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Goal-directed actions are characterized by two main features: the content (i.e., the action goal) and the form, called vitality forms (VF) (i.e., how actions are executed). It is well established that both the action content and the capacity to understand the content of another's action are mediated by a network formed by a set of parietal and frontal brain areas. In contrast, the neural bases of action forms (e.g., gentle or rude actions) have not been characterized. However, there are now studies showing that the observation and execution of actions endowed with VF activate, in addition to the parieto-frontal network, the dorso-central insula (DCI). In the present study, we established-using dynamic causal modeling (DCM)-the direction of information flow during observation and execution of actions endowed with gentle and rude VF in the human brain. Based on previous fMRI studies, the selected nodes for the DCM comprised the posterior superior temporal sulcus (pSTS), the inferior parietal lobule (IPL), the premotor cortex (PM), and the DCI. Bayesian model comparison showed that, during action observation, two streams arose from pSTS: one toward IPL, concerning the action goal, and one toward DCI, concerning the action vitality forms. During action execution, two streams arose from PM: one toward IPL, concerning the action goal and one toward DCI concerning action vitality forms. This last finding opens an interesting question concerning the possibility to elicit VF in two distinct ways: cognitively (from PM to DCI) and affectively (from DCI to PM)., Competing Interests: Competing interests statement:The authors declare no competing interest.

The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience-from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the 'state' of a system-i.e., a specification of the system's future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets., Competing Interests: The authors declare no competing interests., (Copyright: © 2023 Dezhina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Predictive processing (e.g., predictive coding) is a predominant paradigm in cognitive neuroscience. This Primer considers the various levels of commitment neuroscientists have to the neuronal process theories that accompany the principles of predictive processing. Specifically, it reviews and contextualises a recent PLOS Biology study of alpha oscillations and travelling waves. We will see that alpha oscillations emerge naturally under the computational architectures implied by predictive coding-and may tell us something profound about recurrent message passing in brain hierarchies. Specifically, the bidirectional nature of forward and backward waves speaks to opportunities to understand attention and how it nuances bottom-up and top-down influences., Competing Interests: The author has declared that no competing interests exist.

In cognitive neuroscience, electrical brain activity is most commonly recorded at the scalp. In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. Several approaches to this reconstruction have been developed, thereby solving the so-called implicit inverse problem Michel et al. (Clin Neurophysiol 115:2195-2222, 2004). However, a unifying premise against which to validate these source reconstructions is seldom available. The dataset provided in this work, in which brain activity is simultaneously recorded on the scalp (non-invasively) by electroencephalography (EEG) and on the cortex (invasively) by electrocorticography (ECoG), can be of a great help in this direction. These multimodal recordings were obtained from a macaque monkey under wakefulness and sedation. Our primary goal was to establish the connectivity architecture between two sources of interest (frontal and parietal), and to assess how their coupling changes over the conditions. We chose these sources because previous studies have shown that the connections between them are modified by anaesthesia Boly et al. (J Neurosci 32:7082-7090, 2012). Our secondary goal was to evaluate the consistency of the connectivity results when analyzing sources recorded from invasive data (128 implanted ECoG sources) and source activity reconstructed from scalp recordings (19 EEG sensors) at the same locations as the ECoG sources. We conclude that the directed connectivity in the frequency domain between cortical sources reconstructed from scalp EEG is qualitatively similar to the connectivity inferred directly from cortical recordings, using both data-driven (directed transfer function) and biologically grounded (dynamic causal modelling) methods. Furthermore, the connectivity changes identified were consistent with previous findings Boly et al. (J Neurosci 32:7082-7090, 2012). Our findings suggest that inferences about directed connectivity based upon non-invasive electrophysiological data have construct validity in relation to invasive recordings.

