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The precise role played by the hippocampus in supporting cognitive functions such as episodic memory and future thinking is debated, but there is general agreement that it involves constructing representations comprised of numerous elements. Visual scenes have been deployed extensively in cognitive neuroscience because they are paradigmatic multi-element stimuli. However, questions remain about the specificity and nature of the hippocampal response to scenes. Here, we devised a paradigm in which we had participants search pairs of images for either colour or layout differences, thought to be associated with perceptual or spatial constructive processes respectively. Importantly, images depicted either naturalistic scenes or phase-scrambled versions of the same scenes, and were either simple or complex. Using this paradigm during functional MRI scanning, we addressed three questions: 1. Is the hippocampus recruited specifically during scene processing? 2. If the hippocampus is more active in response to scenes, does searching for colour or layout differences influence its activation? 3. Does the complexity of the scenes affect its response? We found that, compared to phase-scrambled versions of the scenes, the hippocampus was more responsive to scene stimuli. Moreover, a clear anatomical distinction was evident, with colour detection in scenes engaging the posterior hippocampus whereas layout detection in scenes recruited the anterior hippocampus. The complexity of the scenes did not influence hippocampal activity. These findings seem to align with perspectives that propose the hippocampus is especially attuned to scenes, and its involvement occurs irrespective of the cognitive process or the complexity of the scenes.

In our everyday lives, we are often required to follow a conversation when background noise is present (“speech-in-noise” perception). Speech-in-noise perception varies widely—and people who are worse at speech-in-noise perception are also worse at fundamental auditory grouping, as assessed by figure-ground tasks. Here, we examined the cortical processes that link difficulties with speech-in-noise perception to difficulties with figure-ground perception using functional magnetic resonance imaging (fMRI). We found strong evidence that the earliest stages of the auditory cortical hierarchy (left core and belt areas) are similarly disinhibited when speech-in-noise and figure-ground tasks are more difficult (i.e., at target-to-masker ratios corresponding to 60% than 90% thresholds)—consistent with increased cortical gain at lower levels of the auditory hierarchy. Overall, our results reveal a common neural substrate for these basic (figure-ground) and naturally relevant (speech-in-noise) tasks—which provides a common computational basis for the link between speech-in-noise perception and fundamental auditory grouping.

Adaptive social behavior and mental well-being depend on not only recognizing emotional expressions but also, inferring the absence of emotion. While the neurobiology underwriting the perception of emotions is well studied, the mechanisms for detecting a lack of emotional content in social signals remain largely unknown. Here, using cutting-edge analyses of effective brain connectivity, we uncover the brain networks differentiating neutral and emotional body language. The data indicate greater activation of the right amygdala and midline cerebellar vermis to nonemotional as opposed to emotional body language. Most important, the effective connectivity between the amygdala and insula predicts people's ability to recognize the absence of emotion. These conclusions extend substantially current concepts of emotion perception by suggesting engagement of limbic effective connectivity in recognizing the lack of emotion in body language reading. Furthermore, the outcome may advance the understanding of overly emotional interpretation of social signals in depression or schizophrenia by providing the missing link between body language reading and limbic pathways. The study thus opens an avenue for multidisciplinary research on social cognition and the underlying cerebrocerebellar networks, ranging from animal models to patients with neuropsychiatric conditions.

Selecting hand actions to manipulate an object is affected both by perceptual factors and by action goals. Affordances may contribute to “stimulus–response” congruency effects driven by habitual actions to an object. In previous studies, we have demonstrated an influence of the congruency between hand and object orientations on response times when reaching to turn an object, such as a cup. In this study, we investigated how the representation of hand postures triggered by planning to turn a cup was influenced by this congruency effect, in an fMRI scanning environment. Healthy participants were asked to reach and turn a real cup that was placed in front of them either in an upright orientation or upside–down. They were instructed to use a hand orientation that was either congruent or incongruent with the cup orientation. As expected, the motor responses were faster when the hand and cup orientations were congruent. There was increased activity in a network of brain regions involving object-directed actions during action planning, which included bilateral primary and extrastriate visual, medial, and superior temporal areas, as well as superior parietal, primary motor, and premotor areas in the left hemisphere. Specific activation of the dorsal premotor cortex was associated with hand–object orientation congruency during planning and prior to any action taking place. Activity in that area and its connectivity with the lateral occipito-temporal cortex increased when planning incongruent (goal-directed) actions. The increased activity in premotor areas in trials where the orientation of the hand was incongruent to that of the object suggests a role in eliciting competing representations specified by hand postures in lateral occipito-temporal cortex.

