High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas.

One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.

Neurophysiological brain mapping of human sleep-wake states.

OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data. METHODS: Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models. RESULTS: The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the 'awakening' of the cortex during REM sleep. CONCLUSIONS: Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension. SIGNIFICANCE: Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a 'continuum' hypothesis in the generative processes of sleep and wakefulness in human brain.

Functional Connectivity in Dorsolateral Frontal Cortex: Intracranial Electroencephalogram Study.

Motivation: Mechanisms underlying the variation in the appearance of electroencephalogram (EEG) over human head are not well characterized. We hypothesized that spatial variation of the EEG, being ultimately linked to variations in cortical neurobiology, was dependent on cortical connectivity patterns. Specifically, we explored the relationship of resting-state functional connectivity derived from intracranial EEG (iEEG) data in seven (N = 7) human epilepsy patients with the intrinsic dynamic variability of the local iEEG. We asked whether primary and association brain areas over the lateral frontal lobe-due to their sharply different connectivity patterns-were thus dissociable in "EEG space." Methods: Functional connectivity between pairs of subdural grid electrodes was averaged to yield an electrode connectivity (EC) whose time-average yielded mean electrode connectivity (mEC), compared with that electrode's time-averaged sample entropy (SE; mean electrode sample entropy, mESE). Results: We found that mEC and mESE were generally in inverse proportion to each other. Extreme values of mEC and mESE occurred over the Rolandic region and were part of a more general rostrocaudal gradient observed in all patients, with larger (smaller) values of mEC (mESE) occurring anteriorly. Conclusions: Brain networks influence brain dynamics. Over the lateral frontal lobe, mEC and mESE demonstrate a rostrocaudal topography, consistent with current notions regarding the structural and functional parcellation of the human frontal lobe. Our findings distinguish the frontal association cortex from primary sensorimotor cortex, effectively "diagnosing" Rolandic iEEG independent of the classical mu rhythm associated with the latter brain region. Impact statement Electroencephalographic rhythms (electroencephalogram [EEG]) exhibit well-recognized spatial variation over the brain surface. How such variation pertains to the biology of the cortex is poorly understood. Here we identify a novel relationship between sample entropy of the local EEG and the connectivity of that local cortical region to the rest of the brain. Due to the differing connectivities of primary and association motor areas, our methods identify new differences in the EEG arising from those respective brain areas. Our work demonstrates that aspects of brain dynamics (i.e., EEG entropy) may be understood in terms of brain architecture (i.e., functional connectivity) and vice versa.

A neurophysiological brain map: Spectral parameterization of the human intracranial electroencephalogram.

OBJECTIVE: A library of intracranial electroencephalography (iEEG) from the normal human brain has recently been made publicly available (Frauscher et al., 2018). The library - which we term the Montreal Neurological Institute Atlas (MNIA) - comprises 30 hours of iEEG from over a hundred epilepsy patients. We present a Fourier spectrum-based model of low dimension that summarizes all of MNIA into a neurophysiological 'brain map'. METHODS: Normalized amplitude spectra of the MNIA data were modelled as log-normal distributions around individual canonical Berger frequencies. The latter were concatenated to yield the composite spectrum with high accuracy. Key model parameters were color-coded into a visual representation on cortical surface models. RESULTS: Each brain region has its own spectral characteristics that together yield a novel composite intracranial EEG brain map. CONCLUSIONS: iEEG from normal brain regions can be accurately modelled with a small number of independent parameters. Our model is based in the canonical Berger bands and naturally suits clinical electroencephalography. SIGNIFICANCE: Due to its applicability to iEEG from all sampled regions, the model suggests a certain universality to brain rhythm generation that is independent of precise cortical location. More generally, our results are a novel abstraction of resting cortical dynamics that may help diagnostics in epileptology, in addition to informing structure-function relationships in the field of human brain mapping.

A Continuous Clustering Algorithm for Detection of Local Sleep in Humans.

