Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization.

Over time, pathological, genetic, environmental, and lifestyle factors can age the brain and diminish its functional capabilities. While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to develop a method for assessing general age-related brain health and function that can be implemented widely and inexpensively. To this end, we trained a machine-learning algorithm on resting-state EEG (RS-EEG) recordings obtained from healthy individuals as the core of a brain-age estimation technique that takes an individual's RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person's estimated brain age. We tested the current version of our machine-learning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and estimated brain ages (r = 0.963 after statistical bias-correction). The test-retest correlation was 0.750 (0.939 after bias-correction) over a period of 1 week. Given these strong results and the ease and low cost of implementation, this technique has the potential for widespread adoption in the clinic, workplace, and home as a method for assessing general brain health and function and for testing the impact of interventions over time.

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.

Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians.

Using a creative production task, jazz improvisation, we tested alternative hypotheses about the flow experience: (A) that it is a state of domain-specific processing optimized by experience and characterized by minimal interference from task-negative default-mode network (DMN) activity versus (B) that it recruits domain-general task-positive DMN activity supervised by the fronto-parietal control network (FPCN) to support ideation. We recorded jazz guitarists' electroencephalograms (EEGs) while they improvised to provided chord sequences. Their flow-states were measured with the Core Flow State Scale. Flow-related neural sources were reconstructed using SPM12. Over all musicians, high-flow (relative to low-flow) improvisations were associated with transient hypofrontality. High-experience musicians' high-flow improvisations showed reduced activity in posterior DMN nodes. Low-experience musicians showed no flow-related DMN or FPCN modulation. High-experience musicians also showed modality-specific left-hemisphere flow-related activity while low-experience musicians showed modality-specific right-hemisphere flow-related deactivations. These results are consistent with the idea that creative flow represents optimized domain-specific processing enabled by extensive practice paired with reduced cognitive control.

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.

An examination of frontal asymmetry in relation to eating in the absence of hunger and loss-of-control eating.

Loss-of-control (LOC) eating involves a subjective feeling that one cannot stop eating or control one's eating. Individuals with LOC eating may exhibit strong appetitive drives and weak inhibitory control, and these two opposing motivations have been related to EEG measurements of frontal asymmetry or lateralized frontal activation. The present study investigated whether frontal asymmetry is related to hedonic hunger, LOC eating severity and frequency, and eating in the absence of hunger (EAH) in the laboratory. Fifty-nine individuals participated in an ostensible taste study after resting-state electroencephalogram (EEG) recordings. After the EEGs, they were provided a meal to eat until fullness, followed by an array of snacks and instructions to eat as much as they would like. The results indicated that several measures of right-frontal asymmetry were related to greater EAH and greater self-reported LOC eating severity. Although right-frontal asymmetry has been theorized to reflect avoidance motivation, recent evidence suggests it may indicate effortful control during approach-avoidance conflicts. Because individuals with LOC eating presumably experience heightened conflict between drives to eat beyond energy needs and to minimize such eating, those experiencing greater LOC may exert greater effort to manage these conflicting motivations. An integration of these neurobiological correlates of LOC eating may help provide a more comprehensive understanding of LOC eating and inform treatments.

Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior.

How does rumination affect reinforcement learning-the ubiquitous process by which we adjust behavior after error in order to behave more effectively in the future? In a within-subject design (n=49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly this impairment could not be attributed to decreased attentional breadth (quantified using a "decay" parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention), yet not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

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.

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders.

Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 individuals, we identified clusters of imaging features with distinct developmental trajectories and built machine learning models to obtain brain-age predictions from each of the clusters. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age. Importantly, brain-ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to distinguish disorder groups (e.g., depression and oppositional defiant disorder) from healthy controls. In summary, we show that MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns that may serve as biomarkers that contribute to our understanding of healthy and pathological brain development and the characterization and diagnosis of psychiatric disorders.

Pre-stimulus brain oscillations predict insight versus analytic problem-solving in an anagram task.

