Deriving theoretical phase locking values of a coupled cortico-thalamic neural mass model using center manifold reduction

Cognitive functions such as sensory processing and memory processes lead to phase synchronization in the electroencephalogram or local field potential between different brain regions. There are a lot of computational researches deriving phase locking values (PLVs), which are an index of phase synchronization intensity, from neural models. However, these researches derive PLVs numerically. To the best of our knowledge, there have been no reports on the derivation of a theoretical PLV. In this study, we propose an analytical method for deriving theoretical PLVs from a cortico-thalamic neural mass model described by a delay differential equation. First, the model for generating neural signals is transformed into a normal form of the Hopf bifurcation using center manifold reduction. Second, the normal form is transformed into a phase model that is suitable for analyzing synchronization phenomena. Third, the Fokker–Planck equation of the phase model is derived and the phase difference distribution is obtained. Finally, the PLVs are calculated from the stationary distribution of the phase difference. The validity of the proposed method is confirmed via numerical simulations. Furthermore, we apply the proposed method to a working memory process, and discuss the neurophysiological basis behind the phase synchronization phenomenon. The results demonstrate the importance of decreasing the intensity of independent noise during the working memory process. The proposed method will be of great use in various experimental studies and simulations relevant to phase synchronization, because it enables the effect of neurophysiological changes on PLVs to be analyzed from a mathematical perspective.     

Biophysical mechanism of the interaction between default mode network and working memory network

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network''s internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11571-021-09674-1.     

A role for Melanin-Concentrating Hormone in learning and memory

The neurobiological substrate of learning process and persistent memory storage involves multiple brain areas. The neocortex and hippocampal formation are known as processing and storage sites for explicit memory, whereas the striatum, amygdala, neocortex and cerebellum support implicit memory. Synaptic plasticity, long-term changes in synaptic transmission efficacy and transient recruitment of intracellular signaling pathways in these brain areas have been proposed as possible mechanisms underlying short- and long-term memory retention. In addition to the classical neurotransmitters (glutamate, GABA), experimental evidence supports a role for neuropeptides in modulating memory processes. This review focuses on the role of the Melanin-Concentrating Hormone (MCH) and receptors on memory formation in animal studies. Possible mechanisms may involve direct MCH modulation of neural circuit activity that support memory storage and cognitive functions, as well as indirect effect on arousal.     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

Learning related interactions among neuronal systems involved in memory processes

Functional neuroimaging techniques using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided new insights in our understanding of brain function from the molecular to the systems level. While subtraction strategy based data analyses have revealed the involvement of distributed brain regions in memory processes, covariance analysis based data analysis strategies allow functional interactions between brain regions of a neuronal network to be assessed. The focus of this chapter is to (1) establish the functional topography of episodic and working memory processes in young and old normal volunteers, (2) to assess functional interactions between modules of networks of brain regions by means of covariance based analyses and systems level modelling and (3) to relate neuroimaging data to the underpinning neural networks. Male normal young and old volunteers without neurological or psychiatric illness participated in neuroimaging studies (PET, fMRI) on working and episodic memory. Distributed brain areas are involved in memory processes (episodic and working memory) in young volunteers and show much of an overlap with respect to the network components. Systems level modelling analyses support the hypothesis of bihemispheric, asymmetric networks subserving memory processes and revealed both similarities in general and differences in the interactions between brain regions during episodic encoding and retrieval as well as working memory. Changes in memory function with ageing are evident from studies in old volunteers activating more brain regions compared to young volunteers and revealing more and stronger influences of prefrontal regions. We finally discuss the way in which the systems level models based on PET and fMRI results have implications for the understanding of the underlying neural network functioning of the brain.     

From cognitive to neural models of working memory

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.     

The relationships between working memory and long-term memory

Recent studies have led to the proposal that working memory operates not as a gateway between sensory input and long-term memory but as a workspace. The core of argument is that access to acquired knowledge and prior learning occurs before information becomes available to working memory. This proposition is a way to accomodate Baddeley's multiple component working memory model and the view that considers that working memory is nothing other than temporary activations of representations and procedures in long-term memory. However, this ‘workspace’ conception of working memory raises the question of the relationships between the central executive system and long-term memory.     

Specificity in the functional architecture of primate prefrontal cortex

Multiple lines of evidence indicate that the performance of complex cognitive processes, such as those involving working memory, depend upon the functional properties of the circuitry of the prefrontal cortex (PFC). In primates, working memory has been proposed to be dependent upon the sustained activity of specific populations of PFC pyramidal cells, with this activity regulated by certain types of GABAergic interneurons. Thus, knowledge of the connectivity between PFC pyramidal cells and interneurons is crucial to the understanding the neural mechanisms that subserve working memory. This paper reviews recent findings that reveal specificity in the spatial organization, synaptic targets and postnatal development of pyramidal cells and interneurons in the primate prefrontal cortex, and considers the relevance of these findings for the neural circuitry that subserves working memory.     

