Brain Activations and Functional Connectivity Patterns Associated with Insight-Based and Analytical Anagram Solving.

Insight is one of the most mysterious problem-solving phenomena involving the sudden emergence of a solution, often preceded by long unproductive attempts to find it. This seemingly unexplainable generation of the answer, together with the role attributed to insight in the advancement of science, technology and culture, stimulate active research interest in discovering its neuronal underpinnings. The present study employs functional Magnetic resonance imaging (fMRI) to probe and compare the brain activations occurring in the course of solving anagrams by insight or analytically, as judged by the subjects. A number of regions were activated in both strategies, including the left premotor cortex, left claustrum, and bilateral clusters in the precuneus and middle temporal gyrus. The activated areas span the majority of the clusters reported in a recent meta-analysis of insight-related fMRI studies. At the same time, the activation patterns were very similar between the insight and analytical solutions, with the only difference in the right sensorimotor region probably explainable by subject motion related to the study design. Additionally, we applied resting-state fMRI to study functional connectivity patterns correlated with the individual frequency of insight anagram solutions. Significant correlations were found for the seed-based connectivity of areas in the left premotor cortex, left claustrum, and left frontal eye field. The results stress the need for optimizing insight paradigms with respect to the accuracy and reliability of the subjective insight/analytical solution classification. Furthermore, the short-lived nature of the insight phenomenon makes it difficult to capture the associated neural events with the current experimental techniques and motivates complementing such studies by the investigation of the structural and functional brain features related to the individual differences in the frequency of insight-based decisions.

Accuracy of Estimating the Area of Cortical Muscle Representations from TMS Mapping Data Using Voronoi Diagrams.

Motor evoked potentials (MEPs) caused by transcranial magnetic stimulation (TMS) provide a possibility of noninvasively mapping cortical muscle representations for clinical and research purposes. The interpretation of such results is complicated by the high variability in MEPs and the lack of a standard optimal mapping protocol. Comparing protocols requires the determination of the accuracy of estimated representation parameters (such as the area), which is problematic without ground truth data. We addressed this problem and obtained two main results: (1) the development of a bootstrapping-based approach for estimating the within-session variability and bias of representation parameters and (2) estimations of the area and amplitude-weighted area accuracies for motor representations using this approach. The method consists in the simulation of TMS mapping results by subsampling MEPs from a single map with a large number of stimuli. We studied the extensor digitorum communis (EDC) and flexor digitorum superficialis (FDS) muscle maps of 15 healthy subjects processed using Voronoi diagrams. We calculated the (decreasing) dependency of the errors in the area and weighted area on the number of stimuli. This result can be used to choose a number of stimuli sufficient for studying the effects of a given size (e.g., the protocol with 150 stimuli leads to relative errors of 7% for the area and 11% for the weighted area in 90% of the maps). The approach is applicable to other parameters (e.g., the center of gravity) and other map processing methods, such as spline interpolation.

Optimization of the Navigated TMS Mapping Algorithm for Accurate Estimation of Cortical Muscle Representation Characteristics.

Navigated transcranial magnetic stimulation (nTMS) mapping of cortical muscle representations allows noninvasive assessment of the state of a healthy or diseased motor system, and monitoring changes over time. These applications are hampered by the heterogeneity of existing mapping algorithms and the lack of detailed information about their accuracy. We aimed to find an optimal motor evoked potential (MEP) sampling scheme in the grid-based mapping algorithm in terms of the accuracy of muscle representation parameters. The abductor pollicis brevis (APB) muscles of eight healthy subjects were mapped three times on consecutive days using a seven-by-seven grid with ten stimuli per cell. The effect of the MEP variability on the parameter accuracy was assessed using bootstrapping. The accuracy of representation parameters increased with the number of stimuli without saturation up to at least ten stimuli per cell. The detailed sampling showed that the between-session representation area changes in the absence of interventions were significantly larger than the within-session fluctuations and thus could not be explained solely by the trial-to-trial variability of MEPs. The results demonstrate that the number of stimuli has no universally optimal value and must be chosen by balancing the accuracy requirements with the mapping time constraints in a given problem.