There are two general strategies for solving a problem. Insight is the sudden realization of a novel idea or problem solution accompanied by an "aha" experience. Analysis occurs in a conscious, deliberate fashion without an "aha" experience. Previous research has shown that brain activity during a preparatory period immediately before a problem is presented can predict whether the subsequently presented problem will be solved by insight or by analysis. Those prior studies used a type of brief verbal problem called compound remote associates (CRA). To determine whether prestimulus activity predicts subsequent insight versus analytic solving for other types of problems, the present study used an anagram task. We examined high-density electroencephalograms (EEGs) immediately preceding the presentation of anagrams and found that during the 2-s prestimulus interval there was greater beta-band activity recorded over right central-parietal cortex prior to analytic solving compared with insightful solving. EEG source reconstruction showed that this activity originated in left mid-cingulate cortex (MCC) and the right postcentral gyrus. In contrast to prior work, we found no evidence of a significant interaction between insight-related prestimulus brain activity and positive mood, although positive mood was associated with greater activity in anterior cingulate cortex and with a larger number of insight solutions. The present MCC results suggest that participants' anagram-solving strategies may be influenced by allostasis, that is, the estimation and marshalling of neurocognitive resources required to cope with an expected task. Specifically, when a participant adequately prepares for an upcoming problem, then MCC activity is high, enabling solution by resource-intensive analytic processing. Alternatively, when preparation is insufficient for analytic processing, then MCC activity is low and subsequent solving occurs by low-demand insight processing. The current findings and explanatory model differ from those of previous studies that used a CRA task, suggesting the possibility of complex interactions between task-type and procedure-type in determining the nature of prestimulus preparation. Future research examining such interactions may yield results that benefit educators who teach students problem-solving strategies.

An insight-related neural reward signal.

Moments of insight, a phenomenon of creative cognition in which an idea suddenly emerges into awareness as an "Aha!" are often reported to be affectively positive experiences. We tested the hypothesis that problem-solving by insight is accompanied by neural reward processing. We recorded high-density EEGs while participants solved a series of anagrams. For each solution, they reported whether the answer had occurred to them as a sudden insight or whether they had derived it deliberately and incrementally (i.e., "analytically'). Afterwards, they filled out a questionnaire that measures general dispositional reward sensitivity. We computed the time-frequency representations of the EEGs for trials with insight (I) solutions and trials with analytic (A) solutions and subtracted them to obtain an I-A time-frequency representation for each electrode. Statistical Parametric Mapping (SPM) analyses tested for significant I-A and reward-sensitivity effects. SPM revealed the time, frequency, and scalp locations of several I â€‹> â€‹A effects. No A â€‹> â€‹I effect was observed. The primary neural correlate of insight was a burst of (I â€‹> â€‹A) gamma-band oscillatory activity over prefrontal cortex approximately 500 â€‹ms before participants pressed a button to indicate that they had solved the problem. We correlated the I-A time-frequency representation with reward sensitivity to discover insight-related effects that were modulated by reward sensitivity. This revealed a separate anterior prefrontal burst of gamma-band activity, approximately 100 â€‹ms after the primary I-A insight effect, which we interpreted to be an insight-related reward signal. This interpretation was supported by source reconstruction showing that this signal was generated in part by orbitofrontal cortex, a region associated with reward learning and hedonically pleasurable experiences such as food, positive social experiences, addictive drugs, and orgasm. These findings support the notion that for many people insight is rewarding. Additionally, these results may explain why many people choose to engage in insight-generating recreational and vocational activities such as solving puzzles, reading murder mysteries, creating inventions, or doing research. This insight-related reward signal may be a manifestation of an evolutionarily adaptive mechanism for the reinforcement of exploration, problem solving, and creative cognition.

Dual-process contributions to creativity in jazz improvisations: An SPM-EEG study.

