Computational and dynamic models in neuroimaging

Pain and Reorganization after Amputation: Is Interoceptive Prediction a Key?

There is an ongoing discussion on the relevance of brain reorganization following amputation for phantom limb pain. Recent attempts to provide explanations for seemingly controversial findings-specifically, maladaptive plasticity versus persistent functional representation as a complementary process-acknowledged that reorganization in the primary somatosensory cortex is not sufficient to explain phantom limb pain satisfactorily. Here we provide theoretical considerations that might help integrate the data reviewed and suppose a possible additional driver of the development of phantom limb pain-namely, an error in interoceptive predictions to somatosensory sensations and movements of the missing limb. Finally, we derive empirically testable consequences based on our considerations to guide future research.

Effective Connectivity within the Papez Circuit in the Multiple Sclerosis Patients: A Comparative Study Using Resting-State fMRI

Background

Multiple sclerosis (MS) disease causes structural and functional damage to brain. Structural imaging of the MS-induced damage cannot adequately describe the functional impairment of the brain in MS patients. Therefore, it seems that advanced functional imaging analysis such as functional magnetic resonance imaging (fMRI) data is needed for better management of this disease.

Objective

The aim of present study was to evaluate the effective connectivity within the Papez circuit in MS patients using resting-state fMRI.

Material and Methods

In this cross-sectional analytical study, 22 healthy individuals and 24 patients with MS underwent resting-state fMRI. After pre-processing of the obtained data, the time series of Cingulate gyrus (CG), Para hippocampus gyrus (PHG), anterior thalamic nuclei (ATN), Mammillary body (MB), and Hippocampus (HPC) were extracted as the main Papez circuit components. The obtained time series were statistically analyzed as an input of the dynamic causal model in order to evaluate the effective connectivity in the Papez circuit.

Results

The power of effective connectivity between each pair of tested nodes in Papez circuit was significantly lower in MS patients than healthy subjects. Also, the effective connectivity level of MS patients in direction of HPC→ATN was higher in men than women. In addition, effective self-connection in CG→CG and MB→MB regions in healthy subjects were higher in women than them in men.

Conclusion

The present study reveals significant difference in effective connectivity of the Papez nodes in MS patients than control group, which can be exploited to diagnosis and predict or evaluate the treatment response of these patients.

Time-of-Day Effects in Resting-State Functional Magnetic Resonance Imaging: Changes in Effective Connectivity and Blood Oxygenation Level Dependent Signal

Introduction:

In the light of the ongoing replication crisis in the field of neuroimaging, it is necessary to assess the possible exogenous and endogenous factors that may affect functional magnetic resonance imaging (fMRI). The current project investigated time-of-day effects in the spontaneous fluctuations (<0.1 Hz) of the blood oxygenation level dependent (BOLD) signal.

Method:

Using data from the human connectome project release S1200, cross-spectral density dynamic causal modeling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. The DCM analysis covered three networks, namely the default mode network, the central executive network, and the saliency network. Hierarchical group-parametric empirical Bayes (PEB) was used to test varying design-matrices against the time-of-day model.

Results:

Hierarchical group-PEB found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect, indicating a diurnal vascular effect that might affect the measured BOLD signal in the absence of any diurnal variations of the underlying neuronal activations and effective connectivity.

Conclusion:

We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies and suggest that time-of-day dependent metabolic variations contribute to reduced reliability in resting-state fMRI studies.

Impact statement

The results from this study suggest that the circadian mechanism influences the blood oxygenation level dependent signal in resting-state functional magnetic resonance imaging (fMRI). The current study urges to record and report the time of fMRI scan acquisition in future research, as it may increase the replicability of findings. Both exploratory and clinical studies would benefit by incorporating this small change in fMRI protocol, which to date has been often overlooked.

