Direct brain stimulation during episodic memory

Exploring individual differences in amygdala-mediated memory modulation

Amygdala activation by emotional arousal during memory formation can prioritize events for long-term memory. Building upon our prior demonstration that brief electrical stimulation to the human amygdala reliably improved long-term recognition memory for images of neutral objects without eliciting an emotional response, our study aims to explore and describe individual differences and stimulation-related factors in amygdala-mediated memory modulation. Thirty-one patients undergoing intracranial monitoring for intractable epilepsy were shown neutral object images paired with direct amygdala stimulation during encoding with recognition memory tested immediately and one day later. Adding to our prior sample, we found an overall memory enhancement effect without subjective emotional arousal at the one-day delay, but not at the immediate delay, for previously stimulated objects compared to not stimulated objects. Importantly, we observed a larger variation in performance across this larger sample than our initial sample, including some participants who showed a memory impairment for stimulated objects. Of the explored individual differences, the factor that most accounted for variability in memory modulation was each participant's pre-operative memory performance. Worse memory performance on standardized neuropsychological tests was associated with a stronger susceptibility to memory modulation in a positive or negative direction. Sex differences and the frequency of interictal epileptiform discharges (IEDs) during testing also accounted for some variance in amygdala-mediated memory modulation. Given the potential and challenges of this memory modulation approach, we discuss additional individual and stimulation factors that we hope will differentiate between memory enhancement and impairment to further optimize the potential of amygdala-mediated memory enhancement for therapeutic interventions.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies

The quest to understand how the development of the brain supports the development of complex cognitive functions is fueled by advances in cognitive neuroscience methods. Intracranial EEG (iEEG) recorded directly from the developing human brain provides unprecedented spatial and temporal resolution for mapping the neurophysiological mechanisms supporting cognitive development. In this paper, we focus on episodic memory, the ability to remember detailed information about past experiences, which improves from childhood into adulthood. We review memory effects based on broadband spectral power and emphasize the importance of isolating narrowband oscillations from broadband activity to determine mechanisms of neural coordination within and between brain regions. We then review evidence of developmental variability in neural oscillations and present emerging evidence linking the development of neural oscillations to the development of memory. We conclude by proposing that the development of oscillations increases the precision of neural coordination and may be an essential factor underlying memory development. More broadly, we demonstrate how recording neural activity directly from the developing brain holds immense potential to advance our understanding of cognitive development.

Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Probing Network Mechanisms of Brain Stimulation in the Mood Circuit

Direct brain stimulation has diverse effects on network activity. In this issue of Neuron, Qiao et al. (2020) demonstrate a novel approach for using stimulation to characterize and modulate interactions between areas of the mood processing network.

Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality

By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research. We then delineate how studies using iEEG have informed our understanding of issues fundamental to human cognition: auditory prediction, working and episodic memory, and internal cognition. We also discuss the potential of iEEG to infer causality through the manipulation or 'engineering' of neurocognitive processes via spatiotemporally precise electrical stimulation. We close by highlighting limitations of iEEG, potential of burgeoning techniques to further increase spatiotemporal precision, and implications for future research using intracranial approaches to understand, restore, and enhance human cognition.

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.

Does data cleaning improve brain state classification?

BACKGROUND

Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations.

NEW METHOD

This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings.

RESULTS

We find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain.

COMPARISON WITH EXISTING METHODS

Researchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations.

CONCLUSIONS

These findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?

Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.