TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Brain modulation after exergaming training in advanced forms of Parkinson's disease: a randomized controlled study

BACKGROUND

Physical activity combined with virtual reality and exergaming has emerged as a new technique to improve engagement and provide clinical benefit for gait and balance disorders in people with Parkinson's disease (PD).

OBJECTIVE

To investigate the effects of a training protocol using a home-based exergaming system on brain volume and resting-state functional connectivity (rs-FC) in persons with PD.

METHODS

A single blind randomized controlled trial was conducted in people with PD with gait and/or balance disorders. The experimental (active) group performed 18 training sessions at home by playing a custom-designed exergame with full body movements, standing in front of a RGB-D Kinect® motion sensor, while the control group played using the computer keyboard. Both groups received the same training program. Clinical scales, gait recordings, and brain MRI were performed before and after training. We assessed the effects of both training on both the grey matter volumes (GVM) and rs-FC, within and between groups.

RESULTS

Twenty-three patients were enrolled and randomly assigned to either the active (n = 11) or control (n = 12) training groups. Comparing pre- to post-training, the active group showed significant improvements in gait and balance disorders, with decreased rs-FC between the sensorimotor, attentional and basal ganglia networks, but with an increase between the cerebellar and basal ganglia networks. In contrast, the control group showed no significant changes, and rs-FC significantly decreased in the mesolimbic and visuospatial cerebellar and basal ganglia networks. Post-training, the rs-FC was greater in the active relative to the control group between the basal ganglia, motor cortical and cerebellar areas, and bilaterally between the insula and the inferior temporal lobe. Conversely, rs FC was lower in the active relative to the control group between the pedunculopontine nucleus and cerebellar areas, between the temporal inferior lobes and the right thalamus, between the left putamen and dorsolateral prefrontal cortex, and within the default mode network.

CONCLUSIONS

Full-body movement training using a customized exergame induced brain rs-FC changes within the sensorimotor, attentional and cerebellar networks in people with PD. Further research is needed to comprehensively understand the neurophysiological effects of such training approaches. Trial registration ClinicalTrials.gov NCT03560089.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia

Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.

Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

A robust understanding of the natural history of apathy in Parkinson disease (PD) is foundational for developing effective clinical management tools. However, large longitudinal studies are lacking while the literature is inconsistent about even cross-sectional associations. We aimed to determine the longitudinal predictors of apathy development in a large cohort of people with PD and its cross-sectional associations and trajectories over time, using sophisticated Bayesian modeling techniques.

METHODS

People with PD followed up in the longitudinal New Zealand Parkinson's progression project were included. Apathy was defined using the neuropsychiatric inventory subscale ≥4, and analyses were also repeated using a less stringent cutoff of ≥1. Both MoCA and comprehensive neuropsychological testing were used as appropriate to the model. Depression was assessed using the hospital anxiety and depression scale. Cross-sectional Bayesian regressions were conducted, and a multistate predictive model was used to identify factors that predict the initial onset of apathy in nonapathetic PD, while also accounting for the competing risk of death. The relationship between apathy presence and mortality was also investigated.

RESULTS

Three hundred forty-six people with PD followed up for up to 14 years across a total of 1,392 sessions were included. Apathy occurrence did not vary significantly across the disease course (disease duration odds ratio [OR] = 0.55, [95% CI 0.28-1.12], affecting approximately 11% or 22% of people at any time depending on the NPI cutoff used. Its presence was associated with a significantly higher risk of death after controlling for all other factors (hazard ratio [HR] = 2.92 [1.50-5.66]). Lower cognition, higher depression levels, and greater motor severity predicted apathy development in those without motivational deficits (HR [cognition] = 0.66 [0.48-0.90], HR [depression] = 1.45 [1.04-2.02], HR [motor severity] = 1.37 [1.01-1.86]). Cognition and depression were also associated with apathy cross-sectionally, along with male sex and possibly lower dopaminergic therapy level, but apathy still occurred across the full spectrum of each variable (OR [cognition] = 0.58 [0.44-0.76], OR [depression] = 1.43 [1.04-1.97], OR [female sex] = 0.45 [0.22-0.92], and OR [levodopa equivalent dose] = 0.78 [0.59-1.04].

