassel
- Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | | | - Jacqueline K Harris
- Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, Ontario, Canada
| | - Daniel J Müller
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Geoffrey B Hall
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
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2
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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3
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Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Front Aging Neurosci 2023; 15:1216163. [PMID: 37539346 PMCID: PMC10394631 DOI: 10.3389/fnagi.2023.1216163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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Affiliation(s)
| | - Andreas Husch
- Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
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4
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Candemir C. Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI. SENSORS (BASEL, SWITZERLAND) 2023; 23:5866. [PMID: 37447716 DOI: 10.3390/s23135866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/17/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. However, there is no sufficient information about the effects of the Gaussian kernel size on group-level results for different cases yet. This study investigates the influence of kernel size on functional connectivity networks and network parameters in whole-brain rs-fMRI and tb-fMRI analyses of healthy adults. The analysis includes {0, 2, 4, 6, 8, 10} mm kernels, commonly used in practical analyses, covering all major brain networks. Graph theoretical measures such as betweenness centrality, global/local efficiency, clustering coefficient, and average path length are examined for each kernel. Additionally, principal component analysis (PCA) and independent component analysis (ICA) parameters, namely kurtosis and skewness, are evaluated for the functional images. The findings demonstrate that kernel size directly affects node connections, resulting in modifications to functional network structures and PCA/ICA parameters. However, network metrics exhibit greater resilience to these changes.
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Affiliation(s)
- Cemre Candemir
- International Computer Institute, Ege University, Izmir 35100, Turkey
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5
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Zhang L, Pini L, Corbetta M. Different MRI structural processing methods do not impact functional connectivity computation. Sci Rep 2023; 13:8589. [PMID: 37237072 DOI: 10.1038/s41598-023-34645-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular technique. This technique can assess several features of brain connectivity, such as inter-regional temporal correlation (functional connectivity), from which graph measures of network organization can be derived. However, these measures are prone to a certain degree of variability depending on the analytical steps during preprocessing. Many studies have investigated the effect of different preprocessing steps on functional connectivity measures; however, no study investigated whether different structural reconstructions lead to different functional connectivity metrics. Here, we evaluated the impact of different structural segmentation strategies on functional connectivity outcomes. To this aim, we compared different metrics computed after two different registration strategies. The first strategy used structural information from the 3D T1-weighted image (unimodal), while the second strategy implemented a multimodal approach, where an additional registration step used the information from the T2-weighted image. The impact of these different approaches was evaluated on a sample of 58 healthy adults. As expected, different approaches led to significant differences in structural measures (i.e., cortical thickness, volume, and gyrification index), with the maximum impact on the insula cortex. However, these differences were only slightly translated to functional metrics. We reported no differences in graph measures and seed-based functional connectivity maps, but slight differences in the insula when we compared the mean functional strength for each parcel. Overall, these results suggested that functional metrics are only slightly different when using a unimodal compared to a multimodal approach, while the structural output can be significantly affected.
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Affiliation(s)
- Lu Zhang
- Padova Neuroscience Center, University of Padova, 35131, Padua, Italy
| | - Lorenzo Pini
- Padova Neuroscience Center, University of Padova, 35131, Padua, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, 35131, Padua, Italy.
- Venetian Institute of Molecular Medicine (VIMM), 35129, Padua, Italy.
- Clinica Neurologica, Department of Neuroscience, University of Padova, 35131, Padua, Italy.
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6
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Manso Jimeno M, Vaughan JT, Geethanath S. Superconducting magnet designs and MRI accessibility: A review. NMR IN BIOMEDICINE 2023:e4921. [PMID: 36914280 DOI: 10.1002/nbm.4921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 02/13/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Presently, magnetic resonance imaging (MRI) magnets must deliver excellent magnetic field (B0 ) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity requirements but require considerable superconducting material. These designs result in large, heavy, and costly systems that aggravate as field strength increases. Furthermore, the tight temperature tolerance of niobium titanium magnets adds instability to the system and requires operation at liquid helium temperature. These issues are crucial factors in the disparity of MR density and field strength use across the globe. Low-income settings show reduced access to MRI, especially to high field strengths. This article summarizes the proposed modifications to MRI superconducting magnet design and their impact on accessibility, including compact, reduced liquid helium, and specialty systems. Reducing the amount of superconductor inevitably entails shrinking the magnet size, resulting in higher field inhomogeneity. This work also reviews the state-of-the-art imaging and reconstruction methods to overcome this issue. Finally, we summarize the current and future challenges and opportunities in the design of accessible MRI.
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Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, New York, USA
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
| | - John Thomas Vaughan
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, New York, USA
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, The Biomedical Engineering and Imaging Institute, New York, New York, USA
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7
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fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines. Brain Topogr 2023; 36:172-191. [PMID: 36575327 PMCID: PMC10014671 DOI: 10.1007/s10548-022-00935-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/18/2022] [Indexed: 12/28/2022]
Abstract
How functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows' structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.
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8
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Warren SL, Moustafa AA. Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review. J Neuroimaging 2023; 33:5-18. [PMID: 36257926 PMCID: PMC10092597 DOI: 10.1111/jon.13063] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 02/01/2023] Open
Abstract
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.
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Affiliation(s)
- Samuel L. Warren
- School of Psychology, Faculty of Society and DesignBond UniversityGold CoastQueenslandAustralia
| | - Ahmed A. Moustafa
- School of Psychology, Faculty of Society and DesignBond UniversityGold CoastQueenslandAustralia
- Department of Human Anatomy and Physiology, Faculty of Health SciencesUniversity of JohannesburgJohannesburgSouth Africa
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9
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Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO, Herholz P, Karakuzu A, Keator DB, Markiewicz CJ, Maumet C, Pernet CR, Pestilli F, Queder N, Schmitt T, Sójka W, Wagner AS, Whitaker KJ, Rieger JW. Open and reproducible neuroimaging: From study inception to publication. Neuroimage 2022; 263:119623. [PMID: 36100172 PMCID: PMC10008521 DOI: 10.1016/j.neuroimage.2022.119623] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/09/2022] [Indexed: 10/31/2022] Open
Abstract
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain.
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Oscar Esteban
- Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rémi Gau
- Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Peer Herholz
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada
| | - Agah Karakuzu
- Biomedical Engineering Institute, Polytechnique Montréal, Montréal, Quebec, Canada; Montréal Heart Institute, Montréal, Quebec, Canada
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | | | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Cyril R Pernet
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Franco Pestilli
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Nazek Queder
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Tina Schmitt
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany
| | - Weronika Sójka
- Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland
| | - Adina S Wagner
- Institute for Neuroscience and Medicine, Research Centre Juelich, Germany
| | | | - Jochem W Rieger
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany.