This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc., (© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

See Roberts and Breakspear (doi:10.1093/brain/awy136) for a scientific commentary on this article.Neurological and psychiatric practice frequently lack diagnostic probes that can assess mechanisms of neuronal communication non-invasively in humans. In N-methyl-d-aspartate (NMDA) receptor antibody encephalitis, functional molecular assays are particularly important given the presence of NMDA antibodies in healthy populations, the multifarious symptomology and the lack of radiological signs. Recent advances in biophysical modelling techniques suggest that inferring cellular-level properties of neural circuits from macroscopic measures of brain activity is possible. Here, we estimated receptor function from EEG in patients with NMDA receptor antibody encephalitis (n = 29) as well as from encephalopathic and neurological patient controls (n = 36). We show that the autoimmune patients exhibit distinct fronto-parietal network changes from which ion channel estimates can be obtained using a microcircuit model. Specifically, a dynamic causal model of EEG data applied to spontaneous brain responses identifies a selective deficit in signalling at NMDA receptors in patients with NMDA receptor antibody encephalitis but not at other ionotropic receptors. Moreover, though these changes are observed across brain regions, these effects predominate at the NMDA receptors of excitatory neurons rather than at inhibitory interneurons. Given that EEG is a ubiquitously available clinical method, our findings suggest a unique re-purposing of EEG data as an assay of brain network dysfunction at the molecular level.

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants' brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling.

Objective: Posttraumatic stress disorder (PTSD) is associated with decreased top-down emotion modulation from medial prefrontal cortex (mPFC) regions, a pathophysiology accompanied by hyperarousal and hyperactivation of the amygdala. By contrast, PTSD patients with the dissociative subtype (PTSD + DS) often exhibit increased mPFC top-down modulation and decreased amygdala activation associated with emotional detachment and hypoarousal. Crucially, PTSD and PTSD + DS display distinct functional connectivity within the PFC, amygdala complexes, and the periaqueductal gray (PAG), a region related to defensive responses/emotional coping. However, differences in directed connectivity between these regions have not been established in PTSD, PTSD + DS, or controls., Methods: To examine directed (effective) connectivity among these nodes, as well as group differences, we conducted resting-state stochastic dynamic causal modeling (sDCM) pairwise analyses of coupling between the ventromedial (vm)PFC, the bilateral basolateral and centromedial (CMA) amygdala complexes, and the PAG, in 155 participants (PTSD [n = 62]; PTSD + DS [n = 41]; age-matched healthy trauma-unexposed controls [n = 52])., Results: PTSD was characterized by a pattern of predominant bottom-up connectivity from the amygdala to the vmPFC and from the PAG to the vmPFC and amygdala. Conversely, PTSD + DS exhibited predominant top-down connectivity between all node pairs (from the vmPFC to the amygdala and PAG, and from the amygdala to the PAG). Interestingly, the PTSD + DS group displayed the strongest intrinsic inhibitory connections within the vmPFC., Conclusions: These results suggest the contrasting symptom profiles of PTSD and its dissociative subtype (hyper- vs. hypo-emotionality, respectively) may be driven by complementary changes in directed connectivity corresponding to bottom-up defensive fear processing versus enhanced top-down regulation. Hum Brain Mapp 38:5551-5561, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Effective connectivity is commonly assessed using blood oxygenation level-dependent (BOLD) signals. In (Havlicek et al., 2015), we presented a novel, physiologically informed dynamic causal model (P-DCM) that extends current generative models. We demonstrated the improvements afforded by P-DCM in terms of the ability to model commonly observed neuronal and vascular transients in single regions. Here, we assess the ability of the novel and previous DCM variants to estimate effective connectivity among a network of five ROIs driven by a visuo-motor task. We demonstrate that connectivity estimates depend sensitively on the DCM used, due to differences in the modeling of hemodynamic response transients; such as the post-stimulus undershoot or adaptation during stimulation. In addition, using a novel DCM for arterial spin labeling (ASL) fMRI that measures BOLD and CBF signals simultaneously, we confirmed our findings (by using the BOLD data alone and in conjunction with CBF). We show that P-DCM provides better estimates of effective connectivity, regardless of whether it is applied to BOLD data alone or to ASL time-series, and that all new aspects of P-DCM (i.e. neuronal, neurovascular, hemodynamic components) constitute an improvement compared to those in the previous DCM variants. In summary, (i) accurate modeling of fMRI response transients is crucial to obtain valid effective connectivity estimates and (ii) any additional hemodynamic data, such as provided by ASL, increases the ability to disambiguate neuronal and vascular effects present in the BOLD signal., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The "dysconnection hypothesis" of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibition-that is, increased cortical excitability-in left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis. Hum Brain Mapp 38:3262-3276, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

