Neural Information Flow Research Articles

Impairment of Cognitive Function and Synaptic Plasticity Associated with Alteration of Information Flow in Theta and Gamma Oscillations in Melamine-Treated Rats

Changes of neural oscillations at a variety of physiological rhythms are effectively associated with cognitive performance. The present study investigated whether the directional indices of neural information flow (NIF) could be used to symbolize the synaptic plasticity impairment in hippocampal CA3-CA1 network in a rat model of melamine. Male Wistar rats were employed while melamine was administered at a dose of 300 mg/kg/day for 4 weeks. Behavior was measured by the Morris water maze(MWM)test. Local field potentials (LFPs) were recorded before long-term potentiation (LTP) induction. Generalized partial directed coherence (gPDC) and phase-amplitude coupling conditional mutual information (PAC_CMI) were used to measure the unidirectional indices in both theta and low gamma oscillations (LG, ∼30–50 Hz). Our results showed that melamine induced the cognition deficits consistent with the reduced LTP in CA1 area. Phase locking values (PLVs) showed that the synchronization between CA3 and CA1 in both theta and LG rhythms was reduced by melamine. In both theta and LG rhythms, unidirectional indices were significantly decreased in melamine treated rats while a similar variation trend was observed in LTP reduction, implying that the effects of melamine on cognitive impairment were possibly mediated via profound alterations of NIF on CA3-CA1 pathway in hippocampus. The results suggested that LFPs activities at these rhythms were most likely involved in determining the alterations of information flow in the hippocampal CA3-CA1 network, which might be associated with the alteration of synaptic transmission to some extent.

Decreased thalamo-cortical connectivity by alteration of neural information flow in theta oscillation in depression-model rats

Alterations in oscillatory brain activity are strongly correlated with cognitive performance in various physiological rhythms. The present study investigated whether the directionality of neural information flow (NIF) could be used to characterize the synaptic plasticity in thalamocortical (TC) pathway, and examined which frequency field oscillations were mostly related to the cognitive deficiency in depression. Two novel algorithms were employed to determine the coupling interaction between the LD thalamus and medial prefrontal cortex (mPFC) in five frequency bands, using the phase signals of local field potentials (LFP) in these two regions. The results showed that the power of neural activity in mPFC was increased in delta, theta and beta frequency bands in depression. However, the nonlinear characteristics of LFP activity were weakened in depression by means of sample entropy measurements. In the analysis of phase dynamics, the phase synchronization values were reduced in theta rhythm in stressed rats. Importantly, the coupling direction index d and the unidirectional influence from LD thalamus to mPFC were significantly reduced at the theta rhythm in rats in depression, and increased after memantine treatment, which were associated with the LTP alterations and cognitive impairment in our previous report. Moreover, the fact that the reduced entropy value was only found in mPFC might implicate postsynaptic effect involved in synaptic plasticity alteration in the depression model. The results suggest that the effects of depression on cognitive deficits are mediated via profound alterations in information flow in the TC pathway, and the directional index at theta rhythm could be used as a measurement of synaptic plasticity.

Tracking neural coding of perceptual and semantic features of concrete nouns

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes.

Decrease of synaptic plasticity associated with alteration of information flow in a rat model of vascular dementia

This investigation examined whether the directional index of neural information flow (NIF) could be employed to characterize the synaptic plasticity in the CA3-CA1 pathway of the hippocampus and assessed which oscillatory rhythm was associated with cognitive impairments induced by vascular dementia (VD). Rats were randomly divided into control and VD groups. The animal model of VD used the two-vessel occlusion (2VO) method. Behavior was measured using the Morris water maze (MWM). Local field potentials (LFPs) from CA3 and CA1 were recorded after behavioral tests, followed by recording long-term potentiation (LTP) of the same CA3-CA1 pathway. General partial directed coherence (gPDC) approach was utilized to determine the directionality of NIF between CA3 and CA1 over five frequency bands, which were delta, theta, alpha, beta, and gamma. The results showed that the escape latencies were significantly prolonged in the VD group, whereas the swimming speeds of these two groups remained constant throughout testing. Moreover, the phase synchronization values between CA3 and CA1 regions were reduced in theta, alpha, beta, and gamma bands in the VD state compared to that in the normal state. The coupling directional index was considerably decreased in the previously given four frequency bands in VD rats, whereas the strength of CA3 driving CA1 was significantly reduced in the same frequency bands. Interestingly, LTP was significantly decreased in the VD group, which was consistent with the LFPs findings. The data suggest that the directionality index of NIF in these physiological oscillatory rhythms could be used as a measure of synaptic plasticity in the hippocampal CA3-CA1 pathway in VD states. The potential mechanism of the relationship between NIF direction and synaptic plasticity in VD state was discussed.

