Impact of Affective Multimedia Content on the Electroencephalogram and Facial Expressions.

Most of the research in the field of affective computing has focused on detecting and classifying human emotions through electroencephalogram (EEG) or facial expressions. Designing multimedia content to evoke certain emotions has been largely motivated by manual rating provided by users. Here we present insights from the correlation of affective features between three modalities namely, affective multimedia content, EEG, and facial expressions. Interestingly, low-level Audio-visual features such as contrast and homogeneity of the video and tone of the audio in the movie clips are most correlated with changes in facial expressions and EEG. We also detect the regions associated with the human face and the brain (in addition to the EEG frequency bands) that are most representative of affective responses. The computational modeling between the three modalities showed a high correlation between features from these regions and user-reported affective labels. Finally, the correlation between different layers of convolutional neural networks with EEG and Face images as input provides insights into human affection. Together, these findings will assist in (1) designing more effective multimedia contents to engage or influence the viewers, (2) understanding the brain/body bio-markers of affection, and (3) developing newer brain-computer interfaces as well as facial-expression-based algorithms to read emotional responses of the viewers.

Impairments in remote memory caused by the lack of Type 2 IP3 receptors.

The second messenger inositol 1,4,5-trisphosphate (IP3 ) is paramount for signal transduction in biological cells, mediating Ca2+ release from the endoplasmic reticulum. Of the three isoforms of IP3 receptors identified in the nervous system, Type 2 (IP3 R2) is the main isoform expressed by astrocytes. The complete lack of IP3 R2 in transgenic mice was shown to significantly disrupt Ca2+ signaling in astrocytes, while leaving neuronal intracellular pathways virtually unperturbed. Whether and how this predominantly nonneuronal receptor might affect long-term memory function has been a matter of intense debate. In this work, we found that the absence of IP3 R2-mediated signaling did not disrupt normal learning or recent (24-48 h) memory. Contrary to expectations, however, mice lacking IP3 R2 exhibited remote (2-4 weeks) memory deficits. Not only did the lack of IP3 R2 impair remote recognition, fear, and spatial memories, but it also prevented naturally occurring post-encoding memory enhancements consequent to memory consolidation. Consistent with the key role played by the downscaling of synaptic transmission in memory consolidation, we found that NMDAR-dependent long-term depression was abnormal in ex vivo hippocampal slices acutely prepared from IP3 R2-deficient mice, a deficit that could be prevented upon supplementation with D-serine - an NMDA-receptor co-agonist whose synthesis depends upon astrocytes' activity. Our results reveal that IP3 R2 activation, which in the brain is paramount for Ca2+ signaling in astrocytes, but not in neurons, can help shape brain plasticity by enhancing the consolidation of newly acquired information into long-term memories that can guide remote cognitive behaviors.

Nonlinear dynamics underlying sensory processing dysfunction in schizophrenia.

Natural systems, including the brain, often seem chaotic, since they are typically driven by complex nonlinear dynamical processes. Disruption in the fluid coordination of multiple brain regions contributes to impairments in information processing and the constellation of symptoms observed in neuropsychiatric disorders. Schizophrenia (SZ), one of the most debilitating mental illnesses, is thought to arise, in part, from such a network dysfunction, leading to impaired auditory information processing as well as cognitive and psychosocial deficits. Current approaches to neurophysiologic biomarker analyses predominantly rely on linear methods and may, therefore, fail to capture the wealth of information contained in whole EEG signals, including nonlinear dynamics. In this study, delay differential analysis (DDA), a nonlinear method based on embedding theory from theoretical physics, was applied to EEG recordings from 877 SZ patients and 753 nonpsychiatric comparison subjects (NCSs) who underwent mismatch negativity (MMN) testing via their participation in the Consortium on the Genetics of Schizophrenia (COGS-2) study. DDA revealed significant nonlinear dynamical architecture related to auditory information processing in both groups. Importantly, significant DDA changes preceded those observed with traditional linear methods. Marked abnormalities in both linear and nonlinear features were detected in SZ patients. These results illustrate the benefits of nonlinear analysis of brain signals and underscore the need for future studies to investigate the relationship between DDA features and pathophysiology of information processing.

Thalamocortical and intracortical laminar connectivity determines sleep spindle properties.

Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night.

