Local Field Potential Activity Research Articles

Supraspinal contributions to defective antagonistic inhibition and freezing of gait in Parkinson's disease.

The neuromuscular circuit mechanisms of freezing of gait in Parkinson's disease have received little study. Technological progress enables researchers chronically to sense local field potential activity of the basal ganglia in patients while walking. To study subthalamic activity and the circuit processes of supraspinal contributions to spinal motor integration, we recorded local field potentials, surface EMG of antagonistic leg muscles and gait kinematics in patients while walking and freezing. To evaluate the specificity of our findings, we controlled our findings to internally generated volitional stops. We found specific activation-deactivation abnormalities of oscillatory activity of the subthalamic nucleus both before and during a freeze. Furthermore, we were able to show with synchronization analyses that subthalamo-spinal circuits entrain the spinal motor neurons to a defective timing and activation pattern. The main neuromuscular correlates when turning into freezing were as follows: (i) disturbed reciprocity between antagonistic muscles; (ii) increased co-contraction of the antagonists; (iii) defective activation and time pattern of the gastrocnemius muscle; and (iv) increased subthalamo-muscular coherence with the gastrocnemius muscles before the freeze. Beyond the pathophysiological insights into the supraspinal mechanisms contributing to freezing of gait, our findings have potential to inform the conceptualization of future neurorestorative therapies.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Schizophrenia pathology reverse-translated into mouse shows hippocampal hyperactivity, psychosis behaviors and hyper-synchronous events.

Decades of research into the function of the medial temporal lobe has driven curiosity around clinical outcomes associated with hippocampal dysfunction, including psychosis. Post-mortem analyses of brain tissue from human schizophrenia brain show decreased expression of the NMDAR subunit GluN1 confined to the dentate gyrus with evidence of downstream hippocampal hyperactivity in CA3 and CA1. Little is known about the mechanisms of the emergence of hippocampal hyperactivity as a putative psychosis biomarker. We have developed a reverse-translation mouse to study critical neural features. We had previously studied a dentate gyrus (DG)-specific GluN1 KO, which displays hippocampal hyperactivity and a psychosis-relevant behavioral phenotype. Here, we expressed an inhibitory DREADD (pAAV-CaMKIIa-hM4D(Gi)-mCherry) in granule cells of the mouse dentate gyrus, and continuously inhibited the region for 21 days in adolescent (6 weeks) and adult (10 weeks) C57BL/6 J mice with DREADD agonist Compound 21 (C21). Following this period, we quantified activity in the hippocampal subfields by assessing cFos expression, hippocampally mediated behaviors, and hippocampal local field potential with an intracerebral probe with continual monitoring over time. DG inhibition during adolescence generates an increase in hippocampal activity in CA3 and CA1, impairs social cognition and spatial working memory, as well as shows evidence of increased activity in local field potentials as spontaneous synchronous bursts of activity, which we term hyper-synchronous events (HSEs) in hippocampus. The same DG inhibition delivered during adulthood in the mouse lacks these outcomes. These results suggest a sensitive period in development in which the hippocampus is susceptible to DG inhibition resulting in hippocampal hyperactivity and psychosis-like behavioral outcomes.

The excessive tonic inhibition of the peri-infarct cortex depresses low gamma rhythm power during post-stroke recovery.

