Cognitive Network Neuroscience

Medaglia, John D.; Lynall, Mary-Ellen; Bassett, Danielle S.

doi:10.1162/jocn_a_00810

Cited by 383 publications

(362 citation statements)

References 217 publications

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“…Our formalization of a clear framework for experimentally testing network-level mechanisms also distinguishes the present report from previous informative reviews of the human FC literature (Bullmore & Sporns, 2009; Medaglia et al, 2015; Sporns, 2014). Additionally, whilst these cited reviews share an emphasis on defining graph theory and summarizing its applications to human imaging, we opt for breadth in summarizing multiple FC analysis methods beyond graph theory.…”

Section: A Framework For Mechanistic Discovery In Network Neuroscimentioning

confidence: 71%

“…An initial focus on changes in regional activation amplitudes (Friston et al, 1994; Kanwisher, 2010) has given way to examination of functional connectivity (FC) between regions and large-scale networks of regions (Biswal, Yetkin, Haughton, & Hyde, 1995; Medaglia, Lynall, & Bassett, 2015; Petersen & Sporns, 2015; Raichle, 2010; Sporns, 2014). This trend provides a macroscopic parallel to the search for the “neural code” in animal neurophysiology, which has also transitioned from analysis of spiking in individual neurons to deciphering patterns of spatiotemporal synchronization in neuronal populations (Fries, 2005; Goldman-Rakic, 1988; Kumar, Rotter, & Aertsen, 2010; Laughlin & Sejnowski, 2003; M.…”

Section: A Framework For Mechanistic Discovery In Network Neuroscimentioning

confidence: 99%

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From connectome to cognition: The search for mechanism in human functional brain networks

2017

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Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for “network mechanisms”, which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or “effective” connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain “stable” across task domains and more “flexible” components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

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Section: A Framework For Mechanistic Discovery In Network Neuroscimentioning

confidence: 71%

Section: A Framework For Mechanistic Discovery In Network Neuroscimentioning

confidence: 99%

From connectome to cognition: The search for mechanism in human functional brain networks

2017

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“…In the second stage, h in (49) is considered given and the optimal s * P is obtained as the minimizer of (h, y, s P ) 2 2 , which is a function of h and y [cf. (46)]. In the first stage, the solution of the second stage s * P (h, y) and the distribution of y are leveraged to write the expectation of the reconstruction error in (49) as¯ (h) := E y [ (h, y, s * P (h, y)) 2 2 ], which only depends on h. The optimum h * is then the minimizer of the expected error¯ (h).…”

Section: A Insufficient Seeding Valuesmentioning

confidence: 99%

“…Brain resting states are associated with high activity in the posterior cingulate (PC) and inferior parietal (IP) cortices whereas active states are associated with high activity in the rostral middle frontal (RMF) and superior parietal (SP) cortices [45], [46].…”

Section: Inducing a Brain Statementioning

confidence: 99%

Reconstruction of Graph Signals Through Percolation from Seeding Nodes

Segarra

Marqués

Leus

et al. 2016

IEEE Trans. Signal Process.

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New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph.Most existing formulations focus on estimating an unknown graph signal by observing its value on a subset of nodes. By contrast, in this paper, we study the problem of inducing a known graph signal using as input a graph signal that is nonzero only for a small subset of nodes (seeding nodes). The sparse signal is then percolated (interpolated) across the graph using a graph filter.Graph filters are a generalization of classical time-invariant systems and represent linear transformations that can be implemented distributedly across the nodes of the graph. Alternatively, one can interpret graph signals as network states and study graph-signal reconstruction as a network-control problem where the target class of states is represented by bandlimited signals. Three setups are investigated. In the first one, a single simultaneous injection takes place on several nodes in the graph. This setting resembles that of reconstruction after sampling, but in this paper the interpolator is constrained to be a graph filter. In the second one, successive value injections take place on a single node. The third one is a generalization where multiple nodes inject multiple signal values. For noiseless settings, conditions under which perfect reconstruction is feasible are given, and the corresponding schemes to recover the desired signal are specified. Scenarios leading to imperfect reconstruction, either due to insufficient or noisy signal value injections, are also analyzed. Moreover, connections with classical interpolation in the time domain are discussed.Specifically, for time-varying signals, where the ideal interpolator after uniform sampling is a (low-pass) filter, our proposed approach and the reconstruction of a sampled signal coincide. Nevertheless, for general graph signals, we show that these two approaches differ. The last part of the paper presents numerical experiments that illustrate the results developed through synthetic graph signals and two real-world signal reconstruction problems: influencing opinions in a social network and inducing a desired brain state in humans.

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“…Noticeably, the increasing utilization of network science with statistical learning [7][8][9] makes network-based approaches a robust tool in cognitive science and computational linguistics [10,11]. Network science has been successfully applied to several pressing issues: feature biases in early word learning [12], semantic concepts [13][14][15][16][17], grammatical relationships [18], spatial learning in human navigation [19], structural and functional brain connections in relationship to various cognitive capacities [20][21][22][23], and scene perception studies 2 Advances in Artificial Intelligence rise to learning, how acquired knowledge might be reflected in observable topological patterns in the human brain, and how micro-and macrolevel brain dynamics support learning.…”

Section: Introductionmentioning

confidence: 99%

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

Chang

2017

Advances in Artificial Intelligence

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One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term "understanding," or the meaning of the word "meaning," is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual's knowledge and state of mind at the time of the network construction. As a result, "understanding" and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed "Path of Understanding" in an iWordNet that characterizes an individual's understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual's iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.

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Cognitive Network Neuroscience

Cited by 383 publications

References 217 publications

From connectome to cognition: The search for mechanism in human functional brain networks

From connectome to cognition: The search for mechanism in human functional brain networks

Reconstruction of Graph Signals Through Percolation from Seeding Nodes

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

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