OBJECTIVE: Research in animal models has shown that many EEG sleep features reflect local conditions, which is a consequence of relative inactivity of neuronal clusters. In humans, the authors previously reported that focal sleep patterns appear on the cortex during the wake state and suggested that this underlies the condition described as drowsiness. The focal changes at individual electrodes appeared as a combination of increased instantaneous amplitude in the delta band and decreased instantaneous frequency in the theta-alpha band during non-REM sleep, with the opposite occurring during the wake state, permitting their categorization as "active" and "inactive." A limitation of the previous work was the use of a binary k-means clustering algorithm, which created the possibility that the findings were biased toward a predominantly inactive state while the study subject was still awake. The present study tested the hypothesis that analyzing the same data by using a continuous rather than binary classifier would overcome this limitation. METHODS: An analysis was performed on records from six patients with refractory epilepsy who were undergoing video-electrocorticographic monitoring with implanted subdural grid electrodes. A fuzzy c-means clustering algorithm was utilized after feature extraction from the recordings to create state classifications for each moment in each recording. A subsequent analysis was performed to determine the relative contributions of instantaneous amplitude versus instantaneous frequency to the classification. RESULTS: Localized state changes consistent with the hypothesis were observed. The contributions from instantaneous frequency and amplitude appeared roughly equal. CONCLUSIONS: This study reveals evidence of local sleep during the wake state in humans.

Focal Changes to Human Electrocorticography With Drowsiness: A Novel Measure of Local Sleep.

Drowsiness may be defined as the progressive loss of cortical processing efficiency that occurs with time passing while awake. This loss of cortical processing efficiency is reflected in focal changes to the electroencephalogram, including islands of increased delta power concurrent with drop-offs in neuronal activity (i.e., focal cortical inactivity). The authors hypothesized that these focal changes are evidenced at individual electrodes by combination of increased instantaneous amplitude in delta band and decreased instantaneous frequency in theta-alpha band, permitting their categorization as "active" and "inactive." An analysis of records from six patients with refractory epilepsy undergoing video-electrocorticographic monitoring was conducted. Feature extraction and state classification on multiple recordings revealed focal changes consistent with the hypothesis, as well as progressively increased numbers of inactive electrodes with time awake. The implications of these findings on the study of sleep, and particularly local sleep, are discussed.

A unified statistical model for the human electrocorticogram.

OBJECTIVE: Extracellular field potentials (ECFs) generated in the cerebral cortex span a vast range of spatiotemporal scales. The process(es) leading to this large dynamic range remain debatable. Here we propose a novel statistical description of the amplitude spectrum of the human electrocorticogram (ECoG). METHODS: Spectral analysis was performed on long-term recordings from epilepsy patients undergoing pre-surgical evaluation with intracranial electrodes. Amplitude spectra were fit with a multi-component Gaussian model on semi-logarithmic axes. RESULTS: The Gaussian formulation provided excellent fits to the data. It also suggested how the changes accompanying the sleep-wake cycle and certain epileptiform transitions could be understood by variation in the parameters of the model. CONCLUSIONS: The proposed continuum model synthesizes several previous observations regarding the statistical structure of the resting human ECoG. It offers a conceptual platform for understanding the EEG changes accompanying the sleep-wake cycle and pathologically hypersynchronous behaviour. SIGNIFICANCE: Statistical characterisation of the spectral distribution of field potentials yield insight into the cortico-cortical interactions that underlie the summated cortical ECFs comprising the ECoG. Such insight is relevant for a synoptic understanding of major state changes in the brain that are diagnosed in clinical practice by visual inspection of the ECoG.

Reactivation of visual-evoked activity in human cortical networks.

In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we demonstrate reactivation of stimulus-evoked activity that is distributed across large areas in the human brain. We performed simultaneous electrocorticography recordings from occipital, parietal, temporal, and frontal areas in awake humans in the presence and absence of sensory stimulation. We found that, in the absence of visual input, repeated exposure to brief natural movies induces robust stimulus-specific reactivation at individual recording sites. The reactivation sites were characterized by greater global connectivity compared with those sites that did not exhibit reactivation. Our results indicate a surprising degree of short-term plasticity across multiple networks in the human brain as a result of repeated exposure to unattended information.

Negative Correlations in Visual Cortical Networks.

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function.

Correlated variability in laminar cortical circuits.

Despite the fact that strong trial-to-trial correlated variability in responses has been reported in many cortical areas, recent evidence suggests that neuronal correlations are much lower than previously thought. Here, we used multicontact laminar probes to revisit the issue of correlated variability in primary visual (V1) cortical circuits. We found that correlations between neurons depend strongly on local network context--whereas neurons in the input (granular) layers showed virtually no correlated variability, neurons in the output layers (supragranular and infragranular) exhibited strong correlations. The laminar dependence of noise correlations is consistent with recurrent models in which neurons in the granular layer receive intracortical inputs from nearby cells, whereas supragranular and infragranular layer neurons receive inputs over larger distances. Contrary to expectation that the output cortical layers encode stimulus information most accurately, we found that the input network offers superior discrimination performance compared to the output networks.

