Local Field Potential Power Spectra Research Articles

Ultrasonic thalamic stimulation modulates neural activity of thalamus and motor cortex in the mouse

Objective. Previous studies have demonstrated that ultrasound thalamic stimulation (UTS) can treat disorders of consciousness. However, it is still unclear how UTS modulates neural activity in the thalamus and cortex. Approach. In this study, we performed UTS in mice and recorded the neural activities including spike and local field potential (LFP) of the thalamus and motor cortex (M1). We analyzed the firing rate of spikes and the power spectrum of LFPs and evaluated the coupling relationship between LFPs from the thalamus and M1 with Granger causality. Main results. Our results clearly indicate that UTS can directly induce neural activity in the thalamus and indirectly induce neural activity in the M1. We also found that there is a strong connection relationship of neural activity between thalamus and M1 under UTS. Significance. These results demonstrate that UTS can modulate the neural activity of the thalamus and M1 in mice. It has the potential to provide guidance for the ultrasound treatment of thalamus-related diseases.

Longitudinal analysis of local field potentials recorded from directional deep brain stimulation lead implants in the subthalamic nucleus

Objective. The electrode–tissue interface surrounding a deep brain stimulation (DBS) lead is known to be highly dynamic following implantation, which may have implications on the interpretation of intraoperatively recorded local field potentials (LFPs). We characterized beta-band LFP dynamics following implantation of a directional DBS lead in the sensorimotor subthalamic nucleus (STN), which is a primary target for treating Parkinson’s disease. Approach. Directional STN-DBS leads were implanted in four healthy, non-human primates. LFPs were recorded over two weeks and again 1–4 months after implantation. Impedance was measured for two weeks post-implant without stimulation to compare the reactive tissue response to changes in LFP oscillations. Beta-band (12–30 Hz) peak power was calculated from the LFP power spectra using both common average referencing (CAR) and intra-row bipolar referencing (IRBR). Results. Resting-state LFPs in two of four subjects revealed a steady increase of beta power over the initial two weeks post-implant whereas the other two subjects showed variable changes over time. Beta power variance across days was significantly larger in the first two weeks compared to 1–4 months post-implant in all three long-term subjects. Further, spatial maps of beta power several hours after implantation did not correlate with those measured two weeks or 1–4 months post-implant. CAR and IRBR beta power correlated across short- and long-term time points. However, depending on the time period, subjects showed a significant bias towards larger beta power using one referencing scheme over the other. Lastly, electrode–tissue impedance increased over the two weeks post-implant but showed no significant correlation to beta power. Significance. These results suggest that beta power in the STN may undergo significant changes following DBS lead implantation. DBS lead diameter and electrode recording configurations can affect the post-implant interpretation of oscillatory features. Such insights will be important for extrapolating results from intraoperative and externalized LFP recordings.

Sex differences in μ-opioid regulation of coerulear-cortical transmission

Stress-induced activation of locus coeruleus (LC)-norepinephrine (NE) projections to the prefrontal cortex are thought to promote cognitive responses to stressors. LC activation by stressors is modulated by endogenous opioids that restrain LC activation and facilitate a return to baseline activity upon stress termination. Sex differences in this opioid influence could be a basis for sex differences in stress vulnerability. Consistent with this, we recently demonstrated that μ-opioid receptor (MOR) expression is decreased in the female rat LC compared to the male LC, and this was associated with sexually distinct consequences of activating MOR in the LC on cognitive flexibility. Given that the LC-NE system affects cognitive flexibility through its projections to the medial prefrontal cortex (mPFC), the present study quantified and compared the effects of LC-MOR activation on mPFC neural activity in male and female rats. Local field potential (LFPs) were recorded from the mPFC of freely behaving male and female rats before and following local LC microinjection of the MOR agonist, DAMGO, or vehicle. Intra-LC DAMGO altered the LFP power spectrum selectively in male but not female rats, resulting in a time-dependent increase in the power in delta and alpha frequency bands. LC microinfusion of ACSF had no effect on either sex. Together, the results are consistent with previous evidence for decreased MOR function in the female rat LC and demonstrate that this translates to a diminished effect on cortical activity that can account for sex differences in cognitive consequences. Decreased LC-MOR function in females could contribute to greater stress-induced activation of the LC and increased vulnerability of females to hyperarousal symptoms of stress-related neuropsychiatric pathologies.