The cortex immediately surrounding a brain ischemic lesion, the peri-infarct cortex, harbors a large part of the potential to recover lost functions. However, our understanding of the neurophysiological conditions in which synaptic plasticity operates, remains limited. Here we hypothesized that the chronic imbalance between excitation and inhibition of the peri-infarct cortex prevents the normalization of the gamma rhythm, a waveband of neural oscillations thought to orchestrate action potential trafficking. Probing the local field potential activity of the forelimb primary sensory cortex (S1FL) located in the peri-infarct cortex of male adult mice, we found a constant, deep reduction of low-gamma oscillation power (L-gamma; 30-50 Hz) precisely during the critical time window for recovery (1 to 3 weeks after stroke). The collapse of L-gamma power negatively corelated with behavioral progress in affected forelimb use. Mapping astrocyte reactivity and GABA-like immunoreactivity in the peri-infarct cortex revealed a parallel high signal, which gradually increased when approaching the lesion. Increasing tonic inhibition with local infusion of GABA or by blocking its recapture reduced L-gamma oscillation power in a magnitude similar to stroke. Conversely, the negative allosteric modulation of tonic GABA conductance using L655,708 or the gliopeptide ODN rescued the L-gamma power of the peri-infarct cortex. Altogether the present data point-out that the chronic excess of ambient GABA in the peri-infarct cortex limits the generation of L-gamma oscillations in the repairing cortex and suggests that rehabilitative interventions aimed at normalizing low-gamma power within the critical period of stroke recovery could optimize the restitution of lost functions.Significance Statement After a stroke, the recovery of lost motor function depends on the reorganization of surviving neural networks. However, the excitation/inhibition balance in the repairing area is suboptimal as it leans excessively towards inhibition. In this work, using an in vivo approach, we demonstrate here that this imbalance, occurring during the critical window for recovery, leads to the collapse of gamma oscillations, a crucial cerebral rhythm for organizing neural communication. This study reinforces the concept of timely therapeutic interventions aimed at correcting the pathological oscillatory regimen of stroke recovery to enhance plasticity.

Clinico-physiological correlates of Parkinson’s disease from multi-resolution basal ganglia recordings

Parkinson’s disease (PD) has been associated with pathological neural activity within the basal ganglia. Herein, we analyzed resting-state single-neuron and local field potential (LFP) activities from people with PD who underwent awake deep brain stimulation surgery of the subthalamic nucleus (STN; n = 125) or globus pallidus internus (GPi; n = 44), and correlated rate-based and oscillatory features with UPDRSIII off-medication subscores. Rate-based single-neuron features did not correlate with PD symptoms. STN single-neuron and LFP low-beta (12–21 Hz) power and burst dynamics showed modest correlations with bradykinesia and rigidity severity, while STN spiketrain theta (4–8 Hz) power correlated modestly with tremor severity. GPi low- and high-beta (21–30 Hz) power and burst dynamics correlated moderately with bradykinesia and axial symptom severity. These findings suggest that elevated single-neuron and LFP oscillations may be linked to symptoms, though modest correlations imply that the pathophysiology of PD may extend beyond resting-state beta oscillations.

Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural–behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural–behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural–behavioral data.

Effects of Phasic Activation of Locus Ceruleus on Cortical Neural Activity and Auditory Discrimination Behavior.

Although the locus ceruleus (LC) is recognized as a crucial modulator for attention and perception by releasing norepinephrine into various cortical regions, the impact of LC-noradrenergic (LC-NE) modulation on auditory discrimination behavior remains elusive. In this study, we firstly recorded local field potential and single-unit activity in multiple cortical regions associated with auditory-motor processing, including the auditory cortex, posterior parietal cortex, secondary motor cortex, anterior cingulate cortex, prefrontal cortex, and orbitofrontal cortex (OFC), in response to optogenetic activation (40 Hz and 0.5 s) of the LC-NE neurons in awake mice (male). We found that phasic LC stimulation induced a persistent high gamma oscillation (50-80 Hz) in the OFC. Phasic activation of LC-NE neurons also resulted in a corresponding increase in norepinephrine levels in the OFC, accompanied by a pupillary dilation response. Furthermore, when mice were performing a go/no-go auditory discrimination task, we optogeneticaly activated the neural projections from LC to OFC and revealed a shortened latency in behavioral responses to sound stimuli and an increased false alarm rate. These impulsive behavioral responses may be associated with the gamma neural activity in the OFC. These findings have broadened our understanding of the neural mechanisms involved in the role of LC in auditory-motor processing.

