Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler\u27s connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants\u27 age within ± 3.6 months error and a prediction

Differences in the functional brain architecture of sustained attention and working memory in youth and adults

Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children—and captured individual differences in later recognition memory—but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA

No Hurst for the Weary: Suppression of Scale-Free Brain Activity as a Measure of Cognitive Effort and Predictor of Working Memory Performance

Since its proposition about 2 decades ago, the theory of assessing the brain as a neural network with self-organized criticality has triggered a multitude of research due to its conceptual appeal. Scale-free brain activity as measured by the Hurst exponent (H) of electrophysiological signals and fMRI BOLD signals has been a hallmark of queries related to the ‘criticality hypothesis’ within the field of cognitive neuroscience, which models brain at rest as a network configured to operate near a critical state. In this dissertation I investigated the significance of H in EEG and fMRI data with regards to cognitive processes involved in working memory and learning. In chapter 1, I utilized global H suppression in EEG to distinguish working memory load from cognitive effort. Results from two visual working memory experiments with varying memory set size provided evidence for the suppression of scale-invariance in EEG due to task difficulty that continues even after working memory capacity has been reached. In contrast, task performance and oscillatory signals of working memory load both plateau beyond working memory capacity. This suggests that H suppression may be used to reliably indicate an effortful state. In chapter 2, I used H measured with fMRI data to predict learning in a dual n-back (DNB) working memory task. I hypothesized that learning potential is higher when brain networks are poised closer to a critical state, and thus higher H can be used to predict more learning and improvement on cognitive tasks. The results show that higher H during learning distinguished task improvers from non-improvers. As a comparison, neither baseline task performance nor fMRI functional connectivity strength reliably classified improvers vs. non-improvers. I then successfully cross-validated the H-based model from the DNB dataset on an independent fMRI dataset of participants performing a completely different working memory task (word completion). Taken together, these results suggest that scale-free brain activity can be used as an objective measure of an individual’s cognitive state and provide support for the utility of the criticality hypothesis

Modeling Users’ Repost Behavior in Online Communities Using a Team of Learning Automata

Today's online communities play an important role in the flow of information such as news, educational contents, entertainment, and so on. Millions of users create different posts in this environment on a daily basis. Users will re-post some posts if they wish. Reposting has a significant effect on the transfer of information between users. Due to the large number of posts, users in these communities face the information overload problem. In this paper, the repost behavior of users in online communities is modeled. Firstly, effective factors have been identified in the behavior of user reposting, and then, using a reinforcement learning approach, users' repost behavior is anticipated. This reinforcement learning method is designed as a game for a team of random learning automata as a common pay-off game. To evaluate the proposed method, three large data sets have been gathered. Various scenarios have been used to evaluate the proposed method. Based on the results, randomized learning automata have great performance due to the features of the environment and online learning power

Increasing Information Reposting Behavior in Online Learning Community

Online learning communities (OLCs) enable their learner to access different types of information through internet based structures anywhere anytime. OLCs are among the strategies used for the production and repost of information by learners interested in a specific area to support asynchronous learning. In this respect, learners become members of a particular domain and begin posting. OLC members consist of different sites with different educational backgrounds as well as different levels of expertise. This causes the sharing of posts which may not be appropriate for different learners. It also reduces data reposting behavior and subsequently decreases participation in information sharing. Furthermore, most learners of these communities take up a lurking position toward the posts. One of the ways proposed to increase information reposts is the selection and display of effective posts for each individual. Effective posts are selected in such a way that they can be more likely to be reposted by learners based on each learner's interests, knowledge and characteristics. The present paper intends to introduce a new method for selecting k effective posts to ensure the increase of information repost and participation in OLCs. In terms of participation in OLCs, learners are divided into two groups of posters and lurkers. Some solutions are proposed to encourage lurking learners to participate in content repostings. Comprehensive evaluations indicated that the proposed method had significantly solved the presented challenges