The neuronal mechanisms underlying arithmetic calculations are not well understood but the differences between mental addition and subtraction could be particularly revealing. Using fMRI and dynamic causal modeling (DCM), this study aimed to identify the distinct neuronal architectures engaged by the cognitive processes of simple addition and subtraction. Our results revealed significantly greater activation during subtraction in regions along the dorsal pathway, including the left inferior frontal gyrus (IFG), middle portion of dorsolateral prefrontal cortex (mDLPFC), and supplementary motor area (SMA), compared with addition. Subsequent analysis of the underlying changes in connectivity - with DCM - revealed a common circuit processing basic (numeric) attributes and the retrieval of arithmetic facts. However, DCM showed that addition was more likely to engage (numeric) retrieval-based circuits in the left hemisphere, while subtraction tended to draw on (magnitude) processing in bilateral parietal cortex, especially the right intraparietal sulcus (IPS). Our findings endorse previous hypotheses about the differences in strategic implementation, dominant hemisphere, and the neuronal circuits underlying addition and subtraction. Moreover, for simple arithmetic, our connectivity results suggest that subtraction calls on more complex processing than addition: auxiliary phonological, visual, and motor processes, for representing numbers, were engaged by subtraction, relative to addition. Hum Brain Mapp 38:3210-3225, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

The relationship between the BOLD response and an applied force was quantified in the cerebellum using a power grip task. To investigate whether the cerebellum responds in an on/off way to motor demands or contributes to motor responses in a parametric fashion, similarly to the cortex, five grip force levels were investigated under visual feedback. Functional MRI data were acquired in 13 healthy volunteers and their responses were analyzed using a cerebellum-optimized pipeline. This allowed us to evaluate, within the cerebellum, voxelwise linear and non-linear associations between cerebellar activations and forces. We showed extensive non-linear activations (with a parametric design), covering the anterior and posterior lobes of the cerebellum with a BOLD-force relationship that is region-dependent. Linear responses were mainly located in the anterior lobe, similarly to the cortex, where linear responses are localized in M1. Complex responses were localized in the posterior lobe, reflecting its key role in attention and executive processing, required during visually guided movement. Given the highly organized responses in the cerebellar cortex, a key question is whether deep cerebellar nuclei show similar parametric effects. We found positive correlations with force in the ipsilateral dentate nucleus and negative correlations on the contralateral side, suggesting a somatotopic organization of the dentate nucleus in line with cerebellar and cortical areas. Our results confirm that there is cerebellar organization involving all grey matter structures that reflect functional segregation in the cortex, where cerebellar lobules and dentate nuclei contribute to complex motor tasks with different BOLD response profiles in relation to the forces. Hum Brain Mapp 38:2566-2579, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

This Opinion article considers the implications for functional anatomy of how we represent temporal structure in our exchanges with the world. It offers a theoretical treatment that tries to make sense of the architectural principles seen in mammalian brains. Specifically, it considers a factorisation between representations of temporal succession and representations of content or, heuristically, a segregation into when and what. This segregation may explain the central role of the hippocampus in neuronal hierarchies while providing a tentative explanation for recent observations of how ordinal sequences are encoded. The implications for neuroanatomy and physiology may have something important to say about how self-organised cell assembly sequences enable the brain to exhibit purposeful behaviour that transcends the here and now., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

This paper presents a review of theoretical and empirical work on repetition suppression in the context of predictive coding. Predictive coding is a neurobiologically plausible scheme explaining how biological systems might perform perceptual inference and learning. From this perspective, repetition suppression is a manifestation of minimising prediction error through adaptive changes in predictions about the content and precision of sensory inputs. Simulations of artificial neural hierarchies provide a principled way of understanding how repetition suppression - at different time scales - can be explained in terms of inference and learning implemented under predictive coding. This formulation of repetition suppression is supported by results of numerous empirical studies of repetition suppression and its contextual determinants., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision - inferred by our behavioural DCM - correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using "eyes open" versus "eyes closed" conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.

The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI., (Copyright © 2015. Published by Elsevier Inc.)

The superior temporal sulcus (STS) in the human and monkey is sensitive to the motion of complex forms such as facial and bodily actions. We used functional magnetic resonance imaging (fMRI) to explore network-level explanations for how the form and motion information in dynamic facial expressions might be combined in the human STS. Ventral occipitotemporal areas selective for facial form were localized in occipital and fusiform face areas (OFA and FFA), and motion sensitivity was localized in the more dorsal temporal area V5. We then tested various connectivity models that modeled communication between the ventral form and dorsal motion pathways. We show that facial form information modulated transmission of motion information from V5 to the STS, and that this face-selective modulation likely originated in OFA. This finding shows that form-selective motion sensitivity in the STS can be explained in terms of modulation of gain control on information flow in the motion pathway, and provides a substantial constraint for theories of the perception of faces and biological motion., (© The Author 2014. Published by Oxford University Press.)

Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among brain regions from neuroimaging data. While the validity of DCM has been investigated in various previous studies, the reliability of DCM parameter estimates across sessions has been examined less systematically. Here, we report results of a software comparison with regard to test-retest reliability of DCM for fMRI, using a challenging scenario where complex models with many parameters were applied to relatively few data points. Specifically, we examined the reliability of different DCM implementations (in terms of the intra-class correlation coefficient, ICC) based on fMRI data from 35 human subjects performing a simple motor task in two separate sessions, one month apart. We constructed DCMs of motor regions with fair to excellent reliability of conventional activation measures. Using classical DCM (cDCM) in SPM5, we found that the test-retest reliability of DCM results was high, both concerning the model evidence (ICC=0.94) and the model parameter estimates (median ICC=0.47). However, when using a more recent DCM version (DCM10 in SPM8), test-retest reliability was reduced notably. Analyses indicated that, in our particular case, the prior distributions played a crucial role in this change in reliability across software versions. Specifically, when using cDCM priors for model inversion in DCM10, this not only restored reliability but yielded even better results than in cDCM. Analyzing each component of the objective function in DCM, we found a selective change in the reliability of posterior mean estimates. This suggests that tighter regularization afforded by cDCM priors reduces the possibility of local extrema in the objective function. We conclude this paper with an outlook to ongoing developments for overcoming the software-dependency of reliability observed in this study, including global optimization and empirical Bayesian procedures., (Copyright © 2014 Elsevier Inc. All rights reserved.)

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference--based upon hierarchical predictive coding--and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context-in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature-ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

[18F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [18F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of brain abnormalities in single patients. In order to perform SPM, a "spatial normalization" of an individual's PET scan is required to match a reference PET template. The PET template currently used for SPM normalization is based on [15O]-H2O images and does not resemble either the specific metabolic features of [18F]-FDG brain scans or the specific morphological characteristics of individual brains affected by neurodegeneration. Thus, our aim was to create a new [18F]-FDG PET aging and dementia-specific template for spatial normalization, based on images derived from both age-matched controls and patients. We hypothesized that this template would increase spatial normalization accuracy and thereby preserve crucial information for research and diagnostic purposes. We investigated the statistical sensitivity and registration accuracy of normalization procedures based on the standard and new template-at the single-subject and group level-independently for subjects with Mild Cognitive Impairment (MCI), probable Alzheimer's Disease (AD), Frontotemporal lobar degeneration (FTLD) and dementia with Lewy bodies (DLB). We found a significant statistical effect of the population-specific FDG template-based normalisation in key anatomical regions for each dementia subtype, suggesting that spatial normalization with the new template provides more accurate estimates of metabolic abnormalities for single-subject and group analysis, and therefore, a more effective diagnostic measure.

This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity--as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

Understanding the neural basis of poor impulse control in Internet addiction (IA) is important for understanding the neurobiological mechanisms of this syndrome. The current study investigated how neuronal pathways implicated in response inhibition were affected in IA using a Go-Stop paradigm and functional magnetic resonance imaging (fMRI). Twenty-three control subjects aged 15.2±0.5 years (mean±S.D.) and eighteen IA subjects aged 15.1±1.4 years were studied. Effective connectivity within the response inhibition network was quantified using (stochastic) dynamic causal modeling (DCM). The results showed that the indirect frontal-basal ganglia pathway was engaged by response inhibition in healthy subjects. However, we did not detect any equivalent effective connectivity in the IA group. This suggests the IA subjects fail to recruit this pathway and inhibit unwanted actions. This study provides a clear link between Internet addiction as a behavioral disorder and aberrant connectivity in the response inhibition network.

