Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.

Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive as to how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation or recall in response to direct electrical stimulation are only now being unravelled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learnt. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioural outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with an open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials will shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioural states. Next-generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.

Astrocytic Kir4.1 channels regulate locomotion by orchestrating neuronal rhythmicity in the spinal network.

Neuronal rhythmogenesis in the spinal cord is correlated with variations in extracellular K+ levels ([K+ ]e ). Astrocytes play important role in [K+ ]e homeostasis and compute neuronal information. Yet it is unclear how neuronal oscillations are regulated by astrocytic K+ homeostasis. Here we identify the astrocytic inward-rectifying K+ channel Kir4.1 (a.k.a. Kcnj10) as a key molecular player for neuronal rhythmicity in the spinal central pattern generator (CPG). By combining two-photon calcium imaging with electrophysiology, immunohistochemistry and genetic tools, we report that astrocytes display Ca2+ transients before and during oscillations of neighboring neurons. Inhibition of astrocytic Ca2+ transients with BAPTA decreases the barium-sensitive Kir4.1 current responsible of K+ clearance. Finally, we show in mice that Kir4.1 knockdown in astrocytes progressively prevents neuronal oscillations and alters the locomotor pattern resulting in lower motor performances in challenging tasks. These data identify astroglial Kir4.1 channels as key regulators of neuronal rhythmogenesis in the CPG driving locomotion.

Bistability at the onset of neuronal oscillations.

The Hodgkin-Huxley (HH) model and squid axon (bathed in reduced Ca2+) fire repetitively for steady current injection. Moreover, for a current-range just suprathreshold, repetitive firing coexists with a stable steady state. Neuronal excitability, as such, shows bistability and hysteresis providing the opportunity for the system to perform as switchable between firing and non-firing states with transient input and providing the backbone as a dynamical mechanism for bursting oscillations. Some conditions for bistability can be derived by intricate analysis (bifurcation theory) and characterized by simulation, but conditions for emergence and robustness of such bistability do not typically follow from intuition. Here, we demonstrate with a semi-quantitative two-variable, V-w, reduction of the HH model features that promote/reduce bistability. Visualization of flow and trajectories in the V-w phase plane provides an intuitive grasp for bistability. The geometry of action potential recovery involves a late phase during which the dynamic negative feedback of [Formula: see text] inactivation and [Formula: see text] activation over/undershoot, respectively, their resting values, thereby leading to hyperexcitabilty and an intrinsically generated opportunity to by-pass the spiral-like stable rest state and initiate the next spike upstroke. We illustrate control of bistability and dependence of the degree of hysteresis on recovery timescales and gating properties. Our dynamical dissection reveals the strongly attracting depolarized phase of the spike, enabling approximations like the resetting feature of adapting integrate-and-fire models. We extend our insights and show that the Morris-Lecar model can also exhibit robust bistability.

Neuronal Oscillatory Signatures in the Developing Mouse Visual Cortex After Short-Term Monocular Deprivation.

Development and maturation in cortical networks depend on neuronal activity. For stabilization and pruning of connections, synchronized oscillations play a crucial role. A fundamental mechanism that enables coordinated activity during brain functioning is formed of synchronized neuronal oscillations in low- (delta and theta) and high- (gamma) frequency bands. The relationship between neural synchrony, cognition, and the perceptual process has been widely studied, but any possible role of neural synchrony in amblyopia has been less explored. We hypothesized that monocular deprivation (MD) during early postnatal life would lead to changes in neuronal activity that would be demonstrated by changes in phase-amplitude coupling (PAC) and altered power in specific oscillatory frequency. Our results demonstrate that functional connectivity in the visual cortex is altered by MD during adolescence. The amplitude of high-frequency oscillations is modulated by the phase of low-frequency oscillations. Demonstration of enhanced delta-gamma and theta-gamma PAC indicates that our results are relevant for a broad range of nested oscillatory markers. These markers are inherent to neuronal processing and are consistent with the hypothesized increase in the intrinsic coupling that arises from neural oscillatory phase alignment. Our results reveal distinct frequency bands exhibit altered power and coherence variations modulated by experience-driven plasticity.

No Evidence for Entrainment: Endogenous Gamma Oscillations and Rhythmic Flicker Responses Coexist in Visual Cortex.