A user-friendly algorithm for adaptive closed-loop phase-locked stimulation

BackgroundClosed-loop phase-locked stimulation experiments are rare due to the unavailability of user-friendly algorithms and devices. Our goal is to provide an algorithm for the detection of oscillatory activity in local field potentials (LFPs) and phase prediction, which is user-friendly and robust to non-stationarities in LFPs of behaving animals. New methodWe propose an algorithm that only requires specification of the frequency range within which oscillatory episodes are tracked. Frequency-specific detection thresholds and filter parameters are adjusted automatically based on the short-time LFP power spectrum. Estimates of instantaneous frequency and instantaneous phase are used for phase extrapolation, taking advantage of Bayesian estimation. We used real LFP signals, recorded from a variety of different species and different brain areas, as well as artificial LFP signals with known properties to assess the detection and prediction performance of our algorithm and three previously published reference algorithms under various conditions. Results and comparison with existing methodsOur algorithm, while significantly more user-friendly than previous approaches, provides a solid detection and prediction performance over a wide range of realistic conditions and, in many cases, has a longer prediction horizon than the reference algorithms. Due to its ability to adjust to changes in the signal, the algorithm is well-prepared to deal with non-stationarities in oscillation frequency, even in the presence of multiple oscillation components. ConclusionsWe have created a universal algorithm for oscillation detection and phase prediction, which performs well and is user-friendly at the same time, making closed-loop phase-locked stimulation experiments easier to accomplish.

Characteristics of globus pallidus internus local field potentials in generalized dystonia patients with TWNK mutation

ObjectiveWe focused on a rare gene mutation causing dystonia in two siblings who received globus pallidus internus deep brain stimulation (GPi-DBS). The aim was to characterize the relationship between neuronal activity patterns and clinical syndromes. MethodsWhole exome sequencing was applied to identify the TWNK (previous symbol C10orf2) mutation; Two siblings with TWNK mutation presented as generalized dystonia with rigidity and bradykinesia; four other sporadic generalized dystonia patients underwent GPi-DBS and local field potentials (LFPs) were recorded. Oscillatory activities were illustrated with power spectra and temporal dynamics measured by the Lempel-Ziv complexity (LZC). ResultsNormalized power spectra of GPi LFPs differed between patients with TWNK mutation and dystonia over the low beta bands. Patients with TWNK mutation had higher low beta power (15–27 Hz, unpaired t-test, corrected P < 0.0022) and lower LZC (15–27 Hz, unpaired t-test, P < 0.01) than other patients with generalized dystonia. On the other hand, the TWNK mutation patients showed decreased low frequency and beta oscillation in the GPi after DBS, as well as improved movement performance. ConclusionThe LFPs were different in TWNK mutation dystonia siblings than other patients with generalized dystonia, which indicate the abnormal LFPs were related to symptoms rather than specific disease. In addition, the inhibited effect on oscillations also provided a potential evidence for DBS treatment on rare movement disorders. SignificanceThis study could potentially aid in the future development of adaptive DBS via rare disease LFPs comparison.

Local field potential power spectra and locomotor activity following treatment with pseudoephedrine in mice

The efficacy of pseudoephedrine (PSE) as a nasal decongestant has been well‑demonstrated; however, PSE is strictly prescribed as a control substance due to its controversial psychostimulant effects. Although standard stimulatory drugs increase exploratory behavior and stimulate the dopamine system, the exact effects of PSE on locomotion and electrical activity in the striatum have not been determined. This study aimed to examine and compare the locomotor activities, local field potential (LFP) and sleep‑wake patterns produced by PSE and morphine, which is a standard drug used to promote psychomotor activity. Male Swiss albino mice were anesthetized and implanted with an intracranial electrode into the striatum. Animals were divided into four groups, which received either saline, PSE or morphine. Locomotor activity and LFP signals were continuously monitored following pseudoephedrine or morphine treatment. One‑way ANOVA revealed that locomotor count was significantly increased by morphine, but not PSE. Frequency analyses of LFP signals using fast Fourier transform also revealed significant increases in spectral powers of low‑ and high‑gamma waves following treatment with morphine, but not PSE. Sleep‑wake analysis also confirmed significant increases in waking and decreases in both non‑rapid eye movement and rapid eye movement sleep following morphine treatment. Sleep‑wakefulness did not appear to be disturbed by PSE treatment. These findings indicate that acute PSE administration, even at high doses, does not have psychostimulatory effects and may be relatively safe for the treatment of non‑chronic nasal congestion.