Visuomotor synchronization: Analysis of the initiation and stable synchronization phases

The initiation phase of visuomotor synchronization and the phase of stable synchronization were studied in an experiment where eight adult subjects synchronized their motor responses with an isochronous sequence of visual stimuli. The period of the sequence varied in a wide range (from 500 to 2200 ms). Analysis of the statistical characteristics of synchronization errors (asynchronies) showed that the phase of stable visuomotor synchronization fit the model of current phase correction of a central timer; as in the case of audiomotor synchronization, the variability of the intervals of the central timer and the phase correction coefficient increased with increasing period of the stimulus sequence. The initiation phase of visuomotor synchronization was characterized by a considerable inter-and intraindividual variability of the form (exponential, linear, or step) and duration (from one to ten responses) of the transition from reacting to a sensory signal to synchronization. The shape and duration of the transitional region depend on the phase correction coefficient and the possibility of using an estimate of the sequence period stored in memory. The obtained data indicate that the initiation stage is not automatic throughout the studied range of periods of the visual stimulus sequence; in particular, working memory plays a substantial role in its organization.     

Working memory and executive function: evidence from neuroimaging

Traditional theories of working memory and executive function, when mapped in straightforward ways into the neural domain, yield predictions that are only partly supported by the recent neuroimaging studies. Neuroimaging studies suggest that some constituent functions, such as maintaining information in active form and manipulating it, are not discretely localized in prefrontal regions. Some hypothesized executive processes, such as goal management, have effects in several cortical regions, including posterior regions. Such results suggest a more dynamic and distributed view of the cortical organization of working memory and executive functions.     

Towards a unifying model of neural net activity in the visual cortex

A neural net model describing the non-linear interactions between axonal spikes is presented. It reconciles aspects of pattern recognition (as action of an associative memory) with those of spike synchronization and phase locking. The stability of the synchronized state is studied in detail.     

When encoding yields remembering: insights from event-related neuroimaging.

To understand human memory, it is important to determine why some experiences are remembered whereas others are forgotten. Until recently, insights into the neural bases of human memory encoding, the processes by which information is transformed into an enduring memory trace, have primarily been derived from neuropsychological studies of humans with select brain lesions. The advent of functional neuroimaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), has provided a new opportunity to gain additional understanding of how the brain supports memory formation. Importantly, the recent development of event-related fMRI methods now allows for examination of trial-by-trial differences in neural activity during encoding and of the consequences of these differences for later remembering. In this review, we consider the contributions of PET and fMRI studies to the understanding of memory encoding, placing a particular emphasis on recent event-related fMRI studies of the Dm effect: that is, differences in neural activity during encoding that are related to differences in subsequent memory. We then turn our attention to the rich literature on the Dm effect that has emerged from studies using event-related potentials (ERPs). It is hoped that the integration of findings from ERP studies, which offer higher temporal resolution, with those from event-related fMRI studies, which offer higher spatial resolution, will shed new light on when and why encoding yields subsequent remembering.     

Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, novel methods of time series analysis have been developed for the examination of task- and performance-related oscillatory activity and its synchronization. Studies employing these advanced techniques revealed that synchronization of oscillatory responses in the beta- and gamma-band is involved in a variety of cognitive functions, such as perceptual grouping, attention-dependent stimulus selection, routing of signals across distributed cortical networks, sensory-motor integration, working memory, and perceptual awareness. Here, we review evidence that certain brain disorders, such as schizophrenia, epilepsy, autism, Alzheimer's disease, and Parkinson's are associated with abnormal neural synchronization. The data suggest close correlations between abnormalities in neuronal synchronization and cognitive dysfunctions, emphasizing the importance of temporal coordination. Thus, focused search for abnormalities in temporal patterning may be of considerable clinical relevance.     

Changes in face expression recognition caused by additional visual-spatial load

Changes in face expression recognition and EEG synchronization arising from additional load on working memory were studied in healthy adults. Two types of additional task--semantic and visuospatial--were used to load working memory in an experiment with a visual set, formed to facial stimuli. During perception of new facial stimuli, both these types of additional task caused an increase of erroneous face expression recognitions in the form of assimilative illusions. Alpha-band (8-10 Hz) EEG synchronization analysis revealed that additional memory load causes a decrease of frontal attention system input in set-forming and set-shifting. As for theta-band (4-7 Hz) synchronization, it changed ambiguously at additional memory load--in right fronto-temporal region coherence function decreased; other coherence connections, especially intra-hemispheric and in the left hemisphere, increased. At issue is the crucial role of fronto-thalamic and cortico-hippocampal systems in plasticity of visual sets formed to facial expressions.     