DISCUSSION

Apathy occurs across the PD time course and is associated with higher mortality. Depressive symptoms and cognitive impairment in particular predict its future development in those with normal motivation.

Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression

BACKGROUND

Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD.

METHODS

Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item).

RESULTS

We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels.

CONCLUSIONS

We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

Background

A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care.

Methods

Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology.

Results

We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05).

Conclusions

Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Incorporating uncertainty within dynamic interoceptive learning

Introduction

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.

Methods

Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.

Results

Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.

Discussion

These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Linking fast and slow: The case for generative models

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Maximizing SNR per unit time in diffusion MRI with multiband T-Hex spirals

PURPOSE

The characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b-values (e.g., b > 30 ms/μm2 ), along many directions, and using multiple b-values, leading to long scan times and extremely low SNR in dMRI images. The purpose of this work is to boost the SNR efficiency of dMRI by combining three particularly efficient spatial encoding techniques and utilizing a high-performance gradient system (Gmax  ≤ 300 mT/m) for efficient diffusion encoding.

METHODS

Spiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T-Hex) are combined and implemented on a 3T MRI system with ultra-strong gradients. Image reconstruction is performed through an iterative cg-SENSE algorithm incorporating static off-resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T-Hex multiband is combined with a more conventional EPI-readout and compared with state-of-the-art blipped-CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging.

RESULTS

High fidelity in vivo images with b-values up to 40 ms/μm2 are obtained. The approach provides superior SNR efficiency over other state-of-the-art multiband diffusion readout schemes.

CONCLUSION

The demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.

Neural inhibition as implemented by an actor-critic model involves the human dorsal striatum and ventral tegmental area

Inhibition is implicated across virtually all human experiences. As a trade-off of being very efficient, this executive function is also prone to many errors. Rodent and computational studies show that midbrain regions play crucial roles during errors by sending dopaminergic learning signals to the basal ganglia for behavioural adjustment. However, the parallels between animal and human neural anatomy and function are not determined. We scanned human adults while they performed an fMRI inhibitory task requiring trial-and-error learning. Guided by an actor-critic model, our results implicate the dorsal striatum and the ventral tegmental area as the actor and the critic, respectively. Using a multilevel and dimensional approach, we also demonstrate a link between midbrain and striatum circuit activity, inhibitory performance, and self-reported autistic and obsessive-compulsive subclinical traits.

The Associative Thalamus: A Switchboard for Cortical Operations and a Promising Target for Schizophrenia

Schizophrenia is a brain disorder that profoundly perturbs cognitive processing. Despite the success in treating many of its symptoms, the field lacks effective methods to measure and address its impact on reasoning, inference, and decision making. Prefrontal cortical abnormalities have been well documented in schizophrenia, but additional dysfunction in the interactions between the prefrontal cortex and thalamus have recently been described. This dysfunction may be interpreted in light of parallel advances in neural circuit research based on nonhuman animals, which show critical thalamic roles in maintaining and switching prefrontal activity patterns in various cognitive tasks. Here, we review this basic literature and connect it to emerging innovations in clinical research. We highlight the value of focusing on associative thalamic structures not only to better understand the very nature of cognitive processing but also to leverage these circuits for diagnostic and therapeutic development in schizophrenia. We suggest that the time is right for building close bridges between basic thalamic research and its clinical translation, particularly in the domain of cognition and schizophrenia.

Individual prediction tendencies do not generalize across modalities

Predictive processing theories, which model the brain as a "prediction machine", explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualized as individual traits or as tendencies that generalize across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalizes across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented with a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that, on a group-level, under conditions of low entropy, stimulus features of high probability are preactivated in the auditory but not in the visual modality. Crucially, the magnitude of the individual tendency to predict sensory events seems not to correlate between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalized across modalities.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity

BACKGROUND

Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia.

