Combined methods reveal task activation dynamics in human brain networks.

A clear understanding of how human brain networks reflect task performance has been lacking, in part due to methodological difficulties. A new study combines the temporal resolution of EEG, MRI source localization, and multivariate modeling to address this need.

Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes.

Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.

Dynamics of hidden brain states when people solve verbal puzzles.

When people try to solve a problem, they go through distinct steps (encoding, ideation, evaluation, etc.) recurrently and spontaneously. To disentangle different cognitive processes that unfold throughout a trial, we applied an unsupervised machine learning method to electroencephalogram (EEG) data continuously recorded while 39 participants attempted 153 Compound Remote Associates problems (CRA). CRA problems are verbal puzzles that can be solved in either insight-leaning or analysis-leaning manner. We fitted a Hidden Markov Model to the time-frequency transformed EEG signals and decoded each trial as a time-resolved state sequence. The model characterizes hidden brain states with spectrally resolved power topography. Seven states were identified with distinct activation patterns in the theta (4-7 Hz), alpha (8-9 Hz and 10-13 Hz), and gamma (25-50 Hz) bands. Notably, a state featuring widespread activation only in alpha-band frequency emerged, from this data-driven approach, which exhibited dynamic characteristics associated with specific temporal stages and outcomes (whether solved with insight or analysis) of the trials. The state dynamics derived from the model overlap and extend previous literature on the cognitive function of alpha oscillation: the "alpha-state" probability peaks before stimulus onset and decreases before response. In trials solved with insight, relative to solved with analysis, the alpha-state is more likely to be visited and maintained during preparation and solving periods, and its probability declines more sharply immediately preceding a response. This novel paradigm provides a way to extract dynamic features that characterize problem-solving stages and potentially provide a novel window into the nature of the underlying cognitive processes.

BOLD cofluctuation 'events' are predicted from static functional connectivity.

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure.

Exploring How Generating Metaphor Via Insight Versus Analysis Affects Metaphor Quality and Learning Outcomes.

Metaphor generation is both a creative act and a means of learning. When learning a new concept, people often create a metaphor to connect the new concept to existing knowledge. Does the manner in which people generate a metaphor, via sudden insight (Aha! moment) or deliberate analysis, influence the quality of generation and subsequent learning outcomes? According to some research, deliberate processing enhances knowledge retention; hence, generation via analysis likely leads to better concept learning. However, other research has shown that solutions generated via insight are better remembered. In the current study, participants were presented with science concepts and descriptions, then generated metaphors for the concepts. They also indicated how they generated each metaphor and rated their metaphor for novelty and aptness. We assessed participants' learning outcomes with a memory test and evaluated the creative quality of the metaphors based on self- and crowd-sourced ratings. Consistent with the deliberate processing benefit, participants became more familiar with the target science concept if they previously generated a metaphor for the concept via analysis compared to via insight. We also found that metaphors generated via analysis did not differ from metaphors generated via insight in quality (aptness or novelty) nor in how well they were remembered. However, participants' self-evaluations of metaphors generated via insight showed more agreement with independent raters, suggesting the role of insight in modulating the creative ideation process. These preliminary findings have implications for understanding the nature of insight during idea generation and its impact on learning.

The influence of insight on risky decision making and nucleus accumbens activation.

During insightful problem solving, the solution appears unexpectedly and is accompanied by the feeling of an AHA!. Research suggests that this affective component of insight can have consequences beyond the solution itself by motivating future behavior, such as risky (high reward and high uncertainty) decision making. Here, we investigate the behavioral and neural support for the motivational role of AHA in decision making involving monetary choices. The positive affect of the AHA! experience has been linked to internal reward. Reward in turn has been linked to dopaminergic signal transmission in the Nucleus Accumbens (NAcc) and risky decision making. Therefore, we hypothesized that insight activates reward-related brain areas, modulating risky decision making. We tested this hypothesis in two studies. First, in a pre-registered online study (Study 1), we demonstrated the behavioral effect of insight-related increase in risky decision making using a visual Mooney identification paradigm. Participants were more likely to choose the riskier monetary payout when they had previously solved the Mooney image with high compared to low accompanied AHA!. Second, in an fMRI study (Study 2), we measured the effects of insight on NAcc activity using a similar Mooney identification paradigm to the one of Study 1. Greater NAcc activity was found when participants solved the Mooney image with high vs low AHA!. Taken together, our results link insight to enhanced NAcc activity and a preference for high but uncertain rewards, suggesting that insight enhances reward-related brain areas possibly via dopaminergic signal transmission, promoting risky decision making.

From correlation to communication: Disentangling hidden factors from functional connectivity changes.

While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, "communication change," utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed "communication change" is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.

Uncovering the Interplay of Oscillatory Processes During Creative Problem Solving: A Dynamic Modeling Approach.