Over the last decade, synchronized resting-state fluctuations of blood oxygenation level-dependent (BOLD) signals between remote brain areas [so-called BOLD resting-state functional connectivity (rs-FC)] have gained enormous relevance in systems and clinical neuroscience. However, the neural underpinnings of rs-FC are still incompletely understood. Using simultaneous positron emission tomography/magnetic resonance imaging we here directly investigated the relationship between rs-FC and local neuronal activity in humans. Computational models suggest a mechanistic link between the dynamics of local neuronal activity and the functional coupling among distributed brain regions. Therefore, we hypothesized that the local activity (LA) of a region at rest determines its rs-FC. To test this hypothesis, we simultaneously measured both LA (glucose metabolism) and rs-FC (via synchronized BOLD fluctuations) during conditions of eyes closed or eyes open. During eyes open, LA increased in the visual system, and the salience network (i.e., cingulate and insular cortices) and the pattern of elevated LA coincided almost exactly with the spatial pattern of increased rs-FC. Specifically, the voxelwise regional profile of LA in these areas strongly correlated with the regional pattern of rs-FC among the same regions (e.g., LA in primary visual cortex accounts for ∼ 50%, and LA in anterior cingulate accounts for ∼ 20% of rs-FC with the visual system). These data provide the first direct evidence in humans that local neuronal activity determines BOLD FC at rest. Beyond its relevance for the neuronal basis of coherent BOLD signal fluctuations, our procedure may translate into clinical research particularly to investigate potentially aberrant links between local dynamics and remote functional coupling in patients with neuropsychiatric disorders.

Detecting the location of salient sounds in the environment rests on the brain's ability to use differences in sounds arriving at both ears. Functional neuroimaging studies in humans indicate that the left and right auditory hemispaces are coded asymmetrically, with a rightward attentional bias that reflects spatial attention in vision. Neuropsychological observations in patients with spatial neglect have led to the formulation of two competing models: the orientation bias and right-hemisphere dominance models. The orientation bias model posits a symmetrical mapping between one side of the sensorium and the contralateral hemisphere, with mutual inhibition of the ipsilateral hemisphere. The right-hemisphere dominance model introduces a functional asymmetry in the brain's coding of space: the left hemisphere represents the right side, whereas the right hemisphere represents both sides of the sensorium. We used Dynamic Causal Modeling of effective connectivity and Bayesian model comparison to adjudicate between these alternative network architectures, based on human electroencephalographic data acquired during an auditory location oddball paradigm. Our results support a hemispheric asymmetry in a frontoparietal network that conforms to the right-hemisphere dominance model. We show that, within this frontoparietal network, forward connectivity increases selectively in the hemisphere contralateral to the side of sensory stimulation. We interpret this finding in light of hierarchical predictive coding as a selective increase in attentional gain, which is mediated by feedforward connections that carry precision-weighted prediction errors during perceptual inference. This finding supports the disconnection hypothesis of unilateral neglect and has implications for theories of its etiology.

The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative has focused scientific attention on the necessary tools to understand the human brain and mind. Here, we outline our collective vision for what we can achieve within a decade with properly targeted efforts and discuss likely technological deliverables and neuroscience progress., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Spontaneous fluctuations in resting state activity can change in response to experience-dependent plasticity and learning. Visual learning is fast and can be elicited in an MRI scanner. Here, we showed that a random dot motion coherence task can be learned within one training session. While the task activated primarily visual and parietal brain areas, learning related changes in neural activity were observed in the hippocampus. Crucially, even this rapid learning affected resting state dynamics both immediately after the learning and 24h later. Specifically, the hippocampus changed its coupling with the striatum, in a way that was best explained as a consolidation of early learning related changes. Our findings suggest that long-lasting changes in neuronal coupling are accompanied by changes in resting state activity., (Copyright © 2013 Elsevier Inc. All rights reserved.)

In this work, we address the problem of using dynamic causal modelling (DCM) to estimate the coupling parameters (effective connectivity) of large models with many regions. This is a potentially important problem because meaningful graph theoretic analyses of effective connectivity rest upon the statistics of the connections (edges). This calls for characterisations of networks with an appreciable number of regions (nodes). The problem here is that the number of coupling parameters grows quadratically with the number of nodes-leading to severe conditional dependencies among their estimates and a computational load that quickly becomes unsustainable. Here, we describe a simple solution, in which we use functional connectivity to provide prior constraints that bound the effective number of free parameters. In brief, we assume that priors over connections between individual nodes can be replaced by priors over connections between modes (patterns over nodes). By using a small number of modes, we can reduce the dimensionality of the problem in an informed way. The modes we use are the principal components or eigenvectors of the functional connectivity matrix. However, this approach begs the question of how many modes to use. This question can be addressed using Bayesian model comparison to optimise the number of modes. We imagine that this form of prior - over the extrinsic (endogenous) connections in large DCMs - may be useful for people interested in applying graph theory to distributed networks in the brain or to characterise connectivity beyond the subgraphs normally examined in DCM., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Certain sounds, for example, the squeal of chalk on a blackboard, are perceived as highly unpleasant. Functional magnetic resonance imaging (fMRI) in humans shows responses in the amygdala and auditory cortex to aversive sounds. Dynamic causal modelling (DCM) of the interaction between auditory cortex and the amygdala revealed that evoked responses to aversive sounds are relayed to the amygdala via the auditory cortex. There is a complex interaction between the -auditory cortex and amygdala involving effective connectivity in both directions. While acoustic features modulate forward connections from auditory cortex to the amygdala, the valence modulates effective connectivity from the amygdala to the auditory cortex. The results support interaction between the auditory cortex and amygdala where stimuli are first processed to a higher (object) level in the auditory cortex before assignment of valence in the amygdala.