Ongoing intracortical neural activity predicts upcoming interictal epileptiform discharges in human epilepsy.

Interictal epileptiform discharges (IEDs) are a hallmark of focal epilepsies. Most previous studies have focused on whether IED events increase seizure likelihood or, on the contrary, act as a protective mechanism. Here, we study instead whether IED events themselves can be predicted based on measured ongoing neural activity. We examined local field potentials (LFPs) and multi-unit activity (MUA) recorded via intracortical 10 × 10 (4 × 4 mm) arrays implanted in two patients with pharmacologically resistant seizures. Seizures in one patient (P1) were characterized by low-voltage fast-activity (LVFA), and IEDs occurred as isolated (100 - 200 ms) spike-wave events. In the other patient (P2), seizures were characterized by complex spike-wave discharges (2 - 3 Hz) and IEDs consisted of bursts of ~ 2 - 3 spike-wave discharges each lasting ~ 300 - 500 ms. We used extreme gradient boosting (XGBoost) classifiers for IED prediction. Inputs to the classifiers consisted of LFP power spectra; In addition, counts of MUA (1-ms and 100-ms time bins) and envelope, as well as leading eigenvalues/eigenvectors of MUA correlation matrices were used as features. Features were computed from moving short-time windows (1 second) immediately preceding IED events (0.3 - 0.5 preictal gap). Classifiers allowed successful IED prediction in both patients, with better results in the case of IED occurring in the LVFA case (area under ROC curve: 0.86). In comparison, LFP features performed comparatively for P1 datasets, while MUA appeared not predictive in the case of P2. Our preliminary results suggest that features of ongoing activity, predictive of upcoming IED events, can be identified based on intracortical recordings, and warrant further investigation in larger datasets. We expect this type of prediction analyses to contribute to a better understanding of the mechanisms underlying the generation of IED events and their contribution to seizure onset.

Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals.

We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures. 4×4 mm2 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0h interictal and preictal recordings. The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations. The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate. Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.

Oscillatory local field potentials of the nucleus accumbens and the anterior limb of the internal capsule in heroin addicts

ObjectivesThe nucleus accumbens (NAc) is known to regulate the motivation and underlie addictive behaviors, and the anterior limb of the internal capsule (ALIC) is involved in several psychiatric disorders. Our study aimed to explore the functions of NAc and ALIC electrophysiologically. MethodsThe local field potentials (LFPs) of the NAc and ALIC were recorded from 7 heroin addicts treated with deep brain stimulation. Correlation analysis was made between LFP powers in various frequency bands and the subjects’ neuropsychological test scores; coherence was calculated for the LFPs in NAc and ALIC. ResultsBoth the NAc and ALIC exhibited prominent theta and alpha frequency band activity in the LFP power spectra. Additionally, a distinct beta band peak was detected in the power spectra of ALIC LFPs, which may represent the activity of striatal bridge cells. There was a significant negative correlation between the power of the theta frequency band of ALIC LFPs and visual analogue scale (VAS) scores indicative of cravings (Spearman’s ρ = −0.758, P = 0.002), and a significant positive correlation was found between the power of the alpha frequency band of NAc LFPs and subjects’ scores on the Hamilton depression inventory (ρ = 0.727, P = 0.005). LFPs of the NAc and ALIC exhibited higher coherence values in the theta and alpha frequency bands. ConclusionsThe results suggest that theta power in the ALIC/dorsal striatum and alpha power in the NAc may be associated with drug cravings and depressive symptoms, respectively, in heroin addicts. For these subjects, the neural activities in the dorsal and ventral striatum were mainly coordinated within the low-frequency band. SignificanceThe study illustrates the neurophysiologic characteristics of heroin addiction and its comorbidities, providing a potential theoretical basis for optimizing deep brain stimulation (DBS) therapy.

