Laura D. Lewis

Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness

The neurophysiological mechanisms by which anesthetic drugs cause loss of consciousness are poorly understood. Anesthetic actions at the molecular, cellular, and systems levels have been studied in detail at steady states of deep general anesthesia. However, little is known about how anesthetics alter neural activity during the transition into unconsciousness. We recorded simultaneous multiscale neural activity from human cortex, including ensembles of single neurons, local field potentials, and intracranial electrocorticograms, during induction of general anesthesia. We analyzed local and global neuronal network changes that occurred simultaneously with loss of consciousness. We show that propofol-induced unconsciousness occurs within seconds of the abrupt onset of a slow (<1 Hz) oscillation in the local field potential. This oscillation marks a state in which cortical neurons maintain local patterns of network activity, but this activity is fragmented across both time and space. Local (<4 mm) neuronal populations maintain the millisecond-scale connectivity patterns observed in the awake state, and spike rates fluctuate and can reach baseline levels. However, neuronal spiking occurs only within a limited slow oscillation-phase window and is silent otherwise, fragmenting the time course of neural activity. Unexpectedly, we found that these slow oscillations occur asynchronously across cortex, disrupting functional connectivity between cortical areas. We conclude that the onset of slow oscillations is a neural correlate of propofol-induced loss of consciousness, marking a shift to cortical dynamics in which local neuronal networks remain intact but become functionally isolated in time and space.

Thalamic reticular nucleus induces fast and local modulation of arousal state

During low arousal states such as drowsiness and sleep, cortical neurons exhibit rhythmic slow wave activity associated with periods of neuronal silence. Slow waves are locally regulated, and local slow wave dynamics are important for memory, cognition, and behaviour. While several brainstem structures for controlling global sleep states have now been well characterized, a mechanism underlying fast and local modulation of cortical slow waves has not been identified. Here, using optogenetics and whole cortex electrophysiology, we show that local tonic activation of thalamic reticular nucleus (TRN) rapidly induces slow wave activity in a spatially restricted region of cortex. These slow waves resemble those seen in sleep, as cortical units undergo periods of silence phase-locked to the slow wave. Furthermore, animals exhibit behavioural changes consistent with a decrease in arousal state during TRN stimulation. We conclude that TRN can induce rapid modulation of local cortical state. DOI: http://dx.doi.org/10.7554/eLife.08760.001

Fast fMRI can detect oscillatory neural activity in humans

Significance A major challenge in neuroscience is our limited ability to image neural signals noninvasively in humans. Oscillations in brain activity are important for perception, attention, and awareness, and progress in cognitive neuroscience depends on localizing these patterns. fMRI is thought to be too slow to measure brain oscillations because it depends on slow changes in blood flow. Here, we use recently developed imaging techniques to show that fMRI can measure faster neural oscillations than previously thought, and responses are 10 times larger than expected. With computational modeling and simultaneous electroencephalography we show that vascular responses are surprisingly fast when brain activity fluctuates rapidly. These results suggest that fMRI can be used to track oscillating brain activity directly during human cognition. Oscillatory neural dynamics play an important role in the coordination of large-scale brain networks. High-level cognitive processes depend on dynamics evolving over hundreds of milliseconds, so measuring neural activity in this frequency range is important for cognitive neuroscience. However, current noninvasive neuroimaging methods are not able to precisely localize oscillatory neural activity above 0.2 Hz. Electroencephalography and magnetoencephalography have limited spatial resolution, whereas fMRI has limited temporal resolution because it measures vascular responses rather than directly recording neural activity. We hypothesized that the recent development of fast fMRI techniques, combined with the extra sensitivity afforded by ultra-high-field systems, could enable precise localization of neural oscillations. We tested whether fMRI can detect neural oscillations using human visual cortex as a model system. We detected small oscillatory fMRI signals in response to stimuli oscillating at up to 0.75 Hz within single scan sessions, and these responses were an order of magnitude larger than predicted by canonical linear models. Simultaneous EEG–fMRI and simulations based on a biophysical model of the hemodynamic response to neuronal activity suggested that the blood oxygen level-dependent response becomes faster for rapidly varying stimuli, enabling the detection of higher frequencies than expected. Accounting for phase delays across voxels further improved detection, demonstrating that identifying vascular delays will be of increasing importance with higher-frequency activity. These results challenge the assumption that the hemodynamic response is slow, and demonstrate that fMRI has the potential to map neural oscillations directly throughout the brain.