Sex differences in the behavioral response to methylphenidate in three adolescent rat strains (WKY, SHR, SD).

Methylphenidate (MPD) is the most widely used drug in the treatment of attention-deficit hyperactivity disorder (ADHD). ADHD has a high incidence in children and can persist in adolescence and adulthood. The relation between sex and the effects of acute and chronic MPD treatment was examined using adolescent male and female rats from three genetically different strains: spontaneously hyperactive rat (SHR), Wistar-Kyoto (WKY) and Sprague-Dawley (SD). Rats from each strain and sex were randomly divided into a control group that received saline injections and three MPD groups that received either 0.6 or 2.5 or 10mg/kg MPD injections. All rats received saline on experimental day 1 (ED1). On ED2 to ED7 and ED11, the rats were injected either with saline or MPD and received no treatment on ED8-ED10. The open field assay was used to assess the dose-response of acute and chronic MPD administration. Significant sex differences were found. Female SHR and SD rats were significantly more active after MPD injections than their male counterparts, while the female WKY rats were less active than the male WKY rats. Dose dependent behavioral sensitization or tolerance to MPD treatment was not observed for SHR or SD rats, but tolerance to MPD was found in WKY rats for the 10mg/kg MPD dose. The use of dose-response protocol and evaluating different locomotor indices provides the means to identify differences between the sexes and the genetic strain in adolescent rats. In addition these differences suggest that the differences to MPD treatment between the sexes are not due to the reproductive hormones.

Levodopa-induced dyskinesias detection based on the complexity of involuntary movements.

Complexity of the biological system output reflects the system's ability to adapt in a changing environment. Disease states and aging, which influence the adaptation of biological systems, modify the complexity of system response to environment changes. The alteration of motor adaptivity seen in Parkinson's disease (PD) has never been properly investigated by using the motor response complexity measured with sample entropy. We show here that the differences in the complexity of the involuntary movements can be efficiently used for the discrimination of the PD treatment state (non-dyskinetic/dyskinetic). We studied the complexity in the involuntary movements of three groups of subjects, normal, non-dyskinetic PD, and dyskinetic PD, by using the sample entropy for multiple time-scales. During experiments the subjects were standing with their arms extended in front of them while performing a cognitive task. PD subjects were tested after levodopa administration in their "best" ON condition. For all subject groups we found that the sample entropy increased almost linearly with the increase of the time-scale. The biggest increase was found in the dyskinetic PD and the least in non-dyskinetic PD subjects. This shows that system adaptivity, as revealed by the complexity analysis of postural displacement, is lower than normal in non-dyskinetic PD patients, but is abnormally increased in dyskinetic patients. The clear difference between the sample entropy-based measures for the non-dyskinetic and dyskinetic patients made possible the automatic, efficient recognition of the dyskinetic condition, based on multi-layer perceptrons.

Efficient coding in heterogeneous neuronal populations.

A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response.

Asymmetric synaptic depression in cortical networks.

Synaptic depression is essential for controlling the balance between excitation and inhibition in cortical networks. Several studies have shown that the depression of intracortical synapses is asymmetric, that is, inhibitory synapses depress less than excitatory ones. Whether this asymmetry has any impact on cortical function is unknown. Here we show that the differential depression of intracortical synapses provides a mechanism through which the gain and sensitivity of cortical circuits shifts over time to improve stimulus coding. We examined the functional consequences of asymmetric synaptic depression by modeling recurrent interactions between orientation-selective neurons in primary visual cortex (V1) that adapt to feedforward inputs. We demonstrate analytically that despite the fact that excitatory synapses depress more than inhibitory synapses, excitatory responses are reduced less than inhibitory ones to increase the overall response gain. These changes play an active role in generating selective gain control in visual cortical circuits. Specifically, asymmetric synaptic depression regulates network selectivity by amplifying responses and sensitivity of V1 neurons to infrequent stimuli and attenuating responses and sensitivity to frequent stimuli, as is indeed observed experimentally.

Spike source localization with tetrodes.

The paper presents in detail a method for approximating the spatial position of neural spike activity from tetrode recordings. The method uses a nonlinear mapping of a set of tetrode tip amplitudes into three-dimensional (3D) space, followed by a self-organizing map clustering technique. Viewed as a spike sorting method, it performs better than tetrode peak amplitudes and it is roughly equivalent with amplitude ratios. The technique's appeal to physical location may be of advantage in many investigations.