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10
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van Lutterveld R, Varkevisser T, Kouwer K, van Rooij SJH, Kennis M, Hueting M, van Montfort S, van Dellen E, Geuze E. Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response. Front Hum Neurosci 2022; 16:730745. [PMID: 36034114 PMCID: PMC9413840 DOI: 10.3389/fnhum.2022.730745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 07/21/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Trauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success. Methods Functional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed. Results No differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion. Discussion These results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.
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Affiliation(s)
- Remko van Lutterveld
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
- *Correspondence: Remko van Lutterveld,
| | - Tim Varkevisser
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
| | - Karlijn Kouwer
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
| | - Sanne J. H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Mitzy Kennis
- ARQ National Psychotrauma Centre, ARQ Centre of Expertise for the Impact of Disasters and Crises, Diemen, Netherlands
| | - Martine Hueting
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
| | - Simone van Montfort
- Department of Intensive Care Medicine, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
- Department of Intensive Care Medicine, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Elbert Geuze
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
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11
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Centeno EGZ, Moreni G, Vriend C, Douw L, Santos FAN. A hands-on tutorial on network and topological neuroscience. Brain Struct Funct 2022; 227:741-762. [PMID: 35142909 PMCID: PMC8930803 DOI: 10.1007/s00429-021-02435-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 11/23/2021] [Indexed: 02/08/2023]
Abstract
The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.
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Affiliation(s)
- Eduarda Gervini Zampieri Centeno
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
- Institut Des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, CNRS, Bordeaux Neurocampus, 146 Rue Léo Saignat, 33000, Bordeaux, France
| | - Giulia Moreni
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Chris Vriend
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Linda Douw
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Fernando Antônio Nóbrega Santos
- Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands.
- Institute for Advanced Studies, University of Amsterdam, Oude Turfmarkt 147, 1012 GC, Amsterdam, The Netherlands.
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12
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An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity. Soft comput 2022. [DOI: 10.1007/s00500-022-06890-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis. Schizophr Res 2022; 240:220-227. [PMID: 35074702 DOI: 10.1016/j.schres.2021.12.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/28/2021] [Accepted: 12/24/2021] [Indexed: 11/21/2022]
Abstract
Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.
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14
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Zito GA, Tarrano C, Jegatheesan P, Ekmen A, Béranger B, Rebsamen M, Hubsch C, Sangla S, Bonnet C, Delorme C, Méneret A, Degos B, Bouquet F, Brissard MA, Vidailhet M, Gallea C, Roze E, Worbe Y. Somatotopy of cervical dystonia in motor-cerebellar networks: Evidence from resting state fMRI. Parkinsonism Relat Disord 2021; 94:30-36. [PMID: 34875561 DOI: 10.1016/j.parkreldis.2021.11.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Cervical dystonia is the most frequent form of isolated focal dystonia. It is often associated with a dysfunction in brain networks, mostly affecting the basal ganglia, the cerebellum, and the somatosensory cortex. However, it is unclear if such a dysfunction is somato-specific to the brain areas containing the representation of the affected body part, and may thereby account for the focal expression of cervical dystonia. In this study, we investigated resting state functional connectivity in the areas within the motor cortex and the cerebellum containing affected and non-affected body representations in cervical dystonia patients. METHODS Eighteen patients affected by cervical dystonia and 21 healthy controls had resting state fMRI. The functional connectivity between the motor cortex and the cerebellum, as well as their corresponding measures of gray matter volume and cortical thickness, were compared between groups. We performed seed-based analyses, selecting the different body representation areas in the precentral gyrus as seed regions, and all cerebellar areas as target regions. RESULTS Compared to controls, patients exhibited increased functional connectivity between the bilateral trunk representation area of the motor cortex and the cerebellar vermis 6 and 7b, respectively. These functional abnormalities did not correlate with structural changes or symptom severity. CONCLUSIONS Our findings indicate that the abnormal function of the motor network is somato-specific to the areas encompassing the neck representation. Functional abnormalities in discrete relevant areas of the motor network could thus contribute to the focal expression of CD.
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Affiliation(s)
- Giuseppe A Zito
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Clément Tarrano
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Prasanthi Jegatheesan
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Asya Ekmen
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Benoît Béranger
- Center for NeuroImaging Research CENIR, Paris Brain Institute, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMR 7225, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging SCAN, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, CH, Switzerland.
| | - Cécile Hubsch
- Department of Neurology, Rothschild Foundation, 25-29 Rue Manin, 75019, Paris, France.
| | - Sophie Sangla
- Department of Neurology, Rothschild Foundation, 25-29 Rue Manin, 75019, Paris, France.
| | - Cécilia Bonnet
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Cécile Delorme
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Aurélie Méneret
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Bertrand Degos
- Neurology Unit, Assistance Publique-Hôpitaux de Paris, Avicenne University Hospital, Sorbonne Paris Nord, 125 Rue de Stalingrad, 93000, Bobigny, France; Center for Interdisciplinary Research in Biology, Collège de France, Inserm U1050, CNRS UMR 7241, PSL University, 11 place Marcelin Berthelot, 75231, Paris, France.
| | - Floriane Bouquet
- Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Marion Apoil Brissard
- Department of Neurology, University of Caen Normandie Hospital Center, Av. de la Côte de Nacre, 14000, Caen, France.
| | - Marie Vidailhet
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Cécile Gallea
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Emmanuel Roze
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurology, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
| | - Yulia Worbe
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, 47-83 Boulevard de l'Hôpital, 75013, Paris, France; Department of Neurophysiology, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, 184 Rue du Faubourg Saint-Antoine, 75012, Paris, France.
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15
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Kristensen DG, Sandberg K. Population receptive fields of human primary visual cortex organised as DC-balanced bandpass filters. Sci Rep 2021; 11:22423. [PMID: 34789812 PMCID: PMC8599479 DOI: 10.1038/s41598-021-01891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/29/2021] [Indexed: 11/22/2022] Open
Abstract
The response to visual stimulation of population receptive fields (pRF) in the human visual cortex has been modelled with a Difference of Gaussians model, yet many aspects of their organisation remain poorly understood. Here, we examined the mathematical basis and signal-processing properties of this model and argue that the DC-balanced Difference of Gaussians (DoG) holds a number of advantages over a DC-biased DoG. Through functional magnetic resonance imaging (fMRI) pRF mapping, we compared performance of DC-balanced and DC-biased models in human primary visual cortex and found that when model complexity is taken into account, the DC-balanced model is preferred. Finally, we present evidence indicating that the BOLD signal DC offset contains information related to the processing of visual stimuli. Taken together, the results indicate that V1 pRFs are at least frequently organised in the exact constellation that allows them to function as bandpass filters, which makes the separation of stimulus contrast and luminance possible. We further speculate that if the DoG models stimulus contrast, the DC offset may reflect stimulus luminance. These findings suggest that it may be possible to separate contrast and luminance processing in fMRI experiments and this could lead to new insights on the haemodynamic response.