Evaluation of movement and brain activity

Clinical neurophysiology studies can contribute important information about the physiology of human movement and the pathophysiology and diagnosis of different movement disorders. Some techniques can be accomplished in a routine clinical neurophysiology laboratory and others require some special equipment. This review, initiating a series of articles on this topic, focuses on the methods and techniques. The methods reviewed include EMG, EEG, MEG, evoked potentials, coherence, accelerometry, posturography (balance), gait, and sleep studies. Functional MRI (fMRI) is also reviewed as a physiological method that can be used independently or together with other methods. A few applications to patients with movement disorders are discussed as examples, but the detailed applications will be the subject of other articles.

Human Cognitive Neuroscience as It Is Taught

Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering, and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n = 307) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analyzed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, and Biology), current researcher status (undergraduate student, PhD student, and post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods vs. non-methods (Psychology/Biology) groups was especially striking for questions related to signal analysis and linear algebra, two areas particularly relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programs in cognitive neuroscience institutions. We will provide practical suggestions on how to achieve this.

Resting-state effective connectivity in the motive circuit of methamphetamine users: A case controlled fMRI study

Methamphetamine (MA) and other psychostimulants target the motive circuit of the brain, which is involved in reward, behavioral sensitization, and relapse to drug-seeking/taking behavior. In spite of this fact, the data regarding the effective connectivity (EC) in this circuit among MA users is scarce. The present study aimed to assess resting-state EC in the motive circuit of MA users during abstinence using the fMRI technique. Seventeen MA users after abstinence and 18 normal controls were examined using a 3 T Siemens fMRI scanner. After extracting time series of the motive circuit, EC differences in the motive circuit were analyzed using dynamic causal modeling (DCM). The findings revealed that abstinent MA users had an enhanced EC from the prefrontal cortex (PFC) to the ventral palladium (VP) (PFC→VP) and on the mediodorsal thalamus (MD) self-loop (MD→MD), but they showed a decreased connectivity on the VP self-loop (VP→VP) compared to healthy controls. The findings suggest that abstinent MA users may suffer from a limited pathology in connectivity within the motive circuit involved in reward, behavioral sensitization, and relapse. The enhanced PFC→VP seems to be a compensatory mechanism to control or regulate the subcortical regions involved in reward and behavioral sensitization. Furthermore, the enhanced connectivity on the MD self-loop and the decreased connectivity on the VP self-loop in abstinent MA users may, at least partially, affect the output of the limbic system, which can be seen in the behavioral sensitization and relapse processes. Nonetheless, further investigation in this area is strongly recommended to elucidate the exact mechanisms involved.

Under the Mind's Hood: What We Have Learned by Watching the Brain at Work

Imagine you were asked to investigate the workings of an engine, but to do so without ever opening the hood. Now imagine the engine fueled the human mind. This is the challenge faced by cognitive neuroscientists worldwide aiming to understand the neural bases of our psychological functions. Luckily, human ingenuity comes to the rescue. Around the same time as the Society for Neuroscience was being established in the 1960s, the first tools for measuring the human brain at work were becoming available. Noninvasive human brain imaging and neurophysiology have continued developing at a relentless pace ever since. In this 50 year anniversary, we reflect on how these methods have been changing our understanding of how brain supports mind.

PyRates-A Python framework for rate-based neural simulations

In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

Language and Sensory Neural Plasticity in the Superior Temporal Cortex of the Deaf

Visual stimuli are known to activate the auditory cortex of deaf people, presenting evidence of cross-modal plasticity. However, the mechanisms underlying such plasticity are poorly understood. In this functional MRI study, we presented two types of visual stimuli, language stimuli (words, sign language, and lip-reading) and a general stimulus (checkerboard) to investigate neural reorganization in the superior temporal cortex (STC) of deaf subjects and hearing controls. We found that only in the deaf subjects, all visual stimuli activated the STC. The cross-modal activation induced by the checkerboard was mainly due to a sensory component via a feed-forward pathway from the thalamus and primary visual cortex, positively correlated with duration of deafness, indicating a consequence of pure sensory deprivation. In contrast, the STC activity evoked by language stimuli was functionally connected to both the visual cortex and the frontotemporal areas, which were highly correlated with the learning of sign language, suggesting a strong language component via a possible feedback modulation. While the sensory component exhibited specificity to features of a visual stimulus (e.g., selective to the form of words, bodies, or faces) and the language (semantic) component appeared to recruit a common frontotemporal neural network, the two components converged to the STC and caused plasticity with different multivoxel activity patterns. In summary, the present study showed plausible neural pathways for auditory reorganization and correlations of activations of the reorganized cortical areas with developmental factors and provided unique evidence towards the understanding of neural circuits involved in cross-modal plasticity.

