Impact of brain parcellation on prediction performance in models of cognition and demographics.

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

Test-retest reliability of Bayesian estimations of the effects of stimulation, prior information and individual traits on pain perception.

BACKGROUND: There is inter-individual variability in the influence of different components (e.g. nociception and expectations) on pain perception. Identifying the individual effect of these components could serve for patient stratification, but only if these influences are stable in time. METHODS: In this study, 30 healthy participants underwent a cognitive pain paradigm in which they rated pain after viewing a probabilistic cue informing of forthcoming pain intensity and then receiving electrical stimulation. The trial information was then used in a Bayesian probability model to compute the relative weight each participant put on stimulation, cue, cue uncertainty and trait-like bias. The same procedure was repeated 2 weeks later. Relative and absolute test-retest reliability of all measures was assessed. RESULTS: Intraclass correlation results showed good reliability for the effect of the stimulation (0.83), the effect of the cue (0.75) and the trait-like bias (0.75 and 0.75), and a moderate reliability for the effect of the cue uncertainty (0.55). Absolute reliability measures also supported the temporal stability of the results and indicated that a change in parameters corresponding to a difference in pain ratings ranging between 0.47 and 1.45 (depending on the parameters) would be needed to consider differences in outcomes significant. The comparison of these measures with the closest clinical data we possess supports the reliability of our results. CONCLUSIONS: These findings support the hypothesis that inter-individual differences in the weight placed on different pain factors are stable in time and could therefore be a possible target for patient stratification. SIGNIFICANCE: Our results demonstrate the temporal stability of the weight healthy individuals place on the different factors leading to the pain response. These findings give validity to the idea of using Bayesian estimations of the influence of different factors on pain as a way to stratify patients for treatment personalization.

Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance.

Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.

A mean-field model of glutamate and GABA synaptic dynamics for functional MRS.

Advances in functional magnetic resonance spectroscopy (fMRS) have enabled the quantification of activity-dependent changes in neurotransmitter concentrations in vivo. However, the physiological basis of the large changes in GABA and glutamate observed by fMRS (>10%) over short time scales of less than a minute remain unclear as such changes cannot be accounted for by known synthesis or degradation metabolic pathways. Instead, it has been hypothesized that fMRS detects shifts in neurotransmitter concentrations as they cycle from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are detectable. The present paper uses a computational modelling approach to demonstrate the viability of this hypothesis. A new mean-field model of the neural mechanisms generating the fMRS signal in a cortical voxel is derived. The proposed macroscopic mean-field model is based on a microscopic description of the neurotransmitter dynamics at the level of the synapse. Specifically, GABA and glutamate are assumed to cycle between three metabolic pools: packaged in the vesicles; active in the synaptic cleft; and undergoing recycling and repackaging in the astrocytic or neuronal cytosol. Computational simulations from the model are used to generate predicted changes in GABA and glutamate concentrations in response to different types of stimuli including pain, vision, and electric current stimulation. The predicted changes in the extracellular and cytosolic pools corresponded to those reported in empirical fMRS data. Furthermore, the model predicts a selective control mechanism of the GABA/glutamate relationship, whereby inhibitory stimulation reduces both neurotransmitters, whereas excitatory stimulation increases glutamate and decreases GABA. The proposed model bridges between neural dynamics and fMRS and provides a mechanistic account for the activity-dependent changes in the glutamate and GABA fMRS signals. Lastly, these results indicate that echo-time may be an important timing parameter that can be leveraged to maximise fMRS experimental outcomes.

Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods.

Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

Frontal alpha asymmetry: A potential biomarker of approach-withdrawal motivation towards pain.

Pain-related catastrophising is a maladaptive coping strategy known to have a strong influence on clinical pain outcomes and treatment efficacy. Notwithstanding, little is known about its neurophysiological correlates. There is evidence to suggest catastrophising is associated with resting-state EEG frontal alpha asymmetry (FAA) patterns reflective of greater relative right frontal activity, which is known to be linked to withdrawal motivation and avoidance of aversive stimuli. The present study aims to investigate whether such a relationship occurs in the situational context of experimental pain. A placebo intervention was also included to evaluate effects of a potential pain-relieving intervention on FAA. 35 participants, including both chronic pain patients and healthy subjects, completed the Pain Catastrophising Scale (PCS) questionnaire followed by EEG recordings during cold pressor test (CPT)-induced tonic pain with or without prior application of placebo cream. There was a negative correlation between FAA and PCS-subscale helplessness scores, but not rumination or magnification, during the pre-placebo CPT condition. Moreover, FAA scores were shown to increase significantly in response to pain, indicative of greater relative left frontal activity that relates to approach-oriented behaviours. Placebo treatment elicited a decrease in FAA in low helplessness scorers, but no significant effects in individuals scoring above the mean on PCS-helplessness. These findings suggest that, during painful events, FAA may reflect the motivational drive to obtain reward of pain relief, which may be diminished in individuals who are prone to feel helpless about their pain. This study provides valuable insights into biomarkers of pain-related catastrophising and prospects of identifying promising targets of brain-based therapies for chronic pain management.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in-sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study.