METHODS

Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time.

RESULTS

Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters.

CONCLUSIONS

Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

The regional variation of laminar thickness in the human isocortex is related to cortical hierarchy and interregional connectivity

The human isocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support function through enabling targeted corticocortical connections. Here, leveraging maps of the 6 cortical layers based on 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with similar laminar thickness patterns have a higher likelihood of structural connections and strength of functional connections. In sum, here we characterize the organization of laminar thickness in the human isocortex and its association with cortico-cortical connectivity, illustrating how laminar organization may provide a foundational principle of cortical function.

The effect of loss incentives on prospective memory in healthy older adults: study protocol of a randomized controlled trial using ultra-high field fMRI

BACKGROUND

Prospective memory is important for our health and independence but declines with age. Hence, interventions to enhance prospective memory, for example by providing an incentive, may promote healthy ageing. The neuroanatomical correlates of prospective memory and the processing of incentive-related prospective memory changes in older adults are not fully understood. In an fMRI study, we will therefore test whether incentives improve prospective memory in older adults and how prospective memory is processed in the brain in general, and when incentives are provided. Since goals and interests change across adulthood, avoiding losses is becoming more important for older adults than achieving gains. We therefore posit that loss-related incentives will enhance prospective memory, which will be subserved by increased prefrontal and midbrain activity.

METHODS

We will include n = 60 healthy older adults (60-75 years of age) in a randomized, single-blind, and parallel-group study. We will acquire 7T fMRI data in an incentive group and a control group (n = 30 each, stratified by education, age, and sex). Before and after fMRI, all participants will complete questionnaires and cognitive tests to assess possible confounders (e.g., income, personality traits, sensitivity to reward or punishment).

DISCUSSION

The results of this study will clarify whether loss-related incentives can enhance prospective memory and how any enhancement is processed in the brain. In addition, we will determine how prospective memory is processed in the brain in general. The results of our study will be an important step towards a better understanding of how prospective memory changes when we get older and for developing interventions to counteract cognitive decline.

A primer on Variational Laplace (VL)

This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.

Echoes from Intrinsic Connectivity Networks in the Subcortex

Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.

Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.

Control dilemma: Evidence of the stability-flexibility trade-off

Cognitive control can be applied flexibly when task goals or environments change (i.e., cognitive flexibility), or stably to pursue a goal in the face of distraction (i.e., cognitive stability). Whether these seemingly contradictory characteristics have an inverse relationship has been controversial, as some studies have suggested a trade-off mechanism between cognitive flexibility and cognitive stability, while others have not found such reciprocal associations. This study investigated the possible antagonistic correlation between cognitive flexibility and stability using a novel version of the flexibility-stability paradigm and the classic cued task switching paradigm. In Experiment 1, we showed that cognitive flexibility was inversely correlated with cognitive stability, as increased distractor proportions were associated with decreased cognitive flexibility and greater cognitive stability. Moreover, cognitive flexibility and stability were regulated by a single control system instead of two independent control mechanisms, as the model selection results indicated that the reciprocally regulated model with one integration parameter outperformed all other models, and the model parameter was inversely linked to cognitive flexibility and stability. We found similar results using the classic cued task switching paradigm in Experiment 2. Therefore, a trade-off between cognitive flexibility and stability was observed from the paradigms used in this study.

Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo

PURPOSE

Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo.

METHODS

Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time.

RESULTS

QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc  = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect.

CONCLUSION

We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.

