Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

Dynamic Changes of the Infralimbic Cortex and Its Regulation of the Prelimbic Cortex in Rats with Chronic Inflammatory Pain

The prelimbic cortex (PL) is actively engaged in pain modulation. The infralimbic cortex (IL) has been reported to regulate the PL. However, how this regulation affects pain remains unclear. In the present study, we recorded temporary hyper-activity of PL pyramidal neurons responding to nociceptive stimuli, but a temporary hypo-function of the IL by in vivo electrophysiological recording in rats with peripheral inflammation. Manipulation of the PL or IL had opposite effects on thermal hyperalgesia. Furthermore, the functional connectivity and chemogenetic regulation between the subregions indicated an inhibitory influence of the IL on the PL. Activation of the pathway from the IL to the PL alleviated thermal hyperalgesia, whereas its inhibition exacerbated chronic pain. Overall, our results suggest a new mechanism underlying the role of the medial prefrontal cortex in chronic pain: hypo-function of the IL leads to hyperactivity of the PL, which regulates thermal hyperalgesia, and thus contributes to the chronicity of pain.

Neural Oscillation Disorder in the Hippocampal CA1 Region of Different Alzheimer's Disease Mice

BACKGROUND

Alzheimer's disease (AD) is a well-known neurodegenerative disease that gradually induces neural network dysfunction and progressive memory deficits. Neural network activity is represented by rhythmic oscillations that influence local field potentials (LFPs). However, changes in hippocampal neural rhythmic oscillations in the early stage of AD remain largely unexplored.

OBJECTIVE

This study investigated neural rhythmic oscillations in 3-month-old APP/PS1 and 5x- FAD mice to assess early neural connectivity in AD.

METHODS

LFPs were recorded from the hippocampal CA1 region with implanted microelectrode arrays while the mice were in the awake resting stage. Welch fast Fourier transforms, continuous wavelet transforms, and phase-amplitude coupling analyses were used to compute the power density of different frequency bands and phase-amplitude modulation indices in the LFPs.

RESULTS

Our results showed impaired theta, low gamma, and high gamma frequency band power in APP/PS1 and 5xFAD mice during the awake resting stage. AD mice also showed decreased delta, alpha, and beta frequency band power. Impaired theta-low gamma and theta-high gamma phaseamplitude coupling were observed in 5xFAD mice.

CONCLUSION

This study revealed neural network activity differences in oscillation power and cross-frequency coupling in the early stage of AD, providing a new perspective for developing biomarkers for early AD diagnosis.

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon

Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40-60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.

Glutamatergic drive along the septo-temporal axis of hippocampus boosts prelimbic oscillations in the neonatal mouse

The long-range coupling within prefrontal-hippocampal networks that account for cognitive performance emerges early in life. The discontinuous hippocampal theta bursts have been proposed to drive the generation of neonatal prefrontal oscillations, yet the cellular substrate of these early interactions is still unresolved. Here, we selectively target optogenetic manipulation of glutamatergic projection neurons in the CA1 area of either dorsal or intermediate/ventral hippocampus at neonatal age to elucidate their contribution to the emergence of prefrontal oscillatory entrainment. We show that despite stronger theta and ripples power in dorsal hippocampus, the prefrontal cortex is mainly coupled with intermediate/ventral hippocampus by phase-locking of neuronal firing via dense direct axonal projections. Theta band-confined activation by light of pyramidal neurons in intermediate/ventral but not dorsal CA1 that were transfected by in utero electroporation with high-efficiency channelrhodopsin boosts prefrontal oscillations. Our data causally elucidate the cellular origin of the long-range coupling in the developing brain.

When memories are stored, or mental tasks performed, different parts of the brain need to communicate with each other to process and extract information from the environment. For example, the communication between two brain areas called the hippocampus and the prefrontal cortex is essential for memory and attention. However, it is still unclear how these interactions are established when the brain develops.

Now, by looking at how the hippocampus and the prefrontal cortex ‘work’ together in newborn mouse pups, Ahlbeck et al. hope to understand how these brain areas start to connect. In particular, the groups of neurons that kick start the development of the circuits required for information processing need to be identified.