As source of sensory information, the body provides a sense of agency and self/non-self-discrimination. The integration of bodily states and sensory inputs with prior beliefs has been linked to the generation of bodily self-consciousness. The ability to detect surprising tactile stimuli is essential for the survival of an organism and for the formation of mental body representations. Despite the relevance for a variety of psychiatric disorders characterized by altered body and self-perception, the neurobiology of these processes is poorly understood. We therefore investigated the effect of psilocybin (Psi), known to induce alterations in self-experience, on tactile mismatch responses by combining pharmacological manipulations with simultaneous electroencephalography–functional magnetic resonance imaging (EEG–fMRI) recording. Psi reduced activity in response to tactile surprising stimuli in frontal regions, the visual cortex, and the cerebellum. Furthermore, Psi reduced tactile mismatch negativity EEG responses at frontal electrodes, associated with alterations of body- and self-experience. This study provides first evidence that Psi alters the integration of tactile sensory inputs through aberrant prediction error processing and highlights the importance of the 5-HT2A system in tactile deviancy processing as well as in the integration of bodily and self-related stimuli. These findings may have important implications for the treatment of psychiatric disorders characterized by aberrant bodily self-awareness.

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software., Highlights • A method is introduced for Bayesian fusion of M/EEG and BOLD fMRI data using dynamic causal modelling. • The key novel contribution is the introduction of neural drive functions, which link models of the two modalities. • Bayesian model comparison is used to select plausible hypotheses about the function of neurovascular coupling.

The initial encoding of visual information primarily from the contralateral visual field is a fundamental organizing principle of the primate visual system. Recently, the presence of such retinotopic sensitivity has been shown to extend well beyond early visual cortex to regions not historically considered retinotopically sensitive. In particular, human scene-selective regions in parahippocampal and medial parietal cortex exhibit prominent biases for the contralateral visual field. Here, we used fMRI to test the hypothesis that the human hippocampus, which is thought to be anatomically connected with these scene-selective regions, would also exhibit a biased representation of contralateral visual space. First, population receptive field (pRF) mapping with scene stimuli revealed strong biases for the contralateral visual field in bilateral hippocampus. Second, the distribution of retinotopic sensitivity suggested a more prominent representation in anterior medial portions of the hippocampus. Finally, the contralateral bias was confirmed in independent data taken from the Human Connectome Project (HCP) initiative. The presence of contralateral biases in the hippocampus, a structure considered by many as the apex of the visual hierarchy, highlights the truly pervasive influence of retinotopy. Moreover, this finding has important implications for understanding how visual information relates to the allocentric global spatial representations known to be encoded therein.SIGNIFICANCE STATEMENTRetinotopic encoding of visual information is an organizing principle of visual cortex. Recent work demonstrates this sensitivity in structures far beyond early visual cortex, including those anatomically connected to the hippocampus. Here, using population receptive field (pRF) modeling in two independent sets of data we demonstrate a consistent bias for the contralateral visual field in bilateral hippocampus. Such a bias highlights the truly pervasive influence of retinotopy, with important implications for understanding how the presence of retinotopy relates to more allocentric spatial representations.

Background There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). New method In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). Results Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity – as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative – and furnish more efficient estimates. Compared with existing methods In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. Conclusions In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.