Conflicting theories identify creativity either with frontal-lobe mediated (Type-2) executive control processes or (Type-1) associative processes that are disinhibited when executive control is relaxed. Musical (jazz) improvisation is an ecologically valid test-case to distinguish between these views because relatively slow, deliberate, executive-control processes should not dominate during high-quality, real-time improvisation. In the present study, jazz guitarists (n â€‹= â€‹32) improvised to novel chord sequences while 64-channel EEGs were recorded. Jazz experts rated each improvisation for creativity, technical proficiency and aesthetic appeal. Surface-Laplacian-transformed EEGs recorded during the performances were analyzed in the scalp-frequency domain using SPM12. Significant clusters of high-frequency (beta-band and gamma-band) activity were observed when higher-quality versus lower-quality improvisations were compared. Higher-quality improvisations were associated with predominantly posterior left-hemisphere activity; lower-quality improvisations were associated with right temporo-parietal and fronto-polar activity. However, after statistically controlling for experience (defined as the number of public performances previously given), performance quality was a function of right-hemisphere, largely right-frontal, activity. These results support the notion that superior creative production is associated with hypofrontality and right-hemisphere activity thereby supporting a dual-process model of creativity in which experience influences the balance between executive and associative processes. This study also highlights the idea that the functional neuroanatomy of creative production depends on whether creativity is defined in terms of the quality of products or the type of cognitive processes involved.

Improved prediction of brain age using multimodal neuroimaging data.

Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects. Our study is a follow-up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs-fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.

Resting-state brain oscillations predict trait-like cognitive styles.

Anecdotal reports suggest the existence of individual differences in peoples' cognitive styles for solving problems, in particular, the tendency to rely on insight (the "aha" phenomenon) versus deliberate analytical thought. We hypothesized that such stable individual differences exist and are associated with trait-like individual differences in resting-state brain activity. We tested this idea by recording participants' resting-state electroencephalograms (RS-EEGs) on 4 occasions over approximately 7 weeks and then tasking them with solving anagrams and compound remote associates problems that are solvable by either strategy. We found that peoples' tendency to solve problems consistently by insight or by analysis spans both tasks and time. Moreover, we discovered trait-like individual differences in the balance between frontal and posterior resting-state brain activity and in temporal-lobe hemispheric asymmetries that predict, at least weeks in advance, the tendency to solve by insight versus analysis. The discovery of an insight-analytic dimension of cognitive style and its neural basis in resting state brain activity suggests new avenues for the development of neuroscience-based methods for intellectual, educational, and vocational assessment.

A Growth-Curve Analysis of the Effects of Future-Thought Priming on Insight and Analytical Problem-Solving.

Research based on construal level theory (CLT) suggests that thinking about the distant future can prime people to solve problems by insight (i.e., an "aha" moment) while thinking about the near future can prime them to solve problems analytically. In this study, we used a novel method to elucidate the time-course of temporal priming effects on creative problem solving. Specifically, we used growth-curve analysis (GCA) to examine the time-course of priming while participants solved a series of brief verbal problems. Participants were tested in two counterbalanced sessions in a within-subject experimental design; one session featured near-future priming and the other featured far-future priming. Our results suggest high-level construal may temporarily enhance analytical thinking; far-future priming caused transient facilitation of analytical solving while near-future priming induced weaker, transient facilitation of insightful solving. However, this effect is short-lived; priming produced no significant differences in the total number of insights and analytical solutions. Given the fleeting nature of these effects, future studies should consider implementing methodology that allows for aspects of the time-course of priming effects to be examined. A method such as GCA may reveal mild effects that would be otherwise missed using other types of analyses.

The role of hunger state and dieting history in neural response to food cues: An event-related potential study.

A history of dieting to lose weight has been shown to be a robust predictor of future weight gain. A potential factor in propensity towards weight gain is the nature of people's reactions to the abundance of highly palatable food cues in the environment. Event Related Potentials (ERPs) have revealed differences in how the brain processes food cues between obese and normal weight individuals, as well as between restrained and unrestrained eaters. However, comparisons by weight status are not informative regarding whether differences predate or follow weight gain in obese individuals and restrained eating has not consistently been found to predict future weight gain. The present study compared ERP responses to food cues in non-obese historic dieters (HDs) to non-obese never dieters (NDs). HDs showed a blunted N1 component relative to NDs overall, and delayed N1 and P2 components compared to NDs in the hungry state, suggesting that early, perceptual processing of food cues differs between these groups, especially when food-deprived. HDs also showed a more hunger-dependent sustained ERP (LPP) compared to NDs. Future research should test ERP-based food cue responsivity as a mediator between dieting history and future weight gain to better identify those most at risk for weight gain as well as the nature of their vulnerability.