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Affiliation(s)
- Daniel Gramm Kristensen
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus University, Nørrebrogade 44, Building 1A, 8000, Aarhus C, Denmark.
| | - Kristian Sandberg
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus University, Nørrebrogade 44, Building 1A, 8000, Aarhus C, Denmark
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16
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Bridgeford EW, Wang S, Wang Z, Xu T, Craddock C, Dey J, Kiar G, Gray-Roncal W, Colantuoni C, Douville C, Noble S, Priebe CE, Caffo B, Milham M, Zuo XN, Vogelstein JT. Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. PLoS Comput Biol 2021; 17:e1009279. [PMID: 34529652 PMCID: PMC8500408 DOI: 10.1371/journal.pcbi.1009279] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/08/2021] [Accepted: 07/14/2021] [Indexed: 11/25/2022] Open
Abstract
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.
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Affiliation(s)
| | - Shangsi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Zeyi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Ting Xu
- Child Mind Institute, New York, New York, United States of America
| | - Cameron Craddock
- Child Mind Institute, New York, New York, United States of America
| | - Jayanta Dey
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | | | - Carlo Colantuoni
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Stephanie Noble
- Yale University, New Haven, Connecticut, United States of America
| | - Carey E. Priebe
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Brian Caffo
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael Milham
- Child Mind Institute, New York, New York, United States of America
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Joshua T. Vogelstein
- Johns Hopkins University, Baltimore, Maryland, United States of America
- Progressive Learning, Baltimore, Maryland, United States of America
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17
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Zhu Z, Lei D, Qin K, Suo X, Li W, Li L, DelBello MP, Sweeney JA, Gong Q. Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level. Diagnostics (Basel) 2021; 11:1416. [PMID: 34441350 PMCID: PMC8391111 DOI: 10.3390/diagnostics11081416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/18/2021] [Accepted: 07/28/2021] [Indexed: 02/05/2023] Open
Abstract
Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.
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Affiliation(s)
- Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA; (D.L.); (M.P.D.)
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
| | - Lingjiang Li
- Mental Health Institute, The Second Xiangya Hospital of Central South University, Changsha 410008, China;
| | - Melissa P. DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA; (D.L.); (M.P.D.)
| | - John A. Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA; (D.L.); (M.P.D.)
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; (Z.Z.); (K.Q.); (X.S.); (W.L.); (J.A.S.)
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610000, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu 610000, China
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18
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Frässle S, Aponte EA, Bollmann S, Brodersen KH, Do CT, Harrison OK, Harrison SJ, Heinzle J, Iglesias S, Kasper L, Lomakina EI, Mathys C, Müller-Schrader M, Pereira I, Petzschner FH, Raman S, Schöbi D, Toussaint B, Weber LA, Yao Y, Stephan KE. TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Front Psychiatry 2021; 12:680811. [PMID: 34149484 PMCID: PMC8206497 DOI: 10.3389/fpsyt.2021.680811] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/10/2021] [Indexed: 12/26/2022] Open
Abstract
Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eduardo A. Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Saskia Bollmann
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Charlestown, MA, United States
| | - Kay H. Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Cao T. Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Olivia K. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Samuel J. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Ekaterina I. Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Interacting Minds Center, Aarhus University, Aarhus, Denmark
| | - Matthias Müller-Schrader
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Frederike H. Petzschner
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sudhir Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lilian A. Weber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yu Yao
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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Yang H, Zhang H, Di X, Wang S, Meng C, Tian L, Biswal B. Reproducible coactivation patterns of functional brain networks reveal the aberrant dynamic state transition in schizophrenia. Neuroimage 2021; 237:118193. [PMID: 34048900 DOI: 10.1016/j.neuroimage.2021.118193] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/28/2021] [Accepted: 05/19/2021] [Indexed: 11/15/2022] Open
Abstract
It is well documented that massive dynamic information is contained in the resting-state fMRI. Recent studies have identified recurring states dominated by similar coactivation patterns (CAPs) and revealed their temporal dynamics. However, the reproducibility and generalizability of the CAP analysis are unclear. To address this question, the effects of methodological pipelines on CAP are comprehensively evaluated in this study, including the preprocessing, network construction, cluster number and three independent cohorts. The CAP state dynamics are characterized by the fraction of time, persistence, counts, and transition probability. Results demonstrate six reliable CAP states and their dynamic characteristics are also reproducible. The state transition probability is found to be positively associated with the spatial similarity. Furthermore, the aberrant CAP states in schizophrenia have been investigated by using the reproducible method on three cohorts. Schizophrenia patients spend less time in CAP states that involve the fronto-parietal network, but more time in CAP states that involve the default mode and salience network. The aberrant dynamic characteristics of CAP states are correlated with the symptom severity. These results reveal the reproducibility and generalizability of the CAP analysis, which can provide novel insights into the neuropathological mechanism associated with aberrant brain network dynamics of schizophrenia.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xin Di
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Shuai Wang
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi 214151, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Lin Tian
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi 214151, China.
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States.
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20
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Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study. Neuroimage 2021; 237:118197. [PMID: 34029737 DOI: 10.1016/j.neuroimage.2021.118197] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 11/21/2022] Open
Abstract
Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.
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21
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Alahmadi AAS. Investigating the sub-regions of the superior parietal cortex using functional magnetic resonance imaging connectivity. Insights Imaging 2021; 12:47. [PMID: 33847819 PMCID: PMC8044280 DOI: 10.1186/s13244-021-00993-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/23/2021] [Indexed: 11/17/2022] Open
Abstract
Objectives Traditionally, the superior parietal lobule (SPL) is usually investigated as one region of interest, particularly in functional magnetic resonance imaging (fMRI) studies. However, cytoarchitectonic analysis has shown that the SPL has a complex, heterogeneous topology that comprises more than seven sub-regions. Since previous studies have shown how the SPL is significantly involved in different neurological functions—such as visuomotor, cognitive, sensory, higher order, working memory and attention—this study aims to investigate whether these cytoarchitecturally different sub-regions have different functional connectivity to different functional brain networks. Methods This study examined 198 healthy subjects using resting-state fMRI and investigated the functional connectivity of seven sub-regions of the SPL to eight regional functional networks. Results The findings showed that most of the seven sub-regions were functionally connected to these targeted networks and that there are differences between these sub-regions and their functional connectivity patterns. The most consistent functional connectivity was observed with the visual and attention networks. There were also clear functional differences between Brodmann area (BA) 5 and BA7. BA5, with its three sub-regions, had strong functional connectivity to both the sensorimotor and salience networks. Conclusion These findings have enhanced our understanding of the functional organisations of the complexity of the SPL and its varied topology and also provide clear evidence of the functional patterns and involvements of the SPL in major brain functions.