One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through noninvasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential noninvasive and nonpharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications. However, evidence supporting its use in patients with chronic pain is lacking. This study explores whether (a) alpha entrainment increase alpha power in patients and (b) whether this increase in alpha correlates with analgesia. In total, 28 patients with chronic pain sat in a comfortable position and underwent 4-min visual stimulation using customised goggles at 10 Hz (alpha) and 7 Hz (control) frequency blocks in a randomised cross-over design. 64-channel electroencephalography and 11-point numeric rating scale pain intensity and pain unpleasantness scores were recorded before and after stimulation. Electroencephalography analysis revealed frontal alpha power was significantly higher when stimulating at 10 Hz when compared to 7 Hz. There was a significant positive correlation between increased frontal alpha and reduction in pain intensity (r = 0.33; P < 0.05) and pain unpleasantness (r = 0.40; P < 0.05) in the 10 Hz block. This study provides the first proof of concept that changes in alpha power resulting from entrainment correlate with an analgesic response in patients with chronic pain. Further studies are warranted to investigate dose-response parameters and equivalence to analgesia provided by medications.

GABA Modulates Frequency-Dependent Plasticity in Humans.

Frequency-dependent reorganization of the primary somatosensory cortex, together with perceptual changes, arises following repetitive sensory stimulation. Here, we investigate the role of GABA in this process. We co-stimulated two finger tips and measured GABA and Glx using magnetic resonance (MR) spectroscopy at the beginning and end of the stimulation. Participants performed a perceptual learning task before and after stimulation. There were 2 sessions with stimulation frequency either at or above the resonance frequency of the primary somatosensory cortex (23 and 39 Hz, respectively). Perceptual learning occurred following above resonance stimulation only, while GABA reduced during this condition. Lower levels of early GABA were associated with greater perceptual learning. One possible mechanism underlying this finding is that cortical disinhibition "unmasks" lateral connections within the cortex to permit adaptation to the sensory environment. These results provide evidence in humans for a frequency-dependent inhibitory mechanism underlying learning and suggest a mechanism-based approach for optimizing neurostimulation frequency.

Entraining Alpha Activity Using Visual Stimulation in Patients With Chronic Musculoskeletal Pain: A Feasibility Study.

Entraining alpha activity with rhythmic visual, auditory, and electrical stimulation can reduce experimentally induced pain. However, evidence for alpha entrainment and pain reduction in patients with chronic pain is limited. This feasibility study investigated whether visual alpha stimulation can increase alpha power in patients with chronic musculoskeletal pain and, secondarily, if chronic pain was reduced following stimulation. In a within-subject design, 20 patients underwent 4-min periods of stimulation at 10 Hz (alpha), 7 Hz (high-theta, control), and 1 Hz (control) in a pseudo-randomized order. Patients underwent stimulation both sitting and standing and verbally rated their pain before and after each stimulation block on a 0-10 numerical rating scale. Global alpha power was significantly higher during 10 Hz compared to 1 Hz stimulation when patients were standing (t = -6.08, p < 0.001). On a more regional level, a significant increase of alpha power was found for 10 Hz stimulation in the right-middle and left-posterior region when patients were sitting. With respect to our secondary aim, no significant reduction of pain intensity and unpleasantness was found. However, only the alpha stimulation resulted in a minimal clinically important difference in at least 50% of participants for pain intensity (50%) and unpleasantness ratings (65%) in the sitting condition. This study provides initial evidence for the potential of visual stimulation as a means to enhance alpha activity in patients with chronic musculoskeletal pain. The brief period of stimulation was insufficient to reduce chronic pain significantly. This study is the first to provide evidence that a brief period of visual stimulation at alpha frequency can significantly increase alpha power in patients with chronic musculoskeletal pain. A further larger study is warranted to investigate optimal dose and individual stimulation parameters to achieve pain relief in these patients.

A tutorial and tool for exploring feature similarity gradients with MRI data.