A new approach to detect the coding rule of the cortical spiking model in the information transmission

Investigation of the role of the local field potential (LFP) fluctuations in encoding the received sensory information by the nervous system remains largely unknown. On the other hand, transmission of these translation rules in information transmission between the structure of sensory stimuli and the cortical oscillations to the bio-inspired artificial neural networks operating at the efficiency of the nervous system is still a vague puzzle. In order to move towards this important goal, computational neuroscience tools can be useful so, we simulated a large-scale network of excitatory and inhibitory spiking neurons with synaptic connections consisting of AMPA and GABA currents as a model of cortical populations. Spiking network was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP) on the MNIST benchmark and we specified how the generated LFP by the network contained information about input patterns. The main result of this article is that the calculated coefficients of Prolate spheroidal wave functions (PSWF) from the input pattern with mean square error (MSE) criterion and power spectrum of LFP with maximum correntropy criterion (MCC) are equal. The more important result is that 82.3% of PSWF coefficients are the same as the connecting weights of the cortical neurons to the classifying neurons after the completion of the training process. Higher compliance percentage of coefficients with synaptic weights (82.3%) gives the expectance us that this coding rule will be able to extend to biological systems. Eventually, we introduced the cortical spiking network as an information channel, which transmits the information of the input pattern in the form of PSWF coefficients to the power spectrum of the output generated LFP.

Effects of alkaloid-rich extract from Mitragyna speciosa (Korth.) Havil. on naloxone-precipitated morphine withdrawal symptoms and local field potential in the nucleus accumbens of mice

Ethnopharmacological relevanceMitragyna speciosa (Korth.) Havil. (M. speciosa) is among the most well-known plants used in ethnic practice of Southeast Asia. It has gained increasing attention as a plant with potential to substitute morphine in addiction treatment program. However, its action on the central nervous system is controversial. Aim of the studyThis study investigated the effects of M. speciosa alkaloid extract on naloxone-precipitated morphine withdrawal and neural signaling in the nucleus accumbens (NAc, brain reward center) of mice. Materials and methodsThe effects of M. speciosa alkaloid extract and mitragynine, a pure major constituent, on naloxone-precipitated morphine withdrawal were examined. Male Swiss Albino (ICR) mice were rendered dependent on morphine before injection with naloxone, a nonspecific opioid antagonist, to induce morphine withdrawal symptoms. The intensity of naloxone-precipitated morphine withdrawal was assessed from jumping behavior and diarrhea induced during a period of morphine withdrawal. To test possible addictive effect of M. speciosa alkaloid extract, mice were implanted with intracranial electrode into the NAc for local field potential (LFP) recording. Following M. speciosa alkaloid extract (80mg/kg) and morphine (15mg/kg) treatment, LFP power spectra and spontaneous motor activity were analyzed in comparison to control levels. ResultsOne-way ANOVA and multiple comparisons revealed that M. speciosa alkaloid extract (80 and 100mg/kg) significantly decreased the number of jumping behavior induced by morphine withdrawal whereas mitragynine did not. Additionally, M. speciosa alkaloid extract significantly decreased dry and wet fecal excretions induced by morphine withdrawal. LFP analysis revealed that morphine significantly decreased alpha (9.7–12Hz) and increased low gamma (30.3–44.9Hz) and high gamma (60.5–95.7Hz) powers in the NAc whereas M. speciosa alkaloid extract did not. Spontaneous motor activity was significantly increased by morphine but not M. speciosa alkaloid extract. ConclusionsTaken together, M. speciosa alkaloid extract, but not mitragynine, attenuated the severity of naloxone-precipitated morphine withdrawal symptoms. Neural signaling in the NAc and spontaneous motor activity were sensitive to morphine but not M. speciosa alkaloid extract. Therefore, treatment with the M. speciosa alkaloid extract may be useful for opiate addiction treatment program.