Encoding through patterns: Regression tree-based neuronal population models

Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed.

Differential requirement for NMDAR activity in SAP97β-mediated regulation of the number and strength of glutamatergic AMPAR-containing synapses

PSD-95-like, disc-large (DLG) family membrane-associated guanylate kinase proteins (PSD/DLG-MAGUKs) are essential for regulating synaptic AMPA receptor (AMPAR) function and activity-dependent trafficking of AMPARs. Using a molecular replacement strategy to replace endogenous PSD-95 with SAP97β, we show that the prototypic β-isoform of the PSD-MAGUKs, SAP97β, has distinct NMDA receptor (NMDAR)-dependent roles in regulating basic properties of AMPAR-containing synapses. SAP97β enhances the number of AMPAR-containing synapses in an NMDAR-dependent manner, whereas its effect on the size of unitary synaptic response is not fully dependent on NMDAR activity. These effects contrast with those of PSD-95α, which increases both the number of AMPAR-containing synapses and the size of unitary synaptic responses, with or without NMDAR activity. Our results suggest that SAP97β regulates synaptic AMPAR content by increasing surface expression of GluA1-containing AMPARs, whereas PSD-95α enhances synaptic AMPAR content presumably by increasing the synaptic scaffold capacity for synaptic AMPARs. Our approach delineates discrete effects of different PSD-MAGUKs on principal properties of glutamatergic synaptic transmission. Our results suggest that the molecular diversity of PSD-MAGUKs can provide rich molecular substrates for differential regulation of glutamatergic synapses in the brain.

A Model-Based Framework for the Analysis of Miniature Post-Synaptic Currents

Miniature post-synaptic currents (mPSCs) have become a primary measure of synaptic modification during development, plasticity, and disease. ‘Minis’ represent the response of postsynaptic receptors to the spontaneous fusion of vesicles. They are a useful assay for the number and strength of synaptic connections, as mini event frequency is related to the number of functional release sites, and event amplitude is a measure of synapse strength [1]. Thus, accurate characterization of synapse dynamics relies critically on statistical analyses of event frequency and amplitude. We develop a new paradigm for mPSC analysis that uses likelihood methods [2] and formal goodness-of-fit assessments [3] to derive accurate statistical descriptions of their frequency and amplitude properties. In particular, we demonstrate that mPSC inter-event intervals and amplitudes within individual cells are well described by exponential and log-normal models. These characterizations allow us to analyze mPSCs at the single-cell level. We employ a parametric bootstrap based on these models to make accurate assessments of uncertainty within and between groups in the setting of small samples. This enables accurate estimation of responses for individual cells or groups, and paired comparisons of beforeand after- manipulations in single cells. We illustrate this approach in the analysis of excitatory mPSCs from acute slices of sensory midbrain. We show that the method may be broadly applicable to excitatory and inhibitory PSCs in other CNS regions, and is robust to changes in event selection parameters and recording conditions. Our method preserves information about the variability of events within individual cells and allows the summary of information across cells in order to make between-group comparisons. The use of an accurate model maximizes the efficiency of the resulting statistics, by taking advantage of the high degree of structure in the data. The framework allows accurate inferences to be made from studies of spontaneous activity, and for the first time extends analysis of synaptic function to the single cell level.

A transient cortical state with sleep-like sensory responses precedes emergence from general anesthesia in humans

During awake consciousness, the brain intrinsically maintains a dynamical state in which it can coordinate complex responses to sensory input. How the brain reaches this state spontaneously is not known. General anesthesia provides a unique opportunity to examine how the human brain recovers its functional capabilities after profound unconsciousness. We used intracranial electrocorticography and scalp EEG in humans to track neural dynamics during emergence from propofol general anesthesia. We identify a distinct transient brain state that occurs immediately prior to recovery of behavioral responsiveness. This state is characterized by large, spatially distributed, slow sensory-evoked potentials that resemble the K-complexes that are hallmarks of stage two sleep. However, the ongoing spontaneous dynamics in this transitional state differ from sleep. These results identify an asymmetry in the neurophysiology of induction and emergence, as the emerging brain can enter a state with a sleep-like sensory blockade before regaining responsivity to arousing stimuli.

Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI

ABSTRACT Recent developments in fMRI acquisition techniques now enable fast sampling with whole‐brain coverage, suggesting fMRI can be used to track changes in neural activity at increasingly rapid timescales. When images are acquired at fast rates, the limiting factor for fMRI temporal resolution is the speed of the hemodynamic response. Given that HRFs may vary substantially in subcortical structures, characterizing the speed of subcortical hemodynamic responses, and how the hemodynamic response shape changes with stimulus duration (i.e. the hemodynamic nonlinearity), is needed for designing and interpreting fast fMRI studies of these regions. We studied the temporal properties and nonlinearities of the hemodynamic response function (HRF) across the human subcortical visual system, imaging superior colliculus (SC), lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1) with high spatiotemporal resolution 7 Tesla fMRI. By presenting stimuli of varying durations, we mapped the timing and nonlinearity of hemodynamic responses in these structures at high spatiotemporal resolution. We found that the hemodynamic response is consistently faster and narrower in subcortical structures than in cortex. However, the nonlinearity in LGN is similar to that in cortex, with shorter duration stimuli eliciting larger and faster responses than would have been predicted by a linear model. Using oscillatory visual stimuli, we tested the frequency response in LGN and found that its BOLD response tracked high‐frequency (0.5Hz) oscillations. The LGN response magnitudes were comparable to V1, allowing oscillatory BOLD signals to be detected in LGN despite the small size of this structure. These results suggest that the increase in the speed and amplitude of the hemodynamic response when neural activity is brief may be the key physiological driver of fast fMRI signals, enabling detection of high‐frequency oscillations with fMRI. We conclude that subcortical visual structures exhibit fast and nonlinear hemodynamic responses, and that these dynamics enable detection of fast BOLD signals even within small deep brain structures when imaging is performed at ultra‐high field.

SAP102 regulates synaptic AMPAR function through a CNIH-2-dependent mechanism

The postsynaptic density (PSD)-95-like, disk-large (DLG) membrane-associated guanylate kinase (PSD/DLG-MAGUK) family of proteins scaffold α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) complexes to the postsynaptic compartment and are postulated to orchestrate activity-dependent modulation of synaptic AMPAR functions. SAP102 is a key member of this family, present from early development, before PSD-95 and PSD-93, and throughout life. Here we investigate the role of SAP102 in synaptic transmission using a cell-restricted molecular replacement strategy, where SAP102 is expressed against the background of acute knockdown of endogenous PSD-95. We show that SAP102 rescues the decrease of AMPAR-mediated evoked excitatory postsynaptic currents (AMPAR eEPSCs) and AMPAR miniature EPSC (AMPAR mEPSC) frequency caused by acute knockdown of PSD-95. Further analysis of the mini events revealed that PSD-95-to-SAP102 replacement but not direct manipulation of PSD-95 increases the AMPAR mEPSC decay time. SAP102-mediated rescue of AMPAR eEPSCs requires AMPAR auxiliary subunit cornichon-2, whereas cornichon-2 knockdown did not affect PSD-95-mediated regulation of AMPAR eEPSC. Combining these observations, our data elucidate that PSD-95 and SAP102 differentially influence basic synaptic properties and synaptic current kinetics potentially via different AMPAR auxiliary subunits. NEW & NOTEWORTHY Synaptic scaffold proteins postsynaptic density (PSD)-95-like, disk-large (DLG) membrane-associated guanylate kinase (PSD-MAGUKs) regulate synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) function. However, the functional diversity among different PSD-MAGUKs remains to be categorized. We show that distinct from PSD-95, SAP102 increase the AMPAR synaptic current decay time, and the effect of SAP102 on synaptic AMPAR function requires the AMPAR auxiliary subunit cornichon-2. Our data suggest that PSD-MAGUKs target and modulate different AMPAR complexes to exert specific experience-dependent modification of the excitatory circuit.