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• W2955135514 abstract "HomeRadiologyVol. 292, No. 2 PreviousNext Reviews and CommentaryFree AccessReviewFunctional Network Dynamics on Functional MRI: A Primer on an Emerging Frontier in NeuroscienceAnand J. C. Eijlers , Alle Meije Wink, Kim A. Meijer, Linda Douw, Jeroen J. G. Geurts, Menno M. SchoonheimAnand J. C. Eijlers , Alle Meije Wink, Kim A. Meijer, Linda Douw, Jeroen J. G. Geurts, Menno M. SchoonheimAuthor AffiliationsFrom the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.Address correspondence to A.J.C.E. (e-mail: [email protected]).Anand J. C. Eijlers Alle Meije WinkKim A. MeijerLinda DouwJeroen J. G. GeurtsMenno M. SchoonheimPublished Online:Jun 25 2019https://doi.org/10.1148/radiol.2019194009See editorial byRobert ZivadinovMoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Eijlers et al and the editorial by Zivadinov and Dwyer in this issue.IntroductionThe global burden of neurological disorders expressed in daily adjusted life years is already exceeding those of HIV, malignant neoplasms, and ischemic heart disease and is projected to rise even further in the decades ahead in both absolute and relative terms (1,2). Some of the largest contributors to the reduction in daily adjusted life years within this category of neurological disorders are Alzheimer disease, cerebrovascular disease, and multiple sclerosis, which are characterized by heterogeneous pathologic processes inflicting damage to various brain regions (1). Clinically, there is partial overlap in symptomatology between these disorders, with patients commonly experiencing a decline in cognitive function, leading to significant morbidity (3,4). How the accumulation of brain pathology affects normal brain function and why this brain function is resilient in some but easily derailed in others is still only poorly understood (5,6). Such disturbances in normal brain function due to structural pathology can be studied using functional brain imaging techniques such as functional MRI (6,7). The large advances in both the acquisition as well as analysis of functional MRI data in recent years not only enable us to unravel the dynamic interplay between brain regions during normal cognitive functioning, but also enable the study of how this normal interplay is disturbed by various neurological disorders (8,9).Functional MRI, from Activation to CommunicationFunctional MRI relies on regional changes in the ratio between oxygenized and deoxygenized blood, the so-called blood-oxygen level dependent (BOLD) signal on a T2*-based MRI sequence, which is thought to reflect underlying changes in neuronal activity (10). Functional MRI was initially mainly used to investigate regional specialization of brain regions by mapping behavioral tasks to specific brain regions (11). This was done by evaluating task-induced regional increases in BOLD signal, which is commonly referred to as the “activation” of brain regions (11). These task-based functional MRI studies have also been applied to investigate cognitive impairment in a range of neurological disorders characterized by impaired cognitive function, and a common finding is a stronger and more widely distributed recruitment (ie, activation) of task-related brain regions in early disease stages, possibly reflecting a compensatory mechanism to limit cognitive dysfunction (12,13). In addition to quantifying the level of activation of brain regions, a more recent application of functional MRI is the study of the communication between brain regions. This communication or functional connectivity is approximated by measuring the coherence of BOLD signal fluctuations between brain regions over time, based on the assumption that when brain regions fire together, they wire together (14). This functional connectivity between brain regions is commonly investigated in the absence of a particular cognitive task, during the so-called resting state (15).Resting-State NetworksBy analyzing functional connectivity patterns between brain regions during the resting state, groups of spatially distributed brain regions demonstrated functional connectivity and were therefore named resting-state networks (16,17). These networks included the sensorimotor and visual networks, primarily involved in the processing of sensorimotor and visual information, respectively, as well as the frontoparietal network, which is primarily involved in different types of cognitive processing (16,17) (Fig 1). Another resting-state network, the default-mode or “rest” network (18,19), was originally discovered as a group of brain regions exhibiting suppressed activity during goal-directed behaviour, which led to the term default mode, as this was interpreted as a sort of baseline activity of the brain (20). This network was initially thought to be mainly involved in introspective and self-referential thought (21), but recent studies have also pointed out its role as a structural core network of brain hubs facilitating cognitive processing (22).Figure 1: Resting-state networks. Four well-known resting-state networks, identified by grouping together brain regions based on signal coherence between time series using an independent component analysis: A, sensorimotor network; B, visual network; C, frontoparietal network, and, D, default mode network.Figure 1:Download as PowerPointOpen in Image Viewer Network NeuroscienceTask-based and resting-state functional connectivity studies have provided a wealth