Verbal working memory and functional large-scale networks in schizophrenia

The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia.

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

Probabilistic delay differential equation modeling of event-related potentials

"Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach.

Anterior insula coordinates hierarchical processing of tactile mismatch responses

The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy-projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace.

Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis

Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science.

Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models

The mechanisms that link a transient neural activity to the corresponding increase of cerebral blood flow (CBF) are termed neurovascular coupling (NVC). They are possibly impaired at early stages of small vessel or neurodegenerative diseases. Investigation of NVC in humans has been made possible with the development of various neuroimaging techniques based on variations of local hemodynamics during neural activity. Specific dynamic models are currently used for interpreting these data that can include biophysical parameters related to NVC. After a brief review of the current knowledge about possible mechanisms acting in NVC we selected seven models with explicit integration of NVC found in the literature. All these models were described using the same procedure. We compared their physiological assumptions, mathematical formalism, and validation. In particular, we pointed out their strong differences in terms of complexity. Finally, we discussed their validity and their potential applications. These models may provide key information to investigate various aspects of NVC in human pathology.

The case for neuropsychoanalysis: Why a dialogue with neuroscience is necessary but not sufficient for psychoanalysis

Recent advances in the cognitive, affective and social neurosciences have enabled these fields to study aspects of the mind that are central to psychoanalysis. These developments raise a number of possibilities for psychoanalysis. Can it engage the neurosciences in a productive and mutually enriching dialogue without compromising its own integrity and unique perspective? While many analysts welcome interdisciplinary exchanges with the neurosciences, termed neuropsychoanalysis, some have voiced concerns about their potentially deleterious effects on psychoanalytic theory and practice. In this paper we outline the development and aims of neuropsychoanalysis, and consider its reception in psychoanalysis and in the neurosciences. We then discuss some of the concerns raised within psychoanalysis, with particular emphasis on the epistemological foundations of neuropsychoanalysis. While this paper does not attempt to fully address the clinical applications of neuropsychoanalysis, we offer and discuss a brief case illustration in order to demonstrate that neuroscientific research findings can be used to enrich our models of the mind in ways that, in turn, may influence how analysts work with their patients. We will conclude that neuropsychoanalysis is grounded in the history of psychoanalysis, that it is part of the psychoanalytic worldview, and that it is necessary, albeit not sufficient, for the future viability of psychoanalysis.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

Lag threads organize the brain's intrinsic activity

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term "lag threads," are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

Optimal inference with suboptimal models: addiction and active Bayesian inference

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent's beliefs - based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment - as opposed to the agent's beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less 'optimally' than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject's generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described 'limited offer' task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work.

On self-feedback connectivity in neural mass models applied to event-related potentials

Neural mass models (NMMs) applied to neuroimaging data often do not emphasise intrinsic self-feedback within a neural population. However, based on mean-field theory, any population of coupled neurons is intrinsically endowed with effective self-coupling. In this work, we examine the effectiveness of three cortical NMMs with different self-feedbacks using a dynamic causal modelling approach. Specifically, we compare the classic Jansen and Rit (1995) model (no self-feedback), a modified model by Moran et al. (2007) (only inhibitory self-feedback), and our proposed model with inhibitory and excitatory self-feedbacks. Using bifurcation analysis, we show that single-unit Jansen-Rit model is less robust in generating oscillatory behaviour than the other two models. Next, under Bayesian inversion, we simulate single-channel event-related potentials (ERPs) within a mismatch negativity auditory oddball paradigm. We found fully self-feedback model (FSM) to provide the best fit to single-channel data. By analysing the posterior covariances of model parameters, we show that self-feedback connections are less sensitive to the generated evoked responses than the other model parameters, and hence can be treated analogously to "higher-order" parameter corrections of the original Jansen-Rit model. This is further supported in the more realistic multi-area case where FSM can replicate data better than JRM and MoM in the majority of subjects by capturing the finer features of the ERP data more accurately. Our work informs how NMMs with full self-feedback connectivity are not only more consistent with the underlying neurophysiology, but can also account for more complex features in ERP data.

Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of "constructs" as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD.

Uncertainty in perception and the Hierarchical Gaussian Filter

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder–Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient—but at the same time intuitive—framework for the resolution of perceptual uncertainty in behaving agents.

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG–fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake–sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.

A neuron-inspired computational architecture for spatiotemporal visual processing: real-time visual sensory integration for humanoid robots

In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to ∼+6%).

Estimating neural response functions from fMRI

This paper proposes a methodology for estimating Neural Response Functions (NRFs) from fMRI data. These NRFs describe non-linear relationships between experimental stimuli and neuronal population responses. The method is based on a two-stage model comprising an NRF and a Hemodynamic Response Function (HRF) that are simultaneously fitted to fMRI data using a Bayesian optimization algorithm. This algorithm also produces a model evidence score, providing a formal model comparison method for evaluating alternative NRFs. The HRF is characterized using previously established "Balloon" and BOLD signal models. We illustrate the method with two example applications based on fMRI studies of the auditory system. In the first, we estimate the time constants of repetition suppression and facilitation, and in the second we estimate the parameters of population receptive fields in a tonotopic mapping study.

Rich club organization supports a diverse set of functional network configurations

Brain function relies on the flexible integration of a diverse set of segregated cortical modules, with the structural connectivity of the brain being a fundamentally important factor in shaping the brain's functional dynamics. Following up on macroscopic studies showing the existence of centrally connected nodes in the mammalian brain, combined with the notion that these putative brain hubs may form a dense interconnected 'rich club' collective, we hypothesized that brain connectivity might involve a rich club type of architecture to promote a repertoire of different and flexibly accessible brain functions. With the rich club suggested to play an important role in global brain communication, examining the effects of a rich club organization on the functional repertoire of physical systems in general, and the brain in particular, is of keen interest. Here we elucidate these effects using a spin glass model of neural networks for simulating stable configurations of cortical activity. Using simulations, we show that the presence of a rich club increases the set of attractors and hence the diversity of the functional repertoire over and above the effects produced by scale free type topology alone. Within the networks' overall functional repertoire rich nodes are shown to be important for enabling a high level of dynamic integrations of low-degree nodes to form functional networks. This suggests that the rich club serves as an important backbone for numerous co-activation patterns among peripheral nodes of the network. In addition, applying the spin glass model to empirical anatomical data of the human brain, we show that the positive effects on the functional repertoire attributed to the rich club phenomenon can be observed for the brain as well. We conclude that a rich club organization in network architectures may be crucial for the facilitation and integration of a diverse number of segregated functions.

Lag structure in resting-state fMRI

The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.

Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself.

Action and language mechanisms in the brain: data, models and neuroinformatics

We assess the challenges of studying action and language mechanisms in the brain, both singly and in relation to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and empirical data that serve systems and cognitive neuroscience.

Computational neuropsychiatry - schizophrenia as a cognitive brain network disorder

Computational modeling of functional brain networks in fMRI data has advanced the understanding of higher cognitive function. It is hypothesized that functional networks mediating higher cognitive processes are disrupted in people with schizophrenia. In this article, we review studies that applied measures of functional and effective connectivity to fMRI data during cognitive tasks, in particular working memory fMRI studies. We provide a conceptual summary of the main findings in fMRI data and their relationship with neurotransmitter systems, which are known to be altered in individuals with schizophrenia. We consider possible developments in computational neuropsychiatry, which are likely to further our understanding of how key functional networks are altered in schizophrenia.