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Affiliation(s)
- Adnan A S Alahmadi
- Department of Diagnostic Radiology, College of Applied Medical Science, King Abdulaziz University , Jeddah, Saudi Arabia.
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22
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series. Biomed Phys Eng Express 2020; 6:055022. [PMID: 33444253 DOI: 10.1088/2057-1976/abaf5e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three different sparsification methods were used. In addition to simple thresholding, spectral sparsification based on effective resistance and sparse autoencoder were performed in order to analyze the effect of sparsification routine on classification results. Also, instead of extracting common features, the correlation matrices were reshaped to a correlation vector and used as a feature vector to enter the classifier. Since the correlation matrix is symmetric, in another analysis half of the feature vector was used, moreover, the Genetic Algorithm (GA) also utilized for feature vector dimension reduction. The non-linear SVM classifier with a polynomial kernel applied. The results showed that the autoencoder sparsification method had the greatest discrimination power with the accuracy of 98.35% for classification when the feature vector was the full correlation matrix.
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Affiliation(s)
- Hessam Ahmadi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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23
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Alahmadi AAS. Effects of different smoothing on global and regional resting functional connectivity. Neuroradiology 2020; 63:99-109. [PMID: 32840683 DOI: 10.1007/s00234-020-02523-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/13/2020] [Indexed: 01/25/2023]
Abstract
PURPOSE Spatial smoothing is an essential pre-processing step in the process of analysing functional magnetic resonance imaging (fMRI) data, both during an experimental task or during resting-state fMRI (rsfMRI). The main benefit of this spatial smoothing step is to artificially increase the signal-to-noise ratio of the fMRI signal. Previous fMRI studies have investigated the impact of spatial smoothing on task fMRI data, while rsfMRI studies usually apply the same analytical process used for the task data. However, this study investigates changes in different rsfMRI analyses, such as ROI-to-ROI, seed-to-voxels and ICA analyses. METHODS Nineteen healthy volunteers were scanned using rsfMRI with three applied smoothing kernels: 0 mm, 4 mm and 8 mm. Appropriate statistical comparisons were made. RESULTS The findings showed that spatial smoothing has a greater effect on rsfMRI data when analysed using seed-to-voxel-based analysis. The effect was less pronounced when analysing data using ROI-ROI or ICA analyses. The results demonstrated that even when analysing the data without the application of spatial smoothing, the results were significant compared with data analysed using a typical smoothing kernel. However, data analysed with lower-smoothing kernels produced greater negative correlations, particularly with the ICA analysis. CONCLUSION The results suggest that a medium smoothing kernel (around 4 mm) may be preferable, as it is comparable with the 8 mm kernel in all of the analyses performed. It is also recommended that the researchers consider analysing the data using two different smoothing kernels, as this will help to confirm the significance of the results and avoid overestimating the findings.
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Affiliation(s)
- Adnan A S Alahmadi
- Department of Radiologic Sciences, College of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia.
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Hassel S, Sharma GB, Alders GL, Davis AD, Arnott SR, Frey BN, Hall GB, Harris JK, Lam RW, Milev R, Müller DJ, Rotzinger S, Zamyadi M, Kennedy SH, Strother SC, MacQueen GM. Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants. Hum Brain Mapp 2020; 41:1400-1415. [PMID: 31794150 PMCID: PMC7267954 DOI: 10.1002/hbm.24883] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 11/10/2019] [Accepted: 11/16/2019] [Indexed: 12/02/2022] Open
Abstract
Task-based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood-oxygen-level dependent (BOLD) signal of such tasks is essential. We assessed test-retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra-class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub-cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task-based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.
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Affiliation(s)
- Stefanie Hassel
- Department of Psychiatry, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Mathison Centre for Mental Health Research and EducationUniversity of CalgaryCalgaryAlbertaCanada
| | - Gulshan B. Sharma
- Department of Psychiatry, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Gésine L. Alders
- Graduate Program in NeuroscienceMcMaster University, and St. Joseph's Healthcare HamiltonHamiltonOntarioCanada
| | - Andrew D. Davis
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonOntarioCanada
| | | | - Benicio N. Frey
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonOntarioCanada
- Mood Disorders Program and Women's Health Concerns ClinicSt. Joseph's HealthcareHamiltonOntarioCanada
| | - Geoffrey B. Hall
- Department of Psychology, Neuroscience and BehaviourMcMaster UniversityHamiltonOntarioCanada
| | | | - Raymond W. Lam
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Roumen Milev
- Department of PsychiatryQueen's University and Providence Care HospitalKingstonOntarioCanada
- Department of PsychologyQueen's UniversityKingstonOntarioCanada
| | - Daniel J. Müller
- Department of PsychiatryCentre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Pharmacogenetic Research Clinic, University of TorontoTorontoOntarioCanada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Department of Psychiatry, Krembil Research CentreUniversity Health Network, University of TorontoTorontoOntarioCanada
- Department of Psychiatry, St. Michael's HospitalUniversity of TorontoTorontoOntarioCanada
| | | | - Sidney H. Kennedy
- Department of Psychiatry, Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Department of Psychiatry, Krembil Research CentreUniversity Health Network, University of TorontoTorontoOntarioCanada
- Department of Psychiatry, St. Michael's HospitalUniversity of TorontoTorontoOntarioCanada
- Keenan Research Centre for Biomedical ScienceLi Ka Shing Knowledge Institute, St. Michael's HospitalTorontoOntarioCanada
| | - Stephen C. Strother
- Rotman Research InstituteTorontoOntarioCanada
- Department of Medical Biophysics, Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Glenda M. MacQueen
- Department of Psychiatry, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Mathison Centre for Mental Health Research and EducationUniversity of CalgaryCalgaryAlbertaCanada
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Dillon K, Wang YP. Resolution-based spectral clustering for brain parcellation using functional MRI. J Neurosci Methods 2020; 335:108628. [PMID: 32035090 PMCID: PMC7061089 DOI: 10.1016/j.jneumeth.2020.108628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 01/03/2020] [Accepted: 02/03/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Brain parcellation is important for exploiting neuroimaging data. Variability in physiology between individuals has led to the need for data-driven approaches to parcellation, with recent research focusing on simultaneously estimating and partitioning the network structure of the brain. NEW METHOD We view data preprocessing, parcellation, and parcel validation from the perspective of predictive modeling. The goal is to identify parcels in a way that best generalizes to unseen data. We utilize an uncertainty quantification approach from image science to define parcels as groups of unresolvable variables in the predictive model. Model parameters are chosen via cross-validation. Parcellation results are compared based on both their repeatability as well as their ability to describe held-out data. RESULTS The approach provides insight and strategies for open questions such as the choice of evaluation metrics, selection of model order, and the optimal tuning of preprocessing steps for functional imaging data. COMPARISON WITH EXISTING METHODS We compare new and established approaches using functional imaging data, where we find the proposed approach produces parcellations which are both more accurate and more repeatable than the current baseline clustering method. The metrics also demonstrate potential problems with overfitting for the baseline method, and with bias for other methods. CONCLUSIONS Clustering of resolution provides a principled and robust approach to brain parcellation which improves on the current baseline.
