Can Feelings "Feel" Wrong? Similarities Between Counter-Normative Emotion Reports and Perceptual Errors.

In popular belief, emotions are regarded as deeply subjective and thus as lacking truth value. Is this reflected at the behavioral or brain level? This work compared counter-normative emotion reports with perceptual-decision errors. Participants (university students; N = 29, 16, 40, and 60 in Experiments 1-4, respectively) were given trials comprising two tasks and were asked to (a) report their pleasant or unpleasant feelings in response to emotion-invoking pictures (emotion report) and (b) indicate the gender of faces (perceptual decision). Focusing on classical error markers, we found that the results of both tasks indicated (a) post-error slowing, (b) speed/accuracy trade-offs, (c) a heavier right tail of the reaction time distribution for errors or counter-normative responses relative to correct or normative responses, and (d) inconclusive evidence for error-related negativity in electroencephalograms. These results suggest that at both the behavioral and the brain levels, the experience of reporting counter-normative emotions is remarkably similar to that accompanying perceptual-decision errors.

Multiscale criticality measures as general-purpose gauges of proper brain function.

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). MsCr measures reflect the behavior of conventional criticality measures (such as the branching parameter) across temporal scale. We applied MsCr to MEG and EEG data in several telling degraded information processing scenarios. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity: MsCr fingerprints showed deviance in the four states of compromised information processing examined in this study, disorders of consciousness, mild cognitive impairment, schizophrenia and even during pre-ictal activity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

EEG-Based Prediction of Cognitive Load in Intelligence Tests.

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well.

Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different.

Ongoing internal cortical activity plays a major role in perception and behavior both in animals and humans. Previously we have shown that spontaneous patterns resembling orientation-maps appear over large cortical areas in the primary visual-cortex of anesthetized cats. However, it remains unknown 1) whether spontaneous-activity in the primate also displays similar patterns and 2) whether a significant difference exists between cortical ongoing-activity in the anesthetized and awake primate. We explored these questions by combining voltage-sensitive-dye imaging with multiunit and local-field-potential recordings. Spontaneously emerging orientation and ocular-dominance maps, spanning up to 6 × 6 mm2, were readily observed in anesthetized but not in awake monkeys. Nevertheless, spontaneous correlated-activity involving orientation-domains was observed in awake monkeys. Under both anesthetized and awake conditions, spontaneous correlated-activity coincided with traveling waves. We found that spontaneous activity resembling orientation-maps in awake animals spans smaller cortical areas in each instance, but over time it appears across all of V1. Furthermore, in the awake monkey, our results suggest that the synaptic strength had been completely reorganized including connections between dissimilar elements of the functional architecture. These findings lend support to the notion that ongoing-activity has many more fast switching representations playing an important role in cortical function and behavior.

Critical dynamics, anesthesia and information integration: Lessons from multi-scale criticality analysis of voltage imaging data.

Critical dynamics are thought to play an important role in neuronal information-processing: near critical networks exhibit neuronal avalanches, cascades of spatiotemporal activity that are scale-free, and are considered to enhance information capacity and transfer. However, the exact relationship between criticality, awareness, and information integration remains unclear. To characterize this relationship, we applied multi-scale avalanche analysis to voltage-sensitive dye imaging data collected from animals of various species under different anesthetics. We found that anesthesia systematically varied the scaling behavior of neural dynamics, a change that was mirrored in reduced neural complexity. These findings were corroborated by applying the same analyses to a biophysically realistic cortical network model, in which multi-scale criticality measures were associated with network properties and the capacity for information integration. Our results imply that multi-scale criticality measures are potential biomarkers for assessing the level of consciousness.

Multi-Electrode Alpha tACS During Varying Background Tasks Fails to Modulate Subsequent Alpha Power.

Transcranial alternating-current stimulation (tACS) for entraining alpha activity holds potential for influencing mental function, both in laboratory and clinical settings. While initial results of alpha entrainment are promising, questions remain regarding its translational potential-namely if tACS alpha entrainment is sufficiently robust to context and to what extent it can be upscaled to multi-electrode arrangements needed to direct currents into precise brain loci. We set out to explore these questions by administering alternating current through a multi-electrode montage (mtACS), while varying background task. A multi-electrode analog of previously employed anterior/posterior stimulation failed to replicate the reported alpha entrainment, suggesting that further work is required to understand the scope of applicability of tACS alpha entrainment.