Hippocampal CA1 local field potential oscillations induced by olfactory cue of liked food

Eating motivation is induced not only by negative energy balance but also food related cues. However, neural processing for acquisition of learned food preference remains to be established. This study aimed to identify hippocampal neural signaling in response to olfactory cue (chocolate scent) after completion of repetitive chocolate sessions. Male Swiss albino mice implanted with intracranial electrode into the hippocampus were used for local field potential (LFP) recording. Animals were given chocolate sessions (a piece of 2g chocolate per each mouse to eat on day 1, 3, 5 and 7). Hippocampal CA1 LFP signals and exploratory behavior of animals receiving chocolate scent were analyzed before and after chocolate sessions. The experiment was performed in a place preference-like apparatus with the zones of normal food pellet and chocolate (both kept in a small perforated cup for smell dispersion) at the opposite ends. Following chocolate sessions, time spent in a chocolate zone and CA1 LFP patterns were analyzed in comparison to control levels. Two-way ANOVA revealed significant increase in time spent seeking for chocolate. Frequency analysis of LFP power spectra revealed significant increases in delta and theta powers. Phase-amplitude analysis showed significant increase in maximal modulation index and decrease in frequency for phase of theta-high gamma coupling. Taken together, neural signaling in the hippocampus was sensitive to chocolate olfactory cue that might underlie learning process in response to repeated chocolate consumptions that primed intense food approaching behavior. Ultimately, these LFP patterns might reflect motivation to eat and predict feeding probability.

SP 4. Distribution of oscillatory activities in MRI confirmed subthalamic nucleus subregions

Objective To explore the differences and similarities of oscillatory activity between subregions of the subthalamic nucleus (STN). Background In Parkinson’s disease (PD), treatment driven changes in bradykinesia and rigidity are associated with changes in exaggerated local field potential (LFP) beta (13–30 Hz) power. The power of these oscillations has recently successfully been applied as a biomarker for adaptive deep brain stimulation. However, it is not yet known where in the STN beta can be best recorded, and it remains unclear whether this activity correlates with OFF-state impairment, as opposed to changes in impairment with treatment. Methods In 39 patients with advanced PD who underwent MRI-guided and MRI-verified STN DBS surgery, 229 bipolar LFP’s were recorded in the postoperative week in the OFF dopaminergic state during rest. The location of the electrode in the visualized STN was divided into 5 subregions: superior, dorsolateral, central, anteromedial and inferior. LFP power spectra were obtained by using fast Fourier transformations and were sequentially normalised, log-transformed and divided into alpha (8–13 Hz) and beta band power. Results 95 (41%) of the LFP’s contacts had one or more contacts in the inferior part of the STN. For the anteromedial, central, dorsolateral and superior part of the STN these numbers were 15 (7%), 112 (49%), 66 (29%) and 92 (40%), respectively (Fig. 1). Maximum LFP beta power showed a significant correlation (r = 0.40, p < 0.0005) with the pre-operative assessment of contra-lateral bradykinesia and rigidity (Fig. 2). This was not the case for maximum alpha peak power (r = 0.03, p = 0.89). Beta peak power (33 ± 38%) was significantly (Z = 7.89, p < 0.0001) higher than alpha peak (6 ± 10%) power. Neither activity predominated in one or more STN subregions (for beta Chi-sq4,,370 = 6.01, p = 0.19, for alpha Chi-sq4,,370 = 5.83, p = 0.21) Conclusions Normalised LFP beta power is robustly correlated with contra-lateral bradykinesia and rigidity but is not limited to the presumed dorsolateral STN.

Subthalamic local field potentials in Parkinson's disease and isolated dystonia: An evaluation of potential biomarkers

