A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

BACKGROUND: Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD: Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS: The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD: Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS: These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

Background: Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method: Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results: The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods: Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions: These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Brain mechanisms underlying the emotion processing bias in treatment-resistant depression.

Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization.

Intracranial recordings in epilepsy patients are increasingly utilized to gain insight into the electrophysiological mechanisms of human cognition. There are currently several practical limitations to conducting research with these patients, including patient and researcher availability and the cognitive abilities of patients, which limit the amount of task-related data that can be collected. Prior studies have synchronized clinical audio, video, and neural recordings to understand naturalistic behaviors, but these recordings are centered on the patient to understand their seizure semiology and thus do not capture and synchronize audiovisual stimuli experienced by patients. Here, we describe a platform for cognitive monitoring of neurosurgical patients during their hospitalization that benefits both patients and researchers. We provide the full specifications for this system and describe some example use cases in perception, memory, and sleep research. We provide results obtained from a patient passively watching TV as proof-of-principle for the naturalistic study of cognition. Our system opens up new avenues to collect more data per patient using real-world behaviors, affording new possibilities to conduct longitudinal studies of the electrophysiological basis of human cognition under naturalistic conditions.

Multi-scale neural decoding and analysis.

Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

The Oscillatory ReConstruction Algorithm adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings.

Neural oscillations are routinely analyzed using methods that measure activity in fixed frequency bands (e.g., alpha, 8-12 Hz), although the frequency of neural signals varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Furthermore, band-limited activity is an often assumed, typically unmeasured model of neural activity, and band definitions vary considerably across studies. Together, these factors mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm ("ORCA"), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands, which incorporates two new methods for frequency band identification. ORCA uses the instantaneous amplitude, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes-open and eyes-closed "resting" conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared with conventional methods and captures the well-known increase in low-frequency activity during eye closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that allows researchers to investigate how nonstationary neural oscillations vary across behaviors, brain regions, individuals, and species.NEW & NOTEWORTHY Neural oscillations show substantial variability within and across individuals and brain regions, yet most existing studies analyze oscillations using canonical, fixed-frequency bands. Thus, there is an ongoing need for tools that capture oscillatory variability in neural signals. Toward this end, Oscillatory ReConstruction Algorithm is a novel and adaptive analytic tool that allows researchers to measure neural oscillations with more precision and less researcher bias.

Functionally distinct high and low theta oscillations in the human hippocampus.

Based on rodent models, researchers have theorized that the hippocampus supports episodic memory and navigation via the theta oscillation, a ~4-10 Hz rhythm that coordinates brain-wide neural activity. However, recordings from humans have indicated that hippocampal theta oscillations are lower in frequency and less prevalent than in rodents, suggesting interspecies differences in theta's function. To characterize human hippocampal theta, we examine the properties of theta oscillations throughout the anterior-posterior length of the hippocampus as neurosurgical subjects performed a virtual spatial navigation task. During virtual movement, we observe hippocampal oscillations at multiple frequencies from 2 to 14 Hz. The posterior hippocampus prominently displays oscillations at ~8-Hz and the precise frequency of these oscillations correlates with the speed of movement, implicating these signals in spatial navigation. We also observe slower ~3 Hz oscillations, but these signals are more prevalent in the anterior hippocampus and their frequency does not vary with movement speed. Our results converge with recent findings to suggest an updated view of human hippocampal electrophysiology. Rather than one hippocampal theta oscillation with a single general role, high- and low-frequency theta oscillations, respectively, may reflect spatial and non-spatial cognitive processes.

The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity.

BACKGROUND: Researchers have used direct electrical brain stimulation to treat a range of neurological and psychiatric disorders. However, for brain stimulation to be maximally effective, clinicians and researchers should optimize stimulation parameters according to desired outcomes. OBJECTIVE: The goal of our large-scale study was to comprehensively evaluate the effects of stimulation at different parameters and locations on neuronal activity across the human brain. METHODS: To examine how different kinds of stimulation affect human brain activity, we compared the changes in neuronal activity that resulted from stimulation at a range of frequencies, amplitudes, and locations with direct human brain recordings. We recorded human brain activity directly with electrodes that were implanted in widespread regions across 106 neurosurgical epilepsy patients while systematically stimulating across a range of parameters and locations. RESULTS: Overall, stimulation most often had an inhibitory effect on neuronal activity, consistent with earlier work. When stimulation excited neuronal activity, it most often occurred from high-frequency stimulation. These effects were modulated by the location of the stimulating electrode, with stimulation sites near white matter more likely to cause excitation and sites near gray matter more likely to inhibit neuronal activity. CONCLUSION: By characterizing how different stimulation parameters produced specific neuronal activity patterns on a large scale, our results provide an electrophysiological framework that clinicians and researchers may consider when designing stimulation protocols to cause precisely targeted changes in human brain activity.

Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex.