Attentional orientation to a spatial cue and reorientation-after invalid cueing-are mediated by two distinct networks in the human brain. A bilateral dorsal frontoparietal network, comprising the intraparietal sulcus (IPS) and the frontal eye fields (FEF), controls the voluntary deployment of attention and may modulate visual cortex in preparation for upcoming stimulation. In contrast, reorienting attention to invalidly cued targets engages a right-lateralized ventral frontoparietal network comprising the temporoparietal junction (TPJ) and ventral frontal cortex. The present fMRI study investigated the functional architecture of these two attentional systems by characterizing effective connectivity during lateralized orienting and reorienting of attention, respectively. Subjects performed a modified version of Posner's location-cueing paradigm. Dynamic causal modeling (DCM) of regional responses in the dorsal and ventral network, identified in a conventional (SPM) whole-brain analysis, was used to compare different functional architectures. Bayesian model selection showed that top-down connections from left and right IPS to left and right visual cortex, respectively, were modulated by the direction of attention. Moreover, model evidence was highest for a model with directed influences from bilateral IPS to FEF, and reciprocal coupling between right and left FEF. Invalid cueing enhanced forward connections from visual areas to right TPJ, and directed influences from right TPJ to right IPS and IFG (inferior frontal gyrus). These findings shed further light on the functional organization of the dorsal and ventral attentional network and support a context-sensitive lateralization in the top-down (backward) mediation of attentional orienting and the bottom-up (forward) effects of invalid cueing.

After more than twenty years busily mapping the human brain, what have we learned from neuroimaging? This review (coda) considers this question from the point of view of structure-function relationships and the two cornerstones of functional neuroimaging; functional segregation and integration. Despite remarkable advances and insights into the brain's functional architecture, the earliest and simplest challenge in human brain mapping remains unresolved: We do not have a principled way to map brain function onto its structure in a way that speaks directly to cognitive neuroscience. Having said this, there are distinct clues about how this might be done: First, there is a growing appreciation of the role of functional integration in the distributed nature of neuronal processing. Second, there is an emerging interest in data-driven cognitive ontologies, i.e., that are internally consistent with functional anatomy. We will focus this review on the growing momentum in the fields of functional connectivity and distributed brain responses and consider this in the light of meta-analyses that use very large data sets to disclose large-scale structure-function mappings in the human brain., (Copyright © 2011. Published by Elsevier Inc.)