Over the past decades, numerous studies have linked cortical gamma oscillations (∼30-100 Hz) to neurocomputational mechanisms. Their functional relevance, however, is still passionately debated. Here, we asked whether endogenous gamma oscillations in the human brain can be entrained by a rhythmic photic drive >50 Hz. Such a noninvasive modulation of endogenous brain rhythms would allow conclusions about their causal involvement in neurocognition. To this end, we systematically investigated oscillatory responses to a rapid sinusoidal flicker in the absence and presence of endogenous gamma oscillations using magnetoencephalography (MEG) in combination with a high-frequency projector. The photic drive produced a robust response over visual cortex to stimulation frequencies of up to 80 Hz. Strong, endogenous gamma oscillations were induced using moving grating stimuli as repeatedly done in previous research. When superimposing the flicker and the gratings, there was no evidence for phase or frequency entrainment of the endogenous gamma oscillations by the photic drive. Unexpectedly, we did not observe an amplification of the flicker response around participants' individual gamma frequencies (IGFs); rather, the magnitude of the response decreased monotonically with increasing frequency. Source reconstruction suggests that the flicker response and the gamma oscillations were produced by separate, coexistent generators in visual cortex. The presented findings challenge the notion that cortical gamma oscillations can be entrained by rhythmic visual stimulation. Instead, the mechanism generating endogenous gamma oscillations seems to be resilient to external perturbation.SIGNIFICANCE STATEMENT We aimed to investigate to what extent ongoing, high-frequency oscillations in the gamma-band (30-100 Hz) in the human brain can be entrained by a visual flicker. Gamma oscillations have long been suggested to coordinate neuronal firing and enable interregional communication. Our results demonstrate that rhythmic visual stimulation cannot hijack the dynamics of ongoing gamma oscillations; rather, the flicker response and the endogenous gamma oscillations coexist in different visual areas. Therefore, while a visual flicker evokes a strong neuronal response even at high frequencies in the gamma-band, it does not entrain endogenous gamma oscillations in visual cortex. This has important implications for interpreting studies investigating the causal and neuroprotective effects of rhythmic sensory stimulation in the gamma-band.

Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms.

Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.

Multistage classification identifies altered cortical phase- and amplitude-coupling in Multiple Sclerosis.

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Changes in this dataset included both, increased and decreased phase- and amplitude-coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions.

Hippocampal-prefrontal interactions during spatial decision-making.

The hippocampus has been linked to memory encoding and spatial navigation, while the prefrontal cortex is associated with cognitive functions such as decision-making. These regions are hypothesized to communicate in tasks that demand both spatial navigation and decision-making processes. However, the electrophysiological signatures underlying this communication remain to be better elucidated. To investigate the dynamics of the hippocampal-prefrontal interactions, we have analyzed their local field potentials and spiking activity recorded from rats performing a spatial alternation task on a figure eight-shaped maze. We found that the phase coherence of theta peaked around the choice point area of the maze. Moreover, Granger causality revealed a hippocampus → prefrontal cortex directionality of information flow at theta frequency, peaking at starting areas of the maze, and on the reverse direction at delta frequency, peaking near the turn onset. Additionally, the patterns of phase-amplitude cross-frequency coupling within and between the regions also showed spatial selectivity, and hippocampal theta and prefrontal delta modulated not only gamma amplitude but also inter-regional gamma synchrony. Finally, we found that the theta rhythm dynamically modulated neurons in both regions, with the highest modulation at the choice area; interestingly, prefrontal cortex neurons were more strongly modulated by the hippocampal theta rhythm than by their local field rhythm. In all, our results reveal maximum electrophysiological interactions between the hippocampus and the prefrontal cortex near the decision-making period of the spatial alternation task, corroborating the hypothesis that a dynamic interplay between these regions takes place during spatial decisions.

Calibrating rhythmic stimulation parameters to individual electroencephalography markers: The consistency of individual alpha frequency in practical lab settings.

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such 'entrainment' is optimized when stimulation frequency is individually calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short electroencephalography (EEG) measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on 4 days (between-sessions), with multiple measurements over an hour on 1 day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional 'maximum' method and a 'Gaussian fit' method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the 'Gaussian fit' method was more reliable than the 'maximum' method. Furthermore, we evaluated how far from an approximated 'true' task-related IAF the selected 'stimulation frequency' was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10 Hz for all participants. For the 'maximum' method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hz. For the 'Gaussian fit' method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.

GSK-3ß Disrupts Neuronal Oscillatory Function to Inhibit Learning and Memory in Male Rats.