Neurodevelopmental changes in working memory and cognitive control

One of the most salient ways in which our behavior changes during childhood and adolescence is that we get better at working towards long-term goals, at ignoring irrelevant information that could distract us from our goals, and at controlling our impulses - in other words, we exhibit improvements in cognitive control. Several recent magnetic resonance imaging studies have examined the developmental changes in brain structure and function that underlie improvements in working memory and cognitive control. Increased recruitment of task-relevant regions in the prefrontal cortex, parietal cortex and striatum over the course of development is associated with better performance in a range of cognitive tasks. Further work is needed to assess the role of experience in shaping the neural circuitry that underlies cognitive control.     

Fear memory and the amygdala: insights from a molecular perspective

The amygdala modulates memory consolidation and the storage of emotionally relevant information in other brain areas, and itself comprises a site of neural plasticity during aversive learning. These processes have been intensively studied in Pavlovian fear conditioning, a leading aversive learning paradigm that is dependent on the structural and functional integrity of the amygdala. The rapidness and persistence, and the relative ease, with which this conditioning paradigm can be applied to a great variety of species have made it an attractive model for neurochemical and electrophysiological investigations on memory formation. In this review we summarise recent studies which have begun to unravel cellular processes in the amygdala that are critical for the formation of long-term fear memory and have identified molecular factors and mechanisms of neural plasticity in this brain area.     

Amplitude modulation of steady-state visual evoked potentials by event-related potentials in a working memory task

Previous studies have shown that the amplitude and phase of the steady-state visual-evoked potential (SSVEP) can be influenced by a cognitive task, yet the mechanism of this influence has not been understood. As the event-related potential (ERP) is the direct neural electric response to a cognitive task, studying the relationship between the SSVEP and ERP would be meaningful in understanding this underlying mechanism. In this work, the traditional average method was applied to extract the ERP directly, following the stimulus of a working memory task, while a technique named steady-state probe topography was utilized to estimate the SSVEP under the simultaneous stimulus of an 8.3-Hz flicker and a working memory task; a comparison between the ERP and SSVEP was completed. The results show that the ERP can modulate the SSVEP amplitude, and for regions where both SSVEP and ERP are strong, the modulation depth is large.     

Hippocampal-prefrontal connectivity predicts midfrontal oscillations and long-term memory performance

The hippocampus and prefrontal cortex interact to support working memory (WM) and long-term memory [1-3]. Neurophysiologically, WM is thought to be subserved by reverberatory activity of distributed networks within the prefrontal cortex (PFC) [2, 4-8], which become synchronized with reverberatory activity in the hippocampus [1, 4]. This electrophysiological synchronization is difficult to study in humans because noninvasive electroencephalography (EEG) cannot measure hippocampus activity. Here, using a novel integration of EEG and diffusion-weighted imaging, it is shown that individuals with relatively stronger anatomical connectivity linking the hippocampus to the right ventrolateral PFC (ventral Brodmann area 46) exhibited slower frequency neuronal oscillations during a WM task. Furthermore, subjects with stronger hippocampus-PFC connectivity were better able to encode the complex pictures used in the WM task into long-term memory. These findings are consistent with models suggesting that electrophysiological oscillations provide a mechanism of long-range interactions [9] and link hippocampus-PFC structural connectivity to PFC rhythmic electrical dynamics and memory performance. More generally, these results highlight the importance of incorporating individual differences when linking structure and function to cognition.     

Debra, a protein mediating lysosomal degradation, is required for long-term memory in Drosophila

A central goal of neuroscience is to understand how neural circuits encode memory and guide behavior changes. Many of the molecular mechanisms underlying memory are conserved from flies to mammals, and Drosophila has been used extensively to study memory processes. To identify new genes involved in long-term memory, we screened Drosophila enhancer-trap P(Gal4) lines showing Gal4 expression in the mushroom bodies, a specialized brain structure involved in olfactory memory. This screening led to the isolation of a memory mutant that carries a P-element insertion in the debra locus. debra encodes a protein involved in the Hedgehog signaling pathway as a mediator of protein degradation by the lysosome. To study debra's role in memory, we achieved debra overexpression, as well as debra silencing mediated by RNA interference. Experiments conducted with a conditional driver that allowed us to specifically restrict transgene expression in the adult mushroom bodies led to a long-term memory defect. Several conclusions can be drawn from these results: i) debra levels must be precisely regulated to support normal long-term memory, ii) the role of debra in this process is physiological rather than developmental, and iii) debra is specifically required for long-term memory, as it is dispensable for earlier memory phases. Drosophila long-term memory is the only long-lasting memory phase whose formation requires de novo protein synthesis, a process underlying synaptic plasticity. It has been shown in several organisms that regulation of proteins at synapses occurs not only at translation level of but also via protein degradation, acting in remodeling synapses. Our work gives further support to a role of protein degradation in long-term memory, and suggests that the lysosome plays a role in this process.     

A model for integrating elementary neural functions into delayed-response behavior

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.