Local field potentials (LFP) recorded from the subthalamic nucleus in patients with Parkinson's disease (PD) demonstrate prominent oscillations in the beta (13–30Hz) frequency range, and reduction of beta band spectral power by levodopa and deep brain stimulation (DBS) is correlated with motor symptom improvement. Several features of beta activity have been theorized to be specific biomarkers of the parkinsonian state, though these have rarely been studied in non-parkinsonian conditions. To compare resting state LFP features in PD and isolated dystonia and evaluate disease-specific biomarkers, we recorded subthalamic LFPs from 28 akinetic-rigid PD and 12 isolated dystonia patients during awake DBS implantation. Spectral power and phase-amplitude coupling characteristics were analyzed. In 26/28 PD and 11/12 isolated dystonia patients, the LFP power spectrum had a peak in the beta frequency range, with similar amplitudes between groups. Resting state power did not differ between groups in the theta (5–8Hz), alpha (8–12Hz), beta (13–30Hz), broadband gamma (50–200Hz), or high frequency oscillation (HFO, 250–350Hz) bands. Analysis of phase-amplitude coupling between low frequency phase and HFO amplitude revealed significant interactions in 19/28 PD and 6/12 dystonia recordings without significant differences in maximal coupling or preferred phase. Two features of subthalamic LFPs that have been proposed as specific parkinsonian biomarkers, beta power and coupling of beta phase to HFO amplitude, were also present in isolated dystonia, including focal dystonias. This casts doubt on the utility of these metrics as disease-specific diagnostic biomarkers.

Spiking network modeling of neuronal dynamics in individual rats

Many researchers base their neuronal models on experimental data, but few systematically calibrate to it, and even fewer use unpooled data from multiple subjects. Some (partial) exceptions are [1], where one model parameter was calibrated to data pooled across four macaques; and [2], where five model parameters were calibrated to unpooled data from 292 humans. To our knowledge, no one has previously calibrated spiking network models to individual subjects - likely because a single model iteration typically takes considerable computational time, compounded by the formidable number of iterations required to perform optimizations on a statistically meaningful number of subjects. In this study, we calibrate and validate a mesoscopic spiking network model against data from individual rats, then use these fits to infer differences in the rats' physiologies. We recorded data from microelectrode arrays implanted in the somatosensory cortices of nine male rats, each of whom received touch stimuli to his left forepaw. The spiking network models consisted of 20,000 Izhikevich neurons, representing a 2x2 mm patch of cortex sampled at approximately 10% true cell density, with cell types (15 across six layers) and connectivities based on empirical data. We calibrated key model parameters (including connection probability and weight, tonic background activity, and the ratio of thalamic to cortical input) to experimental data (including average firing rates and coefficients of variation) using a nonlinear optimization algorithm [3]. The calibrations were validated using the exponents of the local field potential (LFP) power spectra from 5-50 Hz, which were not used for calibration. Changes in the information processing properties of the (simulated) networks were then quantified via interlaminar Granger causality. Experimentally, we found large differences between subjects. Cortical firing rates varied from 3.8 Hz to 19.6 Hz (median 11.9 Hz); coefficients of variation, 0.4 to 1.2 (median 0.9); and peristimulus time histogram peak amplitudes, 48 Hz to 73 Hz (median 62 Hz). Inter-subject differences were significantly greater than intra-subject differences across both sessions and electrodes. The calibrated models reproduced experimental data with an average mismatch of 16%, compared to 30% when uncalibrated. Experimentally, the LFP power spectra exponents varied from -1.4 to -2.7 (median -2.2). While the models had uniformly steeper exponents (range -2.8 to -3.6), calibration reduced mismatch in eight of the nine subjects. Inter-subject differences could be largely accounted for by differences in average synaptic connection probability (which varied by a mean of 24% between subjects, compared to <8% for other fitted parameters). Parameter differences produced changes in how these networks process information - for example, there were significant and complex differences in the patterns of interlaminar information flow between subjects, including reversals in the dominant direction of information flow for some layer pairs. In summary, we found that (1) significant differences exist between individual subjects, (2) these differences can be captured by calibrating spiking network models to data from each individual, and (3) modeling suggests that these differences affect how individuals process information across cortical layers.