Recording the brains of the pups revealed that electrical activity in a particular sub-division of the hippocampus activated neurons in the prefrontal cortex. In fact, a specific population of neurons in this area was needed for the circuits in the prefrontal cortex to mature.

In further experiments, the neurons from this population in the hippocampus were manipulated so they could be artificially activated in the brain using light. When stimulated, these neurons generated electrical activity, which was then relayed through the neurons all the way to the prefrontal cortex. There, this signal triggered local neuronal circuits. Thanks to this activation, these circuits could ‘wire’ together, and start establishing the connections necessary for mental tasks or memory in adulthood.

The brain of the mouse pups used by Ahlbeck et al. was approximately in the same developmental state as the brain of human fetuses in the second or third trimester of pregnancy. These findings may therefore inform on how the hippocampus and the prefrontal cortex start connecting in humans. Problems in the way brain areas interact during early development could be partly responsible for certain neurodevelopmental disorders and mental illnesses, such as schizophrenia. Understanding these processes at the cellular level may therefore be the first step towards finding potential targets for treatment.

Identification of Directed Interactions in Kinematic Data during Running

The knowledge of motion dynamics during running activity is crucial to enhance the development of rehabilitation techniques and injury prevention programs. Recent studies investigated the interaction between joints, using several analysis techniques, as cross-correlation, sensitivity analysis, among others. However, the direction of the joints pairing is still not understood. This paper proposes a study of the influence direction pattern in healthy runners by using kinematic data together with partial directed coherence, a frequency approach of Granger causality. The analysis was divided into three anatomical planes, sagittal, frontal, and transverse, and using data from ankle, knee, hip, and trunk segments. Results indicate a predominance of proximal to distal influence during running, reflecting a centralized anatomic source of movements. These findings highlight the necessity of managing proximal joints movements, in addition to motor control and core (trunk and hip) strengthening training to lumbar spine, knee, and ankle injuries prevention and rehabilitation.

Early-stage attenuation of phase-amplitude coupling in the hippocampus and medial prefrontal cortex in a transgenic rat model of Alzheimer's disease

Alzheimer's disease (AD) is pathologically characterized by amyloid-β peptide (Aβ) accumulation, neurofibrillary tangle formation, and neurodegeneration. Preclinical studies on neuronal impairments associated with progressive amyloidosis have demonstrated some Aβ-dependent neuronal dysfunction including modulation of gamma-aminobutyric acid-ergic signaling. The present work focuses on the early stage of disease progression and uses TgF344-AD rats that recapitulate a broad repertoire of AD-like pathologies to investigate the neuronal network functioning using simultaneous intracranial recordings from the hippocampus (HPC) and the medial prefrontal cortex (mPFC), followed by pathological analyses of gamma-aminobutyric acid (GABA_A ) receptor subunits α1_, α5, and δ, and glutamic acid decarboxylases (GAD65 and GAD67). Concomitant to amyloid deposition and tau hyperphosphorylation, low-gamma band power was strongly attenuated in the HPC and mPFC of TgF344-AD rats in comparison to those in non-transgenic littermates. In addition, the phase-amplitude coupling of the neuronal networks in both areas was impaired, evidenced by decreased modulation of theta band phase on gamma band amplitude in TgF344-AD animals. Finally, the gamma coherence between HPC and mPFC was attenuated as well. These results demonstrate significant neuronal network dysfunction at an early stage of AD-like pathology. This network dysfunction precedes the onset of cognitive deficits and is likely driven by Aβ and tau pathologies. This article is part of the Special Issue "Vascular Dementia".

Reliability of EEG Measures of Interaction: A Paradigm Shift Is Needed to Fight the Reproducibility Crisis