There has been an increasing interest in examining organisational principles of the cerebral cortex (and subcortical regions) using different MRI features such as structural or functional connectivity. Despite the widespread interest, introductory tutorials on the underlying technique targeted for the novice neuroimager are sparse in the literature. Articles that investigate various "neural gradients" (for example based on region studied "cortical gradients," "cerebellar gradients," "hippocampal gradients" etc … or feature of interest "functional gradients," "cytoarchitectural gradients," "myeloarchitectural gradients" etc …) have increased in popularity. Thus, we believe that it is opportune to discuss what is generally meant by "gradient analysis". We introduce basics concepts in graph theory, such as graphs themselves, the degree matrix, and the adjacency matrix. We discuss how one can think about gradients of feature similarity (the similarity between timeseries in fMRI, or streamline in tractography) using graph theory and we extend this to explore such gradients across the whole MRI scale; from the voxel level to the whole brain level. We proceed to introduce a measure for quantifying the level of similarity in regions of interest. We propose the term "the Vogt-Bailey index" for such quantification to pay homage to our history as a brain mapping community. We run through the techniques on sample datasets including a brain MRI as an example of the application of the techniques on real data and we provide several appendices that expand upon details. To maximise intuition, the appendices contain a didactic example describing how one could use these techniques to solve a particularly pernicious problem that one may encounter at a wedding. Accompanying the article is a tool, available in both MATLAB and Python, that enables readers to perform the analysis described in this article on their own data. We refer readers to the graphical abstract as an overview of the analysis pipeline presented in this work.

Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain.

OBJECTIVE: Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states. METHODS: Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman's Test and correlated with changes in pain scores using Pearson's correlation. RESULTS: There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy (r = -0.45, p = 0.03), mean dwell time (r = -0.48, p = 0.04) and transition probability from a low to high state (r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants. CONCLUSION: There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.

A structural connectivity convergence zone in the ventral and anterior temporal lobes: Data-driven evidence from structural imaging.

The hub-and-spoke model of semantic cognition seeks to reconcile embodied views of a fully distributed semantic network with patient evidence, primarily from semantic dementia, who demonstrate modality-independent conceptual deficits associated with atrophy centred on the ventrolateral anterior temporal lobe. The proponents of this model have recently suggested that the temporal cortex is a graded representational space where concepts become less linked to a specific modality as they are processed farther away from primary and secondary sensory cortices and towards the ventral anterior temporal lobe. To explore whether there is evidence that the connectivity patterns of the temporal lobe converge in its ventral anterior end the current study uses three dimensional Laplacian eigenmapping, a technique that allows visualisation of similarity in a low dimensional space. In this space similarity is encoded in terms of distances between data points. We found that the ventral and anterior temporal lobe is in a unique position of being at the centre of mass of the data points within the connective similarity space. This can be interpreted as the area where the connectivity profiles of all other temporal cortex voxels converge. This study is the first to explicitly investigate the pattern of connectivity and thus provides the missing link in the evidence that the ventral anterior temporal lobe can be considered a multi-modal graded hub.

Evidence for frequency-dependent cortical plasticity in the human brain.

Frequency-dependent plasticity (FDP) describes adaptation at the synapse in response to stimulation at different frequencies. Its consequence on the structure and function of cortical networks is unknown. We tested whether cortical "resonance," favorable stimulation frequencies at which the sensory cortices respond maximally, influenced the impact of FDP on perception, functional topography, and connectivity of the primary somatosensory cortex using psychophysics and functional imaging (fMRI). We costimulated two digits on the hand synchronously at, above, or below the resonance frequency of the somatosensory cortex, and tested subjects' accuracy and speed on tactile localization before and after costimulation. More errors and slower response times followed costimulation at above- or below-resonance, respectively. Response times were faster after at-resonance costimulation. In the fMRI, the cortical representations of the two digits costimulated above-resonance shifted closer, potentially accounting for the poorer performance. Costimulation at-resonance did not shift the digit regions, but increased the functional coupling between them, potentially accounting for the improved response time. To relate these results to synaptic plasticity, we simulated a network of oscillators incorporating Hebbian learning. Two neighboring patches embedded in a cortical sheet, mimicking the two digit regions, were costimulated at different frequencies. Network activation outside the stimulated patches was greatest at above-resonance frequencies, reproducing the spread of digit representations seen with fMRI. Connection strengths within the patches increased following at-resonance costimulation, reproducing the increased fMRI connectivity. We show that FDP extends to the cortical level and is influenced by cortical resonance.

Cortical Resonance Frequencies Emerge from Network Size and Connectivity.

Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.

Attentional Bias Predicts Increased Reward Salience and Risk Taking in Bipolar Disorder.