Human orbitofrontal cortex signals decision outcomes to sensory cortex during behavioral adaptations

The ability to respond flexibly to an ever-changing environment relies on the orbitofrontal cortex (OFC). However, how the OFC associates sensory information with predicted outcomes to enable flexible sensory learning in humans remains elusive. Here, we combine a probabilistic tactile reversal learning task with functional magnetic resonance imaging (fMRI) to investigate how lateral OFC (lOFC) interacts with the primary somatosensory cortex (S1) to guide flexible tactile learning in humans. fMRI results reveal that lOFC and S1 exhibit distinct task-dependent engagement: while the lOFC responds transiently to unexpected outcomes immediately following reversals, S1 is persistently engaged during re-learning. Unlike the contralateral stimulus-selective S1, activity in ipsilateral S1 mirrors the outcomes of behavior during re-learning, closely related to top-down signals from lOFC. These findings suggest that lOFC contributes to teaching signals to dynamically update representations in sensory areas, which implement computations critical for adaptive behavior.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

Assumed shared belief about conspiracy theories in social networks protects paranoid individuals against distress

Paranoia is the belief that others intend you harm. It is related to conspiracy theories, wherein those others represent an organized faction, coordinating the harm against self and others, and violating societal norms. Current psychological studies of paranoid conspiracy theorizing focus either on the individual or their broader social network. Likewise, theories of belief formation and updating often contain individual level processes as well as broader interpersonal and organizational factors. Here we examine paranoia and conspiracy theorizing in terms of individual behavioral predictors (performance on a probabilistic reversal learning task which assays belief updating) as well as social sensing: we ask participants to report the features of their social network, including whether their friends and acquaintances share their paranoid conspiratorial beliefs. We find that people who believe paranoid conspiracy theories expect more volatility during the task. They also assume that members of their social network share their paranoid beliefs. Critically, those participants with larger social networks and greater assumed shared belief tend to harbor their conspiratorial beliefs with less emotional distress and expect less volatility in the task. This is evidence that, like political and religious beliefs, conspiracy theories may flourish under a sacred canopy of belief consensus. These data suggest that friends and acquaintances may serve as sources of credulity and moving between them may sustain conspiracy beliefs when there is detraction. This hybrid individual/social account may shed light on clinical paranoia and persecutory delusion, wherein disability is defined normatively, and social supports are fewer.

Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (MRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N=906) of task-free ("resting state") fMRI data from the UK Biobank. Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three year period, 50% of participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p<0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

Pupil dilation tracks divergent learning processes in aware versus unaware Pavlovian conditioning

Evidence regarding unaware differential fear conditioning in humans is mixed and even less is known about the effects of contingency awareness on appetitive conditioning. Phasic pupil dilation responses (PDR) might be more sensitive for capturing implicit learning than other measures, such as skin conductance responses (SCR). Here, we report data from two delay conditioning experiments utilizing PDR (alongside SCR and subjective assessments) to investigate the role of contingency awareness in aversive and appetitive conditioning. In both experiments, valence of unconditioned stimuli (UCS) was varied within participants by administering aversive (mild electric shocks) and appetitive UCSs (monetary rewards). Preceding visual stimuli (CSs) predicted either the reward, the shock (65% reinforcement), or neither UCS. In Exp. 1, participants were fully instructed about CS-UCS contingencies, whereas in Exp. 2, no such information was given. PDR and SCR demonstrated successful differential conditioning in Exp. 1 and in (learned) aware participants in Exp. 2. In non-instructed participants who remained fully unaware of contingencies (Exp. 2), differential modulation of early PDR (immediately after CS onset) by appetitive cues emerged. Associations with model-derived learning parameters further suggest that early PDR in unaware participants mainly reflect implicit learning of expected outcome value, whereas early PDR in aware (instructed/learned-aware) participants presumably index attentional processes (related to uncertainty/prediction error processing). Similar, but less clear results emerged for later PDR (preceding UCS onset). Our data argue in favor of a dual-process account of associative learning, suggesting that value-related processing can take place irrespective of mechanisms involved in conscious memory formation.

Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients

Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.

Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI

Response inhibition and interference resolution are often considered subcomponents of an overarching inhibition system that utilizes the so-called cortico-basal-ganglia loop. Up until now, most previous functional magnetic resonance imaging (fMRI) literature has compared the two using between-subject designs, pooling data in the form of a meta-analysis or comparing different groups. Here, we investigate the overlap of activation patterns underlying response inhibition and interference resolution on a within-subject level, using ultra-high field MRI. In this model-based study, we furthered the functional analysis with cognitive modelling techniques to provide a more in-depth understanding of behaviour. We applied the stop-signal task and multi-source interference task to measure response inhibition and interference resolution, respectively. Our results lead us to conclude that these constructs are rooted in anatomically distinct brain areas and provide little evidence for spatial overlap. Across the two tasks, common BOLD responses were observed in the inferior frontal gyrus and anterior insula. Interference resolution relied more heavily on subcortical components, specifically nodes of the commonly referred to indirect and hyperdirect pathways, as well as the anterior cingulate cortex, and pre-supplementary motor area. Our data indicated that orbitofrontal cortex activation is specific to response inhibition. Our model-based approach provided evidence for the dissimilarity in behavioural dynamics between the two tasks. The current work exemplifies the importance of reducing inter-individual variance when comparing network patterns and the value of UHF-MRI for high resolution functional mapping.

A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing

Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model were compared with the established measures of conditioned pain modulation (CPM) and temporal summation of pain (TSP) assessed by cuff algometry. Subsequent HGF-modelling and estimation of the influence of prior beliefs on perception showed that 70% of subjects had a higher reliance on nociceptive input during perception of acute pain stimuli, whereas 30% showed a stronger weighting of prior expectations over sensory evidence. There was no association between prior weighting estimates and CPM or TSP. The data demonstrates relevant individual differences in prior weighting and suggests an importance of top-down cognitive processes on pain perception. Our new psychophysical testing paradigm provides a method to identify individuals with traits suggesting greater reliance on prior expectations in pain perception, which may be a risk factor for developing chronic pain and may be differentially responsive to learning-based interventions.

Reaction Time "Mismatch Costs" Change with the Likelihood of Stimulus-Response Compatibility

Dyadic interactions require dynamic correspondence between one's own movements and those of the other agent. This mapping is largely viewed as imitative, with the behavioural hallmark being a reaction-time cost for mismatched actions. Yet the complex motor patterns humans enact together extend beyond direct-matching, varying adaptively between imitation, complementary movements, and counter-imitation. Optimal behaviour requires an agent to predict not only what is likely to be observed but also how that observed action will relate to their own motor planning. In 28 healthy adults, we examined imitation and counter-imitation in a task that varied the likelihood of stimulus-response congruence from highly predictable, to moderately predictable, to unpredictable. To gain mechanistic insights into the statistical learning of stimulus-response compatibility, we compared two computational models of behaviour: (1) a classic fixed learning-rate model (Rescorla-Wagner reinforcement [RW]) and (2) a hierarchical model of perceptual-behavioural processes in which the learning rate adapts to the inferred environmental volatility (hierarchical Gaussian filter [HGF]). Though more complex and hence penalized by model selection, the HGF provided a more likely model of the participants' behaviour. Matching motor responses were only primed (faster) in the most experimentally volatile context. This bias was reversed so that mismatched actions were primed when beliefs about volatility were lower. Inferential statistics indicated that matching responses were only primed in unpredictable contexts when stimuli-response congruence was at 50:50 chance. Outside of these unpredictable blocks the classic stimulus-response compatibility effect was reversed: Incongruent responses were faster than congruent ones. We show that hierarchical Bayesian learning of environmental statistics may underlie response priming during dyadic interactions.

Action selection in early stages of psychosis: an active inference approach

BACKGROUND

To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls.

METHODS

Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification.

RESULTS

We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action-state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures.

LIMITATIONS

The sample size is moderate.

CONCLUSION

Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis.