Measures of interaction (connectivity) of the EEG are at the forefront of current neuroscientific research. Unfortunately, test-retest reliability can be very low, depending on the measure and its estimation, the EEG-frequency of interest, the length of the signal, and the population under investigation. In addition, artifacts can hamper the continuity of the EEG signal, and in some clinical situations it is impractical to exclude artifacts. We aimed to examine factors that moderate test-retest reliability of measures of interaction. The study involved 40 patients with a range of neurological diseases and memory impairments (age median: 60; range 21–76; 40% female; 22 mild cognitive impairment, 5 subjective cognitive complaints, 13 temporal lobe epilepsy), and 20 healthy controls (age median: 61.5; range 23–74; 70% female). We calculated 14 measures of interaction based on the multivariate autoregressive model from two EEG-recordings separated by 2 weeks. We characterized test-retest reliability by correlating the measures between the two EEG-recordings for variations of data length, data discontinuity, artifact exclusion, model order, and frequency over all combinations of channels and all frequencies, individually for each subject, yielding a correlation coefficient for each participant. Excluding artifacts had strong effects on reliability of some measures, such as classical, real valued coherence (~0.1 before, ~0.9 after artifact exclusion). Full frequency directed transfer function was highly reliable and robust against artifacts. Variation of data length decreased reliability in relation to poor adjustment of model order and signal length. Variation of discontinuity had no effect, but reliabilities were different between model orders, frequency ranges, and patient groups depending on the measure. Pathology did not interact with variation of signal length or discontinuity. Our results emphasize the importance of documenting reliability, which may vary considerably between measures of interaction. We recommend careful selection of measures of interaction in accordance with the properties of the data. When only short data segments are available and when the signal length varies strongly across subjects after exclusion of artifacts, reliability becomes an issue. Finally, measures which show high reliability irrespective of the presence of artifacts could be extremely useful in clinical situations when exclusion of artifacts is impractical.

Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases

Alterations of interaction (connectivity) of the EEG reflect pathological processes in patients with neurologic disorders. Nevertheless, it is questionable whether these patterns are reliable over time in different measures of interaction and whether this reliability of the measures is the same across different patient populations. In order to address this topic we examined 22 patients with mild cognitive impairment, five patients with subjective cognitive complaints, six patients with right-lateralized temporal lobe epilepsy, seven patients with left lateralized temporal lobe epilepsy, and 20 healthy controls. We calculated 14 measures of interaction from two EEG-recordings separated by 2 weeks. In order to characterize test-retest reliability, we correlated these measures for each group and compared the correlations between measures and between groups. We found that both measures of interaction as well as groups differed from each other in terms of reliability. The strongest correlation coefficients were found for spectrum, coherence, and full frequency directed transfer function (average rho > 0.9). In the delta (2–4 Hz) range, reliability was lower for mild cognitive impairment compared to healthy controls and left lateralized temporal lobe epilepsy. In the beta (13–30 Hz), gamma (31–80 Hz), and high gamma (81–125 Hz) frequency ranges we found decreased reliability in subjective cognitive complaints compared to mild cognitive impairment. In the gamma and high gamma range we found increased reliability in left lateralized temporal lobe epilepsy patients compared to healthy controls. Our results emphasize the importance of documenting reliability of measures of interaction, which may vary considerably between measures, but also between patient populations. We suggest that studies claiming clinical usefulness of measures of interaction should provide information on the reliability of the results. In addition, differences between patient groups in reliability of interactions in the EEG indicate the potential of reliability to serve as a new biomarker for pathological memory decline as well as for epilepsy. While the brain concert of information flow is generally variable, high reliability, and thus, low variability may reflect abnormal firing patterns.

Effects of Hydrogen Sulfide on Modulation of Theta-Gamma Coupling in Hippocampus in Vascular Dementia Rats

Our previous study showed that hydrogen sulfide (H2S) could alleviate the cognitive deficits in vascular dementia (VD) rats associated with the improvement of synaptic plasticity. Neural oscillations are reported to interact with each other through either identical-frequency or cross-frequency coupling. This study examined whether impaired neural couplings could be alleviated by H2S in the hippocampal CA3-CA1 of VD rats and explored its possible mechanism. A VD rat model was established by two-vessel occlusion. Sodium hydrosulfide (NaHS), a kind of H2S donor, was administered intraperitoneally (5.6 mg/kg/day) for 3 weeks. Local field potentials were simultaneously collected in the hippocampal CA3 and CA1. The effects of NaHS on the modulation of theta-gamma coupling were evaluated by using the measurements of both phase-phase coupling and phase-amplitude coupling, while several other approaches including behavior, electrophysiology, western blot, immunofluorescence staining were also employed. The results showed that NaHS significantly prevented spatial learning and memory impairments (p < 0.01). NaHS considerably alleviated the impairment of neural coupling in VD rats in an identical-frequency rhythm and between cross-frequency bands. Moreover, the expression of cystathionine-β-synthase (CBS) was markedly attenuated in VD rats. NaHS elevated the expression of CBS to maintain the intrinsic balance of H2S. Interestingly, it was observed that NaHS increased the protein expression of N-methyl-D-aspartic acid receptor 2A (NMDAR2A) in VD rats. In conclusion, the data suggest that NaHS played the neuroprotective role partly via modulating the expression of NMDAR2A in order to alleviate the impairments of neural couplings in VD rats.

