Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.     

Individual differences in white-matter microstructure reflect variation in functional connectivity during choice

The relation between brain structure and function is of fundamental importance in neuroscience. Comparisons between behavioral and brain-imaging measures suggest that variation in brain structure correlates with the presence of specific skills. Behavioral measures, however, reflect the integrated function of multiple brain regions. Rather than behavior, a physiological index of function could be a more sensitive and informative measure with which to compare structural measures. Here, we test for a relationship between a physiological measure of functional connectivity between two brain areas during a simple decision-making task and a measure of structural connectivity. Paired-pulse transcranial magnetic stimulation indexed functional connectivity between two regions important for action choices: the premotor and motor cortex. Fractional anisotropy (FA), a marker of microstructural integrity, indexed structural connectivity. Individual differences in functional connectivity during action selection show highly specific correlations with FA in localized regions of white-matter interconnecting regions, including the premotor and motor cortex. Probabilistic tractography, a technique for identifying fiber pathways from diffusion-weighted imaging (DWI), was used to reconstruct the anatomical networks linking the component brain regions involved in making decisions. These findings demonstrate a relationship between individual differences in functional and structural connectivity within human brain networks central to action choice.     

Improved Working Memory Performance through Self-Regulation of Dorsal Lateral Prefrontal Cortex Activation Using Real-Time fMRI

Working memory is important for a wide range of high-level cognitive activities. Previous studies have shown that the dorsal lateral prefrontal cortex (DLPFC) plays a critical role in working memory and that behavioral training of working memory can alter the activity of DLPFC. However, it is unclear whether the activation in the DLPFC can be self-regulated and whether any self-regulation can affect working memory behavior. The recently emerged real-time functional magnetic resonance imaging (rtfMRI) technique enables the individuals to acquire self-control of localized brain activation, potentially inducing desirable behavioral changes. In the present study, we employed the rtfMRI technique to train subjects to up-regulate the activation in the left DLPFC, which is linked to verbal working memory. After two rtfMRI training sessions, activation in the left DLPFC was significantly increased, whereas the control group that received sham feedback did not show any increase in DLPFC activation. Pre- and post-training behavioral tests indicated that performance of the digit span and letter memory task was significantly improved in the experimental group. Between-group comparison of behavioral changes showed that the increase of digit span in the experimental group was significantly greater than that in the control group. These findings provide preliminary evidence that working memory performance can be improved through learned regulation of activation in associated brain regions using rtfMRI.     

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.     

Variance in the production of homovanillic acid and 3-methoxy-4-hydroxyphenethyleneglycol by the awake primate brain

Previous experimental results, using a new technique whereby the production rates of the neurotransmitter metabolites homovanillic acid (HVA) and 3-methoxy-4-hydroxyphenethyleneglycol (MHPG) by the awake primate brain are determined, have shown a wide variance in metabolite production among both animal and human subjects. These data suggested that either individual subjects differ in the activity of brain dopamine (DA) or norepinephrine (NE) neurons and/or that the activities of these neurons fluctuate over time. For these reasons a series of experiments were performed in which measures of HVA and MHPG production were obtained at three time points in the same animal (monkeys) over a three hour period. It was found that the group mean values for the production of HVA and MHPG by brain were similar for each of the three time points. However, it was also found that marked variations in HVA and MHPG production occur within a single animal over a three hour period. The coefficients of variation for individual animals for HVA ranged from 9.3 to 31.9% and for MHPG from 10.1 to 62.3%. These variations were not correlated with grossly observable changes in behavioral states. Using an analysis of variance it was found that the variance in MHPG production was significantly greater than that for HVA (F = 6.2, p < 0.05) suggesting that brain NE systems are more liable and/or show greater change than do brain DA systems. These data are interpreted as indicating that in the awake, resting primate brain fluctuations in the activities of DA and NE neurons occur, i.e. there is not a steady, invariant production of metabolites but rather they are produced in pulses of varying lengths. This interpretation of the data is generally consistent with electrophysiological studies which indicate that catecholamine neurons fire in bursts which are then followed by silent periods. Finally, in terms of practical application of the V-A difference technique, these data indicate that replicable group mean estimates of brain HVA and MHPG production can be obtained by averaging values from a single time point whereas accurate information about an individual animal will require multiple samplings.Recent reports from this laboratory have described a method whereby a direct measure of the rates of production of neurotransmitter metabolites such as homovanillic acid (HVA), 3-methoxy-4-hydroxyphenethyleneglycol (MHPG), and 5-hydroxyindoleacetic acid (5-HIAA) by the awake primate brain can be determined (1, 2, 3, 4). Since the quantities of HVA, MHPG, and probably 5-HIAA in the brain vary as a function of the activity of dopamine (DA), norepinephrine (NE), and serotonin (5-HT) neurons (1, 5, 6, 7, 8), it is likely that these measures of neurotransmitter metabolite production reflect the functional state of brain DA, NE, and 5-HT neuronal systems. The experimental results thus far obtained with this technique have shown a wide variance in the rates of neurotransmitter metabolite production across both animal and human subjects even though the subjects were not in clearly different behavioral or emotional states (1, 2, 4, 9). These data suggested that either individual subjects differ markedly in the activities of brain DA, NE, and 5-HT neurotransmitter systems and/or that the activity of these systems fluctuates markedly over time. For these reasons, experiments were undertaken in which repeated measures of HVA and MHPG production by brain within the same animal were determined over a three hour period. The results of these experiments, which are reported here, indicate that there are marked changes in brain metabolite production which occur within animals. The implications of these findings for our understanding of the functioning of brain neurotransmitter systems and for the practical applications of this technique are discussed.     

