A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Bassett, Danielle S.; Mattar, Marcelo G.

doi:10.1016/j.tics.2017.01.010

Cited by 98 publications

(81 citation statements)

References 158 publications

(180 reference statements)

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“…At longer time scales, an important question is whether durable task‐related change can be detected non‐invasively in resting brain activity. Learning and learnability may for example be related to network properties of the system (Advani, Lahiri, & Ganguli, ; Bassett & Mattar, ; Seung, Sompolinsky, & Tishby, ) or to specific phases of brain activity (Carrasquilla, ). More generally, one may ask whether brain activity is more efficient in some dynamical sense as a result of successful NF (Ros et al., ), or more dynamically stable or robust.…”

Section: Appraising Neurofeedbackmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far‐reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain‐behaviour relationships.

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Section: Appraising Neurofeedbackmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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“…Humans can improve their performance in any task by gradually optimizing the implementation of a known strategy, or by devising and then adopting novel, more efficient strategies (Badre et al, 2010;Collins & Frank, 2013;Donoso et al, 2014;Heathcote et al, 2000;Roeder & Ashby, 2016;Schuck et al, 2015). Previous research has shown that practicing a task induces changes not only in the activation level of specific brain regions, but also in the long-range organisation of the relevant brain networks (Bassett & Mattar, 2017;Bassett et al, 2015;Chein & Schneider, 2005;Cole et al, 2013;Patel et al, 2013). However, the network dynamics governing strategy optimization versus the discovery of a new strategy are still unknown.…”

Section: Discussionmentioning

confidence: 99%

“…Compatible effects have been reported when comparing brain connectivity during rest to visuospatial attention (Al-Aidroos, Said, & Turk-Browne, 2012;Spadone et al, 2015), working memory (Cohen & D'Esposito, 2016;Shine et al, 2016) or flexible rule application (Vatansever, Menon, & Stamatakis, 2017). By contrast, the evidence on the network dynamics associated with learning is still limited, also because the analysis tools available until recently had low sensitivity in detecting network changes (Bassett & Mattar, 2017;Bassett et al, 2015). One important study by Bassett and collaborators reported that visuomotor learning was associated with increased functional segregation (i.e.…”

Section: Incremental Task Optimization and Instructed Strategy Changementioning

confidence: 99%

“…This may be understood as a modulation of neural circuits involving multiple brain regions, rather than just a local activation change. Evidence relating learning to brain-wide network changes has become available thanks to the recent development of tools for network dynamics analysis (Bassett et al, 2011;Bassett, Yang, Wymbs, & Grafton, 2015;Bassett & Mattar, 2017;Cole et al, 2013) able to capture the rich information contained in rapidly varying connectivity patterns. Nevertheless, the evidence available to date is mainly focused on motor learning, leaving open the question on how the human brain optimizes known strategies and how this relates to the discovery and implementation of new ones.…”

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confidence: 99%

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Brain network dynamics during spontaneous strategy shifts and incremental task optimization

Allegra

Seyed-Allaei

Amati

et al. 2018

Preprint

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AbstractWith practice, humans may improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. How does the brain support these two fundamental abilities? In the present experiment, subjects performed a simple perceptual decision-making task. They could either use and progressively optimize an instructed strategy based on stimulus position, or spontaneously devise and then use a new strategy based on stimulus color. We investigated how local and long-range BOLD coherence behave during these two types of strategy learning by applying a recently developed unsupervised fMRI analysis technique that was specifically designed to probe the presence of transient correlations. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy: these regions encoded the relevant spatial information, increased the level of local coherence and the strength of connectivity with other relevant regions in the brain (e.g. visual cortex, dorsal attention network). This increase was proportional to the task optimization achieved by subjects, as measured by the reduction of reaction times, and was transiently disrupted when subjects were forced to change strategy. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) together with rostral portion of the fronto-parietal network showed an increase in local coherence and connectivity only in subjects that would at some point spontaneously choose the new strategy. Overall, our findings shed light on the dynamic interactions between regions related with attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being “shut-down” during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.

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“…In this formal modeling approach [28], network nodes represent brain regions or sensors and network edges represent statistical relations or so-called functional connections between regional time series [29]. Recent studies have demonstrated that patterns of functional connections can provide clearer explanations of the learning process than activation alone [30], and changes in those functional connections can track changes in behavior [31]. During BCI tasks, functional connectivity reportedly increases within supplementary and primary motor areas [15] and decreases between motor and higher-order as-sociation areas as performance becomes more automatic [32].…”

Section: Introductionmentioning

confidence: 99%

Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

Stiso¹,

Corsi²,

Vettel³

et al. 2019

Preprint

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Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -non-negative matrix factorization -to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

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A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Cited by 98 publications

References 158 publications

Neurofeedback: Principles, appraisal, and outstanding issues

Neurofeedback: Principles, appraisal, and outstanding issues

Brain network dynamics during spontaneous strategy shifts and incremental task optimization

Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

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