Schizophrenia is a disorder characterized by functional dysconnectivity among distributed brain regions. However, it is unclear how causal influences among large-scale brain networks are disrupted in schizophrenia. In this study, we used dynamic causal modeling (DCM) to assess the hypothesis that there is aberrant directed (effective) connectivity within and between three key large-scale brain networks (the dorsal attention network, the salience network and the default mode network) in schizophrenia during a working memory task. Functional MRI data during an n-back task from 40 patients with schizophrenia and 62 healthy controls were analyzed. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes, we found that intrinsic (within-region) and extrinsic (between-region) effective connectivity involving prefrontal regions were abnormal in schizophrenia. Specifically, in patients (i) inhibitory self-connections in prefrontal regions of the dorsal attention network were decreased across task conditions; (ii) extrinsic connectivity between regions of the default mode network was increased; specifically, from posterior cingulate cortex to the medial prefrontal cortex; (iii) between-network extrinsic connections involving the prefrontal cortex were altered; (iv) connections within networks and between networks were correlated with the severity of clinical symptoms and impaired cognition beyond working memory. In short, this study revealed the predominance of reduced synaptic efficacy of prefrontal efferents and afferents in the pathophysiology of schizophrenia., Highlights • A first use of hierarchical modeling of effective connectivity to characterize large-scale networks in schizophrenia. • Intrinsic and extrinsic effective connectivity involving prefrontal regions were abnormal in schizophrenia. • Diagnostic connections could predict the severity of clinical symptoms and cognition in schizophrenia.

Contains fulltext : 175110.pdf (Publisher’s version ) (Open Access) Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally driven visual experience. 9 p.

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality., Highlights • This guide walks through a group effective connectivity study using DCM and PEB. • Part 1, presented here, covers first level analysis using DCM for fMRI. • It clarifies the specific neural and haemodynamic models in DCM and their priors. • An accompanying dataset is provided with step-by-step analysis instructions.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

Neuroimaging studies in social neuroscience have largely relied on functional connectivity (FC) methods to characterize the functional integration between different brain regions. However, these methods have limited utility in social-cognitive studies that aim to understand the directed information flow among brain areas that underlies complex psychological processes. In this study we combined functional and effective connectivity approaches to characterize the functional integration within the Default Mode Network (DMN) and its role in self-perceived empathy. Forty-two participants underwent a resting state fMRI scan and completed a questionnaire of dyadic empathy. Independent Component Analysis (ICA) showed that higher empathy scores were associated with an increased contribution of the medial prefrontal cortex (mPFC) to the DMN spatial mode. Dynamic causal modelling (DCM) combined with Canonical Variance Analysis (CVA) revealed that this association was mediated indirectly by the posterior cingulate cortex (PCC) via the right inferior parietal lobule (IPL). More specifically, in participants with higher scores in empathy, the PCC had a greater effect on bilateral IPL and the right IPL had a greater influence on mPFC. These results highlight the importance of using analytic approaches that address directed and hierarchical connectivity within networks, when studying complex psychological phenomena, such as empathy., This study was funded by BIAL Foundation (Grant number 87/12); by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653); by the postdoctoral scholarship UMINHO/BPD/18/2017 and by the Portuguese Foundation for Science Doctoral scholarship (PD/BD/105963/2014). This work was conducted at Psychology Research Centre (UID/PSI/01662/2013), University of Minho.

The perception of actions underwrites a wide range of socio-cognitive functions. Previous neuroimaging and lesion studies identified several components of the brain network for visual biological motion (BM) processing, but interactions among these components and their relationship to behavior remain little understood. Here, using a recently developed integrative analysis of structural and effective connectivity derived from high angular resolution diffusion imaging (HARDI) and functional magnetic resonance imaging (fMRI), we assess the cerebro-cerebellar network for processing of camouflaged point-light BM. Dynamic causal modeling (DCM) informed by probabilistic tractography indicates that the right superior temporal sulcus (STS) serves as an integrator within the temporal module. However, the STS does not appear to be a "gatekeeper" in the functional integration of the occipito-temporal and frontal regions: The fusiform gyrus (FFG) and middle temporal cortex (MTC) are also connected to the right inferior frontal gyrus (IFG) and insula, indicating multiple parallel pathways. BM-specific loops of effective connectivity are seen between the left lateral cerebellar lobule Crus I and right STS, as well as between the left Crus I and right insula. The prevalence of a structural pathway between the FFG and STS is associated with better BM detection. Moreover, a canonical variate analysis shows that the visual sensitivity to BM is best predicted by BM-specific effective connectivity from the FFG to STS and from the IFG, insula, and STS to the early visual cortex. Overall, the study characterizes the architecture of the cerebro-cerebellar network for BM processing and offers prospects for assessing the social brain.