Gamma wave oscillation and synchronized neural signaling between the lateral hypothalamus and the hippocampus in response to hunger

The lateral hypothalamus plays an important role in homeostasis. It is sensitive to negative energy balance and believed to interact with other brain regions to mediate food seeking behavior. However, no neural signaling of hunger in the lateral hypothalamus has been studied. Male Swiss albino mice implanted with intracranial electrodes into the lateral hypothalamus and the hippocampus were randomly treated with drinking water for control condition, 18-20 h deprivation of food for hunger condition, and fluid food for satiety condition. Therefore, local field potential (LFP) and locomotor activity of animals were simultaneously recorded. One way ANOVA with Tukey's post hoc test was used for statistical analysis. Frequency analysis of LFP revealed that food deprivation significantly increased the power of gamma oscillation (65-95 Hz) in the lateral hypothalamus and the hippocampus. However, satiety did not change the oscillation in these regions. Moreover, no significant difference among groups was observed for locomotor count and speed. The analysis of coherence values between neural signaling of these two brain areas also confirmed significant increase within a frequency range of 61-92 Hz for hunger. No change in coherence value was induced by satiety. In summary, this study demonstrated neural signaling of the lateral hypothalamus in response to hunger with differential power spectrum of LFP and the interplay with the hippocampus. The data may suggest critical roles of the lateral hypothalamus in detection of negative energy balance and coordination of other higher functions for food related learning or behaviors through the connectivity with the hippocampus.

Effect of focused ultrasound stimulation at different ultrasonic power levels on the local field potential power spectrum**Project supported by the National Natural Science Foundation of China (Grant No. 61273063), China Postdoctoral Science Foundation (Grant No. 2013M540215), and the Natural Science Foundation of Hebei Province, China (Grant No. F2014203161).

Local field potential (LFP) signals of the rat hippocampus were recorded under noninvasive focused ultrasound stimulation (FUS) with different ultrasonic powers. The LFP mean absolute power was calculated with the Welch algorithm at the delta, theta, alpha, beta, and gamma frequency bands. The experimental results demonstrate that the LFP mean absolute power at different frequency bands increases as the ultrasound power increases.

Disrupted hippocampal sharp-wave ripple-associated spike dynamics in a transgenic mouse model of dementia.

High frequency (100-250Hz) neuronal oscillations in the hippocampus, known as sharp-wave ripples (SWRs), synchronise the firing behaviour of groups of neurons and play a key role in memory consolidation. Learning and memory are severely compromised in dementias such as Alzheimer's disease; however, the effects of dementia-related pathology on SWRs are unknown. The frequency and temporal structure of SWRs was disrupted in a transgenic mouse model of tauopathy (one of the major hallmarks of several dementias). Excitatory pyramidal neurons were more likely to fire action potentials in a phase-locked manner during SWRs in the mouse model of tauopathy; conversely, inhibitory interneurons were less likely to fire phase-locked spikes during SWRs. These findings indicate there is reduced inhibitory control of hippocampal network events and point to a novel mechanism which may underlie the cognitive impairments in this model of dementia. Neurons within the CA1 region of the hippocampus are co-activated during high frequency (100-250Hz) sharp-wave ripple (SWR) activity in a manner that probably drives synaptic plasticity and promotes memory consolidation. In this study we have used a transgenic mouse model of dementia (rTg4510 mice), which overexpresses a mutant form of tau protein, to examine the effects of tauopathy on hippocampal SWRs and associated neuronal firing. Tetrodes were used to record simultaneous extracellular action potentials and local field potentials from the dorsal CA1 pyramidal cell layer of 7- to 8-month-old wild-type and rTg4510 mice at rest in their home cage. At this age point these mice exhibit neurofibrillary tangles, neurodegeneration and cognitive deficits. Epochs of sleep or quiet restfulness were characterised by minimal locomotor activity and a low theta/delta ratio in the local field potential power spectrum. SWRs detected off-line were significantly lower in amplitude and had an altered temporal structure in rTg4510 mice. Nevertheless, the average frequency profile and duration of the SWRs were relatively unaltered. Putative interneurons displayed significantly less temporal and phase locking to SWRs in rTg4510 mice, whilst putative pyramidal neurons showed increased temporal and phase locking to SWRs. These findings indicate there is reduced inhibitory control of hippocampal network events and point to a novel mechanism which may contribute to impairments in memory consolidation in this model of dementia.