During sleep, the thalamus generates a characteristic pattern of transient, 11-15 Hz sleep spindle oscillations, which synchronize the cortex through large-scale thalamocortical loops. Spindles have been increasingly demonstrated to be critical for sleep-dependent consolidation of memory, but the specific neural mechanism for this process remains unclear. We show here that cortical spindles are spatiotemporally organized into circular wave-like patterns, organizing neuronal activity over tens of milliseconds, within the timescale for storing memories in large-scale networks across the cortex via spike-time dependent plasticity. These circular patterns repeat over hours of sleep with millisecond temporal precision, allowing reinforcement of the activity patterns through hundreds of reverberations. These results provide a novel mechanistic account for how global sleep oscillations and synaptic plasticity could strengthen networks distributed across the cortex to store coherent and integrated memories.

Synchronization of isolated downstates (K-complexes) may be caused by cortically-induced disruption of thalamic spindling.

Sleep spindles and K-complexes (KCs) define stage 2 NREM sleep (N2) in humans. We recently showed that KCs are isolated downstates characterized by widespread cortical silence. We demonstrate here that KCs can be quasi-synchronous across scalp EEG and across much of the cortex using electrocorticography (ECOG) and localized transcortical recordings (bipolar SEEG). We examine the mechanism of synchronous KC production by creating the first conductance based thalamocortical network model of N2 sleep to generate both spontaneous spindles and KCs. Spontaneous KCs are only observed when the model includes diffuse projections from restricted prefrontal areas to the thalamic reticular nucleus (RE), consistent with recent anatomical findings in rhesus monkeys. Modeled KCs begin with a spontaneous focal depolarization of the prefrontal neurons, followed by depolarization of the RE. Surprisingly, the RE depolarization leads to decreased firing due to disrupted spindling, which in turn is due to depolarization-induced inactivation of the low-threshold Ca2+ current (IT). Further, although the RE inhibits thalamocortical (TC) neurons, decreased RE firing causes decreased TC cell firing, again because of disrupted spindling. The resulting abrupt removal of excitatory input to cortical pyramidal neurons then leads to the downstate. Empirically, KCs may also be evoked by sensory stimuli while maintaining sleep. We reproduce this phenomenon in the model by depolarization of either the RE or the widely-projecting prefrontal neurons. Again, disruption of thalamic spindling plays a key role. Higher levels of RE stimulation also cause downstates, but by directly inhibiting the TC neurons. SEEG recordings from the thalamus and cortex in a single patient demonstrated the model prediction that thalamic spindling significantly decreases before KC onset. In conclusion, we show empirically that KCs can be widespread quasi-synchronous cortical downstates, and demonstrate with the first model of stage 2 NREM sleep a possible mechanism whereby this widespread synchrony may arise.

Thalamic burst firing propensity: a comparison of the dorsal lateral geniculate and pulvinar nuclei in the tree shrew.

Relay neurons in dorsal thalamic nuclei can fire high-frequency bursts of action potentials that ride the crest of voltage-dependent transient (T-type) calcium currents [low-threshold spike (LTS)]. To explore potential nucleus-specific burst features, we compared the membrane properties of dorsal lateral geniculate nucleus (dLGN) and pulvinar nucleus relay neurons using in vitro whole-cell recording in juvenile and adult tree shrew (Tupaia) tissue slices. We injected current ramps of variable slope into neurons that were sufficiently hyperpolarized to de-inactivate T-type calcium channels. In a small percentage of juvenile pulvinar and dLGN neurons, an LTS could not be evoked. In the remaining juvenile neurons and in all adult dLGN neurons, a single LTS could be evoked by current ramps. However, in the adult pulvinar, current ramps evoked multiple LTSs in >70% of recorded neurons. Using immunohistochemistry, Western blot techniques, unbiased stereology, and confocal and electron microscopy, we found that pulvinar neurons expressed more T-type calcium channels (Ca(v) 3.2) and more small conductance potassium channels (SK2) than dLGN neurons and that the pulvinar nucleus contained a higher glia-to-neuron ratio than the dLGN. Hodgkin-Huxley-type compartmental models revealed that the distinct firing modes could be replicated by manipulating T-type calcium and SK2 channel density, distribution, and kinetics. The intrinsic properties of pulvinar neurons that promote burst firing in the adult may be relevant to the treatment of conditions that involve the adult onset of aberrant thalamocortical interactions.

Experimental evidence and modeling studies support a synchronizing role for electrical coupling in the cat thalamic reticular neurons in vivo.