In the interest of saving time: a critique of discrete perception.

A recently proposed model of sensory processing suggests that perceptual experience is updated in discrete steps. We show that the data advanced to support discrete perception are in fact compatible with a continuous account of perception. Physiological and psychophysical constraints, moreover, as well as our awake-primate imaging data, imply that human neuronal networks cannot support discrete updates of perceptual content at the maximal update rates consistent with phenomenology. A more comprehensive approach to understanding the physiology of perception (and experience at large) is therefore called for, and we briefly outline our take on the problem.

Interhemispheric Synchrony of Spontaneous Cortical States at the Cortical Column Level.

In cat early visual cortex, neural activity patterns resembling evoked orientation maps emerge spontaneously under anesthesia. To test if such patterns are synchronized between hemispheres, we performed bilateral imaging in anesthetized cats using a new improved voltage-sensitive dye. We observed map-like activity patterns spanning early visual cortex in both hemispheres simultaneously. Patterns virtually identical to maps associated with the cardinal and oblique orientations emerged as leading principal components of the spontaneous fluctuations, and the strength of transient orientation states was correlated with their duration, providing evidence that these maps are transiently attracting states. A neural mass model we developed reproduced the dynamics of both smooth and abrupt orientation state transitions observed experimentally. The model suggests that map-like activity arises from slow modulations in spontaneous firing in conjunction with interplay between excitation and inhibition. Our results highlight the efficiency and functional precision of interhemispheric connectivity.

System, Subsystem, Hive: Boundary Problems in Computational Theories of Consciousness.

A computational theory of consciousness should include a quantitative measure of consciousness, or MoC, that (i) would reveal to what extent a given system is conscious, (ii) would make it possible to compare not only different systems, but also the same system at different times, and (iii) would be graded, because so is consciousness. However, unless its design is properly constrained, such an MoC gives rise to what we call the boundary problem: an MoC that labels a system as conscious will do so for some-perhaps most-of its subsystems, as well as for irrelevantly extended systems (e.g., the original system augmented with physical appendages that contribute nothing to the properties supposedly supporting consciousness), and for aggregates of individually conscious systems (e.g., groups of people). This problem suggests that the properties that are being measured are epiphenomenal to consciousness, or else it implies a bizarre proliferation of minds. We propose that a solution to the boundary problem can be found by identifying properties that are intrinsic or systemic: properties that clearly differentiate between systems whose existence is a matter of fact, as opposed to those whose existence is a matter of interpretation (in the eye of the beholder). We argue that if a putative MoC can be shown to be systemic, this ipso facto resolves any associated boundary issues. As test cases, we analyze two recent theories of consciousness in light of our definitions: the Integrated Information Theory and the Geometric Theory of consciousness.

Left medial orbitofrontal cortex volume correlates with skydive-elicited euphoric experience.

The medial orbitofrontal cortex has been linked to the experience of positive affect. Greater medial orbitofrontal cortex volume is associated with greater expression of positive affect and reduced medial orbital frontal cortex volume is associated with blunted positive affect. However, little is known about the experience of euphoria, or extreme joy, and how this state may relate to variability in medial orbitofrontal cortex structure. To test the hypothesis that variability in euphoric experience correlates with the volume of the medial orbitofrontal cortex, we measured individuals' (N = 31) level of self-reported euphoria in response to a highly anticipated first time skydive and measured orbitofrontal cortical volumes with structural magnetic resonance imaging. Skydiving elicited a large increase in self-reported euphoria. Participants' euphoric experience was predicted by the volume of their left medial orbitofrontal cortex such that, the greater the volume, the greater the euphoria. Further analyses indicated that the left medial orbitofrontal cortex and amygdalo-hippocampal complex independently explain variability in euphoric experience and that medial orbitofrontal cortex volume, in conjunction with other structures within the mOFC-centered corticolimbic circuit, can be used to predict individuals' euphoric experience.