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., Highlights • We introduce variational RSA, a method for multivariate analysis in neuroimaging. • This treats RSA a standard covariance component estimation problem. • An efficient estimation scheme, variational Laplace, is used to estimate parameters. • Bayesian model comparison is used to optimally test for mixtures of effects. • We illustrate the approach using simulated and empirical fMRI data.

Previous functional MRI (fMRI) studies have associated anterior hippocampus with imagining and recalling scenes, imagining the future, recalling autobiographical memories and visual scene perception. We have observed that this typically involves the medial rather than the lateral portion of the anterior hippocampus. Here, we investigated which specific structures of the hippocampus underpin this observation. We had participants imagine novel scenes during fMRI scanning, as well as recall previously learned scenes from two different time periods (one week and 30 min prior to scanning), with analogous single object conditions as baselines. Using an extended segmentation protocol focussing on anterior hippocampus, we first investigated which substructures of the hippocampus respond to scenes, and found both imagination and recall of scenes to be associated with activity in presubiculum/parasubiculum, a region associated with spatial representation in rodents. Next, we compared imagining novel scenes to recall from one week or 30 min before scanning. We expected a strong response to imagining novel scenes and 1-week recall, as both involve constructing scene representations from elements stored across cortex. By contrast, we expected a weaker response to 30-min recall, as representations of these scenes had already been constructed but not yet consolidated. Both imagination and 1-week recall of scenes engaged anterior hippocampal structures (anterior subiculum and uncus respectively), indicating possible roles in scene construction. By contrast, 30-min recall of scenes elicited significantly less activation of anterior hippocampus but did engage posterior CA3. Together, these results elucidate the functions of different parts of the anterior hippocampus, a key brain area about which little is definitely known.

Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood.To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs.Functional magnetic resonance imaging (fMRI) data were collected on 32 individuals with moderate MDD and 20 control participants who performed a probabilistic reward task. A risky decision task with repeated happiness ratings as a measure of momentary mood was also tested in the laboratory in 74 participants and with a smartphone-based platform in 1833 participants. The study was conducted from November 20, 2012, to February 17, 2015.Blood oxygen level-dependent activity was measured in ventral striatum, a dopamine target area known to represent RPEs. Momentary mood was measured during risky decision making.Of the 52 fMRI participants (mean [SD] age, 34.0 [9.1] years), 30 (58%) were women and 32 had MDD. Of the 74 participants in the laboratory risky decision task (mean age, 34.2 [10.3] years), 44 (59%) were women and 54 had MDD. Of the smartphone group, 543 (30%) had a depression history and 1290 (70%) had no depression history; 918 (50%) were women, and 593 (32%) were younger than 30 years. Contrary to previous results in reinforcement learning tasks, individuals with moderate depression showed intact RPE signals in ventral striatum (z = 3.16; P = .002) that did not differ significantly from controls (z = 0.91; P = .36). Symptom severity correlated with baseline mood parameters in laboratory (ρ = -0.54; P 1 × 10-6) and smartphone (ρ = -0.30; P 1 × 10-39) data. However, participants with depression showed an intact association between RPEs and happiness in a computational model of momentary mood dynamics (z = 4.55; P .001) that was not attenuated compared with controls (z = -0.42; P = .67).The neural and emotional impact of RPEs is intact in major depression. These results suggest that depression does not affect the expression of dopaminergic RPEs and that attenuated RPEs in previous reports may reflect downstream effects more closely related to aberrant behavior. The correlation between symptom severity and baseline mood parameters supports an association between depression and momentary mood fluctuations during cognitive tasks. These results demonstrate a potential for smartphones in large-scale computational phenotyping, which is a goal for computational psychiatry.