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Affiliation(s)
- Keith Dillon
- Department of Electrical and Computer Engineering and Computer Science, University of New Haven, West Haven, CT, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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The Contribution of Functional Magnetic Resonance Imaging to the Understanding of the Effects of Acute Physical Exercise on Cognition. Brain Sci 2020; 10:brainsci10030175. [PMID: 32197357 PMCID: PMC7139910 DOI: 10.3390/brainsci10030175] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 03/04/2020] [Accepted: 03/13/2020] [Indexed: 02/06/2023] Open
Abstract
The fact that a single bout of acute physical exercise has a positive impact on cognition is well-established in the literature, but the neural correlates that underlie these cognitive improvements are not well understood. Here, the use of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers great potential, which is just starting to be recognized. This review aims at providing an overview of those studies that used fMRI to investigate the effects of acute physical exercises on cerebral hemodynamics and cognition. To this end, a systematic literature survey was conducted by two independent reviewers across five electronic databases. The search returned 668 studies, of which 14 studies met the inclusion criteria and were analyzed in this systematic review. Although the findings of the reviewed studies suggest that acute physical exercise (e.g., cycling) leads to profound changes in functional brain activation, the small number of available studies and the great variability in the study protocols limits the conclusions that can be drawn with certainty. In order to overcome these limitations, new, more well-designed trials are needed that (i) use a more rigorous study design, (ii) apply more sophisticated filter methods in fMRI data analysis, (iii) describe the applied processing steps of fMRI data analysis in more detail, and (iv) provide a more precise exercise prescription.
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Escitalopram ameliorates differences in neural activity between healthy comparison and major depressive disorder groups on an fMRI Emotional conflict task: A CAN-BIND-1 study. J Affect Disord 2020; 264:414-424. [PMID: 31757619 DOI: 10.1016/j.jad.2019.11.068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 11/07/2019] [Accepted: 11/12/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND Identifying objective biomarkers can assist in predicting remission/non-remission to treatment, improving remission rates, and reducing illness burden in major depressive disorder (MDD). METHODS Sixteen MDD 8-week remitters (MDD-8), twelve 16-week remitters (MDD-16), 14 non-remitters (MDD-NR) and 30 healthy comparison participants (HC) completed a functional magnetic resonance imaging emotional conflict task at baseline, prior to treatment with escitalopram, and 8 weeks after treatment initiation. Patients were followed 16 weeks to assess remitter status. RESULTS All groups demonstrated emotional Stroop in reaction time (RT) at baseline and Week 8. There were no baseline differences between HC and MDD-8, MDD-16, or MDD-NR in RT or accuracy. By Week 8, MDD-8 demonstrated poorer accuracy compared to HC. Compared to HC, the baseline blood-oxygen level dependent (BOLD) signal was decreased in MDD-8 in brain-stem and thalamus; in MDD-16 in lateral occipital cortex, middle temporal gyrus, and cuneal cortex; in MDD-NR in lingual and occipital fusiform gyri, thalamus, putamen, caudate, cingulate gyrus, insula, cuneal cortex, and middle temporal gyrus. By Week 8, there were no BOLD activity differences between MDD groups and HC. LIMITATIONS The Emotional Conflict Task lacks a neutral (non-emotional) condition, restricting interpretation of how mood may influence perception of non-emotionally valenced stimuli. CONCLUSIONS The Emotional Conflict Task is not an objective biomarker for remission trajectory in patients with MDD receiving escitalopram treatment. Escitalopram may have influenced emotion recognition in MDD groups in terms of augmented accuracy and BOLD signal in response to an Emotional Conflict Task, following 8 weeks of escitalopram treatment.
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Alders GL, Davis AD, MacQueen G, Strother SC, Hassel S, Zamyadi M, Sharma GB, Arnott SR, Downar J, Harris JK, Lam RW, Milev R, Müller DJ, Ravindran A, Kennedy SH, Frey BN, Minuzzi L, Hall GB. Reduced accuracy accompanied by reduced neural activity during the performance of an emotional conflict task by unmedicated patients with major depression: A CAN-BIND fMRI study. J Affect Disord 2019; 257:765-773. [PMID: 31400735 DOI: 10.1016/j.jad.2019.07.037] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 06/12/2019] [Accepted: 07/04/2019] [Indexed: 10/26/2022]
Abstract
METHODS We studied 48 MDD and 30 HC who performed an emotional conflict task in a functional magnetic resonance imaging (fMRI) scanner. RESULTS On the emotional conflict task, MDD and HC demonstrated a robust emotional Stroop effect in reaction time and accuracy. Overall, accuracy was lower in MDD compared to HC with no significant reaction time differences. The fMRI data indicated lower BOLD activation in MDD compared to HC on comparisons of all trials, congruent, incongruent, and incongruent > congruent trials in regions including right inferior temporal gyrus, lateral occipital cortex, and occipital fusiform gyrus. Behavioural and neuroimaging data indicated no group differences in fearful versus happy face processing. LIMITATIONS Inclusion of a neutral condition may have provided a valuable contrast to how MDD and HC process stimuli without emotional valence compared to stimuli with a strong emotional valence. CONCLUSIONS MDD and HC demonstrated a robust emotional Stroop effect. Compared to HC, MDD demonstrated an overall reduced accuracy on the emotional conflict task and reduced BOLD activity in regions important for face perception and emotion information processing, with no differences in responding to fearful versus happy faces. These findings provide support for the theory of emotion context insensitivity in individuals with depression.
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Affiliation(s)
- Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Andrew D Davis
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Glenda MacQueen
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, ON, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Gulshan B Sharma
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Jonathan Downar
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | | | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Arun Ravindran
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Benicio N Frey
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada; Department of Psychology Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Luciano Minuzzi
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | - Geoffrey B Hall
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Department of Psychology Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.