Information theory in neuroscience

Information Theory started and, according to some, ended with Shannon’s seminal paper “A Mathematical Theory of Communication” (Shannon 1948). Because its significance and flexibility were quickly recognized, there were numerous attempts to apply it to diverse fields outside of its original scope. This prompted Shannon to write his famous essay “The Bandwagon” (Shannon 1956), warning against indiscriminate use of the new tool. Nevertheless, non-standard applications of Information Theory persisted. Very soon after Shannon’s initial publication (Shannon 1948), several manuscripts provided the foundations of much of the current use of information theory in neuroscience. MacKay and McCulloch (1952) applied the concept of information to propose limits of the transmission capacity of a nerve cell. This work foreshadowed future work on what can be termed “Neural Information Flow”—how much information moves through the nervous system, and the constraints that information theory imposes on the capabilities of neural systems for communication, computation and behavior. A second set of manuscripts, by Attneave (1954) and Barlow (1961), discussed information as a constraint on neural system structure and function, proposing that neural structure in sensory system is matched to statistical structure of the sensory environment, in a way to optimize information transmission. This is the main idea behind the “Structure from Information” line of research that is still very active today. A third thread, “Reliable Computation with Noisy/Faulty Elements”, started both in the information-theoretic community (Shannon and McCarthy 1956) and neuro-science (Winograd and Cowan 1963). With the advent of integrated circuits that were essentially faultless, interest began to wane. However, as IC technology continues to push towards smaller and faster computational elements (even at the expense of reliability), and as neuromorphic systems are developed with variability designed in (Merolla and Boahen 2006), this topic is gaining in popularity again in the electronics community, and neuroscientists again may have something to contribute to the discussion.

Directional Indicator on Neural Oscillations as a Measure of Synaptic Plasticity in Chronic Unpredictable Stress Rats

To examine whether the directionality index of neural information flow (NIF) over specific oscillatory bands is useful in measuring synaptic plasticity, we employed the IM approach to determine the direction of NIF between the cortex and thalamus in normal and stressed animals. The experiment was performed by inducing long-term potentiation (LTP) of the thalamocortical pathway after recording local field potential (LFP). Additionally, comparison of IM measurement between broad- and narrowbands was performed, while a numerical study was also carried out for assessing the number of data points. The results show that the instantaneous phases extracted from narrowband vary monotonically, while these phases are jagged in broadband. Our data show that there is a predominant driving effect (coupling directional index d >0) from the thalamus to the frontal cortex in normal animals; however, the value of d is significantly reduced in the chronic stressed group in both the delta and theta bands. Furthermore, the field LTP data show that chronic stress decreases medial prefrontal cortex synaptic plasticity, which is certainly in line with the LFP findings. Together, these data suggest that using an IM algorithm, the directionality index of NIF in specific oscillatory frequency bands will probably be used as a measure of synaptic plasticity.

Directionality index of neural information flow as a measure of synaptic plasticity in chronic unpredictable stress rats

This investigation examined whether the directionality of neural information flow could be used to index the measurement of synaptic plasticity in the chronic unpredictable stress (CUS) animals. Evolution map approach (EMA) was employed to determine the direction of information flow between the cortex and thalamus, while the experiment was performed by inducing long-term potentiation of the thalamocortical pathway after recording intracranial EEG at the same two positions in Wistar rats of both normal and stressed groups. The results showed that the coupling direction index was significantly diverted in stressed state compared to that in normal state, while the strength of thalamus driving frontal cortex was considerably reduced in the rats of CUS model. Moreover, the data obtained from LTP experiment exhibited that chronic stress decreased medial prefrontal cortex (mPFC) synaptic plasticity, which was certainly in accordance with the EEG findings. These results demonstrated that using EMA measurement, directionality index of neural information flow may be as a measure of synaptic plasticity.