To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on earlier work, we further developed analytical methods, such as incorporating source separating techniques and identifying the optimal number of states using cross-validation. We extracted brain states characterized by spatio-spectral topographies from time-resolved EEG spectral powers. The data driven approach allowed us to infer the dynamic, trial-by-trial, state sequences, and provided a comprehensive depiction of how various oscillation components interact recurrently throughout the trial. The properties of the states suggest their dissociable cognitive functions. For example, we identified three states with dominant activation in alpha bands but having distinct spatial distributions. People were differentially engaged in these states depending on the stages (e.g., onset or response) and outcomes of the trials (solved with insight or analysis). The current approach, applicable to many tasks requiring extended trial duration, can potentially reconcile findings from previous EEG studies and drive new hypotheses to further our understanding of the complex creative process.

The Ubiquity and Development-Related Abundance Dynamics of Ophiocordyceps Fungi in Soft Scale Insects.

Mutual relationships with symbionts play a crucial role in the evolution and ecology of plant-feeding hemipteran insects. However, there was no specific dominant bacterium observed in soft scales (Coccidae) in the previous studies, it is still unclear whether soft scales have specific primary symbionts. In this study, a nuclear ribosomal internal transcribed spacer (ITS)gene fragment was used to analyze the diversity of fungal communities in 28 Coccidae species based on next-generation sequencing (NGS). Furthermore, samples from different developmental stages of Ceroplastes japonicus were sequenced to illustrate the dynamics of fungal community. Our results showed that Coccidae-associated Ophiocordyceps fungi (COF) were prevalent in all 28 tested species with high relative abundance. Meanwhile, the first and second instars of C. japonicus, two important stages for growth and development, had high relative abundance of COF, while the relative abundances in other stages were low, ranging from 0.68% to 2.07%. The result of fluorescent in situ hybridization showed that the COF were widely present in hemolymph and vertically transmitted from mother to offspring. Our study confirms that the COF have intimate associations with the growth and development of soft scales, and provides new evidence to support that COF are primary fungal symbionts for Coccidae.

Coexistence of Three Dominant Bacterial Symbionts in a Social Aphid and Implications for Ecological Adaptation.

Aphids are associated with an array of symbionts that have diverse ecological and evolutionary effects on their hosts. To date, symbiont communities of most aphid species are still poorly characterized, especially for the social aphids. In this study, high-throughput 16S rDNA amplicon sequencing was used to assess the bacterial communities of the social aphid Pseudoregma bambucicola, and the differences in bacterial diversity with respect to ant attendance and time series were also assessed. We found that the diversity of symbionts in P. bambucicola was low and three dominant symbionts (Buchnera, Pectobacterium and Wolbachia) were stably coexisting. Pectobacterium may help P. bambucicola feed on the hard bamboo stems, and genetic distance analysis suggests that the Pectobacterium in P. bambucicola may be a new symbiont species. Wolbachia may be associated with the transition of reproduction mode or has a nutritional role in P. bambucicola. Statistical tests on the diversity of bacterial communities in P. bambucicola suggest that aphid populations attended by ants usually have a significantly higher evenness than populations without ant attendance but there was no significant difference among aphid populations from different seasons.

Integrating disulfides into a polyethylenimine gene carrier selectively boosts significant transfection activity in lung tissue enabling robust IL-12 gene therapy against metastatic lung cancers.

Bioreducible polyethylenimines (SSPEIs) are promising non-viral carriers for cancer gene therapy. However, the availability of significant gene transfection activity by SSPEIs remains a challenge. Herein, an essential step was taken to ascertain whether or not the disulfide bonds of SSPEIs play a critical role in promoting significant gene transfection activity in different tissues. Initially, a disulfide-linked linear polyethylenimine (denoted as SSLPEI) consisting of one 5.0 kDa LPEI main chain and three disulfide-linked 5.7 kDa LPEI grafts was designed and prepared to possess similar molecular weight with commercialized 25 kDa LPEI as a positive control. The SSLPEI could induce superior in vitro transfection activity in different cells to the LPEI control as well as low cytotoxicity. Notably, such enhanced in vitro transfection effect by the SSLPEI was more marked in type-II alveolar epithelial cells compared to different cancer cells. In a Balb/c nude mouse model bearing SKOV-3 tumor, the SSLPEI caused parallel level of transgene expression with the LPEI control in the tumor but significantly higher level in the mouse lung. Furthermore, the SSLPEI and LPEI groups afforded an identical antitumor efficacy against the SKOV-3 tumor via intravenous delivery of a shRNA for silencing VEGF expression in the tumor. However, via intravenous delivery of an interleukin-12 (IL-12) gene into metastatic lung cancers in a C57BL/6 mouse model, the SSLPEI group exerted markedly higher IL-12 expression level in the mouse lung and peripheral blood as compared to the LPEI group, thereby boosting IL-12 immunotherapy against the lung metastasis with longer medium survival time. The results of this work elicit that the disulfide bonds of SSPEIs play a pivotal role in enhancing gene transfection activity selectively in the lung tissue rather than solid tumor, enabling high translational potential of SSPEIs for non-viral gene therapy against metastatic lung cancers.