Diagnosing Developmental Dyscalculia on the Basis of Reliable Single Case FMRI Methods: Promises and Limitations

FMRI-studies are mostly based on a group study approach, either analyzing one group or comparing multiple groups, or on approaches that correlate brain activation with clinically relevant criteria or behavioral measures. In this study we investigate the potential of fMRI-techniques focusing on individual differences in brain activation within a test-retest reliability context. We employ a single-case analysis approach, which contrasts dyscalculic children with a control group of typically developing children. In a second step, a support-vector machine analysis and cluster analysis techniques served to investigate similarities in multivariate brain activation patterns. Children were confronted with a non-symbolic number comparison and a non-symbolic exact calculation task during fMRI acquisition. Conventional second level group comparison analysis only showed small differences around the angular gyrus bilaterally and the left parieto-occipital sulcus. Analyses based on single-case statistical procedures revealed that developmental dyscalculia is characterized by individual differences predominantly in visual processing areas. Dyscalculic children seemed to compensate for relative under-activation in the primary visual cortex through an upregulation in higher visual areas. However, overlap in deviant activation was low for the dyscalculic children, indicating that developmental dyscalculia is a disorder characterized by heterogeneous brain activation differences. Using support vector machine analysis and cluster analysis, we tried to group dyscalculic and typically developing children according to brain activation. Fronto-parietal systems seem to qualify for a distinction between the two groups. However, this was only effective when reliable brain activations of both tasks were employed simultaneously. Results suggest that deficits in number representation in the visual-parietal cortex get compensated for through finger related aspects of number representation in fronto-parietal cortex. We conclude that dyscalculic children show large individual differences in brain activation patterns. Nonetheless, the majority of dyscalculic children can be differentiated from controls employing brain activation patterns when appropriate methods are used.     

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.     

A comparison of survey and focal follow methods for estimating individual activity budgets of cetaceans

Activity budget data are essential for determining behavioral responses to physiological and ecological variables. Yet, few studies are available to investigate the robustness, accuracy, and biases of the methods used to estimate activity budgets for cetaceans. In this study, we compare activity budgets of 55 adult female bottlenose dolphins in Shark Bay, Australia derived from two methods: surveys (n = 6,903) and focal follows (n = 1,185, totaling 2,721 h of observation). Activity budgets estimated from survey data differed in all behavioral states compared to focal follow data. However, when controlling for temporal autocorrelation, only time spent socializing and time spent traveling remained disparate between the methods. To control for biases associated with assigning group‐level behavior to individuals, we also compared survey and focal follow activity budgets for lone females. Here we found differences between methods in time spent foraging and traveling regardless of whether we controlled for temporal autocorrelation, which suggests detection biases likely play a role in explaining differences in activity budget estimates between the two methodologies. Our results suggest that surveys are less representative of individual‐level activity budgets, and thus, when individual‐level knowledge about behavior is needed, focal follows are preferred.     

Changes in Task-Related Functional Connectivity across Multiple Spatial Scales Are Related to Reading Performance

Reading requires the interaction of a distributed set of cortical areas whose distinct patterns give rise to a wide range of individual skill. However, the nature of these neural interactions and their relation to reading performance are still poorly understood. Functional connectivity analyses of fMRI data can be used to characterize the nature of interactivity of distributed brain networks, yet most previous studies have focused on connectivity during task-free (i.e., “resting state”) conditions. Here, we report new methods for assessing task-related functional connectivity using data-driven graph theoretical methods and describe how large-scale patterns of connectivity relate to individual variability in reading performance among children. We found that connectivity patterns of subjects performing a reading task could be decomposed hierarchically into multiple sub-networks, and we observed stronger long-range interaction between sub-networks in subjects with higher task accuracy. Additionally, we found a network of hub regions known to be critical to reading that displays increased short-range synchronization in higher accuracy subjects. These individual differences in task-related functional connectivity reveal that increased interaction between distant regions, coupled with selective local integration within key regions, is associated with better reading performance. Importantly, we show that task-related neuroimaging data contains far more information than usually extracted via standard univariate analyses – information that can meaningfully relate neural connectivity patterns to cognition and task.     

