A network engineering perspective on probing and perturbing cognition with neurofeedback

Bassett, Danielle S.; Khambhati, Ankit N.

doi:10.1111/nyas.13338

Cited by 56 publications

(39 citation statements)

References 175 publications

(410 reference statements)

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“…Q w is zero if nodes are placed at random into modules or if all nodes are in the same cluster. To test the modularity of the empirically observed networks, we compared them to the modularity distribution (N = 100) of random networks as described above (35). The within-module degree Zi indicates how well node i is connected to other nodes within the module mi.…”

Section: Modularity Analyses and Z-p Parameter Spacementioning

confidence: 99%

Network Topology Dynamics of Circulating Biomarkers and Cognitive Performance in Older Cytomegalovirus-seropositive or -seronegative Men and Women

Benedetto

Müller

Rauskolb

et al. 2019

Preprint

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Background: Cytokines are signaling molecules operating within complex cascade patterns and having exceptional modulatory functions. They impact various physiological processes such as neuroendocrine and metabolic interactions, neurotrophins' metabolism, neuroplasticity, and may affect behavior and cognition. In our previous study, we found that sex and Cytomegalovirus (CMV)-serostatus may modulate levels of circulating pro-and antiinflammatory cytokines, metabolic factors, immune cells, and cognitive performance, as well as associations between them. Results: In the present study, we used a graph-theoretical approach to investigate the network topology dynamics of 22 circulating biomarkers and 11 measures of cognitive performance in 161 older participants recruited to undergo a six-months training intervention. For network construction, we applied coefficient of determination (R 2 ) that was calculated for all possible pairs of variables (N = 33) in four groups (CMVmen and women; CMV + men and women). Network topology has been evaluated by clustering coefficient (CC) and characteristic path length (CPL) as well as local (Elocal) and global (Eglobal) efficiency, showing the degree of network segregation (CC and Elocal) and integration (CPL and Eglobal). We found that networks under consideration showed small-world networks properties with more random characteristics. Mean CC, as well as local and global efficiency were highest and CPL shortest in CMVmales (having lowest inflammatory status and highest cognitive performance). CMVand CMV + females did not show any significant differences. Modularity analyses showed that the networks exhibit in all cases highly differentiated modular organization (with Q-value ranged between 0.397 and 0.453). Conclusions: In this work, we found that segregation and integration properties of the network were notably stronger in the group with balanced inflammatory status. We were also able to confirm our previous findings that CMV-infection and sex modulate multiple circulating biomarkers and cognitive performance and that balanced inflammatory and metabolic status in elderly contributes to better cognitive functioning. Thus, network analyses provide a useful strategy for visualization and quantitative description of multiple interactions between various circulating pro-and anti-inflammatory biomarkers, hormones, neurotrophic and metabolic factors, immune cells, and measures of cognitive performance and can be in general applied for analyzing interactions between different physiological systems.

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Section: Modularity Analyses and Z-p Parameter Spacementioning

confidence: 99%

Network Topology Dynamics of Circulating Biomarkers and Cognitive Performance in Older Cytomegalovirus-seropositive or -seronegative Men and Women

Benedetto

Müller

Rauskolb

et al. 2019

Preprint

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“…To evaluate the cortical changes at the network level, we considered functional connectivity (FC) patterns that have been previously shown to be sensitive to BCI-related tasks (24) as well as to learning processes (25).…”

Section: Functional Connectivity and Network Analysismentioning

confidence: 99%

Spatiotemporal neural correlates of brain-computer interface learning

Corsi

Chávez

Schwartz

et al. 2018

Preprint

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Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects.After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

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“…As neuronal activity evolves in time, functional brain states undergo transitions, traversing a path through a dynamic state-space landscape. Perturbations applied to a set of control nodes, either from an extrinsic source or from internal dynamics, can modulate these trajectories (Bassett and Khambhati, 2017). This input energy depends on the choice of control nodes, and the strength and pattern of structural connections (Kim et al,6 2018; Wu-Yan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

Jeganathan

Perry

Bassett

et al. 2017

Preprint

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Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR (n=84, mean age 22.4) individuals, patients with BD (n=38, mean age 23.9), and age-and gender-matched controls (n=96, mean age 22.6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippocampal, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula (p=0.004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus (p=0.008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions. Highlights• Control theory estimates how neuronal fluctuations spread from local networks.• We compare brain controllability in bipolar disorder and their high-risk relatives.• These groups have impaired controllability in networks supporting cognitive and emotional control. • Weaker connectivity as well as topological alterations contribute to these changes. AbbreviationsCN = control; HR = high-risk; BD = bipolar disorder; MRI = magnetic resonance imaging; fMRI = functional MRI; dMRI = diffusion MRI.4

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A network engineering perspective on probing and perturbing cognition with neurofeedback

Cited by 56 publications

References 175 publications

Network Topology Dynamics of Circulating Biomarkers and Cognitive Performance in Older Cytomegalovirus-seropositive or -seronegative Men and Women

Network Topology Dynamics of Circulating Biomarkers and Cognitive Performance in Older Cytomegalovirus-seropositive or -seronegative Men and Women

Spatiotemporal neural correlates of brain-computer interface learning

Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

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