Molecules and mechanisms of dendrite development inDrosophila

Neurons are one of the most morphologically diverse cell types, in large part owing to their intricate dendrite branching patterns. Dendrites are structures that are specialized to receive and process inputs in neurons, thus their specific morphologies reflect neural connectivity and influence information flow through circuits. Recent studies in Drosophila on the molecular basis of dendrite diversity, dendritic guidance, the cell biology of dendritic branch patterning and territory formation have identified numerous intrinsic and extrinsic cues that shape diverse features of dendrites. As we discuss in this review, many of the mechanisms that are being elucidated show conservation in diverse systems.

Manganese enhanced MRI reveals functional circuitry in response to odorant stimuli

To investigate the circuitry involved in detecting odorants in the rodent brain, we developed a method using manganese-enhanced MRI (MEMRI) to map the flow of neural information from the olfactory sensory neurons (OSNs) to the central layers of the olfactory bulb. Studies have shown that Mn 2+ enters active neurons and is transported anterogradely to axon terminals where it can cross synapses to functionally trace neural networks. Thus, by delivering MnCl 2 directly into the nasal cavity of mice and then exposing them to defined odorants, Mn 2+ is preferentially taken up by activated OSNs. Using the time course of the MRI signal, we generated maps of Mn 2+ accumulation in the olfactory bulb for both glomerular and mitral cell layers. Results demonstrated that overlapping yet distinct enhancement patterns were produced by exposure to either octanal, acetophenone, or carvone. Notably, areas of Mn 2+ accumulation in the mitral cell layer were similar to those in the glomerular layer consistent with neural information that passes from specific OSNs to specific mitral cells. Finally, by correlating specific Mn 2+ signal peaks to genetically labeled glomeruli that are known to be activated by the odorant octanal, we show that MEMRI maps can be resolved at the level of individual glomeruli.

Investigating signal integration with canonical correlation analysis of fMRI brain activation data

How the brain integrates signals from specific areas has been a longstanding critical question for neurobiologists. Two recent observations suggest a new approach to fMRI data analysis of this question. First, in many instances, the brain analyzes inputs by decomposing the information along several salient dimensions. For example, earlier work demonstrated that the brain splits a monetary gamble in terms of expected reward (ER) and variance of the reward (risk) [Preuschoff, K., Bossaerts, P., Quartz, S., 2006. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381–390]. However, since ER and risk activate separate brain regions, the brain needs to integrate these activations to obtain an overall evaluation of the gamble. Second, recent evidence suggests that the correlation of the activity between neurons may serve a specific organizational purpose [Romo, R., Hernandez, A., Zainos, A., Salinas, E., 2003. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657; Salinas, E., Sejnowski, T.J., 2001. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539]. Specifically, it is hypothesized that correlations allow brain regions to integrate several signals in a way that minimizes noise. Under this hypothesis, we show here that canonical correlation analysis of fMRI data identifies how the signals from several regions are combined. A general linear model then verifies whether the identified combination indeed activates a projection area in the brain. We illustrate the proposed procedure on data recorded while human subjects played a simple card game. We show that the brain adds the signals of ER and risk to form a measure that activates the medial prefrontal cortex, consistent with the role of this brain structure in the evaluation of monetary gambles.

Computational Inference of Neural Information Flow Networks

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Correlated neuronal activity and the flow of neural information.

For years we have known that cortical neurons collectively have synchronous or oscillatory patterns of activity, the frequencies and temporal dynamics of which are associated with distinct behavioural states. Although the function of these oscillations has remained obscure, recent experimental and theoretical results indicate that correlated fluctuations might be important for cortical processes, such as attention, that control the flow of information in the brain.