I Know How You Feel: The Warm-Altruistic Personality Profile and the Empathic Brain

The ability to empathize with other people is a critical component of human social relationships. Empathic processing varies across the human population, however it is currently unclear how personality traits are associated with empathic processing. This study was designed to test the hypothesis that specific personality traits are associated with behavioral and biological indicators of improved empathy. Extraversion and Agreeableness are personality traits designed to measure individual differences in social-cognitive functioning, however each trait-dimension includes elements that represent interpersonal social functioning and elements that do not represent interpersonal social functioning. We tested the prediction that interpersonal elements of Extraversion (Warmth) and Agreeableness (Altruism) are associated with empathy and non-interpersonal elements of Extraversion and Agreeableness are not associated with empathy. We quantified empathic processing behaviorally (empathic accuracy task using video vignettes) and within the brain (fMRI and an emotional perspective taking task) in 50 healthy subjects. Converging evidence shows that highly warm and altruistic people are well skilled in recognizing the emotional states of other people and exhibit greater activity in brain regions important for empathy (temporoparietal junction and medial prefrontal cortex) during emotional perspective taking. A mediation analysis further supported the association between warm-altruistic personality and empathic processing; indicating that one reason why highly warm-altruistic individuals may be skilled empathizers is that they engage the temporoparietal junction and medial prefrontal cortex more. Together, these findings advance the way the behavioral and neural basis of empathy is understood and demonstrates the efficacy of personality scales to measure individual differences in interpersonal social function.     

Tropical dung beetle morphological traits predict functional traits and show intraspecific differences across land uses

Functional traits and functional diversity measures are increasingly being used to examine land use effects on biodiversity and community assembly rules. Morphological traits are often used directly as functional traits. However, behavioral characteristics are more difficult to measure. Establishing methods to derive behavioral traits from morphological measurements is necessary to facilitate their inclusion in functional diversity analyses. We collected morphometric data from over 1,700 individuals of 12 species of dung beetle to establish whether morphological measurements can be used as predictors of behavioral traits. We also compared morphology among individuals collected from different land uses (primary forest, logged forest, and oil palm plantation) to identify whether intraspecific differences in morphology vary among land use types. We show that leg and eye measurements can be used to predict dung beetle nesting behavior and period of activity and we used this information to confirm the previously unresolved nesting behavior for Synapsis ritsemae. We found intraspecific differences in morphological traits across different land use types. Phenotypic plasticity was found for traits associated with dispersal (wing aspect ratio and wing loading) and reproductive capacity (abdomen size). The ability to predict behavioral functional traits from morphology is useful where the behavior of individuals cannot be directly observed, especially in tropical environments where the ecology of many species is poorly understood. In addition, we provide evidence that land use change can cause phenotypic plasticity in tropical dung beetle species. Our results reinforce recent calls for intraspecific variation in traits to receive more attention within community ecology.     

Individual Topographic Variability Is Inherent to Cortical Physiology but Task-Related Differences May Be Noise

The observation of highly variable sets of association neocortical areas across individuals, containing the estimated generators of Slow Potentials (SPs) and beta oscillations, lead to the persistence in individual analyses. This brought to notice an unexpected within individual topographic similarity between task conditions, despite our original interest in task-related differences. A recent related work explored the quantification of the similarity in beta topography between largely differing tasks. In this article, we used Independent Component Analysis (ICA) for the decomposition of beta activity from a visual attention task, and compared it with quiet resting, recorded by 128-channel EEG in 62 subjects. We statistically tested whether each ICA component obtained in one condition could be explained by a linear regression model based on the topographic patterns from the other condition, in each individual. Results were coherent with the previous report, showing a high topographic similarity between conditions. From an average of 12 beta component maps obtained for each task, over 80% were satisfactorily explained by the complementary task. Once more, the component maps including those considered unexplained, putatively “task-specific”, had their scalp distribution and estimated cortical sources highly variable across subjects. These findings are discussed along with other studies based on individual data and the present fMRI results, reinforcing the increasingly accepted view that individual variability in sets of active neocortical association areas is not noise, but intrinsic to cortical physiology. Actual ‘noise’, mainly stemming from group “brain averaging” and the emphasis on statistical differences as opposed to similarities, may explain the overall hardship in replication of the vast literature on supposed task-specific forms of activity, and the ever inconclusive status of a universal functional mapping of cortical association areas. A new hypothesis, that individuals may use the same idiosyncratic sets of areas, at least by their fraction of activity in the sub-delta and beta range, in various non-sensory-motor forms of conscious activities, is a corollary of the discussed variability.     