Neurofeedback training can modulate task-relevant memory replay rate in rats.

Hippocampal replay - the time-compressed, sequential reactivation of ensembles of neurons related to past experience - is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

Neurofeedback training can modulate task-relevant memory replay rate in rats

Hippocampal replay – the time-compressed, sequential reactivation of ensembles of neurons related to past experience – is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

Insula uses overlapping codes for emotion in self and others.

In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.

Deep brain stimulation pulse sequences to optimally modulate frequency-specific neural activity

Objective. Precise neuromodulation systems are needed to identify the role of neural oscillatory dynamics in brain function and to advance the development of brain stimulation therapies tailored to each patient’s signature of brain dysfunction. Low-frequency, local field potentials (LFPs) are of increasing interest for the development of these systems because they can reflect the synaptic inputs to a recorded neuronal population and can be chronically recorded in humans. In this computational study, we aim to identify stimulation pulse patterns needed to optimally maximize the suppression or amplification of frequency-specific neural activity. Approach. We derived DBS pulse patterns to minimize or maximize the 2-norm of frequency-specific neural oscillations using a generalized mathematical model of spontaneous and stimulation-evoked LFP activity, and a subject-specific model of neural dynamics in the pallidum of a Parkinson’s disease patient. We leveraged convex and mixed-integer optimization tools to identify these pulse patterns, and employed constraints on the pulse frequency and amplitude that are required to keep electrical stimulation within its safety envelope. Main results. Our analysis revealed that a combination of phase, amplitude, and frequency pulse modulation is needed to attain optimal suppression or amplification of the targeted oscillations. Phase modulation is sufficient to modulate oscillations with a constant amplitude envelope. To attain optimal modulation for oscillations with a time-varying envelope, a trade-off between frequency and amplitude pulse modulation is needed. The optimized pulse sequences were invariant to changes in the dynamics of stimulation-evoked neural activity, including changes in damping and natural frequency or complexity (i.e. generalized vs. patient-specific model). Significance. Our results provide insight into the structure of pulse patterns for future closed-loop brain stimulation strategies aimed at controlling neural activity precisely and in real-time.

Congenital deafness reduces alpha-gamma cross-frequency coupling in the auditory cortex

Neurons within a neuronal network can be grouped by bottom-up and top-down influences using synchrony in neuronal oscillations. This creates the representation of perceptual objects from sensory features. Oscillatory activity can be differentiated into stimulus-phase-locked (evoked) and non-phase-locked (induced). The former is mainly determined by sensory input, the latter by higher-level (cortical) processing. Effects of auditory deprivation on cortical oscillations have been studied in congenitally deaf cats (CDCs) using cochlear implant (CI) stimulation. CI-induced alpha, beta, and gamma activity were compromised in the auditory cortex of CDCs. Furthermore, top-down information flow between secondary and primary auditory areas in hearing cats, conveyed by induced alpha oscillations, was lost in CDCs. Here we used the matching pursuit algorithm to assess components of such oscillatory activity in local field potentials recorded in primary field A1. Additionally to the loss of induced alpha oscillations, we also found a loss of evoked theta activity in CDCs. The loss of theta and alpha activity in CDCs can be directly related to reduced high-frequency (gamma-band) activity due to cross-frequency coupling. Here we quantified such cross-frequency coupling in adult 1) hearing-experienced, acoustically stimulated cats (aHCs), 2) hearing-experienced cats following acute pharmacological deafening and subsequent CIs, thus in electrically stimulated cats (eHCs), and 3) electrically stimulated CDCs. We found significant cross-frequency coupling in all animal groups in > 70% of auditory-responsive sites. The predominant coupling in aHCs and eHCs was between theta/alpha phase and gamma power. In CDCs such coupling was lost and replaced by alpha oscillations coupling to delta/theta phase. Thus, alpha/theta oscillations synchronize high-frequency gamma activity only in hearing-experienced cats. The absence of induced alpha and theta oscillations contributes to the loss of induced gamma power in CDCs, thereby signifying impaired local network activity.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas ofhigh beta power.