Statistical parametric mapping (SPM) locates significant clusters based on a ratio of signal to noise (a 'contrast' of the parameters divided by its standard error) meaning that very low noise regions, for example outside the brain, can attain artefactually high statistical values. Similarly, the commonly applied preprocessing step of Gaussian spatial smoothing can shift the peak statistical significance away from the peak of the contrast and towards regions of lower variance. These problems have previously been identified in positron emission tomography (PET) (Reimold et al., 2006) and voxel-based morphometry (VBM) (Acosta-Cabronero et al., 2008), but can also appear in functional magnetic resonance imaging (fMRI) studies. Additionally, for source-reconstructed magneto- and electro-encephalography (M/EEG), the problems are particularly severe because sparsity-favouring priors constrain meaningfully large signal and variance to a small set of compactly supported regions within the brain. (Acosta-Cabronero et al., 2008) suggested adding noise to background voxels (the 'haircut'), effectively increasing their noise variance, but at the cost of contaminating neighbouring regions with the added noise once smoothed. Following theory and simulations, we propose to modify--directly and solely--the noise variance estimate, and investigate this solution on real imaging data from a range of modalities., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Neuronal responses exhibit two stimulus or task-related components: evoked and induced. The functional role of induced responses has been ascribed to 'top-down' modulation through backward connections and lateral interactions; as opposed to the bottom-up driving processes that may predominate in evoked components. The implication is that evoked and induced components may reflect different neuronal processes. The conventional way of separating evoked and induced responses assumes that they can be decomposed linearly; in that induced responses are the average of the power minus the power of the average (the evoked component). However, this decomposition may not hold if both components are generated by nonlinear processes. In this work, we propose a Dynamic Causal Model that models evoked and induced responses at the same time. This allows us to explain both components in terms of shared mechanisms (coupling) and changes in coupling that are necessary to explain any induced components. To establish the face validity of our approach, we used Bayesian Model Selection to show that the scheme can disambiguate between models of synthetic data that did and did not contain induced components. We then repeated the analysis using MEG data during a hand grip task to ask whether induced responses in motor control circuits are mediated by 'top-down' or backward connections. Our result provides empirical evidence that induced responses are more likely to reflect backward message passing in the brain, while evoked and induced components share certain characteristics and mechanisms., (Copyright © 2011 Elsevier Inc. All rights reserved.)

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work.

Recent studies have established a relation between ongoing brain activity fluctuations and intertrial variability in evoked neural responses, perception, and motor performance. Here, we extended these investigations into the domain of cognitive control. Using functional neuroimaging and a sparse event-related design (with long and unpredictable intervals), we measured ongoing activity fluctuations and evoked responses in volunteers performing a Stroop task with color-word interference. Across trials, prestimulus activity of several regions predicted subsequent response speed and across subjects this effect scaled with the Stroop effect size, being significant only in subjects manifesting behavioral interference. These effects occurred only in task relevant as the dorsal anterior cingulate and dorsolateral prefrontal cortex as well as ventral visual areas sensitive to color and visual words. Crucially, in subjects showing a Stroop effect, reaction times were faster when prestimulus activity was higher in task-relevant (color) regions and slower when activity was higher in irrelevant (word form) regions. These findings suggest that intrinsic brain activity fluctuations modulate neural mechanisms underpinning selective voluntary attention and cognitive control. Rephrased in terms of predictive coding models, ongoing activity can hence be considered a proxy of the precision (gain) with which prediction error signals are transmitted upon sensory stimulation.

In this work, we show that electrophysiological responses during pitch perception are best explained by distributed activity in a hierarchy of cortical sources and, crucially, that the effective connectivity between these sources is modulated with pitch strength. Local field potentials were recorded in two subjects from primary auditory cortex and adjacent auditory cortical areas along the axis of Heschl's gyrus (HG) while they listened to stimuli of varying pitch strength. Dynamic causal modeling was used to compare system architectures that might explain the recorded activity. The data show that representation of pitch requires an interaction between nonprimary and primary auditory cortex along HG that is consistent with the principle of predictive coding.

In recent years there has been growing interest in multivariate analyses of neuroimaging data, which can be used to detect distributed patterns of activity that encode an experimental factor of interest. In this setting, it has become common practice to study the correlations between patterns to make inferences about the way a brain region represents stimuli or tasks (known as representational similarity analysis). Although it would be of great interest to compare these correlations from different regions, direct comparisons are currently not possible. This is because sample correlations are strongly influenced by voxel-selection, fMRI noise, and nonspecific activation patterns, all of which can differ widely between regions. Here, we present a multivariate modeling framework in which the measured patterns are decomposed into their constituent parts. The model is based on a standard linear mixed model, in which pattern components are considered to be randomly distributed over voxels. The model allows one to estimate the true correlations of the underlying neuronal pattern components, thereby enabling comparisons between different regions or individuals. The pattern estimates also allow us to make inferences about the spatial structure of different response components. Thus, the new model provides a theoretical and analytical framework to study the structure of distributed neural representations., (Copyright © 2011 Elsevier Inc. All rights reserved.)