Directional connectivity between frontal and posterior brain regions is altered with increasing concentrations of propofol

Recent studies using electroencephalography (EEG) suggest that alteration of coherent activity between the anterior and posterior brain regions might be used as a neurophysiologic correlate of anesthetic-induced unconsciousness. One way to assess causal relationships between brain regions is given by renormalized partial directed coherence (rPDC). Importantly, directional connectivity is evaluated in the frequency domain by taking into account the whole multichannel EEG, as opposed to time domain or two channel approaches. rPDC was applied here in order to investigate propofol induced changes in causal connectivity between four states of consciousness: awake (AWA), deep sedation (SED), loss (LOC) and return of consciousness (ROC) by gathering full 10/20 system human EEG data in ten healthy male subjects. The target-controlled drug infusion was started at low rate with subsequent gradual stepwise increases at 10 min intervals in order to carefully approach LOC (defined as loss of motor responsiveness to a verbal stimulus). The direction of the causal EEG-network connections clearly changed from AWA to SED and LOC. Propofol induced a decrease (p = 0.002-0.004) in occipital-to-frontal rPDC of 8-16 Hz EEG activity and an increase (p = 0.001-0.040) in frontal-to-occipital rPDC of 10-20 Hz activity on both sides of the brain during SED and LOC. In addition, frontal-to-parietal rPDC within 1-12 Hz increased in the left hemisphere at LOC compared to AWA (p = 0.003). However, no significant changes were detected between the SED and the LOC states. The observed decrease in back-to-front EEG connectivity appears compatible with impaired information flow from the posterior sensory and association cortices to the executive prefrontal areas, possibly related to decreased ability to perceive the surrounding world during sedation. The observed increase in the opposite (front-to-back) connectivity suggests a propofol concentration dependent association and is not directly related to the level of consciousness per se.

Performance comparison between gPDC and PCMI for measuring directionality of neural information flow

BACKGROUND

General partial directed coherence (gPDC) and permutation conditional mutual information (PCMI) have been widely used to analyze neural activities. These two algorithms are representative of linear and nonlinear methods, respectively. However, there is little known about the difference between their performances in measurements of neural information flow (NIF).

NEW METHOD

Comparison of these two approaches was effectively performed based on the neural mass model (NMM) and real local field potentials.

RESULTS

The results showed that the sensitivity of PCMI was more robust than that of gPDC. The coupling strengths calculated by PCMI were closer to theoretical values in the bidirectional mode of NMM. Furthermore, there was a small Coefficient of Variance (C.V.) for the PCMI results. The gPDC was more sensitive to alterations in the directionality index or the coupling strength of NMM; the gPDC method was more likely to detect a difference between two distinct types of coupling strengths compared to that of PCMI, and gPDC performed well in the identification of the coupling strength in the unidirectional mode.

COMPARISON TO EXISTING METHOD(S)

A comparison between gPDC and PCMI was performed and the advantages of the approaches are discussed.

CONCLUSIONS

The performance of the PCMI is better than that of gPDC in measuring the characteristics of connectivity between neural populations. However, gPDC is recommended to distinguish the differences in connectivity between two states in the same pathway or to detect the coupling strength of the unidirectional mode, such as the hippocampal CA3-CA1 pathway.

Influence of slow oscillation on hippocampal activity and ripples through cortico-hippocampal synaptic interactions, analyzed by a cortical-CA3-CA1 network model

Hippocampal sharp wave-ripple complexes (SWRs) involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO) during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway (TA). The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences.