Neural Correlates of Aggression in Medication-Naive Children with ADHD: Multivariate Analysis of Morphometry and Tractography.

Aggression is widely observed in children with attention deficit/hyperactivity disorder (ADHD) and has been frequently linked to frustration or the unsatisfied anticipation of reward. Although animal studies and human functional neuroimaging implicate altered reward processing in aggressive behaviors, no previous studies have documented the relationship between fronto-accumbal circuitry-a critical cortical pathway to subcortical limbic regions-and aggression in medication-naive children with ADHD. To address this, we collected behavioral measures and parental reports of aggression and impulsivity, as well as structural and diffusion MRI, from 30 children with ADHD and 31 healthy controls (HC) (mean age, 10±2.1 SD). Using grey matter morphometry and probabilistic tractography combined with multivariate statistical modeling (partial least squares regression and support vector regression), we identified anomalies within the fronto-accumbal circuit in childhood ADHD, which were associated with increased aggression. More specifically, children with ADHD showed reduced right accumbal volumes and frontal-accumbal white matter connectivity compared with HC. The magnitude of the accumbal volume reductions within the ADHD group was significantly correlated with increased aggression, an effect mediated by the relationship between the accumbal volume and impulsivity. Furthermore, aggression, but not impulsivity, was significantly explained by multivariate measures of fronto-accumbal white matter connectivity and cortical thickness within the orbitofrontal cortex. Our multi-modal imaging, combined with multivariate statistical modeling, indicates that the fronto-accumbal circuit is an important substrate of aggression in children with ADHD. These findings suggest that strategies aimed at probing the fronto-accumbal circuit may be beneficial for the treatment of aggressive behaviors in childhood ADHD.

Small-world network properties in prefrontal cortex correlate with predictors of psychopathology risk in young children: a NIRS study.

Near infrared spectroscopy (NIRS) is an emerging imaging technique that is relatively inexpensive, portable, and particularly well suited for collecting data in ecological settings. Therefore, it holds promise as a potential neurodiagnostic for young children. We set out to explore whether NIRS could be utilized in assessing the risk of developmental psychopathology in young children. A growing body of work indicates that temperament at young age is associated with vulnerability to psychopathology later on in life. In particular, it has been shown that low effortful control (EC), which includes the focusing and shifting of attention, inhibitory control, perceptual sensitivity, and a low threshold for pleasure, is linked to conditions such as anxiety, depression and attention deficit hyperactivity disorder (ADHD). Physiologically, EC has been linked to a control network spanning among other sites the prefrontal cortex. Several psychopathologies, such as depression and ADHD, have been shown to result in compromised small-world network properties. Therefore we set out to explore the relationship between EC and the small-world properties of PFC using NIRS. NIRS data were collected from 44 toddlers, ages 3-5, while watching naturalistic stimuli (movie clips). Derived complex network measures were then correlated to EC as derived from the Children's Behavior Questionnaire (CBQ). We found that reduced levels of EC were associated with compromised small-world properties of the prefrontal network. Our results suggest that the longitudinal NIRS studies of complex network properties in young children hold promise in furthering our understanding of developmental psychopathology.

Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity.

INTRODUCTION: Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. METHODS: HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. RESULTS: Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. CONCLUSIONS: Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.

Combining classification with fMRI-derived complex network measures for potential neurodiagnostics.

Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method's applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.

Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.

There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.

The NIRS Analysis Package: noise reduction and statistical inference.

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.

A stand-alone method for anatomical localization of NIRS measurements.

