Denis Rubin

Chemosensory Cues to Conspecific Emotional Stress Activate Amygdala in Humans

Alarm substances are airborne chemical signals, released by an individual into the environment, which communicate emotional stress between conspecifics. Here we tested whether humans, like other mammals, are able to detect emotional stress in others by chemosensory cues. Sweat samples collected from individuals undergoing an acute emotional stressor, with exercise as a control, were pooled and presented to a separate group of participants (blind to condition) during four experiments. In an fMRI experiment and its replication, we showed that scanned participants showed amygdala activation in response to samples obtained from donors undergoing an emotional, but not physical, stressor. An odor-discrimination experiment suggested the effect was primarily due to emotional, and not odor, differences between the two stimuli. A fourth experiment investigated behavioral effects, demonstrating that stress samples sharpened emotion-perception of ambiguous facial stimuli. Together, our findings suggest human chemosensory signaling of emotional stress, with neurobiological and behavioral effects.

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.

Second-hand stress: inhalation of stress sweat enhances neural response to neutral faces

This study investigated whether human chemosensory-stress cues affect neural activity related to the evaluation of emotional stimuli. Chemosensory stimuli were obtained from the sweat of 64 male donors during both stress (first-time skydive) and control (exercise) conditions, indistinguishable by odor. We then recorded event-related potentials (ERPs) from an unrelated group of 14 participants while they viewed faces morphed with neutral-to-angry expressions and inhaled nebulized stress and exercise sweat in counter-balanced blocks, blind to condition. Results for the control condition ERPs were consistent with previous findings: the late positive potential (LPP; 400-600 ms post stimulus) in response to faces was larger for threatening than both neutral and ambiguous faces. In contrast, the stress condition was associated with a heightened LPP across all facial expressions; relative to control, the LPP was increased for both ambiguous and neutral faces in the stress condition. These results suggest that stress sweat may impact electrocortical activity associated with attention to salient environmental cues, potentially increasing attentiveness to otherwise inconspicuous stimuli.

Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: Adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series

In a well-regulated control system, excitatory and inhibitory components work closely together with minimum lag; in response to inputs of finite duration, outputs should show rapid rise and, following the inputs termination, immediate return to baseline. The efficiency of this response can be quantified using the power spectrum densitys scaling parameter beta, a measure of self-similarity, applied to the first derivative of the raw signal. In this study, we adapted power spectrum density methods, previously used to quantify autonomic dysregulation (heart rate variability), to neural time series obtained via functional MRI. The negative feedback loop we investigated was the limbic system, using affect-valent faces as stimuli. We hypothesized that trait anxiety would be related to efficiency of regulation of limbic responses, as quantified by power-law scaling of fMRI time series. Our results supported this hypothesis, showing moderate to strong correlations of trait anxiety and beta (r=0.45-0.54) for the amygdala, orbitofrontal cortex, hippocampus, superior temporal gyrus, posterior insula, and anterior cingulate. Strong anticorrelations were also found between the amygdalas beta and wake heart rate variability (r=-0.61), suggesting a robust relationship between dysregulated limbic outputs and their autonomic consequences.

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.

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.

Power spectrum scale invariance identifies prefrontal dysregulation in paranoid schizophrenia

Theory and experimental evidence suggest that complex living systems function close to the boundary of chaos, with erroneous organization to an improper dynamical range (too stiff or chaotic) underlying system‐wide dysregulation and disease. We hypothesized that erroneous organization might therefore also characterize paranoid schizophrenia, via optimization abnormalities in the prefrontal‐limbic circuit regulating emotion. To test this, we acquired fMRI scans from 35 subjects (N = 9 patients with paranoid schizophrenia and N = 26 healthy controls), while they viewed affect‐valent stimuli. To quantify dynamic regulation, we analyzed the power spectrum scale invariance (PSSI) of fMRI time‐courses and computed the geometry of time‐delay (Poincaré) maps, a measure of variability. Patients and controls showed distinct PSSI in two clusters (k1: Z = 4.3215, P = 0.00002 and k2: Z = 3.9441, P = 0.00008), localized to the orbitofrontal/medial prefrontal cortex (Brodmann Area 10), represented by β close to white noise in patients (β ≈ 0) and in the pink noise range in controls (β ≈ −1). Interpreting the meaning of PSSI differences, the Poincaré maps indicated less variability in patients than controls (Z = −1.9437, P = 0.05 for k1; Z = −2.5099, P = 0.01 for k2). That the dynamics identified Brodmann Area 10 is consistent with previous schizophrenia research, which implicates this area in deficits of working memory, executive functioning, emotional regulation and underlying biological abnormalities in synaptic (glutamatergic) transmission. Our results additionally cohere with a large body of work finding pink noise to be the normal range of central function at the synaptic, cellular, and small network levels, and suggest that patients show less supple responsivity of this region. Hum Brain Mapp, 2011.

The fast spiral-SelMQC technique for in vivo MR spectroscopic imaging of polyunsaturated fatty acids in human breast tissue†

The selective multiple‐quantum coherence transfer method has been applied to image polyunsaturated fatty acids (PUFA) distributions in human breast tissues in vivo for cancer detection, with complete suppression of the unwanted lipid and water signals in a single scan. The Cartesian k‐space mapping of PUFA in vivo using the selective multiple‐quantum coherence transfer (Sel‐MQC) chemical shift imaging (CSI) technique, however, requires excessive MR scan time. In this article, we report a fast Spiral‐SelMQC sequence using a rapid spiral k‐space sampling scheme. The Spiral‐SelMQC images of PUFA distribution in human breast were acquired using two‐interleaved spirals on a 3 T GE Signa magnetic resonance imaging scanner. Approximately 160‐fold reduction of acquisition time was observed as compared with the corresponding selective multiple‐quantum coherence transfer CSI method with an equivalent number of scans, permitting acquisition of high‐resolution PUFA images in minutes. The reconstructed Spiral‐SelMQC PUFA images of human breast tissues achieved a sub‐millimeter resolution of 0.54 × 0.54 or 0.63 × 0.63 mm2/pixel for field of view = 14 or 16 cm, respectively. The Spiral‐SelMQC parameters for PUFA detection were optimized in 2D selective multiple‐quantum coherence transfer experiments to suppress monounsaturated fatty acids and other lipid signals. The fast in vivo Spiral‐SelMQC imaging method will be applied to study human breast cancer and other human diseases in extracranial organs. Magn Reson Med, 2011.

Lost emotion: Disrupted brain-based tracking of dynamic affective episodes in anxiety and depression

In our day-to-day lives we are confronted with dynamic sensory inputs that elicit a continuously evolving emotional response. Insight into the brain basis of the dynamic nature of emotional reactivity may be critical for understanding chronic symptoms of anxiety and depression. Here, individuals with generalized anxiety disorder, major depressive disorder, and healthy controls watched a video with dynamic affective content while fMRI activity was recorded. Across all participants there was a large-scale tracking of affective content in emotion processing regions and the default mode network. Anxious and depressed individuals displayed less brain-based coupling within these regions and the extent of this uncoupling correlated with variability in emotional numbing. Thus, abnormal neural tracking of affective information during dynamic emotional episodes appears to represent a disconnection between affective cues in the environment and an individuals response to these cues-providing a putative neural basis for context insensitive affective reactivity and emotional numbing.