A network neuroscience of neurofeedback for clinical translation

Murphy, Anthony B.; Bassett, Danielle S.

doi:10.1016/j.cobme.2017.03.003

Cited by 17 publications

(9 citation statements)

References 64 publications

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“…Specifically, assuming a model of brain dynamics allows us to determine which nodes could most easily drive transitions in the state of brain activity via some stimulatory input. The network control theory that we use here to study the internal modulation of brain state (via undertaking a task requiring executive function) also makes explicit predictions about the external modulation of brain state (via stimulation or neurofeedback) 62 . Indeed, average controllability values were associated with sex-dependent, symmetric patterns of brain activity ( Fig.…”

Section: Implications For Cognitive and Clinical Neurosciencementioning

confidence: 99%

Sex differences in network controllability as a predictor of executive function in youth

Cornblath

Tang

Baum³

et al. 2019

NeuroImage

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Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in executive function in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 882 youth aged 8-22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy-versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, average controllability values in the medial frontal cortex and subcortex, both higher in males, are negatively related to executive function. Finally, we find that average controllability predicts sex-dependent individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.

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Section: Implications For Cognitive and Clinical Neurosciencementioning

confidence: 99%

Sex differences in network controllability as a predictor of executive function in youth

Cornblath

Tang

Baum³

et al. 2019

NeuroImage

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“…27 Given that the efficacy of the established pharmacological and behavioral treatment options for these disorders is limited, 28 it has been advocated to employ hemodynamic imaging-informed NF to modulate the identified neural alterations in a regionalspecific manner to alleviate psychiatric symptoms and normalize functional deficits. [28][29][30][31] To facilitate translation of hemodynamic NF into clinical applications, the present randomized, sham-controlled study aims at evaluating the feasibility to employ fNIRS-informed NF as strategy to modulate brain activity. Initial studies have demonstrated the feasibility to use fNIRS-informed NF to allow participants to acquire regulatory control over brain activity in motor-related regions.…”

Section: Introductionmentioning

confidence: 99%

Functional near-infrared spectroscopy-informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility

Jiang

Gong

et al. 2019

Neurophoton.

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“…In the first study of its kind, applying an integrated IEEG-fMRI method, we have shown that established markers of memory encoding correspond to meaningful network differences captured by rsfMRI. In clinical terms, knowing a region is a hub, playing an important role in multi-regional and multi-functional connectivity, may inform technologies trying to identify the most effective targets to electrically or pharmacologically stimulate for cognitive enhancement in areas such as memory Ezzyat Y et al, 2018;Kucewicz MT et al, 2018), or perhaps be used to identify the areas with sufficient influence over targets to generate effective neuro-feedback loops (Hohenfeld C et al, 2017;Murphy AC et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

Chaitanya

Hinds

Kragel

et al. 2019

Preprint

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High-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFAmemory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding. AbbreviationsHFA -High-frequency activity MEM -Brain regions showing HFA associated with verbal-memory encoding CN -Controls Nodes P.REC -Percentage of words recalled during IEEG memory testing CVLT -California Verbal Learning Test IEEG -Invasive Electroencephalography BC -Betweenness Centrality CC-Clustering Coefficient PC -Participation Coefficient Highlights 1. High frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.2. HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.3. HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.4. HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding. 5. HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.resting-state fMRI (rsfMRI) data to characterize the network architecture of a task-defined HFAmemory network and understand its relationship to the functional connectivity of a broader set of intrinsic resting-state networks. Graph theory has been a useful tool for mapping functional networks and characterizing their properties during task and at rest. Three important graph indices that are essential in characterizing brain regions and their functionality include: (1) clustering coefficient (CC), which captures network segregation and local information processing and (2) betweenness centrality (BC), which captures hubness and the degree of importance held by a region that connects two or more modules and (3) participation coefficient (PC) which measures the extent to which regions within a network connect to networks other than its own, with a higher participation coefficient indicating that the regions connect with a variety of o...

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A network neuroscience of neurofeedback for clinical translation

Cited by 17 publications

References 64 publications

Sex differences in network controllability as a predictor of executive function in youth

Sex differences in network controllability as a predictor of executive function in youth

Functional near-infrared spectroscopy-informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

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