Alterations in glycogen synthase kinase-3ß (GSK-3ß) activity have been implicated in disorders of cognitive impairment, including Alzheimer's disease and schizophrenia. Cognitive dysfunction is also characterized by the dysregulation of neuronal oscillatory activity, macroscopic electrical rhythms in brain that are critical to systems communication. A direct functional relationship between GSK-3ß and neuronal oscillations has not been elucidated. Therefore, in the present study, using an adeno-associated viral vector containing a persistently active mutant form of GSK-3ß, GSK-3ß(S9A), the impact of elevated kinase activity in prefrontal cortex (PFC) or ventral hippocampus (vHIP) of rats on neuronal oscillatory activity was evaluated. GSK-3ß(S9A)-induced changes in learning and memory were also assessed and the phosphorylation status of tau protein, a substrate of GSK-3ß, examined. It was demonstrated that increasing GSK-3ß(S9A) activity in either the PFC or vHIP had similar effects on neuronal oscillatory activity, enhancing theta and/or gamma spectral power in one or both regions. Increasing PFC GSK-3ß(S9A) activity additionally suppressed high gamma PFC-vHIP coherence. These changes were accompanied by deficits in recognition memory, spatial learning, and/or reversal learning. Elevated pathogenic tau phosphorylation was also evident in regions where GSK-3ß(S9A) activity was upregulated. The neurophysiological and learning and memory deficits induced by GSK-3ß(S9A) suggest that aberrant GSK-3ß signalling may not only play an early role in cognitive decline in Alzheimer's disease but may also have a more central involvement in disorders of cognitive dysfunction through the regulation of neurophysiological network function.

Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk.

Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high-risk populations other than infants at familial risk.

Crossmodal Phase Reset and Evoked Responses Provide Complementary Mechanisms for the Influence of Visual Speech in Auditory Cortex.

Natural conversation is multisensory: when we can see the speaker's face, visual speech cues improve our comprehension. The neuronal mechanisms underlying this phenomenon remain unclear. The two main alternatives are visually mediated phase modulation of neuronal oscillations (excitability fluctuations) in auditory neurons and visual input-evoked responses in auditory neurons. Investigating this question using naturalistic audiovisual speech with intracranial recordings in humans of both sexes, we find evidence for both mechanisms. Remarkably, auditory cortical neurons track the temporal dynamics of purely visual speech using the phase of their slow oscillations and phase-related modulations in broadband high-frequency activity. Consistent with known perceptual enhancement effects, the visual phase reset amplifies the cortical representation of concomitant auditory speech. In contrast to this, and in line with earlier reports, visual input reduces the amplitude of evoked responses to concomitant auditory input. We interpret the combination of improved phase tracking and reduced response amplitude as evidence for more efficient and reliable stimulus processing in the presence of congruent auditory and visual speech inputs.SIGNIFICANCE STATEMENT Watching the speaker can facilitate our understanding of what is being said. The mechanisms responsible for this influence of visual cues on the processing of speech remain incompletely understood. We studied these mechanisms by recording the electrical activity of the human brain through electrodes implanted surgically inside the brain. We found that visual inputs can operate by directly activating auditory cortical areas, and also indirectly by modulating the strength of cortical responses to auditory input. Our results help to understand the mechanisms by which the brain merges auditory and visual speech into a unitary perception.

Neural oscillations track natural but not artificial fast speech: Novel insights from speech-brain coupling using MEG.

Neural oscillations contribute to speech parsing via cortical tracking of hierarchical linguistic structures, including syllable rate. While the properties of neural entrainment have been largely probed with speech stimuli at either normal or artificially accelerated rates, the important case of natural fast speech has been largely overlooked. Using magnetoencephalography, we found that listening to naturally-produced speech was associated with cortico-acoustic coupling, both at normal (∼6 syllables/s) and fast (∼9 syllables/s) rates, with a corresponding shift in peak entrainment frequency. Interestingly, time-compressed sentences did not yield such coupling, despite being generated at the same rate as the natural fast sentences. Additionally, neural activity in right motor cortex exhibited stronger tuning to natural fast rather than to artificially accelerated speech, and showed evidence for stronger phase-coupling with left temporo-parietal and motor areas. These findings are highly relevant for our understanding of the role played by auditory and motor cortex oscillations in the perception of naturally produced speech.

Multivariate patterns and long-range temporal correlations of alpha oscillations are associated with flexible manipulation of visual working memory representations.

The ability to flexibly manipulate memory representations is embedded in visual working memory (VWM) and can be tested using paradigms with retrospective cues. Although valid retrospective cues often facilitate memory recall, invalid ones may or may not result in performance costs. We investigated individual differences in utilising retrospective cues and evaluated how these individual differences are associated with brain oscillatory activity at rest. At the behavioural level, we operationalised flexibility as the ability to make effective use of retrospective cues or disregard them if required. At the neural level, we tested whether individual differences in such flexibility were associated with properties of resting-state alpha oscillatory activity (8-12 Hz). To capture distinct aspects of these brain oscillations, we evaluated their power spectral density and temporal dynamics using long-range temporal correlations (LRTCs). In addition, we performed multivariate patterns analysis (MVPA) to classify individuals' level of behavioural flexibility based on these neural measures. We observed that alpha power alone (magnitude) at rest was not associated with flexibility. However, we found that the participants' ability to manipulate VWM representations was correlated with alpha LRTC and could be decoded using MVPA on patterns of alpha power. Our findings suggest that alpha LRTC and multivariate patterns of alpha power at rest may underlie some of the individual differences in using retrospective cues in working memory tasks.