Human cognition requires the coordination of neural activity across widespread brain networks. Here, we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies within 2 to 15 Hz. These oscillatory clusters displayed spatial phase gradients, indicating that they formed traveling waves that propagated at ∼0.25-0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Human traveling theta and alpha waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. Our findings suggest that oscillations support brain connectivity by organizing neural processes across space and time.

Lateralized hippocampal oscillations underlie distinct aspects of human spatial memory and navigation.

The hippocampus plays a vital role in various aspects of cognition including both memory and spatial navigation. To understand electrophysiologically how the hippocampus supports these processes, we recorded intracranial electroencephalographic activity from 46 neurosurgical patients as they performed a spatial memory task. We measure signals from multiple brain regions, including both left and right hippocampi, and we use spectral analysis to identify oscillatory patterns related to memory encoding and navigation. We show that in the left but not right hippocampus, the amplitude of oscillations in the 1-3-Hz "low theta" band increases when viewing subsequently remembered object-location pairs. In contrast, in the right but not left hippocampus, low-theta activity increases during periods of navigation. The frequencies of these hippocampal signals are slower than task-related signals in the neocortex. These results suggest that the human brain includes multiple lateralized oscillatory networks that support different aspects of cognition.

Phase-tuned neuronal firing encodes human contextual representations for navigational goals.

We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a virtual navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person's prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

Electrical Stimulation in Hippocampus and Entorhinal Cortex Impairs Spatial and Temporal Memory.

The medial temporal lobe (MTL) is widely implicated in supporting episodic memory and navigation, but its precise functional role in organizing memory across time and space remains elusive. Here we examine the specific cognitive processes implemented by MTL structures (hippocampus and entorhinal cortex) to organize memory by using electrical brain stimulation, leveraging its ability to establish causal links between brain regions and features of behavior. We studied neurosurgical patients of both sexes who performed spatial-navigation and verbal-episodic memory tasks while brain stimulation was applied in various regions during learning. During the verbal memory task, stimulation in the MTL disrupted the temporal organization of encoded memories such that items learned with stimulation tended to be recalled in a more randomized order. During the spatial task, MTL stimulation impaired subjects' abilities to remember items located far away from boundaries. These stimulation effects were specific to the MTL. Our findings thus provide the first causal demonstration in humans of the specific memory processes that are performed by the MTL to encode when and where events occurred.SIGNIFICANCE STATEMENT Numerous studies have implicated the medial temporal lobe (MTL) in encoding spatial and temporal memories, but they have not been able to causally demonstrate the nature of the cognitive processes by which this occurs in real-time. Electrical brain stimulation is able to demonstrate causal links between a brain region and a given function with high temporal precision. By examining behavior in a memory task as subjects received MTL stimulation, we provide the first causal evidence demonstrating the role of the MTL in organizing the spatial and temporal aspects of episodic memory.

Electrophysiological Signatures of Spatial Boundaries in the Human Subiculum.

Environmental boundaries play a crucial role in spatial navigation and memory across a wide range of distantly related species. In rodents, boundary representations have been identified at the single-cell level in the subiculum and entorhinal cortex of the hippocampal formation. Although studies of hippocampal function and spatial behavior suggest that similar representations might exist in humans, boundary-related neural activity has not been identified electrophysiologically in humans until now. To address this gap in the literature, we analyzed intracranial recordings from the hippocampal formation of surgical epilepsy patients (of both sexes) while they performed a virtual spatial navigation task and compared the power in three frequency bands (1-4, 4-10, and 30-90 Hz) for target locations near and far from the environmental boundaries. Our results suggest that encoding locations near boundaries elicited stronger theta oscillations than for target locations near the center of the environment and that this difference cannot be explained by variables such as trial length, speed, movement, or performance. These findings provide direct evidence of boundary-dependent neural activity localized in humans to the subiculum, the homolog of the hippocampal subregion in which most boundary cells are found in rodents, and indicate that this system can represent attended locations that rather than the position of one's own body.SIGNIFICANCE STATEMENT Spatial computations using environmental boundaries are an integral part of the brain's spatial mapping system. In rodents, border/boundary cells in the subiculum and entorhinal cortex reveal boundary coding at the single-neuron level. Although there is good reason to believe that such representations also exist in humans, the evidence has thus far been limited to functional neuroimaging studies that broadly implicate the hippocampus in boundary-based navigation. By combining intracranial recordings with high-resolution imaging of hippocampal subregions, we identified a neural marker of boundary representation in the human subiculum.

Direct Electrical Stimulation of the Human Entorhinal Region and Hippocampus Impairs Memory.

Deep brain stimulation (DBS) has shown promise for treating a range of brain disorders and neurological conditions. One recent study showed that DBS in the entorhinal region improved the accuracy of human spatial memory. Based on this line of work, we performed a series of experiments to more fully characterize the effects of DBS in the medial temporal lobe on human memory. Neurosurgical patients with implanted electrodes performed spatial and verbal-episodic memory tasks. During the encoding periods of both tasks, subjects received electrical stimulation at 50 Hz. In contrast to earlier work, electrical stimulation impaired memory performance significantly in both spatial and verbal tasks. Stimulation in both the entorhinal region and hippocampus caused decreased memory performance. These findings indicate that the entorhinal region and hippocampus are causally involved in human memory and suggest that refined methods are needed to use DBS in these regions to improve memory.