Machine learning approaches: from theory to application in schizophrenia

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

A neurocomputational model of the mismatch negativity

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering--a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude--and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and--more generally--illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

Beyond the lesion: neuroimaging foundations for post-stroke recovery

A shift is emerging in the way in which we view post-stroke recovery. This shift, supported by evidence from neuroimaging studies, encourages us to look beyond the lesion and to identify viable brain networks with capacity for plasticity. In this article, the authors review current advances in neuroimaging techniques and the new insights that they have contributed. The ability to quantify salvageable tissue, evidence of changes in remote networks, changes of functional and structural connectivity, and alterations in cortical thickness are reviewed in the context of their impact on post-stroke recovery. The value of monitoring spared structural connections and functional connectivity of brain networks within and across hemispheres is highlighted.

Imaging pain: a potent means for investigating pain mechanisms in patients

Chronic pain is a state of physical suffering strongly associated with feelings of anxiety, depression and despair. Disease pathophysiology, psychological state, and social milieu can influence chronic pain, but can be difficult to diagnose based solely on clinical presentation. Here, we review brain neuroimaging research that is shaping our understanding of pain mechanisms, and consider how such knowledge might lead to useful diagnostic tools for the management of persistent pain in individual patients.

Uncertainty increases pain: evidence for a novel mechanism of pain modulation involving the periaqueductal gray

Predictions about sensory input exert a dominant effect on what we perceive, and this is particularly true for the experience of pain. However, it remains unclear what component of prediction, from an information-theoretic perspective, controls this effect. We used a vicarious pain observation paradigm to study how the underlying statistics of predictive information modulate experience. Subjects observed judgments that a group of people made to a painful thermal stimulus, before receiving the same stimulus themselves. We show that the mean observed rating exerted a strong assimilative effect on subjective pain. In addition, we show that observed uncertainty had a specific and potent hyperalgesic effect. Using computational functional magnetic resonance imaging, we found that this effect correlated with activity in the periaqueductal gray. Our results provide evidence for a novel form of cognitive hyperalgesia relating to perceptual uncertainty, induced here by vicarious observation, with control mediated by the brainstem pain modulatory system.

The impact of neurodegeneration on network connectivity: a study of change detection in frontotemporal dementia

The neural response to unpredictable auditory events is suggested to depend on frontotemporal interactions. We used magnetoencephalography in patients with behavioral variant frontotemporal dementia to study change detection and to examine the impact of disease on macroscopic network connectivity underlying this core cognitive function. In patients, the amplitudes of auditory cortical responses to predictable standard tones were normal but were reduced for unpredictable deviant tones. Network connectivity, in terms of coherence among frontal, temporal, and parietal sources, was also abnormal in patients. In the beta frequency range, left frontotemporal coherence was reduced. In the gamma frequency range, frontal interhemispheric coherence was reduced whereas parietal interhemispheric coherence was enhanced. These results suggest impaired change detection resulting from dysfunctional frontotemporal interactions. They also provide evidence of a rostro-caudal reorganization of brain networks in disease. The sensitivity of magnetoencephalography to cortical network changes in behavioral variant frontotemporal dementia enriches the understanding of neurocognitive systems as well as showing potential for studies of experimental therapies for neurodegenerative disease.

Functional and effective connectivity: a review

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

The human somatosensory system: from perception to decision making

Pioneering human and animal research has yielded a better understanding of the brain networks involved in somatosensory perception and decision making. New methodical achievements in combination with computational formalization allow research questions to be addressed which increasingly reflect not only the complex sensory demands of real environments, but also the cognitive ones. Here, we review the latest research on somatosensory perception and decision making with a special focus on the recruitment of supplementary brain networks which are dependent on the situation-associated sensory and cognitive demands. We also refer to literature on sensory-motor integration processes during visual decision making to delineate the complexity and dynamics of how sensory information is relayed to the motor output system. Finally, we review the latest literature which provides novel evidence that other everyday life situations, such as semantic decision making or social interactions, appear to depend on tactile experiences; suggesting that the sense of touch, being the first sense to develop ontogenetically, may essentially support later development of other conceptual knowledge.

