Neural Activity Research Articles

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on and norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Is a wandering mind a novelty-seeking mind? The curious case of incubation.

The Novelty-Seeking Model can explain incubation's effect on creativity by assuming an adaptive decision threshold. During an impasse, the threshold for novelty becomes too high and biased to previous neural activity, hindering progress. Incubation "resets" this threshold through attentional decoupling, allowing for spontaneous ideas to emerge from subsequent mind wandering or other activities that attract attention, facilitating progress.

Social context modulates multibrain broadband dynamics and functional brain-to-brain coupling in the group of mice

Although mice are social, multiple animals’ neural activities are rarely explored. To characterise the neural activities during multi-brain interaction, we simultaneously recorded local field potentials (LFP) in the prefrontal cortex of four mice. The social context and locomotive states predominately modulated the entire LFP structure. The power of lower frequency bands—delta to alpha—were correlated with each other and anti-correlated with gamma power. The high-to-low-power ratio (HLR) provided a useful measure to understand LFP changes along the change of behavioural and locomotive states. The HLR during huddled conditions was lower than that during non-huddled conditions, dividing the social context into two. Multi-brain analyses of HLR indicated that the mice in the group displayed high cross-correlation. The mice in the group often showed unilateral precedence of HLR by Granger causality analysis, possibly comprising a hierarchical social structure. Overall, this study shows the importance of the social environment in brain dynamics and emphasises the simultaneous multi-brain recordings in social neuroscience.

Idiosyncratic patterns of interhemispheric connectivity in the face and scene networks of the human brain

Abstract Neuroimaging studies have revealed a network of regions in both hemispheres of the human brain that respond selectively to faces. Neural models of face processing have typically focused on functional connectivity between regions in the same hemisphere (intrahemispheric), with a particular bias toward the right hemisphere. Here, we explored the role of interhemispheric connectivity using fMRI. We used three datasets to compare functional connectivity, as shown by correlations between the time-courses of neural activity of face regions during different natural viewing paradigms. We found higher correlations of neural activity between corresponding interhemispheric regions (e.g., rFFA–lFFA) than between intrahemispheric regions (e.g., rFFA–rOFA), indicating a bias towards higher interhemispheric than intrahemispheric connectivity. A similar interhemispheric bias was evident in scene-selective regions. In contrast, we did not find an interhemispheric bias in early visual regions (V1–V3), where intrahemispheric connectivity between corresponding regions was generally higher than interhemispheric connectivity. Next, we asked whether the higher interhemispheric connectivity in the face and scene networks between corresponding regions was consistent across participants. We found that the interhemispheric bias was significantly attenuated when we compared the time-course of response across participants. This shows that interhemispheric bias in connectivity between corresponding regions in the face and scene networks is specific to the individual. These findings raise the possibility that idiosyncratic variation in interhemispheric connectivity may explain individual differences in perception.

Cortical networks relating to arousal are differentially coupled to neural activity and hemodynamics.

Even in the absence of specific sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide-field voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head-fixed male and female mice expressing the voltage-sensitive fluorescent FRET sensor Butterfly 1.2. We find that global voltage and hemodynamic signals are both positively correlated with changes in arousal with a maximum correlation of 0.5 and 0.25 respectively at a time lag of 0 seconds. We next show that arousal influences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensory-motor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies greater than 1Hz. We finally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity.Significance Statement We leverage wide-field voltage imaging to examine the relation between cortical changes in membrane potential dynamics and hemodynamics. These two signals are then examined with respect to changes in arousal, as measured by pupil diameter, in awake head fixed mice. Our results show similarities as well as important differences in the correlation of arousal with neuronal population activity dynamics and the hemodynamic signal. Further, the spatial activity correlation maps with arousal depended differentially on the behavioral state of the animal in a frequency dependent manner. Our results indicate that the modulation of brain networks by arousal is dynamically regulated, and only partly overlap between functional networks determined from hemodynamic or voltage activity.

Parvalbumin Interneuron Dysfunction in Neurological Disorders: Focus on Epilepsy and Alzheimer’s Disease

Parvalbumin expressing (PV+) GABAergic interneurons are fast spiking neurons that provide powerful but relatively short-lived inhibition to principal excitatory cells in the brain. They play a vital role in feedforward and feedback synaptic inhibition, preventing run away excitation in neural networks. Hence, their dysfunction can lead to hyperexcitability and increased susceptibility to seizures. PV+ interneurons are also key players in generating gamma oscillations, which are synchronized neural oscillations associated with various cognitive functions. PV+ interneuron are particularly vulnerable to aging and their degeneration has been associated with cognitive decline and memory impairment in dementia and Alzheimer’s disease (AD). Overall, dysfunction of PV+ interneurons disrupts the normal excitatory/inhibitory balance within specific neurocircuits in the brain and thus has been linked to a wide range of neurodevelopmental and neuropsychiatric disorders. This review focuses on the role of dysfunctional PV+ inhibitory interneurons in the generation of epileptic seizures and cognitive impairment and their potential as targets in the design of future therapeutic strategies to treat these disorders. Recent research using cutting-edge optogenetic and chemogenetic technologies has demonstrated that they can be selectively manipulated to control seizures and restore the balance of neural activity in the brains of animal models. This suggests that PV+ interneurons could be important targets in developing future treatments for patients with epilepsy and comorbid disorders, such as AD, where seizures and cognitive decline are directly linked to specific PV+ interneuron deficits.