Thalamic reticular (RE) neurons are crucially implicated in brain rhythms. Here, we report that RE neurons of adult cats, recorded and stained intracellularly in vivo, displayed spontaneously occurring spikelets, which are characteristic of central neurons that are coupled electrotonically via gap junctions. Spikelets occurred spontaneously during spindles, an oscillation in which RE neurons play a leading role, as well as during interspindle lulls. They were significantly different from excitatory postsynaptic potentials and also distinct from fast prepotentials that are presumably dendritic spikes generated synaptically. Spikelets were strongly reduced by halothane, a blocker of gap junctions. Multi-site extracellular recordings performed before, during and after administration of halothane demonstrated a role for electrical coupling in the synchronization of spindling activity within the RE nucleus. Finally, computational models of RE neurons predicted that gap junctions between these neurons could mediate the spread of low-frequency activity at great distances. These experimental and modeling data suggest that electrotonic coupling within the RE nucleus plays an important role in the generation and synchronization of low-frequency (spindling) activities in the thalamus.

Model of thalamocortical slow-wave sleep oscillations and transitions to activated States.

During natural slow-wave sleep (SWS) in nonanesthetized cats, silent (down) states alternate with active (up) states; the down states are absent during rapid-eye-movement sleep and waking. Oscillations (<1 Hz) in SWS and transformation to an activated awake state were investigated with intracellular recordings in vivo and with computational models of the corticothalamic system. Occasional summation of the miniature EPSPs during the hyperpolarized (silent) phase of SWS oscillation activated the persistent sodium current and depolarized the membrane of cortical pyramidal (PY) cells sufficiently for spike generation. In the model, this triggered the active phase, which was maintained by lateral PY-PY excitation and persistent sodium current. Progressive depression of the excitatory interconnections and activation of Ca2+-dependent K+ current led to termination of the 20-25 Hz activity after 500-1000 msec. Including thalamocortical (TC) and thalamic reticular neurons in the model increased the duration of the active epochs up to 1-1.5 sec and introduced waning spindle sequences. An increase in acetylcholine activity, which is associated with activated states, was modeled by the reduction in the K+ leak current in PY and TC cells and by a decrease in intracortical PY-PY synaptic conductances. These changes eliminated the hyperpolarizing phases of network activity and transformed cortical neurons to tonic firing at 15-20 Hz. During the transition from SWS to the activated state, the input resistance of cortical neurons gradually increased and, in a fully activated state, reached the same or even higher values as during silent phases of SWS oscillations. The model describes many essential features of SWS and activated states in the thalamocortical system as well as the transition between them.

Frequency-selective augmenting responses by short-term synaptic depression in cat neocortex.

Thalamic stimulation at frequencies between 5 and 15 Hz elicits incremental or 'augmenting' cortical responses. Augmenting responses can also be evoked in cortical slices and isolated cortical slabs in vivo. Here we show that a realistic network model of cortical pyramidal cells and interneurones including short-term plasticity of inhibitory and excitatory synapses replicates the main features of augmenting responses as obtained in isolated slabs in vivo. Repetitive stimulation of synaptic inputs at frequencies around 10 Hz produced postsynaptic potentials that grew in size and carried an increasing number of action potentials resulting from the depression of inhibitory synaptic currents. Frequency selectivity was obtained through the relatively weak depression of inhibitory synapses at low frequencies, and strong depression of excitatory synapses together with activation of a calcium-activated potassium current at high frequencies. This network resonance is a consequence of short-term synaptic plasticity in a network of neurones without intrinsic resonances. These results suggest that short-term plasticity of cortical synapses could shape the dynamics of synchronized oscillations in the brain.

The initiation of bursts in thalamic neurons and the cortical control of thalamic sensitivity.

Thalamic neurons generate high-frequency bursts of action potentials when a low-threshold (T-type) calcium current, located in soma and dendrites, becomes activated. Computational models were used to investigate the bursting properties of thalamic relay and reticular neurons. These two types of thalamic cells differ fundamentally in their ability to generate bursts following either excitatory or inhibitory events. Bursts generated with excitatory inputs in relay cells required a high degree of convergence from excitatory inputs, whereas moderate excitation drove burst discharges in reticular neurons from hyperpolarized levels. The opposite holds for inhibitory rebound bursts, which are more difficult to evoke in reticular neurons than in relay cells. The differences between the reticular neurons and thalamocortical neurons were due to different kinetics of the T-current, different electrotonic properties and different distribution patterns of the T-current in the two cell types. These properties enable the cortex to control the sensitivity of the thalamus to inputs and are also important for understanding states such as absence seizures.