Integrated analysis of anatomical and electrophysiological human intracranial data.

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings.

It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network's spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning.

Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson's Disease.

Oscillations in neural activity play a critical role in neural computation and communication. There is intriguing new evidence that the nonsinusoidal features of the oscillatory waveforms may inform underlying physiological and pathophysiological characteristics. Time-domain waveform analysis approaches stand in contrast to traditional Fourier-based methods, which alter or destroy subtle waveform features. Recently, it has been shown that the waveform features of oscillatory beta (13-30 Hz) events, a prominent motor cortical oscillation, may reflect near-synchronous excitatory synaptic inputs onto cortical pyramidal neurons. Here we analyze data from invasive human primary motor cortex (M1) recordings from patients with Parkinson's disease (PD) implanted with a deep brain stimulator (DBS) to test the hypothesis that the beta waveform becomes less sharp with DBS, suggesting that M1 input synchrony may be decreased. We find that, in PD, M1 beta oscillations have sharp, asymmetric, nonsinusoidal features, specifically asymmetries in the ratio between the sharpness of the beta peaks compared with the troughs. This waveform feature is nearly perfectly correlated with beta-high gamma phase-amplitude coupling (r = 0.94), a neural index previously shown to track PD-related motor deficit. Our results suggest that the pathophysiological beta generator is altered by DBS, smoothing out the beta waveform. This has implications not only for the interpretation of the physiological mechanism by which DBS reduces PD-related motor symptoms, but more broadly for our analytic toolkit in general. That is, the often-overlooked time-domain features of oscillatory waveforms may carry critical physiological information about neural processes and dynamics.SIGNIFICANCE STATEMENT To better understand the neural basis of cognition and disease, we need to understand how groups of neurons interact to communicate with one another. For example, there is evidence that parkinsonian bradykinesia and rigidity may arise from an oversynchronization of afferents to the motor cortex, and that these symptoms are treatable using deep brain stimulation. Here we show that the waveform shape of beta (13-30 Hz) oscillations, which may reflect input synchrony onto the cortex, is altered by deep brain stimulation. This suggests that mechanistic inferences regarding physiological and pathophysiological neural communication may be made from the temporal dynamics of oscillatory waveform shape.

Rhythmic Components in Extracranial Brain Signals Reveal Multifaceted Task Modulation of Overlapping Neuronal Activity.

Oscillatory neuronal activity is implicated in many cognitive functions, and its phase coupling between sensors may reflect networks of communicating neuronal populations. Oscillatory activity is often studied using extracranial recordings and compared between experimental conditions. This is challenging, because there is overlap between sensor-level activity generated by different sources, and this can obscure differential experimental modulations of these sources. Additionally, in extracranial data, sensor-level phase coupling not only reflects communicating populations, but can also be generated by a current dipole, whose sensor-level phase coupling does not reflect source-level interactions. We present a novel method, which is capable of separating and characterizing sources on the basis of their phase coupling patterns as a function of space, frequency and time (trials). Importantly, this method depends on a plausible model of a neurobiological rhythm. We present this model and an accompanying analysis pipeline. Next, we demonstrate our approach, using magnetoencephalographic (MEG) recordings during a cued tactile detection task as a case study. We show that the extracted components have overlapping spatial maps and frequency content, which are difficult to resolve using conventional pairwise measures. Because our decomposition also provides trial loadings, components can be readily contrasted between experimental conditions. Strikingly, we observed heterogeneity in alpha and beta sources with respect to whether their activity was suppressed or enhanced as a function of attention and performance, and this happened both in task relevant and irrelevant regions. This heterogeneity contrasts with the common view that alpha and beta amplitude over sensory areas are always negatively related to attention and performance.

Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep.

During systems-level consolidation, mnemonic representations initially reliant on the hippocampus are thought to migrate to neocortical sites for more permanent storage, with an eminent role of sleep for facilitating this information transfer. Mechanistically, consolidation processes have been hypothesized to rely on systematic interactions between the three cardinal neuronal oscillations characterizing non-rapid eye movement (NREM) sleep. Under global control of de- and hyperpolarizing slow oscillations (SOs), sleep spindles may cluster hippocampal ripples for a precisely timed transfer of local information to the neocortex. We used direct intracranial electroencephalogram recordings from human epilepsy patients during natural sleep to test the assumption that SOs, spindles and ripples are functionally coupled in the hippocampus. Employing cross-frequency phase-amplitude coupling analyses, we found that spindles were modulated by the up-state of SOs. Notably, spindles were found to in turn cluster ripples in their troughs, providing fine-tuned temporal frames for the hypothesized transfer of hippocampal memory traces.

Uncovering phase-coupled oscillatory networks in electrophysiological data.