Elucidating the underlying components of metacognitive systematic bias in the human dorsolateral prefrontal cortex and inferior parietal cortex

Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.

MAPPING THYROID HORMONE ACTION IN THE HUMAN BRAIN.

Normal brain development, mood, and cognitive functions depend on thyroid hormone (TH) action. However, little is known about how TH mediates its actions in the human brain. This is because of limited access to human brains deprived of TH during fetal and early postnatal life, as well as from adults with altered thyroid status. One way to partially bypass these limitations is by using magnetic resonance imaging and spectroscopy, two neuroimaging techniques that provide detailed, non-invasive information on human brain structure and function. Another way is using human-induced pluripotent stem cells (hiPSCs)-derived three-dimensional in vitro systems, known as brain organoids, which allow for studying fundamental aspects of the early stages of human brain development. This narrative review focuses on neuroimaging and brain organoid studies. Neuroimaging of human brains performed in individuals with different thyroid conditions provides information on the volume, myelination, blood flow, neural activity, and connectivity of different areas. Such studies show that a suboptimal thyroid status can impact human brain development and its normal function throughout life. This is true not only for patients with sporadic congenital hypothyroidism, during pregnancy or early after birth, but also for adult patients with hypo- or hyperthyroidism and for patients carrying mutations that manifest as impaired sensitivity to TH-and even for normal individuals during aging. Studies using brain organoids generated from iPSCs of healthy individuals or patients with thyroid genetic conditions provide insights into how TH can impact the early development of the human cerebral cortex. The developmental alterations in children born to mothers with different degrees of gestational hypothyroidism or who developed hypothyroidism early in life are remarkable, affecting multiple brain regions and pathways, including the cerebral cortex, hippocampus, cerebellum, interhemispheric and corticospinal tracts, and associative nuclei. The data connecting such changes to poor neurological outcomes in adult patients with hypothyroidism represent an objective link between thyroid-specific functional brain alterations and behavior. Growing brain organoids require TH, proving critical for human neuro- and oligodendrogenesis. These models proved useful in screening drugs with potential therapeutics for patients with genetic thyroid diseases.

Suspending the embodied self in meditation attenuates beta oscillations in posterior medial cortex.

Human experience is imbued by the sense of being an embodied agent. The investigation of such basic self-consciousness has been hampered by the difficulty of comprehensively modulating it in the laboratory while reliably capturing ensuing subjective changes. The present preregistered study fills this gap by combining advanced meditative states with principled phenomenological interviews: Forty-six long-term meditators (19 female, 27 male) were instructed to modulate and attenuate their embodied self-experience during magnetoencephalographic monitoring. Results showed frequency specific (high beta band) activity reductions in frontoparietal and posterior medial cortices (PMC). Importantly, PMC reductions were driven by a subgroup describing radical embodied self disruptions, including suspension of agency and dissolution of a localised first-person perspective. Neural changes were correlated with lifetime meditation and interview-derived experiential changes, but not with classical self-reports. The results demonstrate the potential of integrating in-depth first-person methods into neuroscientific experiments. Furthermore, they highlight neural oscillations in the PMC as a central process supporting the embodied sense of self.Significance statement Human consciousness is not only characterised by the contents of experience, but also by an implicit sense of subjectivity, delineating the perceiving, embodied agent from her environment. The current study addressed this basic sense of self by scrutinising meditation-induced states of self-boundary dissolution using a neurophenomenological approach: In-depth phenomenological interviews assessed core dimensions of self-experience during such states (agency, self-location, first-person perspective), while magnetoencephalography monitored neural oscillatory activity. Full-blown suspension of self-experience was associated with robust reductions of beta-band power in the posterior medial cortex. These results pinpoint a neural candidate mediating our embodied perspective on the world and highlight the unique potential of integrating rigorous phenomenological methods with neuroscientific experiments for the study of consciousness.