of information on brain function in general and on how cognitive functions are performed in particular (14,23). In recent decades it has, however, become increasingly clear that the functioning of complex systems, such as the cognitive functions performed by the human brain, cannot be fully grasped by investigating individual brain regions or individual connections in isolation (24). This growing realization that the behaviour of complex systems is shaped by the interactions among their constituent elements has led to the ascendance of network neuroscience (24). This research field studies the brain as a network and has shown that some of the key organizational principles of the brain are shared by other complex systems, such as social networks, the organization of the power grid, and the internet (8,24,25). A functional brain network or functional connectome reflects the wiring diagram of the brain and can be constructed by using functional MRI data, by computing functional connectivity strengths between all pairs of brain regions (Fig 2) (24).Figure 2: Construction of a functional brain network. Functional MRI (fMRI) provides a blood oxygen level–dependent signal for each voxel across time. Parcellation of the brain into individual brain regions and averaging time courses across voxels within a region provides a time course for each region. Next, the functional connectivity is computed between each pair of brain regions, resulting in a connectivity matrix. Finally, the connectivity matrix can be modified (eg, thresholded) to obtain the final brain network.Figure 2:Download as PowerPointOpen in Image Viewer Brain Network Organizational CharacteristicsThis functional network can then be analyzed using mathematical approaches derived from the field of graph theory to reveal whole-brain and regional network properties (8). One of the key global (ie, whole-brain) network properties is the presence of a small-world network type of organization (26). This organization is characterized by the combination of a high number of local connections between brain regions (likely facilitating local processing) and a lower number of long distance connections (which create a short path length between distant brain regions and likely facilitate the efficient global integration of information) (26). A study in patients with Alzheimer disease showed that this small-world organization is altered in this patient group, due to a disruption of local connectivity (27). It is thought that normal brain function requires an optimal combination of both local, modular processing by specialized groups of brain regions and the efficient global integration of information. The amount of segregation of the brain into such smaller modules is captured by the network measure modularity (8,28). A study in patients with multiple sclerosis demonstrated a shift towards a more modular architecture, which was related to impaired cognitive functioning (29). In addition to investigating these global (summary) measures of network organization, a large body of research is dedicated to unravelling the role that individual brain regions play within this network.One group of brain regions in particular, the so-called hub regions, is receiving a lot of attention (30). These highly connected brain regions are thought to be important for the neural integration of information and to play a vital role in a diverse set of cognitive functions (30). One of the key measures to investigate the importance of brain regions is the network measure “centrality,” which is based on how strongly a brain region is (functionally) connected to other brain regions. Different types of centrality can be computed, with degree centrality based on the average connection number, eigenvector centrality based on both the connection strength and importance of the connected brain regions, and betweenness centrality based on the number of shortest routes between distant brain regions that run through a particular brain region.Functional Network DynamicsMost studies that have investigated the functional connection strength between brain regions or the brain network organization so far have relied on the BOLD signal coherence between brain regions across the duration of an entire functional scan, also referred to as stationary functional connectivity (14). This provides an indication of how strongly brain regions communicate on average or how the functional brain network is organized on average (14). Accumulating evidence, however, indicates that functional connectivity between brain regions is nonstationary and alternates between periods of low and high functional coupling over time (8,31). These fluctuations in the coupling strength between brain regions suggest that the level of communication between brain regions is not constant but fluctuates over time, perhaps reflecting ongoing changes in processing requirements placed upon different brain regions to enable a multitude of cognitive functions (31). To better understand cognitive function, it is therefore essential to not only study the average functional connectivity strength between brain regions, but also evaluate the time-varying nature of this coupling strength. Or, in network terms, it is essential to not only study the average network organization or average functional connectome, but also assess functional connectome dynamics or the functional “chronnectome” (32).Disclosures of Conflicts of Interest: A.J.C.E. Activities related to the present article: disclosed receipt of institutional grant from Stichting MS Research (grant numbers 08-650, 13-820, and 14-358e). Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. A.M.W. Activities related to the present article: disclosed institutional grants from EuroPOND (ERC Horizon grant 666992), EPAD (EU IMI grant 115736), and AMYPAD (EU IMI grant 115952). Activities not related to the present article: disclosed institutional grants from EuroPOND (ERC Horizon grant 666992), EPAD (EU IMI grant 115736), and AMYPAD (EU IMI grant 115952). Other relationships: disclosed no relevant relationships. K.A.M. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed institutional grant from Biogen. Other relationships: disclosed no relevant relationships. L.D. disclosed no relevant relationships. J.J.G.G. Activities related to the present article: disclosed institutional grant from Dutch MS Research Foundation. Activities not related to the present article: disclosed employment with Amsterdam University Medical Center and Netherlands Organisation for Scientific Research; receipt of payment from Biogen Idec, Novartis Pharma, and Sonofi Genzyme to institution for investigator-initiated trial or sponsor-initiated trial. Other relationships: disclosed work as an editor for the Multiple Sclerosis Journal. M.M.S. Activities related to the present article: disclosed institutional grant from Dutch MS Research Foundation (grant numbers 08-650, 13-820, and 14-358e). Activities not related to the present article: disclosed payment received by institution from Biogen and Sanofi Genzyme for consultancy; institutional research grant from Biogen; and payment to institution from ExceMed for lectures, including service on speakers’ bureaus. Other relationships: disclosed no relevant relationships.Supported in part by Stichting MS Research (grant numbers 08-650, 13-820, 14-358e).References1. GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017;16(11):877–897. Medline, Google Scholar2. Chin JH, Vora N. The global burden of neurologic diseases. Neurology 2014;83(4):349–351. Crossref, Medline, Google Scholar3. Benedict RHB, DeLuca J, Enzinger C, Geurts JJG, Krupp LB, Rao SM. Neuropsychology of multiple sclerosis: looking back and moving forward. J Int Neuropsychol Soc 2017;23(9-10):832–842. Crossref, Medline, Google Scholar4. Prins ND, Scheltens P. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol 2015;11(3):157–165. Crossref, Medline, Google Scholar5. Chard D, Trip SA. 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Crossref, Medline, Google ScholarArticle HistoryReceived: Nov 21 2018Revision requested: Feb 19 2019Revision received: Apr 6 2019Accepted: Apr 29 2019Published online: June 25 2019Published in print: Aug 2019 FiguresReferencesRelatedDetailsCited ByA hands-on tutorial on network and topological neuroscienceEduarda Gervini ZampieriCenteno, GiuliaMoreni, ChrisVriend, LindaDouw, Fernando Antônio NóbregaSantos2022 | Brain Structure and Function, Vol. 227, No. 3Altered functional brain states predict cognitive decline 5 years after a clinically isolated syndromeIsmailKoubiyr, Tommy AABroeders, MathildeDeloire, BrunoBrochet, ThomasTourdias, Jeroen JGGeurts, Menno MichielSchoonheim, AurélieRuet2022 | Multiple Sclerosis Journal, Vol. 28, No. 12Management Based on Multimodal Brain Monitoring May Improve Functional Connectivity and Post-operative Neurocognition in Elderly Patients Undergoing Spinal SurgeryShuyiYang, WeiXiao, HaoWu, YangLiu, ShuaiFeng, JieLu, TianlongWang2021 | Frontiers in Aging Neuroscience, Vol. 13Visualizing the Central Nervous System: Imaging Tools for Multiple Sclerosis and Neuromyelitis Optica Spectrum DisordersJosephKuchling, FriedemannPaul2020 | Frontiers in Neurology, Vol. 11Accompanying This ArticleReduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple SclerosisJun 25 2019RadiologyNetwork Dynamics and Cognitive Impairment in Multiple Sclerosis: Functional MRI–based Decoupling of Complex RelationshipsJun 25 2019RadiologyRecommended Articles Reduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple SclerosisRadiology2019Volume: 292Issue: 2pp. 449-457Network Dynamics and Cognitive Impairment in Multiple Sclerosis: Functional MRI–based Decoupling of Complex RelationshipsRadiology2019Volume: 292Issue: 2pp. 458-459Altered Nucleus Basalis Connectivity Predicts Treatment Response in Mild Cognitive ImpairmentRadiology2018Volume: 289Issue: 3pp. 775-785Determinants of Cognitive Impairment in Patients with Multiple Sclerosis with and without AtrophyRadiology2018Volume: 288Issue: 2pp. 544-551Functional Brain Connectome and Its Relation to Hoehn and Yahr Stage in Parkinson DiseaseRadiology2017Volume: 285Issue: 3pp. 904-913See More RSNA Education Exhibits Magnetoencephalography (MEG): Taking Functional Brain Imaging to New HeightsDigital Posters2020Deep-Brain: A Cutting-edge Concept for Outstanding Functional Resolution in fMRIDigital Posters2020Establishing a Functional-MRI Service: Lessons Learned and Pitfalls to Avoid Digital Posters2019 RSNA Case Collection Multiple sclerosis RSNA Case Collection2020Semantic dementiaRSNA Case Collection2020Mesial temporal sclerosisRSNA Case Collection2020 Vol. 292, No. 2 Metrics Altmetric Score PDF download" @default.
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• W2955135514 title "Functional Network Dynamics on Functional MRI: A Primer on an Emerging Frontier in Neuroscience" @default.
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