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A K, Prakash SS, P S, Carshia S A. Investigations on the Functional connectivity disruptive patterns of progressive neurodegenerative disorders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:800-803. [PMID: 31946016 DOI: 10.1109/embc.2019.8856919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's Disease (AD) and Parkinson's Disease (PD) are neurodegenerative diseases of the brain that affects the memory and motor regions respectively. Neurological disorders are the result of alterations at molecular level due to complex mechanisms between genetic and environmental factors. Classical approaches are focused on determining how disruptions in functional connectivity occur in the memory regions of AD and motor regions of PD. There have been studies stating that in addition to dementia, motor abnormalities may also be observed in Alzheimer's patients and on the other hand, dementia may occur in Parkinson's patients a year or more after the onset of motor symptoms. In this work, to substantiate this hypothesis the brain connectivity patterns and functional topology of motor and memory regions in AD and PD patients were analyzed and compared. Resting state functional connectivity (rs-fMRI) has been found to demonstrate the brain networks in both AD and PD. Graph theoretical modelling is being significantly used in studying the topology of the brain networks. The results show the disruption of connectivity in motor regions in later stages of AD in addition to memory regions and conversely in PD the memory regions were found to have disrupted connectivity in addition to the motor regions. Further, the Z scores of intra and inter hemispheric regions in AD and PD also indicate the disruption in connectivity as the disease progresses.
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Yeung AWK, Lee JCM, Tanabe HC, Ng SKS, Khong PL, Leung WK, Goto TK. Short Version Dental Anxiety Inventory Score May Predict the Response in the Insular Cortex to Stimuli Mimicking Dental Treatment. Front Hum Neurosci 2019; 13:204. [PMID: 31244634 PMCID: PMC6579880 DOI: 10.3389/fnhum.2019.00204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 05/29/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Dental anxiety is a common reason for avoiding dental visits and is associated with poor dental status. The short version of Dental Anxiety Inventory (SDAxI) is an easy-to-use, multi-faceted questionnaire for assessing the level of trait dental anxiety. However, there was no neurophysiological data indicating if its score associates with the state anxiety when an individual is under real/mock dental environment. We hypothesized that there exists such an association. Materials and Methods: Twenty systemic healthy adults with dental attendance experience and self-claimed free of dental phobia were recruited in this cross-sectional study, with their dental anxiety level assessed by SDAxI. Functional magnetic resonance imaging recorded their brain signals in response to audiovisual footages resembling dental scaler or turbine in action. After the brain imaging, they gave fear ratings to the footages in visual analog scale (VAS). Results: Participants' SDAxI scores positively correlated with their responses in the insular cortex (r2 = 0.388-0.445, P < 0.005). Their SDAxI scores also positively correlated with their fear ratings of the footages (r 2 = 0.415-0.555, P < 0.005). Discussion: Our findings indicated a possible neurobiological relevance of SDAxI, and reinforced its neurobiological validity in assessing dental anxiety level of dental attenders.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | | | - Hiroki C Tanabe
- Department of Psychology, Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
| | - Sam Kwai Sang Ng
- Periodontology, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Wai Keung Leung
- Periodontology, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Tazuko K Goto
- Oral and Maxillofacial Radiology, Applied Oral Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.,Department of Oral and Maxillofacial Radiology, Tokyo Dental College, Tokyo, Japan
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31
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Grouped sparse Bayesian learning for voxel selection in multivoxel pattern analysis of fMRI data. Neuroimage 2019; 184:417-430. [DOI: 10.1016/j.neuroimage.2018.09.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/01/2018] [Accepted: 09/12/2018] [Indexed: 11/21/2022] Open
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32
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fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 2018; 16:111-116. [PMID: 30532080 PMCID: PMC6319393 DOI: 10.1038/s41592-018-0235-4] [Citation(s) in RCA: 1440] [Impact Index Per Article: 240.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022]
Abstract
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.
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de Ridder M, Klein K, Kim J. A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging. Brain Inform 2018; 5:5. [PMID: 29968092 PMCID: PMC6170942 DOI: 10.1186/s40708-018-0083-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 06/18/2018] [Indexed: 11/10/2022] Open
Abstract
Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.
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Affiliation(s)
- Michael de Ridder
- Biomedical and Multimedia Information Technology Research Group, University of Sydney, Sydney, Australia.
| | - Karsten Klein
- Department of Computer and Information Science, Universität Konstanz, Konstanz, Germany
| | - Jinman Kim
- Biomedical and Multimedia Information Technology Research Group, University of Sydney, Sydney, Australia
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Rupawala M, Dehghani H, Lucas SJE, Tino P, Cruse D. Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness. Front Neurol 2018; 9:350. [PMID: 29872420 PMCID: PMC5972220 DOI: 10.3389/fneur.2018.00350] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/30/2018] [Indexed: 12/19/2022] Open
Abstract
Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behavior from spontaneous behavior. As many such behaviors are minimal and inconsistent, behavioral assessments are susceptible to diagnostic errors. Advanced neuroimaging tools can bypass behavioral responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. The majority of reports to date have employed the neuroimaging methods of functional magnetic resonance imaging, positron emission tomography, and electroencephalography (EEG). However, each neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.). Here, we describe a burgeoning technique of non-invasive optical neuroimaging—functional near-infrared spectroscopy (fNIRS)—and review its potential to address the clinical challenges of prolonged disorders of consciousness. We also outline the potential for simultaneous EEG to complement the fNIRS signal and suggest the future directions of research that are required in order to realize its clinical potential.
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Affiliation(s)
- Mohammed Rupawala
- Centre for Doctoral Training in Physical Sciences for Health, University of Birmingham, Birmingham, United Kingdom
| | - Hamid Dehghani
- Centre for Doctoral Training in Physical Sciences for Health, University of Birmingham, Birmingham, United Kingdom.,School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Samuel J E Lucas
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Damian Cruse
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
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35
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Abstract
Big Data is increasingly prevalent in science and data analysis. We provide a short tutorial for adapting to these changes and making the necessary adjustments to the academic culture to keep Biostatistics truly impactful in scientific research.
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Affiliation(s)
- Ekaterina Smirnova
- Assistant Professor, Department of Mathematical Sciences, University of Montana, 32 Campus Dr, Missoula, MT 59812
| | - Andrada Ivanescu
- Assistant Professor, Department of Mathematical Sciences, Montclair University, 1 Normal Avenue Montclair, NJ 07043
| | - Jiawei Bai
- Assistant Scientist, Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St. Baltimore, MD 21205 USA
| | - Ciprian M Crainiceanu
- Professor, Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St. Baltimore, MD 21205 USA
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36
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Raut SV, Yadav DM. A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli. BIOMED ENG-BIOMED TE 2018; 63:163-175. [DOI: 10.1515/bmt-2016-0194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 01/12/2017] [Indexed: 11/15/2022]
Abstract
Abstract
This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.
