Effects of meditation experience on functional connectivity of distributed brain networks

This study sought to examine the effect of meditation experience on brain networks underlying cognitive actions employed during contemplative practice. In a previous study, we proposed a basic model of naturalistic cognitive fluctuations that occur during the practice of focused attention meditation. This model specifies four intervals in a cognitive cycle: mind wandering (MW), awareness of MW, shifting of attention, and sustained attention. Using subjective input from experienced practitioners during meditation, we identified activity in salience network regions during awareness of MW and executive network regions during shifting and sustained attention. Brain regions associated with the default mode were active during MW. In the present study, we reasoned that repeated activation of attentional brain networks over years of practice may induce lasting functional connectivity changes within relevant circuits. To investigate this possibility, we created seeds representing the networks that were active during the four phases of the earlier study, and examined functional connectivity during the resting state in the same participants. Connectivity maps were then contrasted between participants with high vs. low meditation experience. Participants with more meditation experience exhibited increased connectivity within attentional networks, as well as between attentional regions and medial frontal regions. These neural relationships may be involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction, that are often reported with meditation practice. Furthermore, because altered connectivity of brain regions in experienced meditators was observed in a non-meditative (resting) state, this may represent a transference of cognitive abilities “off the cushion” into daily life

Establishing the cognitive signature of human brain networks derived from structural and functional connectivity

© 2018, The Author(s). Numerous neuroimaging studies have identified various brain networks using task-free analyses. While these networks undoubtedly support higher cognition, their precise functional characteristics are rarely probed directly. The frontal, temporal, and parietal lobes contain the majority of the tertiary association cortex, which are key substrates for higher cognition including executive function, language, memory, and attention. Accordingly, we established the cognitive signature of a set of contrastive brain networks on the main tertiary association cortices, identified in two task-independent datasets. Using graph-theory analysis, we revealed multiple networks across the frontal, temporal, and parietal cortex, derived from structural and functional connectivity. The patterns of network activity were then investigated using three task-active fMRI datasets to generate the functional profiles of the identified networks. We employed representational dissimilarity analysis on these functional data to quantify and compare the representational characteristics of the networks. Our results demonstrated that the topology of the task-independent networks was strongly associated with the patterns of network activity in the task-active fMRI. Our findings establish a direct relationship between the brain networks identified from task-free datasets and higher cognitive functions including cognitive control, language, memory, visuospatial function, and perception. Not only does this study support the widely held view that higher cognitive functions are supported by widespread, distributed cortical networks, but also it elucidates a methodological approach for formally establishing their relationship

A Neural Network Model of Continual Learning with Cognitive Control

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.Comment: 7 pages, 5 figures, paper accepted as a talk to CogSci 2022 (https://escholarship.org/uc/item/3gn3w58z

Twitter and the Press: an Ego-Centred Analysis

Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, whereby individuals cannot maintain meaningful interactions with more than 150 people, on average. In this work, we focus on the ego networks of journalists on Twitter, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users. Their ego network structure is very aligned with reference models derived from the social brain hypothesis and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter than with other social interaction means. Moreover, the ego networks of journalists are much stabler than those of politicians and generic users, and the ego-alter ties are often information-driven.Comment: Accepted at OSNED 2018 - colocated with WWW'1

Building accurate radio environment maps from multi-fidelity spectrum sensing data

In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated