Insight occurs when a person suddenly reinterprets a stimulus, situation, or event to produce a nonobvious, nondominant interpretation. This can take the form of a solution to a problem (an "aha ...moment"), comprehension of a joke or metaphor, or recognition of an ambiguous percept. Insight research began a century ago, but neuroimaging and electrophysiological techniques have been applied to its study only during the past decade. Recent work has revealed insight-related coarse semantic coding in the right hemisphere and internally focused attention preceding and during problem solving. Individual differences in the tendency to solve problems insightfully rather than in a deliberate, analytic fashion are associated with different patterns of resting-state brain activity. Recent studies have begun to apply direct brain stimulation to facilitate insight. In sum, the cognitive neuroscience of insight is an exciting new area of research with connections to fundamental neurocognitive processes.
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.
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 > A effects. No A > I effect was observed. The primary neural correlate of insight was a burst of (I > A) gamma-band oscillatory activity over prefrontal cortex approximately 500 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 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.
•Problem-solving by insight and analysis recruit different brain activity.•Reward sensitivity modulates brain activity during problem solving.•Insights evoke a neural reward signal in people high in reward sensitivity.•The insight reward signal is too quick to be conscious, post-solution appraisal.•This reward signal implies that creative cognition is intrinsically motivating.
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.
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.
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 = 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.
•Neural and behavioral evidence for an expertise-dependent, dual-process model of creativity.•Performance quality/creativity a function of right-hemispheric activity.•Superior creative production is associated with hypofrontality and the inhibition of executive Type-2 processes.•Sophisticated EEG analysis (SPM12; surface-Laplacian transformations) and machine-learning (k-fold validation) techniques.•Collection of high-density EEG during live guitar improvisation.
Abstract Accumulating evidence suggests that top-down processes, reflected by frontal-midline theta-band (4–8 Hz) electroencephalogram (EEG) oscillations, strengthen the activation of a memory set ...during short-term memory (STM) retention. In addition, the amplitude of posterior alpha-band (8–13 Hz) oscillations during STM retention is thought to reflect a mechanism that protects fragile STM activations from interference by gating bottom-up sensory inputs. The present study addressed two important questions about these phenomena. First, why have previous studies not consistently found memory set-size effects on frontal-midline theta? Second, how does posterior alpha participate in STM retention? To answer these questions, large-scale network connectivity during STM retention was examined by computing EEG wavelet coherence during the retention period of a modified Sternberg task using visually-presented letters as stimuli. The results showed (a) increasing theta-band coherence between frontal-midline and left temporal-parietal sites with increasing memory load, and (b) increasing alpha-band coherence between midline parietal and left temporal/parietal sites with increasing memory load. These findings support the view that theta-band coherence, rather than amplitude, is the key factor in selective top-down strengthening of the memory set and demonstrate that posterior alpha-band oscillations associated with sensory gating are involved in STM retention by participating in the STM network.
The Aha! Moment: The Cognitive Neuroscience of Insight Kounios, John; Beeman, Mark
Current directions in psychological science : a journal of the American Psychological Society,
08/2009, Volume:
18, Issue:
4
Journal Article
Peer reviewed
A sudden comprehension that solves a problem, reinterprets a situation, explains a joke, or resolves an ambiguous percept is called an insight (i.e., the "Aha! moment"). Psychologists have studied ...insight using behavioral methods for nearly a century. Recently, the tools of cognitive neuroscience have been applied to this phenomenon. A series of studies have used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the neural correlates of the "Aha! moment" and its antecedents. Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales. Elucidation of these precursors suggests interventional opportunities for the facilitation of insight.
People can solve problems in more than one way. Two general strategies involve (A) methodical, conscious, search of problem-state transformations, and (B) sudden insight, with abrupt emergence of the ...solution into consciousness. This study elucidated the influence of initial resting brain-state on subjects’ subsequent strategy choices. High-density electroencephalograms (EEGs) were recorded from subjects at rest who were subsequently directed to solve a series of anagrams. Subjects were divided into two groups based on the proportion of anagram solutions derived with self-reported insight versus search. Reaction time and accuracy results were consistent with different cognitive problem-solving strategies used for solving anagrams with versus without insight. Spectral analyses yielded group differences in resting-state EEG supporting hypotheses concerning insight-related attentional diffusion and right-lateralized hemispheric asymmetry. These results reveal a relationship between resting-state brain activity and problem-solving strategy, and, more generally, a dependence of event-related neural computations on the preceding resting state.
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