The neurobiology of depression and antidepressant action

We present a comprehensive overview of the neurobiology of unipolar major depression and antidepressant drug action, integrating data from affective neuroscience, neuro- and psychopharmacology, neuroendocrinology, neuroanatomy, and molecular biology. We suggest that the problem of depression comprises three sub-problems: first episodes in people with low vulnerability ('simple' depressions), which are strongly stress-dependent; an increase in vulnerability and autonomy from stress that develops over episodes of depression (kindling); and factors that confer vulnerability to a first episode (a depressive diathesis). We describe key processes in the onset of a 'simple' depression and show that kindling and depressive diatheses reproduce many of the neurobiological features of depression. We also review the neurobiological mechanisms of antidepressant drug action, and show that resistance to antidepressant treatment is associated with genetic and other factors that are largely similar to those implicated in vulnerability to depression. We discuss the implications of these conclusions for the understanding and treatment of depression, and make some strategic recommendations for future research.

Auditory evoked bursts in mouse visual cortex during isoflurane anesthesia

General anesthesia is not a uniform state of the brain. Ongoing activity differs between light and deep anesthesia and cortical response properties are modulated in dependence of anesthetic dosage. We investigated how anesthesia level affects cross-modal interactions in primary sensory cortex. To examine this, we continuously measured the effects of visual and auditory stimulation during increasing and decreasing isoflurane level in the mouse visual cortex and the subiculum (from baseline at 0.7 to 2.5 vol % and reverse). Auditory evoked burst activity occurred in visual cortex after a transition during increase of anesthesia level. At the same time, auditory and visual evoked bursts occurred in the subiculum, even though the subiculum was unresponsive to both stimuli previous to the transition. This altered sensory excitability was linked to the presence of burst suppression activity in cortex, and to a regular slow burst suppression rhythm (∼0.2 Hz) in the subiculum. The effect disappeared during return to light anesthesia. The results show that pseudo-heteromodal sensory burst responses can appear in brain structures as an effect of an anesthesia induced state change.

Single units in the medial prefrontal cortex with anxiety-related firing patterns are preferentially influenced by ventral hippocampal activity

The medial prefrontal cortex (mPFC) and ventral hippocampus (vHPC) functionally interact during innate anxiety tasks. To explore the consequences of this interaction, we examined task-related firing of single units from the mPFC of mice exploring standard and modified versions of the elevated plus maze (EPM), an innate anxiety paradigm. Hippocampal local field potentials (LFPs) were simultaneously monitored. The population of mPFC units distinguished between safe and aversive locations within the maze, regardless of the nature of the anxiogenic stimulus. Strikingly, mPFC units with stronger task-related activity were more strongly coupled to theta-frequency activity in the vHPC LFP. Lastly, task-related activity was inversely correlated with behavioral measures of anxiety. These results clarify the role of the vHPC-mPFC circuit in innate anxiety and underscore how specific inputs may be involved in the generation of behaviorally relevant neural activity within the mPFC.

Cross-correlation of instantaneous amplitudes of field potential oscillations: a straightforward method to estimate the directionality and lag between brain areas

Researchers performing multi-site recordings are often interested in identifying the directionality of functional connectivity and estimating lags between sites. Current techniques for determining directionality require spike trains or involve multivariate autoregressive modeling. However, it is often difficult to sample large numbers of spikes from multiple areas simultaneously, and modeling can be sensitive to noise. A simple, model-independent method to estimate directionality and lag using local field potentials (LFPs) would be of general interest. Here we describe such a method using the cross-correlation of the instantaneous amplitudes of filtered LFPs. The method involves four steps. First, LFPs are band-pass filtered; second, the instantaneous amplitude of the filtered signals is calculated; third, these amplitudes are cross-correlated and the lag at which the cross-correlation peak occurs is determined; fourth, the distribution of lags obtained is tested to determine if it differs from zero. This method was applied to LFPs recorded from the ventral hippocampus and the medial prefrontal cortex in awake behaving mice. The results demonstrate that the hippocampus leads the mPFC, in good agreement with the time lag calculated from the phase locking of mPFC spikes to vHPC LFP oscillations in the same dataset. We also compare the amplitude cross-correlation method to partial directed coherence, a commonly used multivariate autoregressive model-dependent method, and find that the former is more robust to the effects of noise. These data suggest that the cross-correlation of instantaneous amplitude of filtered LFPs is a valid method to study the direction of flow of information across brain areas.