This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme--both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Perception entails interactions between activated brain visual areas and the records of previous sensations, allowing for processes like figure-ground segregation and object recognition. The aim of this study was to characterize top-down effects that originate in the visual cortex and that are involved in the generation and perception of form. We performed a functional magnetic resonance imaging experiment, where subjects viewed 3 groups of stimuli comprising oriented lines with different levels of recognizable high-order structure (none, collinearity, and meaning). Our results showed that recognizable stimuli cause larger activations in anterior visual and frontal areas. In contrast, when stimuli are random or unrecognizable, activations are greater in posterior visual areas, following a hierarchical organization where areas V1/V2 were less active with "collinearity" and the middle occipital cortex was less active with "meaning." An effective connectivity analysis using dynamic causal modeling showed that high-order visual form engages higher visual areas that generate top-down signals, from multiple levels of the visual hierarchy. These results are consistent with a model in which if a stimulus has recognizable attributes, such as collinearity and meaning, the areas specialized for processing these attributes send top-down messages to the lower levels to facilitate more efficient encoding of visual form.

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

This article reviews the substantial impact computational neuroscience has had on neuroimaging over the past years. It builds on the distinction between models of the brain as a computational machine and computational models of neuronal dynamics per se; i.e., models of brain function and biophysics. Both sorts of model borrow heavily from computational neuroscience, and both have enriched the analysis of neuroimaging data and the type of questions we address. To illustrate the role of functional models in imaging neuroscience, we focus on optimal control and decision (game) theory; the models used here provide a mechanistic account of neuronal computations and the latent (mental) states represent by the brain. In terms of biophysical modelling, we focus on dynamic causal modelling, with a special emphasis on recent advances in neural-mass models for hemodynamic and electrophysiological time series. Each example emphasises the role of generative models, which embed our hypotheses or questions, and the importance of model comparison (i.e., hypothesis testing). We will refer to this theme, when trying to contextualise recent trends in relation to each other., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

Humans have the arguably unique ability to understand the mental representations of others. For success in both competitive and cooperative interactions, however, this ability must be extended to include representations of others' belief about our intentions, their model about our belief about their intentions, and so on. We developed a "stag hunt" game in which human subjects interacted with a computerized agent using different degrees of sophistication (recursive inferences) and applied an ecologically valid computational model of dynamic belief inference. We show that rostral medial prefrontal (paracingulate) cortex, a brain region consistently identified in psychological tasks requiring mentalizing, has a specific role in encoding the uncertainty of inference about the other's strategy. In contrast, dorsolateral prefrontal cortex encodes the depth of recursion of the strategy being used, an index of executive sophistication. These findings reveal putative computational representations within prefrontal cortex regions, supporting the maintenance of cooperation in complex social decision making.

Theories of the positive symptoms of schizophrenia hypothesize a role for aberrant reinforcement signaling driven by dysregulated dopamine transmission. Recently, we provided evidence of aberrant reward learning in symptomatic, but not asymptomatic patients with schizophrenia, using a novel paradigm, the Salience Attribution Test (SAT). The SAT is a probabilistic reward learning game that employs cues that vary across task-relevant and task-irrelevant dimensions; it provides behavioral indices of adaptive and aberrant reward learning. As an initial step prior to future clinical studies, here we used functional magnetic resonance imaging to examine the neural basis of adaptive and aberrant reward learning during the SAT in healthy volunteers. As expected, cues associated with high relative to low reward probabilities elicited robust hemodynamic responses in a network of structures previously implicated in motivational salience; the midbrain, in the vicinity of the ventral tegmental area, and regions targeted by its dopaminergic projections, i.e. medial dorsal thalamus, ventral striatum and prefrontal cortex (PFC). Responses in the medial dorsal thalamus and polar PFC were strongly correlated with the degree of adaptive reward learning across participants. Finally, and most importantly, differential dorsolateral PFC and middle temporal gyrus (MTG) responses to cues with identical reward probabilities were very strongly correlated with the degree of aberrant reward learning. Participants who showed greater aberrant learning exhibited greater dorsolateral PFC responses, and reduced MTG responses, to cues erroneously inferred to be less strongly associated with reward. The results are discussed in terms of their implications for different theories of associative learning., (Copyright 2009 Elsevier Inc. All rights reserved.)

In this, the 21st century, human-brain mapping celebrates 21 years of cognitive activation studies. This review looks at imaging neuroscience and key ideas it has pursued; some ideas portend exciting developments, and others have failed gloriously. In terms of achievements, there is much to celebrate, in the sense that it is difficult to imagine modern neuroscience without brain imaging. I will look at recent advances from the perspectives of functional segregation and integration in the brain, paying special attention to approaches that deal with the distributed and integrated nature of neuronal processing and the questions they address.