Tomer Fekete

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.

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.

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 Childrens 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.

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.

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.

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.

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.

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.

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

Abstract 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.

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

&NA; 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.