5-MeO-DMT induces sleep-like LFP spectral signatures in the hippocampus and prefrontal cortex of awake rats

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a potent classical psychedelic known to induce changes in locomotion, behaviour, and sleep in rodents. However, there is limited knowledge regarding its acute neurophysiological effects. Local field potentials (LFPs) are commonly used as a proxy for neural activity, but previous studies investigating psychedelics have been hindered by confounding effects of behavioural changes and anaesthesia, which alter these signals. To address this gap, we investigated acute LFP changes in the hippocampus (HP) and medial prefrontal cortex (mPFC) of freely behaving rats, following 5-MeO-DMT administration. 5-MeO-DMT led to an increase of delta power and a decrease of theta power in the HP LFPs, which could not be accounted for by changes in locomotion. Furthermore, we observed a dose-dependent reduction in slow (20–50 Hz) and mid (50–100 Hz) gamma power, as well as in theta phase modulation, even after controlling for the effects of speed and theta power. State map analysis of the spectral profile of waking behaviour induced by 5-MeO-DMT revealed similarities to electrophysiological states observed during slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. Our findings suggest that the psychoactive effects of classical psychedelics are associated with the integration of waking behaviours with sleep-like spectral patterns in LFPs.

Linking cognitive integrity to working memory dynamics in the aging human brain.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and magnetoencephalographic (MEG) recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuo-spatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/non-match decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared to younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.Significance statement Several cognitive functions decline during aging, and this process is aggravated in MCI - a condition constituting a primary risk factor for developing dementia. One function susceptible to age-related cognitive decline is working memory: the ability to maintain information online for the flexible control of behavior, which entails persistent stimulus-selective neural activity in different regions of the cerebral cortex. We used computational modeling of behavioral and neural recordings to show that the stability of working memory contents is reduced in older human subjects and predicts overall cognitive decline in MCI patients. Our findings provide new mechanistic insight into cognitive aging and MCI and highlight working memory stability as an objective marker of the mechanisms underlying cognitive impairment.

Assessing the Relationship Between Body Mass Index and Neural Activity of Prefrontal Cortex in Overweight Adults Using EEG-Resting State Data: A Wavelet Transform Analysis

Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapy-based treatment methods for overweight management.

Cortico-limbic interactions and carotid atherosclerotic burden during chronic stress exposure.

Chronic stress associates with cardiovascular disease, but mechanisms remain incompletely defined. Advanced imaging was used to identify stress-related neural imaging phenotypes associated with atherosclerosis. Twenty-seven individuals with post-traumatic stress disorder (PTSD), 45 trauma-exposed controls without PTSD, and 22 healthy controls underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI). Atherosclerotic inflammation and burden were assessed using 18F-FDG PET (as maximal target-to-background ratio, TBR max) and MRI, respectively. Inflammation was assessed using high-sensitivity C-reactive protein (hsCRP) and leucopoietic imaging (18F-FDG PET uptake in spleen and bone marrow). Stress-associated neural network activity (SNA) was assessed on 18F-FDG PET as amygdala relative to ventromedial prefrontal cortex (vmPFC) activity. MRI diffusion tensor imaging assessed the axonal integrity (AI) of the uncinate fasciculus (major white matter tract connecting vmPFC and amygdala). Median age was 37 years old and 54% of participants were female. There were no significant differences in atherosclerotic inflammation between participants with PTSD and controls; adjusted mean difference in TBR max (95% confidence interval) of the aorta 0.020 (-0.098, 0.138), and of the carotids 0.014 (-0.091, 0.119). Participants with PTSD had higher hsCRP, spleen activity, and aorta atherosclerotic burden (normalized wall index). Participants with PTSD also had higher SNA and lower AI. Across the cohort, carotid atherosclerotic burden (standard deviation of wall thickness) associated positively with SNA and negatively with AI independent of Framingham risk score. In this study of limited size, participants with PTSD did not have higher atherosclerotic inflammation than controls. Notably, impaired cortico-limbic interactions (higher amygdala relative to vmPFC activity or disruption of their intercommunication) associated with carotid atherosclerotic burden. Larger studies are needed to refine these findings.

Distinctive neurophysiological signatures of analgesia after inflammatory pain in the ACC of freely moving mice.