Phase-amplitude coupling supports phase coding in human ECoG.

Prior studies have shown that high-frequency activity (HFA) is modulated by the phase of low-frequency activity. This phenomenon of phase-amplitude coupling (PAC) is often interpreted as reflecting phase coding of neural representations, although evidence for this link is still lacking in humans. Here, we show that PAC indeed supports phase-dependent stimulus representations for categories. Six patients with medication-resistant epilepsy viewed images of faces, tools, houses, and scenes during simultaneous acquisition of intracranial recordings. Analyzing 167 electrodes, we observed PAC at 43% of electrodes. Further inspection of PAC revealed that category specific HFA modulations occurred at different phases and frequencies of the underlying low-frequency rhythm, permitting decoding of categorical information using the phase at which HFA events occurred. These results provide evidence for categorical phase-coded neural representations and are the first to show that PAC coincides with phase-dependent coding in the human brain.

More than spikes: common oscillatory mechanisms for content specific neural representations during perception and memory.

Although previous research into the mechanisms underlying sensory and episodic representations has primarily focused on changes in neural firing rate, more recent evidence suggests that neural oscillations also contribute to these representations. Here, we argue that multiplexed oscillatory power and phase contribute to neural representations at the mesoscopic scale, complementary to neuronal firing. Reviewing recent studies which used oscillatory activity to decipher content-specific neural representations, we identify oscillatory mechanisms common to both sensory and episodic memory representations and incorporate these into a model of episodic encoding and retrieval. This model advances the idea that oscillations provide a reference frame for phase-coded item representations during memory encoding and that shifts in oscillatory frequency and phase coordinate ensemble activity during memory retrieval.

Multiple interacting brain areas underlie successful spatiotemporal memory retrieval in humans.

Emerging evidence suggests that our memories for recent events depend on a dynamic interplay between multiple cortical brain regions, although previous research has also emphasized a primary role for the hippocampus in episodic memory. One challenge in determining the relative importance of interactions between multiple brain regions versus a specific brain region is a lack of analytic approaches to address this issue. Participants underwent neuroimaging while retrieving the spatial and temporal details of a recently experienced virtual reality environment; we then employed graph theory to analyze functional connectivity patterns across multiple lobes. Dense, large-scale increases in connectivity during successful memory retrieval typified network topology, with individual participant performance correlating positively with overall network density. Within this dense network, the hippocampus, prefrontal cortex, precuneus, and visual cortex served as "hubs" of high connectivity. Spatial and temporal retrieval were characterized by distinct but overlapping "subnetworks" with higher connectivity within posterior and anterior brain areas, respectively. Together, these findings provide new insight into the neural basis of episodic memory, suggesting that the interactions of multiple hubs characterize successful memory retrieval. Furthermore, distinct subnetworks represent components of spatial versus temporal retrieval, with the hippocampus acting as a hub integrating information between these two subnetworks.

The spectro-contextual encoding and retrieval theory of episodic memory.

The spectral fingerprint hypothesis, which posits that different frequencies of oscillations underlie different cognitive operations, provides one account for how interactions between brain regions support perceptual and attentive processes (Siegel etal., 2012). Here, we explore and extend this idea to the domain of human episodic memory encoding and retrieval. Incorporating findings from the synaptic to cognitive levels of organization, we argue that spectrally precise cross-frequency coupling and phase-synchronization promote the formation of hippocampal-neocortical cell assemblies that form the basis for episodic memory. We suggest that both cell assembly firing patterns as well as the global pattern of brain oscillatory activity within hippocampal-neocortical networks represents the contents of a particular memory. Drawing upon the ideas of context reinstatement and multiple trace theory, we argue that memory retrieval is driven by internal and/or external factors which recreate these frequency-specific oscillatory patterns which occur during episodic encoding. These ideas are synthesized into a novel model of episodic memory (the spectro-contextual encoding and retrieval theory, or "SCERT") that provides several testable predictions for future research.

Modafinil augments oscillatory power in middle frequencies during rule selection.

Control-related cognitive processes are associated with cortical oscillations and modulated by catecholamine neurotransmitters. It remains unclear how catecholamine systems modulate control-related oscillations. We tested modafinil effects on rule-related 4-30 Hz oscillations, with double-blind, placebo-controlled (within-subjects) testing of 22 healthy adults, using EEG during cognitive control task performance. EEG data underwent time-frequency decomposition with Morlet wavelets to determine power of 4-30 Hz oscillations. Modafinil enhanced oscillatory power associated with high-control rule selection in theta, alpha, and beta ranges, with a frontotemporal topography and minimal effects during rule maintenance. Augmentation of catecholamine signaling enhances middle-frequency cortical oscillatory power associated with rule selection, which may subserve diverse subcomponent processes in proactive cognitive control.