Structural imaging techniques in schizophrenia

OBJECTIVE

The aim of this overview study is to translate the technical terminology regarding structural Magnetic Resonance Imaging (sMRI) post-processing analysis into a clinical clear description.

METHOD

We resumed and explained the most popular post-processing methods for structural MRI (sMRI) data applied in psychiatry and their main contributions to the comprehension of the biological basis of schizophrenia.

RESULTS

The region-of-interest (ROI) technique allows to investigate specific brain region size by manual tracing; it is anatomically precise and requires a priori hypothesis, but also it is time-consuming and operator-dependent. The voxel-based morphometry (VBM) detects gray matter density across the whole brain by comparing voxel to voxel; it is operator-independent, does not require a priori hypothesis, and is relatively fast; however, it is limited by multiple comparisons and poor anatomical definition. Finally, computational neuroanatomical analyses have recently been applied to automatically discriminate subjects with schizophrenia from healthy subjects on the basis of MRI images.

CONCLUSION

Structural MRI represents a useful tool in understanding the biological underpinnings of schizophrenia and in planning focused interventions, thus assisting clinicians especially in the early phases of the illness.

Time-delayed mutual information of the phase as a measure of functional connectivity

We propose a time-delayed mutual information of the phase for detecting nonlinear synchronization in electrophysiological data such as MEG. Palus already introduced the mutual information as a measure of synchronization. To obtain estimates on small data-sets as reliably as possible, we adopt the numerical implementation as proposed by Kraskov and colleagues. An embedding with a parametric time-delay allows a reconstruction of arbitrary nonstationary connective structures--so-called connectivity patterns--in a wide class of systems such as coupled oscillatory or even purely stochastic driven processes. By using this method we do not need to make any assumptions about coupling directions, delay times, temporal dynamics, nonlinearities or underlying mechanisms. For verifying and refining the methods we generate synthetic data-sets by a mutual amplitude coupled network of Rössler oscillators with an a-priori known connective structure. This network is modified in such a way, that the power-spectrum forms a 1/f power law, which is also observed in electrophysiological recordings. The functional connectivity measure is tested on robustness to additive uncorrelated noise and in discrimination of linear mixed input data. For the latter issue a suitable de-correlation technique is applied. Furthermore, the compatibility to inverse methods for a source reconstruction in MEG such as beamforming techniques is controlled by dedicated dipole simulations. Finally, the method is applied on an experimental MEG recording.

Critical fluctuations in cortical models near instability

Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where non-linearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power law scaling, and bistable switching have been suggested as generic indicators of the approach to bifurcation in non-linear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen–Rit model) of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations.

Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective

A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment.

Evidence for neural encoding of Bayesian surprise in human somatosensation

Accumulating empirical evidence suggests a role of Bayesian inference and learning for shaping neural responses in auditory and visual perception. However, its relevance for somatosensory processing is unclear. In the present study we test the hypothesis that cortical somatosensory processing exhibits dynamics that are consistent with Bayesian accounts of brain function. Specifically, we investigate the cortical encoding of Bayesian surprise, a recently proposed marker of Bayesian perceptual learning, using EEG data recorded from 15 subjects. Capitalizing on a somatosensory mismatch roving paradigm, we performed computational single-trial modeling of evoked somatosensory potentials for the entire peri-stimulus time period in source space. By means of Bayesian model selection, we find that, at 140 ms post-stimulus onset, secondary somatosensory cortex represents Bayesian surprise rather than stimulus change, which is the conventional marker of EEG mismatch responses. In contrast, at 250 ms, right inferior frontal cortex indexes stimulus change. Finally, at 360 ms, our analyses indicate additional perceptual learning attributable to medial cingulate cortex. In summary, the present study provides novel evidence for anatomical-temporal/functional segregation in human somatosensory processing that is consistent with the Bayesian brain hypothesis.

When two become one: the limits of causality analysis of brain dynamics

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.

Distributed processing; distributed functions?

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.