Preclinical assessments of pain have often relied upon behavioral measurements and anesthetized neurophysiological recordings. Current technologies enabling large scale neural recordings, however, have the potential to unveil quantifiable pain signals in conscious animals for preclinical studies. Although pain processing is distributed across many brain regions, the anterior cingulate cortex (ACC) is of particular interest in isolating these signals given its suggested role in the affective ('unpleasant') component of pain. Here, we explored the utility of the ACC towards preclinical pain research using head-mounted miniaturized microscopes to record calcium transients in freely moving male mice expressing GCaMP6f under the Thy1 promoter. We verified the expression of GCaMP6f in excitatory neurons and found no intrinsic behavioral differences in this model. Using a multimodal stimulation paradigm across naive, pain, and analgesic conditions, we found that while ACC population activity roughly scaled with stimulus intensity, single cell representations were highly flexible. We found only low magnitude increases in population activity after CFA, and insufficient evidence for the existence of a robust nociceptive ensemble in the ACC. However, we found a temporal sharpening of response durations and generalized increases in pairwise neural correlations in the presence of the mechanistically distinct analgesics gabapentin or ibuprofen after (but not before) CFA induced inflammatory pain. This increase was not explainable by changes in locomotion alone. Taken together, these results highlight challenges in isolating distinct pain signals amongst flexible representations in the ACC but suggest a neurophysiological hallmark of analgesia after pain that generalizes to at least two analgesics.Significance Statement Our study measured neural activity in the anterior cingulate cortex (ACC) of transgenic mice to improve measures of pain and analgesia in preclinical models. We found that although ACC population activity scaled with stimulus intensity and could be decoded, single cell representations of sensory stimuli were flexible. Low magnitude increases in ACC population activity were observed after pain, but subpopulations with specific activity changes driven by pain/analgesia were difficult to disambiguate from intrinsic variability. Interestingly, responses were temporally sharpened and exhibited increased cell to cell correlations in the presence of two distinct analgesics after CFA but not before. These distinct neural signatures of analgesia occurring only after pain may broaden our understanding of central mechanisms of pain and analgesia.

A hyperpolarizing neuron recruits undocked innexin hemichannels to transmit neural information in Caenorhabditis elegans

While depolarization of the neuronal membrane is known to evoke the neurotransmitter release from synaptic vesicles, hyperpolarization is regarded as a resting state of chemical neurotransmission. Here, we report that hyperpolarizing neurons can actively signal neural information by employing undocked hemichannels. We show that UNC-7, a member of the innexin family in Caenorhabditis elegans, functions as a hemichannel in thermosensory neurons and transmits temperature information from the thermosensory neurons to their postsynaptic interneurons. By monitoring neural activities in freely behaving animals, we find that hyperpolarizing thermosensory neurons inhibit the activity of the interneurons and that UNC-7 hemichannels regulate this process. UNC-7 is required to control thermotaxis behavior and functions independently of synaptic vesicle exocytosis. Our findings suggest that innexin hemichannels mediate neurotransmission from hyperpolarizing neurons in a manner that is distinct from the synaptic transmission, expanding the way of neural circuitry operations.

Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work.

Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.

A non-canonical visual cortical-entorhinal pathway contributes to spatial navigation

Visual information is important for accurate spatial coding and memory-guided navigation. As a crucial area for spatial cognition, the medial entorhinal cortex (MEC) harbors diverse spatially tuned cells and functions as the major gateway relaying sensory inputs to the hippocampus containing place cells. However, how visual information enters the MEC has not been fully understood. Here, we identify a pathway originating in the secondary visual cortex (V2) and directly targeting MEC layer 5a (L5a). L5a neurons served as a network hub for visual processing in the MEC by routing visual inputs from multiple V2 areas to other local neurons and hippocampal CA1. Interrupting this pathway severely impaired visual stimulus-evoked neural activity in the MEC and performance of mice in navigation tasks. These observations reveal a visual cortical-entorhinal pathway highlighting the role of MEC L5a in sensory information transmission, a function typically attributed to MEC superficial layers before.

De novo motor learning creates structure in neural activity that shapes adaptation

Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population’s existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural ‘structure’—organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.

Development of a novel, concentric micro-ECoG array enabling simultaneous detection of a single location by multiple electrode sizes.

Detection of the epileptogenic zone is critical, especially for patients with
drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during
spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians'
surgical decisions and improve patient outcomes. We designed, fabricated,
and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with
a unique embedded configuration. This array was compared to a commercially available
microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex
elicited by somatosensory evoked potentials and pilocarpine-induced seizures. Main results
Evoked potentials and spatial maps recorded by the multi-scale array ("micros", "mesos", and
"macros" refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively)
were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher
peak amplitudes and greater signal power than those recorded by the larger mesos and macro.
Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-
scale, similar to the commercially available array. The micros had greater SNR than the mesos
and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical
stimulation experimentation, the mesos successfully elicited motor effects. 
Previous studies have compared macro- and microelectrodes for localizing seizure activity in
adjacent regions. The multi-scale design validated here is the first to simultaneously measure
macro- and microelectrode signals from the same overlapping cortical area. This enables direct
comparison of microelectrode recordings to the macroelectrode recordings used in standard
neurosurgical practice. Previous studies have also shown that cortical regions generating
high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More
accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients.

The speech neuroprosthesis.

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore naturalcommunication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinicalepilepsy monitoringand subsequently in individuals with paralysis as part ofearlyfeasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapidandsubstantial progress indecoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.