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys.

Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and β activity increased during contralateral grasp. In contrast, β oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.

480 Effect of Chronic DBS on Ventral Capsule Ventral Striatum-Specific Circadian Rhythm in Refractory Obsessive-Compulsive Disorder

INTRODUCTION: Deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) improves symptoms in ∼66% of patients with treatment-resistant obsessive-compulsive disorder (OCD). However, a common adverse effect of VC/VS DBS is hypomania, marked by decreased need for sleep and increased risk-taking behaviors. Modern DBS devices have enabled 24-hour collection of neural data, allowing for tracking of circadian rhythms reflected by VC/VS local field potentials (LFPs). METHODS: We implanted six patients with treatment-resistant OCD with a DBS device configured for constant VC/VS stimulation and recording. Spectral power in the 9 ± 2.5 Hz band was estimated onboard the device every 10 minutes for a median of 249 days (range 77-608). We assessed the rhythmicity of power in this spectral band by fitting 7-day retrospective rolling windows of per-day normalized LFP power to cosine functions using least-squared regression. Output metrics included acrophase (time of peak activity), amplitude, and fit significance. We quantified trends in acrophase over time using Pearson correlation. RESULTS: All six patients demonstrated consistent 24-hour cyclic LFP activity bilaterally. Two of the six had diurnal peaks which shifted significantly earlier across the study period. The remaining four patients had nocturnal peaks, all shifting significantly later over time. Notably, a patient in the latter group experienced three days of hypomania after DBS initiation associated with circadian disruptions even after clinical symptom resolution. Only after clinical DBS adjustment 2 weeks later did circadian rhythm rapidly return to pre-hypomanic levels. CONCLUSIONS: Study patients demonstrated evolving circadian VC/VS LFP activity across the course of DBS therapy, usually independent of cyclic whole-brain activity (i.e. sleep). Conversely, disruptions to these circadian patterns, as observed here during hypomania, may serve as biomarkers of region-specific stimulation side effects.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas’ relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Objective. Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain–machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Approach. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior. Main results. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson–Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior. Significance. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

Brain-wide human oscillatory local field potential activity during visual working memory

Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.

Aberrant Cortical Activity in 5xFAD Mice in Response to Social and Non-Social Olfactory Stimuli.

Neuroimaging studies investigating the behavioral and psychological symptoms of dementia (BPSD)- such as apathy, anxiety, and depression- have linked some of these symptoms with altered neural activity. However, inconsistencies in operational definitions and rating scales, limited scope of assessments, and poor temporal resolution of imaging techniques have hampered human studies. Many transgenic (Tg) mouse models of Alzheimer's disease (AD) exhibit BPSD-like behaviors concomitant with AD-related neuropathology, allowing examination of how neural activity may relate to BPSD-like behaviors with high temporal and spatial resolution. To examine task-dependent neural activity in the medial prefrontal cortex (mPFC) of AD-model mice in response to social and non-social olfactory stimuli. We previously demonstrated age-related decreases in social investigation in Tg 5xFAD females, and this reduced social investigation is evident in Tg 5xFAD females and males by 6 months of age. In the present study, we examine local field potential (LFP) in the mPFC of awake, behaving 5xFAD females and males at 6 months of age during exposure to social and non-social odor stimuli in a novel olfactometer. Our results indicate that Tg 5xFAD mice exhibit aberrant baseline and task-dependent LFP activity in the mPFC- including higher relative delta (1-4 Hz) band power and lower relative power in higher bands, and overall stronger phase-amplitude coupling- compared to wild-type controls. These results are consistent with previous human and animal studies examining emotional processing, anxiety, fear behaviors, and stress responses, and suggest that Tg 5xFAD mice may exhibit altered arousal or anxiety.