Near-infrared spectroscopy (NIRS) is a non-invasive cortical imaging technique that provides many of the advantages of cortical fMRI with additional benefits of low cost, portability, and increased temporal resolution-features that make it potentially ideal for clinical diagnostic applications. However, the usefulness of NIRS is contingent on the ability to reliably localize the measured signal cortically. Although this can be achieved by supplementing NIRS data collection with an MRI scan, a much more appealing alternative is to use a portable magnetic measuring system to record the locations of optodes. Previous work has shown that optode skull measurements can be projected to the brain consistently within reasonable error bounds. Yet, as we show, if this is done without explicitly modeling the geometry of the holder securing the NIR optodes to participants' heads, considerable bias in the projection loci results. Here, we describe an algorithm that not only overcomes this bias but also corrects for measurement error in both optode position and skull reference points (which are used to register the measurements to standard brain templates) by applying geometric constraints. This method has been implemented as part of our NIRS Analysis Package (NAP), a public domain Matlab toolbox for analysis of NIRS data.

Towards a computational theory of experience.

A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate--that is, unmediated--fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must satisfy for it to constitute not just a sufficient but a necessary, and therefore naturalistic and intrinsic, explanation of qualia. We show that a common assumption behind many neurocomputational theories of the mind, according to which mind states can be formalized solely in terms of instantaneous vectors of activities of representational units such as neurons, does not meet the requisite conditions, in part because it relies on inactive units to shape presently experienced qualia and implies a homogeneous representation space, which is devoid of intrinsic structure. We then sketch a naturalistic computational theory of qualia, which posits that experience is realized by dynamical activity-space trajectories (rather than points) and that its richness is measured by the representational capacity of the trajectory space in which it unfolds.

Arousal increases the representational capacity of cortical tissue.

Arousal patently transforms the faculties of complex organisms. Although typical changes in cortical activity such as seen in EEG and LFP measurements are associated with change in state of arousal, it remains unclear what in the constitution of such state dependent activity enables this profound enhancement of ability. We put forward the hypothesis that arousal modulates cortical activity by rendering it more fit to represent information. We argue that representational capacity is of a dual nature-it requires not only that cortical tissue generate complex activity (i.e. spatiotemporal neuronal events), but also a complex cortical activity space (which is comprised of such spatiotemporal events). We explain that the topological notion of complexity-homology-is the pertinent measure of the complexity of neuronal activity spaces, as homological structure indicates not only the degree to which underlying activity is inherently clustered but also registers the effective dimensionality of the configurations formed by such clusters. Changes of this sort in the structure of cortical activity spaces can serve as the basis of the enhanced capacity to make perceptual/behavioral distinctions brought about by arousal. To show the feasibility of these ideas, we analyzed voltage sensitive dye imaging (VSDI) data acquired from primate visual cortex in disparate states of arousal. Our results lend some support to the theory: first as arousal increased so did the complexity of activity (that is the complexity of VSDI movies). Moreover, the complexity of structure of activity space (that is VSDI movie space) as measured by persistent homology-a multi scale topological measure of complexity-increased with arousal as well.

Removal of spatial biological artifacts in functional maps by local similarity minimization.

Functional maps obtained by various technologies, including optical imaging techniques, f-MRI, PET, and others, may be contaminated with biological artifacts such as vascular patterns or large patches of parenchyma. These artifacts originate mostly from changes in the microcirculation that result from either activity-dependent changes in volume or from oximetric changes that do not co-localize with neuronal activity per se. Standard methods do not always suffice to reduce such artifacts, in which case conspicuous spatial artifacts mask details of the underlying activity patterns. Here we propose a simple algorithm that efficiently removes spatial biological artifacts contaminating high-resolution functional maps. We validated this procedure by applying it to cortical maps resulting from optical imaging, based either on voltage-sensitive dye signals or on intrinsic signals. To remove vascular spatial patterns we first constructed a template of typical artifacts (vascular/cardiac pulsation/vasomotion), using principle components derived from baseline information obtained in the absence of stimulation. Next, we modified this template by means of local similarity minimization (LSM), achieved by measuring neighborhood similarity between contaminated data and the artifact template and then abolishing the similarity. LSM thus removed spatial patterns originating from the cortical vasculature components, including large fields of capillary parenchyma, helping to unveil details of neuronal activity patterns that were otherwise masked by these vascular artifacts. Examples obtained from our imaging experiments with anaesthetized cats and behaving monkeys showed that the LSM method is both general and reproducible, and is often superior to other available procedures.