Task-evoked pupillary responses track precision-weighted prediction errors and learning rate during interceptive visuomotor actions

In this study, we examined the relationship between physiological encoding of surprise and the learning of anticipatory eye movements. Active inference portrays perception and action as interconnected inference processes, driven by the imperative to minimise the surprise of sensory observations. To examine this characterisation of oculomotor learning during a hand-eye coordination task, we tested whether anticipatory eye movements were updated in accordance with Bayesian principles and whether trial-by-trial learning rates tracked pupil dilation as a marker of 'surprise'. Forty-four participants completed an interception task in immersive virtual reality that required them to hit bouncing balls that had either expected or unexpected bounce profiles. We recorded anticipatory eye movements known to index participants' beliefs about likely ball bounce trajectories. By fitting a hierarchical Bayesian inference model to the trial-wise trajectories of these predictive eye movements, we were able to estimate each individual's expectations about bounce trajectories, rates of belief updating, and precision-weighted prediction errors. We found that the task-evoked pupil response tracked prediction errors and learning rates but not beliefs about ball bounciness or environmental volatility. These findings are partially consistent with active inference accounts and shed light on how encoding of surprise may shape the control of action.

Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility

BACKGROUND

Recent advances in computational psychiatry have identified latent cognitive and perceptual states that predispose to psychotic symptoms. Behavioral data fit to Bayesian models have demonstrated an overreliance on priors (i.e., prior overweighting) during perception in select samples of individuals with hallucinations, corresponding to increased precision of prior expectations over incoming sensory evidence. However, the clinical utility of this observation depends on the extent to which it reflects static symptom risk or current symptom state.

METHODS

To determine whether task performance and estimated prior weighting relate to specific elements of symptom expression, a large, heterogeneous, and deeply phenotyped sample of hallucinators (n = 249) and nonhallucinators (n = 209) performed the conditioned hallucination (CH) task.

RESULTS

We found that CH rates predicted stable measures of hallucination status (i.e., peak frequency). However, CH rates were more sensitive to hallucination state (i.e., recent frequency), significantly correlating with recent hallucination severity and driven by heightened reliance on past experiences (priors). To further test the sensitivity of CH rate and prior weighting to symptom severity, a subset of participants with hallucinations (n = 40) performed a repeated-measures version of the CH task. Changes in both CH frequency and prior weighting varied with changes in auditory hallucination frequency on follow-up.

CONCLUSIONS

These results indicate that CH rate and prior overweighting are state markers of hallucination status, potentially useful in tracking disease development and treatment response.

An optimized protocol for assessing changes in mouse whole-brain activity using opto-fMRI

Functional magnetic resonance imaging (fMRI) in mouse brain, paired with spatially and temporally defined manipulations, offers a powerful tool to causally explain the effect of specific neuronal activity on brain network dynamics. Here, we present an optimized protocol to measure cell-type-specific contributions to changes in whole-brain dynamics in mice using optogenetics (opto)-fMRI. This protocol details the injection of ChR2-expressing AAV, the implantation of optical fiber, the steps to perform opto-BOLD (blood-oxygenation-level-dependent) fMRI recording, and data analysis. For complete details on the use and execution of this protocol, please refer to Grimm et al. (2021).

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

A computational framework can extract spatio-spectro-temporal neural signatures corresponding to hierarchical prediction and prediction errors in a local-global auditory task.

Causal Analysis of Activity in Social Brain Areas During Human-Agent Conversation

This article investigates the differences in cognitive and neural mechanisms between human-human and human-virtual agent interaction using a dataset recorded in an ecologically realistic environment. We use Convergent Cross Mapping (CCM) to investigate functional connectivity between pairs of regions involved in the framework of social cognitive neuroscience, namely the fusiform gyrus, superior temporal sulcus (STS), temporoparietal junction (TPJ), and the dorsolateral prefrontal cortex (DLPFC)-taken as prefrontal asymmetry. Our approach is a compromise between investigating local activation in specific regions and investigating connectivity networks that may form part of larger networks. In addition to concording with previous studies, our results suggest that the right TPJ is one of the most reliable areas for assessing processes occurring during human-virtual agent interactions, both in a static and dynamic sense.

Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography

The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.

Mental State of Inpatients With COVID-19: A Computational Psychiatry Approach

BACKGROUND

The overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with coronavirus disease 2019 (COVID-19). The relationship between severe acute respiratory syndrome with the coronavirus 2 (SARS-CoV-2) infection and specific mental disturbances remains poorly understood.

AIM

To reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena.

MATERIALS AND METHODS

Descriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables coded from the main psychopathological symptoms. Dispersion analysis with p level 0.05 was used to reveal the clusters differences in demography, parameters of inflammation, and respiration function collected on the basis of the original medical records.

RESULTS

Three resulting clusters of patients were identified: (1) persons with anxiety; disorders of fluency and tempo of thinking, mood, attention, and motor-volitional sphere; reduced insight; and pessimistic plans for the future (n = 11); (2) persons without psychopathology (n = 37); and (3) persons with disorientation; disorders of memory, attention, fluency, and tempo of thinking; and reduced insight (n = 7). The development of a certain type of impaired mental state was specifically associated with the following: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, and platelet count.

CONCLUSION

Anxiety and/or mood disturbances with psychomotor retardation as well as symptoms of impaired consciousness, memory, and insight may be considered as neuropsychiatric manifestations of COVID-19 and should be used for clinical risk assessment.

Optogenetic activation of striatal D1R and D2R cells differentially engages downstream connected areas beyond the basal ganglia

The basal ganglia (BG) are a group of subcortical nuclei responsible for motor and executive function. Central to BG function are striatal cells expressing D1 (D1R) and D2 (D2R) dopamine receptors. D1R and D2R cells are considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. However, whether they maintain a uniform function across the striatum and what influence they exert outside the BG is unclear. Here, we address these questions by combining optogenetic activation of D1R and D2R cells in the mouse ventrolateral caudoputamen with fMRI. Striatal D1R/D2R stimulation evokes distinct activity within the BG-thalamocortical network and differentially engages cerebellar and prefrontal regions. Computational modeling of effective connectivity confirms that changes in D1R/D2R output drive functional relationships between these regions. Our results suggest a complex functional organization of striatal D1R/D2R cells and hint toward an interconnected fronto-BG-cerebellar network modulated by striatal D1R and D2R cells.

Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

Regenerative Virtual Therapy: The Use of Multisensory Technologies and Mindful Attention for Updating the Altered Representations of the Bodily Self

The term “regenerative medicine” (RM) indicates an emerging trend in biomedical sciences that aims at replacing, engineering, or regenerating human cells, tissues, or organs to restore or establish normal function. So far, the focus of RM has been the physical body. Neuroscience, however, is now suggesting that mental disorders can be broadly characterized by a dysfunction in the way the brain computes and integrates the representations of the inner and outer body across time [bodily self-consciousness (BSC)]. In this perspective, we proposed a new kind of clinical intervention, i.e., “Regenerative Virtual Therapy” (RVT), which integrates knowledge from different disciplines, from neuroscience to computational psychiatry, to regenerate a distorted or faulty BSC. The main goal of RVT was to use technology-based somatic modification techniques to restructure the maladaptive bodily representations behind a pathological condition. Specifically, starting from a Bayesian model of our BSC (i.e., body matrix), we suggested the use of mindful attention, cognitive reappraisal, and brain stimulation techniques merged with high-rewarding and novel synthetic multisensory bodily experience (i.e., a virtual reality full-body illusion in sync with a low predictabIlity interoceptive modulation) to rewrite a faulty experience of the body and to regenerate the wellbeing of an individual. The use of RVT will also offer an unprecedented experimental overview of the dynamics of our bodily representations, allowing the reverse-engineering of their functioning for hacking them using advanced technologies.

Advances in spiral fMRI: A high-resolution study with single-shot acquisition

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.

A model for learning based on the joint estimation of stochasticity and volatility

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.