Deep brain stimulation in Parkinson's disease patients and routine 6-OHDA rodent models: Synergies and pitfalls.

The history of deep brain stimulation for Parkinson's disease (PD) represented a paradigmatic cross-talk between mammalian disease models and clinical evidence in humans. Fascinating were the results achieved by high frequency stimulation (HFS) into the subthalamic nucleus (STN) of MPTP-treated primates. An analogous strategy relieved tremor and hypokinetic parameters in PD patients. The 6-hydroxydopamine (6-OHDA) rodent model has mastered decades of research, contributing to understanding of the PD pathology. However, this review wonders about the actual synergy between the routine neurotoxic models and PD patients underlying STN-DBS. At first, some findings collected following 6-OHDA, promoted dogmatic visions, as the wrong contention that suppression of STN glutamate was the key therapeutic player. Instead, changes of glutamate release are negligible in humans during transition to ON-state. Besides, the imbalance of basal ganglia endogenous band frequencies, the beta (ß) band increase and the cortical-basal ganglia synchronization, undisputedly shared by models and PD patients, do not govern the whole spectrum of non-motor PD signs, difficult to investigate in rodents. Furthermore, the tonic release of dopamine, inferred during HFS in rodents, was not replicated in humans. Finally, neurotoxic rodent models describe a 'pure' dopamine depletion sparing pathways crucial in parkinsonian phenotypes, that is, noradrenergic and cholinergic ones. Although the utilization of neurotoxic models is still providing major advancements, we pore over these contradictions and try to support possible amendments of neurotoxic models (advocating modern 'in vivo' approaches and recordings extending towards motor thalamus) for pursing the development of new DBS technology.

Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy.

Cross-frequency phase-amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

Midfrontal theta phase coordinates behaviorally relevant brain computations during cognitive control.

Neural oscillations are thought to provide a cyclic time frame for orchestrating brain computations. Following this assumption, midfrontal theta oscillations have recently been proposed to temporally organize brain computations during conflict processing. Using a multivariate analysis approach, we show that brain-behavior relationships during conflict tasks are modulated according to the phase of ongoing endogenous midfrontal theta oscillations recorded by scalp EEG. We found reproducible results in two independent datasets, using two different conflict tasks: brain-behavior relationships (correlation between reaction time and theta power) were theta phase-dependent in a subject-specific manner, and these "behaviorally optimal" theta phases were also associated with fronto-parietal cross-frequency dynamics emerging as theta phase-locked beta power bursts. These effects were present regardless of the strength of conflict. Thus, these results provide empirical evidence that midfrontal theta oscillations are involved in cyclically orchestrating brain computations likely related to response execution during the tasks rather than purely related to conflict processing. More generally, this study supports the hypothesis that phase-based computation is an important mechanism giving rise to cognitive processing.

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.

Dissociated neuronal phase- and amplitude-coupling patterns in the human brain.

Coupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described: phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical amplitude-coupling and phase-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Our results show that cortical phase- and amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.

Interindividual differences in memory system local field potential activity predict behavioral strategy on a dual-solution T-maze.

Individuals can use diverse behavioral strategies to navigate their environment including hippocampal-dependent place strategies reliant upon cognitive maps and striatal-dependent response strategies reliant upon egocentric body turns. The existence of multiple memory systems appears to facilitate successful navigation across a wide range of environmental and physiological conditions. The mechanisms by which these systems interact to ultimately generate a unitary behavioral response, however, remain unclear. We trained 20 male, Sprague-Dawley rats on a dual-solution T-maze while simultaneously recording local field potentials that were targeted to the dorsolateral striatum and dorsal hippocampus. Eight rats spontaneously exhibited a place strategy while the remaining 12 rats exhibited a response strategy. Interindividual differences in behavioral strategy were associated with distinct patterns of LFP activity between the dorsolateral striatum and dorsal hippocampus. Specifically, striatal-hippocampal theta activity was in-phase in response rats and out-of-phase in place rats and response rats exhibited elevated striatal-hippocampal coherence across a wide range of frequency bands. These contrasting striatal-hippocampal activity regimes were (a) present during both maze-learning and a 30 min premaze habituation period and (b) could be used to train support vector machines to reliably predict behavioral strategy. Distinct patterns of neuronal activity across multiple memory systems, therefore, appear to bias behavioral strategy selection and thereby contribute to interindividual differences in behavior.