BACKGROUND: There is amassing evidence that risky decision-making in bipolar disorder is related to reward-based differences in frontostriatal regions. However, the roles of early attentional and later cognitive processes remain unclear, limiting theoretical understanding and development of targeted interventions. METHODS: Twenty euthymic bipolar disorder and 19 matched control participants played a Roulette task in which they won and lost money. Event-related potentials and source analysis were used to quantify predominantly sensory-attentional (N1), motivational salience (feedback-related negativities [FRN]), and cognitive appraisal (P300) stages of processing. We predicted that the bipolar disorder group would show increased N1, consistent with increased attentional orienting, and reduced FRN, consistent with a bias to perceive outcomes more favorably. RESULTS: As predicted, the bipolar disorder group showed increased N1 and reduced FRN but no differences in P300. N1 amplitude was additionally associated with real-life risk taking, and N1 source activity was reduced in visual cortex but increased activity in precuneus, frontopolar, and premotor cortex, compared to those of controls. CONCLUSIONS: These findings demonstrate an early attentional bias to reward that potentially drives risk taking by priming approach behavior and elevating reward salience in the frontostriatal pathway. Although later cognitive appraisals of these inputs may be relatively intact in remission, interventions targeting attention orienting may also be effective in long-term reduction of relapse.

Brain Signals of Face Processing as Revealed by Event-Related Potentials.

We analyze the functional significance of different event-related potentials (ERPs) as electrophysiological indices of face perception and face recognition, according to cognitive and neurofunctional models of face processing. Initially, the processing of faces seems to be supported by early extrastriate occipital cortices and revealed by modulations of the occipital P1. This early response is thought to reflect the detection of certain primary structural aspects indicating the presence grosso modo of a face within the visual field. The posterior-temporal N170 is more sensitive to the detection of faces as complex-structured stimuli and, therefore, to the presence of its distinctive organizational characteristics prior to within-category identification. In turn, the relatively late and probably more rostrally generated N250r and N400-like responses might respectively indicate processes of access and retrieval of face-related information, which is stored in long-term memory (LTM). New methods of analysis of electrophysiological and neuroanatomical data, namely, dynamic causal modeling, single-trial and time-frequency analyses, are highly recommended to advance in the knowledge of those brain mechanisms concerning face processing.

Theta-gamma coupling during episodic retrieval in the human EEG.

Recent findings indicate that phase-amplitude coupling between neuronal oscillations in the theta- (3-6 Hz) and the gamma-band (30-100 Hz) plays a functional role in memory processes. Here, using electroencephalography, we provide further evidence for coupling between prefrontal theta and parietal gamma during successful memory retrieval in the human brain. In a pictorial recognition task, the coupling between prefrontal theta phase and parietal gamma amplitude was quantified using the modulation index, 100-1500 ms after stimulus onset. Results show an increased coupling for remembered, as opposed to forgotten and new stimuli (i.e. a "recognition effect" and an "old/new effect"). Phase-amplitude coupling between the prefrontal theta phase and posterior gamma amplitudes is hypothesized to reflect long range communication between prefrontal control processes and the activation of posterior object representations accompanying mnemonic processing.

Timing matters: the processing of pitch relations.

The human central auditory system can automatically extract abstract regularities from a variant auditory input. To this end, temporarily separated events need to be related. This study tested whether the timing between events, falling either within or outside the temporal window of integration (~350 ms), impacts the extraction of abstract feature relations. We utilized tone pairs for which tones within but not across pairs revealed a constant pitch relation (e.g., pitch of second tone of a pair higher than pitch of first tone, while absolute pitch values varied across pairs). We measured the mismatch negativity (MMN; the brain's error signal to auditory regularity violations) to second tones that rarely violated the pitch relation (e.g., pitch of second tone lower). A Short condition in which tone duration (90 ms) and stimulus onset asynchrony between the tones of a pair were short (110 ms) was compared to two conditions, where this onset asynchrony was long (510 ms). In the Long Gap condition, the tone durations were identical to Short (90 ms), but the silent interval was prolonged by 400 ms. In Long Tone, the duration of the first tone was prolonged by 400 ms, while the silent interval was comparable to Short (20 ms). Results show a frontocentral MMN of comparable amplitude in all conditions. Thus, abstract pitch relations can be extracted even when the within-pair timing exceeds the integration period. Source analyses indicate MMN generators in the supratemporal cortex. Interestingly, they were located more anterior in Long Gap than in Short and Long Tone. Moreover, frontal generator activity was found for Long Gap and Long Tone. Thus, the way in which the system automatically registers irregular abstract pitch relations depends on the timing of the events to be linked. Pending that the current MMN data mirror established abstract rule representations coding the regular pitch relation, neural processes building these templates vary with timing.

A formal model of interpersonal inference.

INTRODUCTION: We propose that active Bayesian inference-a general framework for decision-making-can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. METHOD: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. RESULTS: (1) Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to "mentalizing" in the psychological literature, is based upon the outcomes of interpersonal exchanges. (2) We show how some well-known social-psychological phenomena (e.g., self-serving biases) can be explained in terms of active interpersonal inference. (3) Mentalizing naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one's own qualities and preferences. CONCLUSION: We inaugurate a Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted.