|March 18, 2018|
Relay cells are prevalent throughout sensory systems and receive
two types of inputs: driving and modulating. The driving input contains
receptive field properties that must be transmitted while the modulating
input alters the specifics of transmission. For example, the motor
thalamus contains relay neurons that receive a driving input from
motor cortex which encodes a motor plan, and a modulating input
from the basal ganglia, which suppress/facilitate movements that
are not intended/intended. In this paper, we analyze a biophysical
based model of a relay cell and use systems theoretic tools toconstruct
analytic bounds on how well the cell transmits a driving input as
a function of the neuron's electrophysiological properties, the
modulating input, and the driving signal parameters. We assume that
the modulating input belongs to a class of sinusoidal signals and
that the driving input is an irregular train of pulses with inter-pulse
intervals obeying an exponential distribution. Our analysis applies
any nth order model as long as the neuron does not spike without
a driving input pulse and exhibits a refractory period. Our bounds
on relay reliability contain performance obtained through simulation
of a second and third order model, and suggest, for instance, that
if the frequency of the modulating input increases and the DC offset
decreases, then relay increases. Our analysis shows how the biophysical
properties of the neuron (e.g. ion channel dynamics) define the
oscillatory patterns needed in the modulating input (e.g. cells
local field potentials) for appropriately timed relay of sensory
information. We show how our bounds predict experimentally observed
neural activity in the basal ganglia in (i) health, (ii) in Parkinson's
disease (PD), and (iii) in PD during therapeutic deep brain stimulation.
Our bounds also predict different rhythms that emerge in the lateral
geniculate nucleus in the thalamus during different attentional
Synchrony control in a population of bursting neurons using a common
input, a mathematical study.
Intrinsically bursting neurons arise in many contexts.
The governing equations for models of such cells usually have multiple
timescales, giving rise to solutions that have complex responses
to perturbations. I will present a mathematical study of a population
of elliptic bursting neurons driven by a common input. In particular,
we investigate the effects of the driving signals attributes
(strength, periodicity, etc.) on the synchronization and desynchronization
of the population. This study suggests possible applications to
therapeutic methods based on electrical stimulation of neural areas.
Such treatments, sometimes known as deep brain stimulation, are
often used as treatments of disorders such as Parkinsons disease.
High Frequency STN DBS Elicits Reinforcement Mechanisms in the
Motor Striatum in Normal and MPTP Non-Human Primates
We investigate the effects of subthalamic nucleus (STN) Deep Brain Stimulation (DBS) on striatum in the normal and Parkinsonian state by using single unit recordings and point process modeling tools. In particular, two non-human primates (macaca mulata) were treated with STN DBS (25 up to 130 pulses-per-second [pps]) while microelectrode recordings were acquired in striatum. In one animal, recordings were acquired before and after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which induced Parkinsons disease-like movement disorders. 185 neurons (118 normal, 67 MPTP) were collected and, for each neuron, a point process model captured DBS-induced discharge patterns.
We found that, in normal animals, spike trains at rest were Poisson processes with non-stationary recurrent patterns (RPs, period: 3-7ms), and were mildly changed by DBS below 100pps (<30% of neurons affected). With 130pps DBS, instead, up to 57% of neurons either increased or decreased the discharge rate and 66% had significant post-stimulus response. The population-mean rate decreased, the RPs were reduced, and the neuronal discharge patterns were significantly different from Poisson processes. MPTP lowered the population-mean rate and evoked significant inter-neuronal dependencies (INDs) at rest. Compared to normal, DBS below 100pps in the MPTP-treated animal increased RPs and INDs over the value at rest, while 130pps DBS elicited faster and wider post-stimulus activation.
In conclusion, 130pps STN DBS reduced non-stationary dynamics by regularizing the discharge patterns both in normal and MPTP-treated striatum, while the combination of dopamine depletion and DBS below 100pps enhanced such dynamics. This suggests that pattern regularization in striatum might contribute to the therapeutic effect of STN DBS, presumably by reinforcing the orthodromic effects along the basal ganglia-thalamo-cortical loop.
Modulations in Oscillatory Activity of the Globus Pallidus internus
Neurons During a Directed Hand Movement Task A Primary Mechanism
for Motor Planning
Globus Pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through particular modulations in their oscillatory patterns.We built point process models of the neuronal spiking activity for 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We then analyzed the parameters of these models and (i) identified which neurons are task-related,i.e., directionally tuned, and (ii) assessed modulation of oscillations in the beta and gamma bands for non-task related and task-related neurons separately.We found that, during the planning stages of movement, a significant number of directionally tuned neurons display an increase in gamma oscillations and a suppression of beta oscillations (a cross-over effect). This cross-over effect is not observed in the non-directionally tuned neurons. These results suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates. Moreover our findings, taken together with previous work, suggest that increased beta band oscillations prevalent throughout the basal ganglia in Parkinsons disease patients are more difficult to suppress, limiting the occurrences of the cross-over effect during movement planning and thereby impairing movements in patients.
Fine temporal patterning of intermittent synchronized oscillations
in hippocampal and prefrontal circuits of the rat under repeated
use of amphetamine
Characterizing how neural processing regimes facilitate communication within and between the prefrontal cortex (PFC) and hippocampus (HC) has received considerable attention due to the well-established roles of each structure in the control of behavior and cognitive functions. The neural processing regimes that mediate communication within, and between, each of these regions are hypothesized to be critically impaired in schizophrenia. Schizophrenia is associated with elevated synaptic dopamine concentrations which is often modeled via administration of dopamine releasing agents, such as amphetamine.
Effects of Disinhibition in a Cortical Working Memory Circuit with
Relevance to Schizophrenia
Cortical working memory circuits generate stimulus-selective persistent activity through the interplay of recurrent excitation and inhibition. The balance of excitation and inhibition is critical for the function of cortical circuits, and disruption of this balance is believed to underlie multiple mental disorders such as schizophrenia. In particular, the cognitive dysfunction of schizophrenia, with a core deficit in working memory, is hypothesized to be due to the synapse-level antagonism of NMDA receptors onto inhibitory interneurons. This hinders the recruitment of feedback inhibition by local pyramidal-cell activity. However, it is poorly understood how synaptic disinhibition leads to specific behavioral deficits in working memory. To elucidate this connection, we incorporate this synaptic disinhibition mechanism into a computational model of spatial working memory, implemented with spiking neurons and realistic synaptic conductances.
We find that working memory function is very sensitive to the balance of excitation and inhibition, with even a small reduction in the strength of feedback inhibition recruitment degrading neural activity and behavior in a pronounced way. At the neural level, disinhibition broadens the tuning curves of working memory cells, and decreases the signal-to-noise ratio of the population code. At the behavioral level, we characterize two specific deficits. First, behavioral variability, due to drift of the population firing pattern over time, is increased by disinhibition, degrading performance. Second, resistance to intervening distractors is degraded by disinhibition, due to an increased overlap in the neural representations of the target and the distractor. We also present experimental support for the idea neural disinhibition by ketamine induces broadening of spatial working memory representations.
Our findings detail the importance of excitation-inhibition balance for the cognitive function of cortical microcircuits. The model makes specific predictions for the neural and behavioral effects of synaptic disinhibition, which can be tested in humans and animal models under pharmacological manipulation or in disease states. We propose important features of experimental task design for sensitive probing of these effects.
CPP alters hippocampal CA1 oscillations in rat: simulation and
Several different NMDA receptor (NMDAR) antagonists are used to produce pharmacological models for schizophrenia. These antagonists have different molecular mechanisms of action: uncompetitive (e.g. ketamine, which blocks the receptor pore) and competitive (e.g.CPP, which displaces glutamate). Because competitive antagonism results in more glutamate availability, glutamate receptors can be activated in excess on the same synapses. In this study, we modeled the effects of competitive blockade by simultaneously blocking NMDAR and increasing activity of AMPAR. We looked at alterations of network oscillations and synchronization using our biophysical model of the CA1 region of hippocampus using the NEURON simulator. We then compared the results with data obtained from the CA1 region of hippocampus in rats injected with CPP.
Supported by NIMH (R01MH086638). The authors would like to thank Larry Eberle (SUNY Downstate) for assistance with computer programming and Neurosimulation Lab computer support; Michael Hines (Yale) and Ted Carnevale (Yale) for NEURON simulator support.
1. Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW: Ketamine disrupts ? modulation of ? in a computer model of hippocampus. J Neurosci 2011, 31(32):1173311743.
Date received: March 19, 2012
Default mode network connectivity indicates episodic memory capacity
in patients with temporal lobe epilepsy
FMRI resting state connectivity holds great promise for assessing cognitive capacities in patients with neuropsychiatric or neurological diseases because it is relatively easy to acquire and shows reliable changes in several patient populations. The main goal of this study was to evaluate the clinical utility of the default mode network (DMN), a robust resting state network, in assessing episodic memory capacity in patients with unilateral mesial temporal lobe epilepsy (mTLE) who have focal damage to, and in some cases surgical removal of, one node of the DMN, the hippocampus (HC).
We used six-minutes fMRI resting state data to evaluate functional connectivity from a seed (i.e., posterior cingulate cortex, PCC) in 19 healthy participants and 20 patients with right and 18 patients with left seizure onset. First, we examined changes in connectivity between patients and controls and second, we correlated PCC connectivity with presurgical memory performance and postsurgical memory decline in patients with mTLE.
We found that stronger PCC-HC connectivity in the damaged hemisphere was associated with better presurgical memory and also predicted greater postsurgical memory decline. Unexpectedly, stronger PCC-HC connectivity in the contralateral hemisphere was associated with less postsurgical memory decline, indicating functional reserve. This prediction was corroborated by showing that postsurgical PCC connectivity to the remaining HC increased and supported postsurgical memory function.
Our findings indicate the capability of a short resting state scan to accurately reflect episodic memory capacity in patients with selective impairment in this domain. These findings demonstrate the clinical significance of the brains intrinsic activity and connectivity in evaluating functional integrity and cognitive capacity.
A Multivariate Bayesian Framework for Modeling Brain Dynamics at
Seizure Onsets in Drug-Resistant Epilepsy Patients
The onset of an unequivocal electrographic seizure in epileptic subjects is associated with multiple nonlinear and spatially distributed changes in brain dynamics, whose identification and detection are currently challenging problems. We hypothesize that early seizure-related (i.e., ictal) changes in the brain dynamics can be captured by (i) combining information from multiple simultaneously recorded intra-cortical electroencephalographic signals (iEEG) from the affected brain, (ii) by computing a network-based statistic that describes the dynamics of such signals, and (iii) by modeling the temporal evolution of this statistic as a function of the current and past observations.
In particular, we first compute a time-varying cross-power-based connectivity matrix that combines multiple iEEG channels to map the whole brain. Then, we extract from this matrix the temporal sequence of leading singular values (one value for each instance of the connectivity matrix) and corresponding sequence of leading singular vectors. We assume that these sequences are stochastic processes generated by hidden Markov models (HMM) with two hidden clinical states (normal and ictal) in each HMM. Each sequence has a time-varying stochastic characterization that depends on the hidden clinical state and is described by a history-dependent generalized linear model. Finally, the Bayesian a posteriori probability of being in the ictal state is introduced and is conditioned on the HMM dynamics, the stochastic model of the sequence of singular values and vectors, and the past observations of singular values and vectors.
We apply our framework to iEEG recordings from four drug resistant epileptic patients (168 hours of continuous recordings, 26-44 electrodes, 33 clinically-annotated seizures) and show that the proposed statistics significantly modulate in normal vs. ictal state while the Bayesian a posteriori probability is generally low (i.e., <0.5) and selectively increases (i.e., almost 1) only at the onset of the annotated seizures, thus accurately capturing the change in brain dynamics.
Date received: March 1, 2012
Genesis of interictal spikes in CA1: A computational Investigation
Interictal spikes (IIS) refer to abnormal synchronized neuronal discharges, which are hallmarks of epilepsy and can be confirmed through electroencephalography (EEG) . In vitro analysis of resected temporal lobe tissue from patients with refractory temporal lobe epilepsy of hippocampal origin has revealed the presence of IIS in the CA1 subfield. IIS are abolished when the Schaffer collateral (SC) input from the CA3 subfield is cut . The CA1 undergoes morphological changes including cell death and the emergence of recurrent connections between excitatory neurons following brain injury that triggers epileptic seizures .A question then arises: How are the morphological network changes and input variability through SC related to the CA1s ability to generate IIS? To address this issue, we develop a computational model of the CA1 network.
We characterize the morphological changes within the CA1 network by changing the percentage of recurrent synaptic connections (average number of incoming synapses onto CA1 pyramidal cells). Input variability is studied in two ways: (i) By changing the fraction of CA1 pyramidal cells that receive input from SC, and (ii) by changing the synchronization of incoming input by varying the temporal window in which SC input arrives. The CA1 computational network is comprised of 280 neurons of which 80% are pyramidal (excitatory) and 20% are inhibitory interneurons. Each neuron is modeled using the Hodgkin-Huxley framework of conductance based point neuron models.
Since pathological burst firing of pyramidal neurons, referred to as the paroxysmal depolarization shift (PDS) are known as the cellular correlates for IIS, we begin by identifying a typical temporal profile of a cellular PDS event that contributes to an observed IIS. We begin with experimental data on IIS recorded from the CA1 subfield of an in vivo self-sustaining status electrical status epilepticus animal model of chronic limbic epilepsy . We then generate template IIS events using a subset of artificial PDS constructs. By matching the temporal profile of the template IIS with the experimental IIS, we identify the characteristics for PDS constructs that can generate experimentally observable IIS. We then use this information to tune the synaptic parameters of a minimal network of a pyramidal cell coupled with interneurons that is capable of generating a PDS event that matches the temporal profile of the artificial PDS construct in response to stimulation from external synaptic input. The minimal model parameters are then incorporated into a detailed CA1 network model to study conditions under which experimentally observable IIS are produced.
We find that the CA1 network with low recurrent connectivity, mimicking
the topology of a normal brain, has a very low probability of producing
an IIS except when a large fraction of CA1 neurons (>80%) receives
a quasi-synchronous barrage of input (events occurring within a
temporal window of 20 ms) via SC. However, as we increase recurrent
connectivity of CA1 (>40%) we find that an IIS can be evoked
in the CA1 network even in the presence of low synchrony SC input
(>80 ms temporal window) and a low fraction of SC input to CA1
pyramidal cells (>30%). For sufficiently high recurrent connections
(40%), the model produces a sequence of IIS in response to sparse
asynchronous SC input. These results indicate that as the CA1 becomes
increasingly excitable resulting from recurrent excitatory connections,
the ability to produce IIS increases with less dependence on input
variability. This in turn, suggests that the CA1s susceptibility
to produce IIS increases following brain injury, a finding that
has been reported in earlier experimental studies .
2. Dzhala V I, Staley K J: Transition from interictal to ictal activity in limbic networks in vitro. Journal of Neuroscience 2003, 23(21):7873-7880
3. Lehmann T N, Gabriel S, Kovacs R, Eilers A, Kivi A, Schulze K, Lanksch W R, Meencke H J, Heinemann U: Alterations of neuronal connectivity in area CA1 of hippocampal slices from temporal lobe epilepsy patients and from pilocarpine-treated epileptic rats. Epilepsia 2000, 41(Suppl. 6):S190-S194
4. Talathi S.S., Hwang D.U., Ditto W.L., Mareci T., Sepulveda H., Spano M., Carney P.R., Circadian control of neural excitability in an animal model of temporal lobe epilepsy, Neurosci Lett, 2009, 455:145-9
5. Buzsaki G, Hsu M, Slamka C, Gage F H, Horvath Z: Emergence and Propagation of Interictal Spikes in the Subcortically Denervated Hippocampus. Hippocampus 1991. 1(2): 163 180
Mollecular mechanisms of synaptic plasticity at CA3-CA1 synapses
define the properties of learning during theta cycles in a model
of the hippocampal CA1 microcircuit
Hippocampal CA1 pyramidal neurons can oscillate in a theta rhythm (4-10Hz) integrating excitatory synaptic inputs from entorhinal cortex (EC) and CA3 neurons, inhibitory synaptic inputs from many classes of interneuron such as axo-axonic, basket, bistratified, oriens lacunosum-moleculare cells. Pattern storage due to synaptic changes at CA3-CA1 Schaffer collateral synapses is induced by the associated EC and CA3 pattern presentation and occurs most strongly out-of-phase with the peak of CA1 pyramidal cell activity during theta oscillations. Pattern recall is triggered by the CA3 inputs only and it is observed in phase with the firing of pyramidal neurons. It is not known precisely how synaptic learning is induced during these theta cycles.
The results inply that molecular mechanisms of synaptic plasticity together with the spatio-temporal pattern of synaptic inputs define the properties of associative learning in the CA1 region of the hippocampus.
This research was funded by a grant (No. MIP-93/2010) from the Research Council of Lithuania.
Cutsuridis V, Cobb S, Graham BP. Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus, 20:423-446, 2010.
Graupner M, Brunel N. STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways. PloS Computational Biology 3: 2299-2323, 2007.
Pi HJ, Lisman JE. Coupled Phosphatase and Kinase Switches Produce the Tristability Required for Long-Term Potentiation and Long-Term Depression. Journal of Neuroscience 28(49):13132-13138, 2008.
Poirazi P, Brannon T, Mel BW. Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37:977-987, 2003.
Control of slow population activities by fast-spiking interneurons
via network multistability
Slow population activities (SPAs) exist in the brain and have frequencies below approximately 5 Hz. Despite SPAs being prominent in several cortical areas and serving many putative functions, their mechanisms are not well understood. We study a specific type of in vitro GABAergic, inhibitory-based SPAs exhibited by rodent hippocampus. We use a multi-pronged approach consisting of experiment, simulation and mathematical analyses to uncover mechanisms responsible for hippocampal SPAs. Our results show that hippocampal SPAs are an emergent phenomenon where the slowness of the network is due to interactions between synaptic and cellular characteristics of individual fast-spiking, inhibitory interneurons. Our simulations quantify characteristics underlying hippocampal SPAs. In particular, for hippocampal SPAs to occur, we predict that (i) individual fast-spiking interneurons should have frequency-current (f-I) curves that exhibit a suitably sized kink where the slope of the curve decreases more abruptly in the gamma frequency range with increasing current, and (ii) be well-connected with one another. Our mathematical analyses show that the combination of synaptic and intrinsic conditions as predicted by our simulations promote network multistability. Population slow time scales occur when excitatory fluctuations drive the network between different stable network firing states. Since many of the parameters we use are extracted from experiments, we propose that our network models capture a fundamental, operating mechanism in biological, hippocampal networks.