Functional plasticity in MS

Schoonheim, Menno M.; Filippi, Massimo

doi:10.1212/wnl.0b013e31826d602e

Cited by 17 publications

(17 citation statements)

References 10 publications

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“…Indeed, persistent hyperactivation in some regions may place neurons under undue metabolic stress, reducing their viability and rendering them susceptible to degeneration 56,88 . This mechanism may explain the consistent reports of increased task-evoked activation or functional connectivity in the brains of patients with early-stage neurodegenerative disease -whether it is Alzheimer disease, Huntington disease or multiple sclerosis -followed by declines in these measures at later disease stages [89][90][91][92] .…”

Section: Effective Connectivitymentioning

confidence: 96%

“…For example, abnormal development of the corpus callosum in humans often results in the emergence of longitudinal fibres (such as Probust bundles) that run parallel to the inter-hemispheric fissure and that are not seen in healthy controls 172 . Such mis-wiring will result in a higher frequency of qualitative differences in brain connectivity (that is, the presence of a set of connections in patients that is not apparent in healthy individuals; see This pattern of increased activity followed by subsequent degeneration has been mimicked in computational models 88 and may reflect evidence of an early, adaptive and plastic response that is gradually overwhelmed as pathological burden increases 91 . In this regard, compensation could precede subsequent decline and transneuronal degeneration in some cases.…”

Section: Box 2 | Timing Is Everythingmentioning

confidence: 99%

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The connectomics of brain disorders

2015

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Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

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Section: Effective Connectivitymentioning

confidence: 96%

Section: Box 2 | Timing Is Everythingmentioning

confidence: 99%

The connectomics of brain disorders

2015

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“…This way, clinical symptoms can be analyzed in the context of both structural and functional changes in the brain. The observable functional changes may indicate clinically important or manifest reorganization within networks Schoonheim and Filippi, 2012). Reorganization of networks might be triggered by structural damage and act as a compensational mechanism aimed at preserving function.…”

Section: Conclusion and Potential Future Directions For Clinical Resmentioning

confidence: 99%

Gray matter damage in multiple sclerosis: Impact on clinical symptoms

et al. 2015

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“…While some studies restricted their analysis to the DMN or motor regions (Bonavita et al, 2011;Dogonowski et al, 2013;Lowe et al, 2002;Rocca et al, 2010), others have reported changes in several large-scale resting state networks, including the sensorimotor and executive control networks (Hawellek et al, 2011;Richiardi et al, 2012;Rocca et al, 2012;Roosendaal et al, 2010). Together, these results are very promising for characterizing the functional pathology of MS, but the relationship between structural damage and functional changes is still poorly understood (Schoonheim and Filippi, 2012).…”

Section: Introductionmentioning

confidence: 99%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

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Functional plasticity in MS

Cited by 17 publications

References 10 publications

The connectomics of brain disorders

The connectomics of brain disorders

Gray matter damage in multiple sclerosis: Impact on clinical symptoms

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

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