Phase consistent neuronal oscillations are ubiquitous in electrophysiological recordings, and they may reflect networks of phase-coupled neuronal populations oscillating at different frequencies. Because neuronal oscillations may reflect rhythmic modulations of neuronal excitability, phase-coupled oscillatory networks could be the functional building block for routing information through the brain. Current techniques are not suited for directly characterizing such networks. To be able to extract phase-coupled oscillatory networks we developed a new method, which characterizes networks by phase coupling between sites. Importantly, this method respects the fact that neuronal oscillations have energy in a range of frequencies. As a consequence, we characterize these networks by between-site phase relations that vary as a function of frequency, such as those that result from between-site temporal delays. Using human electrocorticographic recordings we show that our method can uncover phase-coupled oscillatory networks that show interesting patterns in their between-site phase relations, such as travelling waves. We validate our method by demonstrating it can accurately recover simulated networks from a realistic noisy environment. By extracting phase-coupled oscillatory networks and investigating patterns in their between-site phase relations we can further elucidate the role of oscillations in neuronal communication.

Phase-amplitude coupling in rat orbitofrontal cortex discriminates between correct and incorrect decisions during associative learning.

Cross-frequency interactions between oscillations in local field potentials (LFPs) are thought to support communication between brain structures by temporally coordinating neural activity. It is unknown, however, whether such interactions differentiate between different levels of performance in decision-making tasks. Here, we investigated theta (4-12 Hz) to gamma (30-100 Hz) phase-amplitude coupling in LFP recordings from rat orbitofrontal cortex. Across subsequent periods of a task in which rats learned to discriminate two odors associated with positive and negative outcomes, theta-to-gamma phase-amplitude coupling (PAC) was highest during the odor-sampling task period that preceded a Go/NoGo decision. This task-dependent modulation could not be explained by changes in oscillatory power and appeared to be time-locked to odor onset, not to the timing of the behavioral response. We found that PAC strength during odor sampling correlated with learning, as indexed by improved performance across trials. Moreover, this increase in PAC magnitude was apparent only on trials with correct Go and NoGo decisions, but not incorrect Go decisions. In addition, we found that PAC preferred coupling phase showed consistency over sessions only for correct, but not incorrect trials. In conclusion, orbitofrontal cortex theta-gamma PAC strength differentiates between different levels of performance in an olfactory decision-making task and may play a role in the generation and utilization of stimulus-based outcome predictions, necessary for adaptive decision-making.

Event-related potentials and oscillatory brain responses associated with semantic and Stroop-like interference effects in overt naming.

Picture-word interference is a widely employed paradigm to investigate lexical access in word production: Speakers name pictures while trying to ignore superimposed distractor words. The distractor can be congruent to the picture (pictured cat, word cat), categorically related (pictured cat, word dog), or unrelated (pictured cat, word pen). Categorically related distractors slow down picture naming relative to unrelated distractors, the so-called semantic interference. Categorically related distractors slow down picture naming relative to congruent distractors, analogous to findings in the colour-word Stroop task. The locus of semantic interference and Stroop-like effects in naming performance has recently become a topic of debate. Whereas some researchers argue for a pre-lexical locus of semantic interference and a lexical locus of Stroop-like effects, others localise both effects at the lexical selection stage. We investigated the time course of semantic and Stroop-like interference effects in overt picture naming by means of event-related potentials (ERP) and time-frequency analyses. Moreover, we employed cluster-based permutation for statistical analyses. Naming latencies showed semantic and Stroop-like interference effects. The ERP waveforms for congruent stimuli started diverging statistically from categorically related stimuli around 250 ms. Deflections for the categorically related condition were more negative-going than for the congruent condition (the Stroop-like effect). The time-frequency analysis revealed a power increase in the beta band (12-30 Hz) for categorically related relative to unrelated stimuli roughly between 250 and 370 ms (the semantic effect). The common time window of these effects suggests that both semantic interference and Stroop-like effects emerged during lexical selection.

Phase-amplitude coupling in human electrocorticography is spatially distributed and phase diverse.

Spatially distributed phase-amplitude coupling (PAC) is a possible mechanism for selectively routing information through neuronal networks. If so, two key properties determine its selectivity and flexibility, phase diversity over space, and frequency diversity. To investigate these issues, we analyzed 42 human electrocorticographic recordings from 27 patients performing a working memory task. We demonstrate that (1) spatially distributed PAC occurred at distances >10 cm, (2) involved diverse preferred coupling phases, and (3) involved diverse frequencies. Using a novel technique [N-way decomposition based on the PARAFAC (for Parallel Factor analysis) model], we demonstrate that (4) these diverse phases originated mainly from the phase-providing oscillations. With these properties, PAC can be the backbone of a mechanism that is able to separate spatially distributed networks operating in parallel.