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Affiliation(s)
- Savita V. Raut
- JSPM’s Rajarshi Shahu College of Engineering , Pune , India
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37
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Seshamani S, Blazejewska AI, Mckown S, Caucutt J, Dighe M, Gatenby C, Studholme C. Detecting default mode networks in utero by integrated 4D fMRI reconstruction and analysis. Hum Brain Mapp 2018; 37:4158-4178. [PMID: 27510837 DOI: 10.1002/hbm.23303] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 05/03/2016] [Accepted: 06/20/2016] [Indexed: 11/05/2022] Open
Abstract
Recently, there has been considerable interest, especially for in utero imaging, in the detection of functional connectivity in subjects whose motion cannot be controlled while in the MRI scanner. These cases require two advances over current studies: (1) multiecho acquisitions and (2) post processing and reconstruction that can deal with significant between slice motion during multislice protocols to allow for the ability to detect temporal correlations introduced by spatial scattering of slices into account. This article focuses on the estimation of a spatially and temporally regular time series from motion scattered slices of multiecho fMRI datasets using a full four-dimensional (4D) iterative image reconstruction framework. The framework which includes quantitative MRI methods for artifact correction is evaluated using adult studies with and without motion to both refine parameter settings and evaluate the analysis pipeline. ICA analysis is then applied to the 4D image reconstruction of both adult and in utero fetal studies where resting state activity is perturbed by motion. Results indicate quantitative improvements in reconstruction quality when compared to the conventional 3D reconstruction approach (using simulated adult data) and demonstrate the ability to detect the default mode network in moving adults and fetuses with single-subject and group analysis. Hum Brain Mapp 37:4158-4178, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sharmishtaa Seshamani
- Department of Pediatrics, Radiology and Bioengineering, Biomedical Image Computing Group, University of Washington, Seattle, Washington, 98195.
| | - Anna I Blazejewska
- Department of Pediatrics, Biomedical Image Computing Group, University of Washington, Seattle, Washington, 98195
| | - Susan Mckown
- Department of Radiology, University of Washington, Seattle, Washington, 98195
| | - Jason Caucutt
- Department of Pediatrics, Radiology, Bioengineering, Institute of Translational Health Sciences, University of Washington, Seattle, Washington, 98195
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, Washington, 98195
| | - Christopher Gatenby
- Department of Radiology, University of Washington, Seattle, Washington, 98195
| | - Colin Studholme
- Departments of Pediatrics, Bioengineering and Radiology, Biomedical Image Computing Group, University of Washington, Seattle, Washington, 98195
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Pfeifer MD, Scholkmann F, Labruyère R. Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results. Front Hum Neurosci 2018; 11:641. [PMID: 29358912 PMCID: PMC5766679 DOI: 10.3389/fnhum.2017.00641] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 12/18/2017] [Indexed: 11/13/2022] Open
Abstract
Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers' standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.
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Affiliation(s)
- Mischa D. Pfeifer
- Rehabilitation Center for Children and Adolescents, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland
| | - Felix Scholkmann
- Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Rob Labruyère
- Rehabilitation Center for Children and Adolescents, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, Zurich, Switzerland
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Caballero-Gaudes C, Reynolds RC. Methods for cleaning the BOLD fMRI signal. Neuroimage 2017; 154:128-149. [PMID: 27956209 PMCID: PMC5466511 DOI: 10.1016/j.neuroimage.2016.12.018] [Citation(s) in RCA: 325] [Impact Index Per Article: 46.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 12/05/2016] [Accepted: 12/08/2016] [Indexed: 01/13/2023] Open
Abstract
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.
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Affiliation(s)
| | - Richard C Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, USA
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40
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Optimizing fMRI preprocessing pipelines for block-design tasks as a function of age. Neuroimage 2017; 154:240-254. [PMID: 28216431 DOI: 10.1016/j.neuroimage.2017.02.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 01/04/2017] [Accepted: 02/10/2017] [Indexed: 11/21/2022] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a powerful neuroimaging tool, which is often hampered by significant noise confounds. There is evidence that our ability to detect activations in task fMRI is highly dependent on the preprocessing steps used to control noise and artifact. However, the vast majority of studies examining preprocessing pipelines in fMRI have focused on young adults. Given the widespread use of fMRI for characterizing the neurobiology of aging, it is critical to examine how the impact of preprocessing choices varies as a function of age. In this study, we employ the NPAIRS cross-validation framework, which optimizes pipelines based on metrics of prediction accuracy (P) and spatial reproducibility (R), to compare the effects of pipeline optimization between young (21-33 years) and older (61-82 years) cohorts, for three different block-design contrasts. Motion is shown to be a greater issue in the older cohort, and we introduce new statistical approaches to control for potential biases due to head motion during pipeline optimization. In comparison, data-driven methods of physiological noise correction show comparable benefits for both young and old cohorts. Using our optimization framework, we demonstrate that the optimal pipelines tend to be highly similar across age cohorts. In addition, there is a comparable, significant benefit of pipeline optimization across age cohorts, for (P, R) metrics and independent validation measures of activation overlap (both between-subject, within-session and within-subject, between-session). The choice of task contrast consistently shows a greater impact than the age cohort, for (P, R) metrics and activation overlap. Finally, adaptive pipeline optimization per task run shows improved sensitivity to age-related changes in brain activity, particularly for weaker, more complex cognitive contrasts. The current study provides the first detailed examination of preprocessing pipelines across age cohorts, demonstrating a significant benefit of adaptive pipeline optimization across age groups.
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41
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Zhang J, Guo Z, Liu X, Jia X, Li J, Li Y, Lv D, Chen W. Abnormal functional connectivity of the posterior cingulate cortex is associated with depressive symptoms in patients with Alzheimer's disease. Neuropsychiatr Dis Treat 2017; 13:2589-2598. [PMID: 29066900 PMCID: PMC5644530 DOI: 10.2147/ndt.s146077] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Depressive symptoms are significant and very common psychiatric complications in patients with Alzheimer's disease (AD), which can aggravate the decline in social function. However, changes in the functional connectivity (FC) of the brain in AD patients with depressive symptoms (D-AD) remain unclear. OBJECTIVE To investigate whether any differences exist in the FC of the posterior cingulate cortex (PCC) between D-AD patients and non-depressed AD patients (nD-AD). MATERIALS AND METHODS We recruited 15 D-AD patients and 17 age-, sex-, educational level-, and Mini-Mental State Examination (MMSE)-matched nD-AD patients to undergo tests using the Neuropsychiatric Inventory, Hamilton Depression Rating Scale, and 3.0T resting-state functional magnetic resonance imaging. Bilateral PCC were selected as the regions of interest and between-group differences in the PCC FC network were assessed using Student's t-test. RESULTS Compared with the nD-AD group, D-AD patients showed increased PCC FC in the right amygdala, right parahippocampus, right superior temporal pole, right middle temporal lobe, right middle temporal pole, and right hippocampus (AlphaSim correction; P<0.05). In the nD-AD group, MMSE scores were positively correlated with PCC FC in the right superior temporal pole and right hippocampus (false discovery rate corrected; P<0.05). CONCLUSION Differences were detected in PCC FC between nD-AD and D-AD patients, which may be related to depressive symptoms. Our study provides a significant enhancement to our understanding of the functional mechanisms underlying D-AD.
