Focal lesions induce large-scale percolation of sleep-like intracerebral activity in awake humans.

Focal cortical lesions are known to result in large-scale functional alterations involving distant areas; however, little is known about the electrophysiological mechanisms underlying these network effects. Here, we addressed this issue by analysing the short and long distance intracranial effects of controlled structural lesions in humans. The changes in Stereo-Electroencephalographic (SEEG) activity after Radiofrequency-Thermocoagulation (RFTC) recorded in 21 epileptic subjects were assessed with respect to baseline resting wakefulness and sleep activity. In addition, Cortico-Cortical Evoked Potentials (CCEPs) recorded before the lesion were employed to interpret these changes with respect to individual long-range connectivity patterns. We found that small structural ablations lead to the generation and large-scale propagation of sleep-like slow waves within the awake brain. These slow waves match those recorded in the same subjects during sleep, are prevalent in perilesional areas, but can percolate up to distances of 60 mm through specific long-range connections, as predicted by CCEPs. Given the known impact of slow waves on information processing and cortical plasticity, demonstrating their intrusion and percolation within the awake brain add key elements to our understanding of network dysfunction after cortical injuries

How can I investigate causal brain networks with iEEG?

While many human imaging methodologies probe the structural and functional connectivity of the brain, techniques to investigate cortical networks in a causal and directional manner are critical but limited. The use of iEEG enables several approaches to directly characterize brain regions that are functionally connected and in some cases also establish directionality of these connections. In this chapter we focus on the basis, method and application of the cortico-cortical evoked potential (CCEP), whereby electrical pulses applied to one set of intracranial electrodes yields an electrically-induced brain response at local and remote regions. In this chapter, CCEPs are first contextualized within common brain connectivity methods used to define cortical networks and how CCEP adds unique information. Second, the practical and analytical considerations when using CCEP are discussed. Third, we review the neurophysiology underlying CCEPs and the applications of CCEPs including exploring functional and pathological brain networks and probing brain plasticity. Finally, we end with a discussion of limitations, caveats, and directions to improve CCEP utilization in the future.Comment: Forthcoming chapter in "Intracranial EEG for Cognitive Neuroscience

Long-range phase synchronization of high-gamma activity in human cortex

AbstractInter-areal synchronization of neuronal oscillations below 100 Hz is ubiquitous in cortical circuitry and thought to regulate neuronal communication. In contrast, faster activities are generally considered to be exclusively local-circuit phenomena. We show with human intracerebral recordings that 100–300 Hz high-gamma activity (HGA) may be synchronized between widely distributed regions. HGA synchronization was not attributable to artefacts or to epileptic pathophysiology. Instead, HGA synchronization exhibited a reliable cortical connectivity and community structures, and a laminar profile opposite to that of lower frequencies. Importantly, HGA synchronization among functional brain systems during non-REM sleep was distinct from that in resting state. Moreover, HGA synchronization was transiently enhanced for correctly inhibited responses in a Go/NoGo task. These findings show that HGA synchronization constitutes a new, functionally significant form of neuronal spike-timing relationships in brain activity. We suggest that HGA synchronization reflects the temporal microstructure of spiking-based neuronal communication per se in cortical circuits

Characterizing Unstructured Motor Behaviors in the Epilepsy Monitoring Unit

Key advancements in recording hardware, data computation, clinical care, and cognitive science continue to drive new possibilities in how humans and machines can interact directly through thought. Neural data analyses with these advancements has progressed neuroscience research in functional brain mapping and brain-computer interfaces (BCIs). Much of our knowledge about BCIs is informed by data collected through carefully controlled experiments. Constraining BCI experiments with structured paradigms allows researchers to collect a high number of consistent data in a short amount of time, while also controlling for external confounds. Very little is currently known about how well these task-based relationships extend to daily life, in part because collecting data outside of the lab is challenging. To further understand natural brain activity, we must study more complex behaviors in more environmentally relevant settings. The results of this dissertation address three general challenges to studying neural correlates to unstructured behaviors. First, we continuously monitored unstructured human movements in the epilepsy monitoring unit using a video sensor synchronized to clinical intracortical electrodes. Second, we annotated unstructured behaviors from these video using both manual and computer vision methods. Finally, analyzed neural features with respect to unstructured human movements, and evaluated the performance of features identified in previous task-based studies. The preliminary nature of this work means that a majority of our demonstrations are whether the continuous paradigm can be leveraged, how one might go about leveraging it, and evaluations that tie our results back to earlier task-based studies. Our advances here motivate future works that focus more intently on what types of behaviors and neural signal features to explore

Granger causality analysis of intraoperative interictal sEEG data predicts the seizure focus