Modelling local field potential features during network gamma oscillations

While the physics of local field potential (LFP) generation are well-established, the complexity of neural network dynamics means that interpreting LFP measurements in terms of the underlying neural activity is very difficult [1]. Recent studies have therefore investigated forward models of the LFP: calculating the LFP due to known arrangements of neuronal current sources [1]. We use this approach to study the spatio-temporal features of the LFP recorded during persistent gamma-oscillations in vitro. The mechanism by which this activity regime is generated is well-known [2], and many multi-electrode array recordings are available that allow us to compare the spatio-temporal properties of the experimentally measured and simulated LFPs. For simplicity, we implemented a model of a neocortical slice containing only layer 2/3, using our VERTEX simulation tool. The model contained a gamma oscillation-generating region surrounded by regions that did not participate in the gamma oscillation (i.e. the neurons in these regions fired randomly). These enforced conditions allowed us to study the spatial profile of the LFP from differently sized gamma oscillation-generating patches, while minimising the number of variables we changed between simulations. Additionally, we could make observations about properties of the LFP during random activity in the network. We describe three key results from our simulations. Firstly, we found that perisomatic synaptic currents on pyramidal neurons resulting from basket interneuron firing dominate the LFP during gamma oscillations, in agreement with recent experimental results [3,4]. We predict that basket interneurons will also contribute the majority of the LFP signal during random, uncorrelated activity because of the location of their synapses at pyramidal neuron somas. This contribution is amplified during oscillatory activity because of increased basket cell synchrony relative to other neuron types. Secondly, we investigated how gamma-frequency power from a localised gammagenerating region in the model spread due to volume conduction. We found that the spatial spread of the oscillation in the LFP increased above and below the neurons’ somadepth, and depended on the level of the random surround activity. Finally, we investigated frequency scaling in the LFP power spectrum across space. We showed that LFPs at the level of the pyramidal cells’ apical dendrites exhibit a low-pass filtering effect (as predicted previously [5]), which is absent from LFPs recorded at the soma level because of the relative dominance and localisation of the basket cell input. We confirmed that this matched the frequency scaling in our comparison experimental data recorded in macaque neocortical slices, and, based on the frequency characteristics in our model, propose that the experimentally recorded LFP could contain contributions from spiking activity at frequencies down to, and perhaps below, 100 Hz.

Phase transitions in cortical dynamics explain improved information processing under attention

A major aspect of cortical information processing is its rapid adaptation to the actual behavioral demands. Highly representative for such processes is selective visual attention, which has been shown to improve performance in visual perception (see e.g. [1]). The neural mechanisms supporting these effects have been investigated in electrophysiological studies: Besides firing rate modulations and tuning curve changes in visual cortical neurons, they revealed a strong increase of oscillations in the gamma frequency band (35-90 Hz) [2]. These findings indicate that gamma oscillations might play an important role in optimizing information processing under attention, but their functional role is currently not yet understood. The dynamics of cortical networks exhibit certain properties that have been suggested to be linked to optimization of information processing: criticality and balance. Experimental studies indicate that cortical networks operate near a 'critical' state in which scale-free avalanches of spike events occur [3], generating neural patterns which are 'rich' in structure [4]. These critical states are indicators of a phase transition from an asynchronous regime of activity in the network towards a synchronous regime. Furthermore, it has been observed that excitatory and inhibitory synaptic currents to cortical neurons are balanced [5], which allows cells to detect rapid changes in the stimulus and provides an explanation for the high variability of neural activity. Here we explore the relationship between the phase transition towards synchrony, balance, and enhanced stimulus representation in a simple network of integrate-and-fire neurons driven by an external stimulus. By increasing the efficacy of recurrent couplings, attention improves spontaneous synchronization and renders activation patterns for different stimuli more distinct. We compare this result to experimental evidence demonstrating that object representation in cortical local field potentials (LFPs) is improved in macaque monkeys under attention [6], and find a near-perfect match between model and LFP data. By analyzing the LFPs and avalanche statistics of the simulated network activity, we show that the enhancement of discriminability of LFP power spectra is strongly correlated with phase transitions towards synchrony. More importantly, our results indicate that networks operating near phase transitions reside in a regime where a dramatic enhancement of stimulus representation may require only fine modulations of synaptic strengths. Taken together, our study implies that attention drives the cortex towards a critical state, inducing oscillatory activity and hereby improving the discriminability of different stimuli represented in the corresponding network activity. Furthermore, our framework suggests a novel role for synchronization in cortical information processing.