Patterns of Individual Variation in Visual Pathway Structure and Function in the Sighted and Blind

Many structural and functional brain alterations accompany blindness, with substantial individual variation in these effects. In normally sighted people, there is correlated individual variation in some visual pathway structures. Here we examined if the changes in brain anatomy produced by blindness alter the patterns of anatomical variation found in the sighted. We derived eight measures of central visual pathway anatomy from a structural image of the brain from 59 sighted and 53 blind people. These measures showed highly significant differences in mean size between the sighted and blind cohorts. When we examined the measurements across individuals within each group we found three clusters of correlated variation, with V1 surface area and pericalcarine volume linked, and independent of the thickness of V1 cortex. These two clusters were in turn relatively independent of the volumes of the optic chiasm and lateral geniculate nucleus. This same pattern of variation in visual pathway anatomy was found in the sighted and the blind. Anatomical changes within these clusters were graded by the timing of onset of blindness, with those subjects with a post-natal onset of blindness having alterations in brain anatomy that were intermediate to those seen in the sighted and congenitally blind. Many of the blind and sighted subjects also contributed functional MRI measures of cross-modal responses within visual cortex, and a diffusion tensor imaging measure of fractional anisotropy within the optic radiations and the splenium of the corpus callosum. We again found group differences between the blind and sighted in these measures. The previously identified clusters of anatomical variation were also found to be differentially related to these additional measures: across subjects, V1 cortical thickness was related to cross-modal activation, and the volume of the optic chiasm and lateral geniculate was related to fractional anisotropy in the visual pathway. Our findings show that several of the structural and functional effects of blindness may be reduced to a smaller set of dimensions. It also seems that the changes in the brain that accompany blindness are on a continuum with normal variation found in the sighted.     

Non-fluent aphasia and neural reorganization after speech therapy: insights from human sleep electrophysiology and functional magnetic resonance imaging

Stroke is associated with long-term functional deficits. Behavioral interventions are often effective in promoting functional recovery and plastic changes. Recent studies in normal subjects have shown that sleep, and particularly slow wave activity (SWA), is tied to local brain plasticity and may be used as a sensitive marker of local cortical reorganization after stroke. In a pilot study, we assessed the local changes induced by a single exposure to a therapeutic session of IMITATE (Intensive Mouth Imitation and Talking for Aphasia Therapeutic Effects), a behavioral therapy used for recovery in patients with post-stroke aphasia. In addition, we measured brain activity changes with functional magnetic resonance imaging (fMRI) in a language observation task before, during and after the full IMITATE rehabilitative program. Speech production improved both after a single exposure and the full therapy program as measured by the Western Aphasia Battery (WAB) Repetition subscale. We found that IMITATE induced reorganization in functionally-connected, speech-relevant areas in the left hemisphere. These preliminary results suggest that sleep hd-EEGs, and the topographical analysis of SWA parameters, are well suited to investigate brain plastic changes underpinning functional recovery in neurological disorders.     

Automated discrimination of brain pathological state attending to complex structural brain network properties: the shiverer mutant mouse case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert''s clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp^shi/Mbp^shi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.     

Imaging genetic influences in human brain function

The association between genes and brain function using functional brain imaging techniques is an emerging and promising area of research that will help to better characterize the influence of genes on cognition and behavior as well as the link between genetic susceptibility and neuropsychiatric disorders. Neurophysiological imaging provides information regarding the effect of genes on brain function at the level of information processing, and neurochemical imaging provides information on the intrinsic mechanisms on how these genes affect the brain response. In this review, we highlight recent studies that have begun to explore the influence of genetic mutations on brain function with these techniques. The results, even from these few studies, illustrate the potential of these techniques to provide a more sensitive assay than behavioral measures used alone. The results also show that neuroimaging techniques can elucidate the influence of genes on brain function in relatively small sample populations, sometimes even in the absence of significant differences in behavioral measures.     

Effects of AKAP5 Pro100Leu Genotype on Working Memory for Emotional Stimuli

Recent investigations addressing the role of the synaptic multiadaptor molecule AKAP5 in human emotion and behavior suggest that the AKAP5 Pro100Leu polymorphism (rs2230491) contributes to individual differences in affective control. Carriers of the less common Leu allele show a higher control of anger as indicated by behavioral measures and dACC brain response on emotional distracters when compared to Pro homozygotes. In the current fMRI study we used an emotional working memory task according to the n-back scheme with neutral and negative emotional faces as target stimuli. Pro homozygotes showed a performance advantage at the behavioral level and exhibited enhanced activation of the amygdala and fusiform face area during working memory for emotional faces. On the other hand, Leu carriers exhibited increased activation of the dACC during performance of the 2-back condition. Our results suggest that AKAP5 Pro100Leu effects on emotion processing might be task-dependent with Pro homozygotes showing lower control of emotional interference, but more efficient processing of task-relevant emotional stimuli.     

Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.