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Affiliation(s)
- Jiangtao Zhang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zhongwei Guo
- Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiaozheng Liu
- China-USA Neuroimaging Research Institute & Department of Radiology, the Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xize Jia
- Center for Cognitive Brain Disorders & Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China
| | - Jiapeng Li
- Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yaoyao Li
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
| | - Danmei Lv
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
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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: 112] [Impact Index Per Article: 14.0] [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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43
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Samadi S, Soltanian-Zadeh H, Jutten C. Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps. Brain Topogr 2016; 29:661-78. [DOI: 10.1007/s10548-016-0506-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Accepted: 07/11/2016] [Indexed: 11/30/2022]
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44
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Abstract
Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. Significance statement: Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience.
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45
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Chen JE, Glover GH. Functional Magnetic Resonance Imaging Methods. Neuropsychol Rev 2015; 25:289-313. [PMID: 26248581 PMCID: PMC4565730 DOI: 10.1007/s11065-015-9294-9] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Accepted: 07/28/2015] [Indexed: 12/11/2022]
Abstract
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,
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46
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Craddock RC, Tungaraza RL, Milham MP. Connectomics and new approaches for analyzing human brain functional connectivity. Gigascience 2015; 4:13. [PMID: 25810900 PMCID: PMC4373299 DOI: 10.1186/s13742-015-0045-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 01/18/2015] [Indexed: 11/10/2022] Open
Abstract
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a “big data” problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.
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Affiliation(s)
- R Cameron Craddock
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
| | - Rosalia L Tungaraza
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
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47
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Karaman M, Nencka AS, Bruce IP, Rowe DB. Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data. Brain Connect 2014; 4:649-61. [PMID: 25132113 DOI: 10.1089/brain.2014.0278] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations. While these solutions make the data more "appealing," many commonly used processing operations induce artificial correlations in the acquired data. As such, it becomes increasingly more difficult to derive the true underlying covariance structure once the data have been processed. As the goal of nontask fMRI studies is to determine, utilize, and analyze the true covariance structure of acquired data, such processing can lead to inaccurate and misleading conclusions drawn from the data if they are unaccounted for in the final connectivity analysis. In this article, we develop a framework that represents the spatiotemporal processing and reconstruction operations as linear operators, providing a means of precisely quantifying the correlations induced or modified by such processing rather than by performing lengthy Monte Carlo simulations. A framework of this kind allows one to appropriately model the statistical properties of the processed data, optimize the data processing pipeline, characterize excessive processing, and draw more accurate functional connectivity conclusions.
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Affiliation(s)
- Muge Karaman
- 1 Department of Mathematics, Statistics, and Computer Science, Marquette University , Milwaukee, Wisconsin
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48
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Bernier M, Chamberland M, Houde JC, Descoteaux M, Whittingstall K. Using fMRI non-local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography. Front Hum Neurosci 2014; 8:715. [PMID: 25309391 PMCID: PMC4160992 DOI: 10.3389/fnhum.2014.00715] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 08/26/2014] [Indexed: 11/23/2022] Open
Abstract
In recent years, there has been ever-increasing interest in combining functional magnetic resonance imaging (fMRI) and diffusion magnetic resonance imaging (dMRI) for better understanding the link between cortical activity and connectivity, respectively. However, it is challenging to detect and validate fMRI activity in key sub-cortical areas such as the thalamus, given that they are prone to susceptibility artifacts due to the partial volume effects (PVE) of surrounding tissues (GM/WM interface). This is especially true on relatively low-field clinical MR systems (e.g., 1.5 T). We propose to overcome this limitation by using a spatial denoising technique used in structural MRI and more recently in diffusion MRI called non-local means (NLM) denoising, which uses a patch-based approach to suppress the noise locally. To test this, we measured fMRI in 20 healthy subjects performing three block-based tasks : eyes-open closed (EOC) and left/right finger tapping (FTL, FTR). Overall, we found that NLM yielded more thalamic activity compared to traditional denoising methods. In order to validate our pipeline, we also investigated known structural connectivity going through the thalamus using HARDI tractography: the optic radiations, related to the EOC task, and the cortico-spinal tract (CST) for FTL and FTR. To do so, we reconstructed the tracts using functionally based thalamic and cortical ROIs to initiates seeds of tractography in a two-level coarse-to-fine fashion. We applied this method at the single subject level, which allowed us to see the structural connections underlying fMRI thalamic activity. In summary, we propose a new fMRI processing pipeline which uses a recent spatial denoising technique (NLM) to successfully detect sub-cortical activity which was validated using an advanced dMRI seeding strategy in single subjects at 1.5 T.
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Affiliation(s)
- Michaël Bernier
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada ; Department of Diagnostic Radiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
| | - Maxime Chamberland
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
| | - Jean-Christophe Houde
- Computer Science Department, Faculty of Science, University of Sherbrooke Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Computer Science Department, Faculty of Science, University of Sherbrooke Sherbrooke, QC, Canada
| | - Kevin Whittingstall
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada ; Department of Diagnostic Radiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
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Model specification and the reliability of fMRI results: implications for longitudinal neuroimaging studies in psychiatry. PLoS One 2014; 9:e105169. [PMID: 25166022 PMCID: PMC4148299 DOI: 10.1371/journal.pone.0105169] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Accepted: 07/18/2014] [Indexed: 01/24/2023] Open
Abstract
Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N = 46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC = 0.31) in the default pipeline to fair (mvICC = 0.45) in the full alternative pipeline – an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed.
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Ding W, Lin E, Ribeiro A, Sarunic MV, Tibbits GF, Beg MF. Automatic cycle averaging for denoising approximately periodic spatiotemporal signals. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1749-1759. [PMID: 24835215 DOI: 10.1109/tmi.2014.2323201] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Optical mapping has become a common tool for cardiovascular research, providing high resolution spatiotemporal data of cardiac action potential propagation. However, noise in cardiac optical mapping (COM) data hampers quantitative measurements and analyses. Spatial and temporal filters have been used to increase the signal-to-noise ratio (SNR) of COM data. Although these help reduce noise and increase SNR, they also lower the spatial and temporal resolution of the data, which is not desirable. This paper utilizes the approximate periodicity of COM data to perform denoising by averaging cycles. We consider the entire approximately periodic spatiotemporal (APST) COM data as a concatenation of random samples generated from a deterministic single cycle spatiotemporal signal. The image difference signal (IDS) was defined and calculated to provide "global" periodicity information. Parameters for cycle segmentation, scaling and alignment were estimated based on the IDS. Finally, these parameters were used to segment, align and average cycles from each individual signal, which produces a clean and denoised single cycle spatiotemporal signal. The novel, fully automated pipeline was validated both qualitatively and quantitatively on zebrafish heart optical mapping data.
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