Anodal tDCS to Right Dorsolateral Prefrontal Cortex Facilitates Performance for Novice Jazz Improvisers but Hinders Experts.

Research on creative cognition reveals a fundamental disagreement about the nature of creative thought, specifically, whether it is primarily based on automatic, associative (Type-1) or executive, controlled (Type-2) processes. We hypothesized that Type-1 and Type-2 processes make differential contributions to creative production that depend on domain expertise. We tested this hypothesis with jazz pianists whose expertise was indexed by the number of public performances given. Previous fMRI studies of musical improvisation have reported that domain expertise is characterized by deactivation of the right-dorsolateral prefrontal cortex (r-DLPFC), a brain area associated with Type-2 executive processing. We used anodal, cathodal, and sham transcranial direct current stimulation (tDCS) applied over r-DLPFC with the reference electrode on the contralateral mastoid (1.5 mA for 15 min, except for sham) to modulate the quality of the pianists' performances while they improvised over chords with drum and bass accompaniment. Jazz experts rated each improvisation for creativity, esthetic appeal, and technical proficiency. There was no main effect of anodal or cathodal stimulation on ratings compared to sham; however, a significant interaction between anodal tDCS and expertise emerged such that stimulation benefitted musicians with less experience but hindered those with more experience. We interpret these results as evidence for a dual-process model of creativity in which novices and experts differentially engage Type-1 and Type-2 processes during creative production.

Insight solutions are correct more often than analytic solutions.

How accurate are insights compared to analytical solutions? In four experiments, we investigated how participants' solving strategies influenced their solution accuracies across different types of problems, including one that was linguistic, one that was visual and two that were mixed visual-linguistic. In each experiment, participants' self-judged insight solutions were, on average, more accurate than their analytic ones. We hypothesised that insight solutions have superior accuracy because they emerge into consciousness in an all-or-nothing fashion when the unconscious solving process is complete, whereas analytic solutions can be guesses based on conscious, prematurely terminated, processing. This hypothesis is supported by the finding that participants' analytic solutions included relatively more incorrect responses (i.e., errors of commission) than timeouts (i.e., errors of omission) compared to their insight responses.

Sudden insight is associated with shutting out visual inputs.

Creative ideas seem often to appear when we close our eyes, stare at a blank wall, or gaze out of a window--all signs of shutting out distractions and turning attention inward. Prior research has demonstrated that attention-related brain areas are differently active when people solve problems with sudden insight (the Aha! phenomenon), relative to deliberate, analytic solving. We directly investigated the relationship between attention deployment and problem solving by recording eye movements and blinks, which are overt indicators of attention, as people solved short, visually presented problems. In the preparation period, before problems eventually solved by insight, participants blinked more frequently and longer, and made fewer fixations, than before problems eventually solved by analysis. Immediately prior to solutions, participants blinked longer and looked away from the problem more often when solving by insight than when solving analytically. These phenomena extend prior research with a direct demonstration of dynamic differences in attention as people solve problems with sudden insight versus analytically.

P50: A candidate ERP biomarker of prodromal Alzheimer's disease.

INTRODUCTION: Reductions of cerebrospinal fluid (CSF) amyloid-beta (Aß42) and elevated phosphorylated-tau (p-Tau) reflect in vivo Alzheimer's disease (AD) pathology and show utility in predicting conversion from mild cognitive impairment (MCI) to dementia. We investigated the P50 event-related potential component as a noninvasive biomarker of AD pathology in non-demented elderly. METHODS: 36 MCI patients were stratified into amyloid positive (MCI-AD, n=17) and negative (MCI-Other, n=19) groups using CSF levels of Aß42. All amyloid positive patients were also p-Tau positive. P50s were elicited with an auditory oddball paradigm. RESULTS: MCI-AD patients yielded larger P50s than MCI-Other. The best amyloid-status predictor model showed 94.7% sensitivity, 94.1% specificity and 94.4% total accuracy. DISCUSSION: P50 predicted amyloid status in MCI patients, thereby showing a relationship with AD pathology versus MCI from another etiology. The P50 may have clinical utility for inexpensive pre-screening and assessment of Alzheimer's pathology.