The purpose of the study was to investigate whether interictal stereotactic EEG data recorded during surgical implantation of depth electrodes can be used to predict the seizure focus in patients with epilepsy. We retrospectively studied 21 patients who underwent invasive stereotactic EEG monitoring and surgical treatment at Boston Children’s Hospital. Interictal data were analyzed using the method of Granger Causality (GC) statistics to calculate causal interactions between brain regions. In 10 cases, intraoperative GC analysis accurately identified the seizure focus, as supported by their statistically significant rank order sum P values. The remaining 11 cases failed to achieve statistical significance for intraoperative GC analysis. When we examined the visual representations of the causality network described by GC, we observed that despite insignificant rank order P values, GC analysis could still be valuable in revealing the seizure network. It is apparent that there needs to be additional, more in-depth investigations regarding the value of using intraoperative interictal EEG data for defining the seizure focus. Future studies should focus on identifying clinical factors that may affect the utility of intraoperative GC analysis and also on experimenting how GC analysis could aid in decision-making during electrode implantation.2022-06-05T00:00:00

Standards for data acquisition and software‐based analysis of in vivo electroencephalography recordings from animals. A TASK1‐WG5 report of the AES/ILAE Translational Task Force of the ILAE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/1/epi13909.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/2/epi13909_am.pd

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal- to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We ap- plied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were similar to 70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around similar to 2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. Significance Statement Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface

Imaging physiological brain activity and epilepsy with Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) allows reconstructing conductivity changes into images. EIT detects fast impedance changes occurring over milliseconds, due to ion channel opening, and slow impedance changes, appearing in seconds, due to cell swelling/increased blood flow. The purpose of this work was to examine the feasibility of using EIT for imaging a gyrencephalic brain with implanted depth electrodes during seizures. Chapter 1 summarises the principles of EIT. In Chapter 2, it is investigated whether recent technical improvements could enable EIT to image slow impedance changes upon visual stimulation non-invasively. This was unsuccessful so the remaining studies were undertaken on intracranial recordings. Chapter 3 presents a computer modelling study using data from patients, for whom the detection of simulated seizure-onset perturbations for both, fast and slow impedance changes, were improved with EIT compared to stereotactic electroencephalography (SEEG) detection or EEG inverse-source modelling. Chapter 4 describes the development of a portable EIT system that could be used on patients. The system does not require averaging and post-hoc signal processing to remove switching artefacts, which was the case previously. Chapter 5 describes the use of the optimised method in chemically-induced focal epilepsy in anaesthetised pigs implanted with depth electrodes. This shows for the first time EIT was capable of producing reproducible images of the onset and spread of seizure-related slow impedance changes in real-time. Chapter 6 presents a study on imaging ictal/interictal-related fast impedance changes. The feasibility of reconstructing ictal-related impedance changes is demonstrated for one pig and interictal-related impedance changes were recorded for the first time in humans. Chapter 7 summarises all work and future directions. Overall, this work suggests EIT in combination with SEEG has a potential to improve the diagnostic yield in epilepsy and demonstrates EIT can be performed safely and ethically creating a foundation for further clinical trials

Neural activity in the human anterior thalamus during natural vision

In natural vision humans and other primates explore environment by active sensing, using saccadic eye movements to relocate the fovea and sample different bits of information multiple times per second. Saccades induce a phase reset of ongoing neuronal oscillations in primary and higher-order visual cortices and in the medial temporal lobe. As a result, neuron ensembles are shifted to a common state at the time visual input propagates through the system (i.e., just after fixation). The extent of the brain’s circuitry that is modulated by saccades is not yet known. Here, we evaluate the possibility that saccadic phase reset impacts the anterior nuclei of the thalamus (ANT). Using recordings in the human thalamus of three surgical patients during natural vision, we found that saccades and visual stimulus onset both modulate neural activity, but with distinct field potential morphologies. Specifically, we found that fixation-locked field potentials had a component that preceded saccade onset. It was followed by an early negativity around 50 ms after fixation onset which is significantly faster than any response to visual stimulus presentation. The timing of these events suggests that the ANT is predictively modulated before the saccadic eye movement. We also found oscillatory phase concentration, peaking at 3–4 Hz, coincident with suppression of Broadband High-frequency Activity (BHA; 80–180 Hz), both locked to fixation onset supporting the idea that neural oscillations in these nuclei are reorganized to a low excitability state right after fixation onset. These findings show that during real-world natural visual exploration neural dynamics in the human ANT is influenced by visual and oculomotor events, which supports the idea that ANT, apart from their contribution to episodic memory, also play a role in natural vision