Understanding the brain mechanisms involved in diagnostic reasoning may contribute to the development of methods that reduce errors in medical practice. In this study we identified similar brain systems for diagnosing diseases, prescribing treatments, and naming animals and objects using written information as stimuli. Employing time resolved modeling of blood oxygen level dependent (BOLD) responses enabled time resolved (400 milliseconds epochs) analyses. With this approach it was possible to study neural processes during successive stages of decision making. Our results showed that highly diagnostic information, reducing uncertainty about the diagnosis, decreased monitoring activity in the frontoparietal attentional network and may contribute to premature diagnostic closure, an important cause of diagnostic errors. We observed an unexpected and remarkable switch of BOLD activity within a right lateralized set of brain regions related to awareness and auditory monitoring at the point of responding. We propose that this neurophysiological response is the neural substrate of awareness of one’s own (verbal) response. Our results highlight the intimate relation between attentional mechanisms, uncertainty, and decision making and may assist the advance of approaches to prevent premature diagnostic closure.

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.

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user’s needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users., NeuroImage, 156, ISSN:1053-8119, ISSN:1095-9572

The left ventral occipito-temporal cortex (LvOT) is thought to be essential for the rapid parallel letter processing that is required for skilled reading. Here we investigate whether rapid written word identification in skilled readers can be supported by neural pathways that do not involve LvOT. Hypotheses were derived from a stroke patient who acquired dyslexia following extensive LvOT damage. The patient followed a reading trajectory typical of that associated with pure alexia, re-gaining the ability to read aloud many words with declining performance as the length of words increased. Using functional MRI and dynamic causal modelling (DCM), we found that, when short (three to five letter) familiar words were read successfully, visual inputs to the patient’s occipital cortex were connected to left motor and premotor regions via activity in a central part of the left superior temporal sulcus (STS). The patient analysis therefore implied a left hemisphere “reading-without-LvOT” pathway that involved STS. We then investigated whether the same reading-without-LvOT pathway could be identified in 29 skilled readers and whether there was inter-subject variability in the degree to which skilled reading engaged LvOT. We found that functional connectivity in the reading-without-LvOT pathway was strongest in individuals who had the weakest functional connectivity in the LvOT pathway. This observation validates the findings of our patient’s case study. Our findings highlight the contribution of a left hemisphere reading pathway that is activated during the rapid identification of short familiar written words, particularly when LvOT is not involved. Preservation and use of this pathway may explain how patients are still able to read short words accurately when LvOT has been damaged., Highlights ► Word reading can succeed after damage to the left occipito-temporal cortex. ► This is enabled by an alternative pathway via the left superior temporal sulcus. ► Connectivity analysis demonstrated the existence of this pathway in normal readers. ► We hypothesise that this pathway integrates semantics with phonology. ► Our work stresses the importance of mapping alternative degenerate reading pathways.

We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental stimuli enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance/covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their log model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which is taken into account by the Bayesian methods we describe when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7 T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain., Highlights • We introduce a Bayesian toolbox for population receptive field (pRF) mapping. • Neuronal and haemodynamic parameters are estimated per voxel or per region. • Hypotheses can be tested by comparing pRF models based on their evidence. • The uncertainty over parameters (such as pRF size) is estimated and visualised. • We establish face validity using simulations and test-rest reliability with 7 T fMRI.

The posterior parahippocampal cortex (PHC) supports a range of cognitive functions, in particular scene processing. However, it has recently been suggested that PHC engagement during functional MRI simply reflects the representation of three-dimensional local space. If so, PHC should respond to space in the absence of scenes, geometric layout, objects or contextual associations. It has also been reported that PHC activation may be influenced by low-level visual properties of stimuli such as spatial frequency. Here, we tested whether PHC was responsive to the mere sense of space in highly simplified stimuli, and whether this was affected by their spatial frequency distribution. Participants were scanned using functional MRI while viewing depictions of simple three-dimensional space, and matched control stimuli that did not depict a space. Half the stimuli were low-pass filtered to ascertain the impact of spatial frequency. We observed a significant interaction between space and spatial frequency in bilateral PHC. Specifically, stimuli depicting space (more than nonspatial stimuli) engaged the right PHC when they featured high spatial frequencies. In contrast, the interaction in the left PHC did not show a preferential response to space. We conclude that a simple depiction of three-dimensional space that is devoid of objects, scene layouts or contextual associations is sufficient to robustly engage the right PHC, at least when high spatial frequencies are present. We suggest that coding for the presence of space may be a core function of PHC, and could explain its engagement in a range of tasks, including scene processing, where space is always present.