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.     

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.     

Human connectomics

Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.     

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.     

Functional neuroimaging

Functional neuroimaging represents an area of brain imaging that has undergone tremendous advancements in the last decade. It is now possible to design experiments that elucidate the functional interplay between brain regions that give rise to specific human cognitive processes. Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) form the core technologies that have allowed such studies. This article reviews the basis of these techniques, their strengths and limitations, the underlying neurophysiology, and the future of functional neuroimaging.     

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.     

Mapping early brain development in autism

Although the neurobiology of autism has been studied for more than two decades, the majority of these studies have examined brain structure 10, 20, or more years after the onset of clinical symptoms. The pathological biology that causes autism remains unknown, but its signature is likely to be most evident during the first years of life when clinical symptoms are emerging. This review highlights neurobiological findings during the first years of life and emphasizes early brain overgrowth as a key factor in the pathobiology of autism. We speculate that excess neuron numbers may be one possible cause of early brain overgrowth and produce defects in neural patterning and wiring, with exuberant local and short-distance cortical interactions impeding the function of large-scale, long-distance interactions between brain regions. Because large-scale networks underlie socio-emotional and communication functions, such alterations in brain architecture could relate to the early clinical manifestations of autism. As such, autism may additionally provide unique insight into genetic and developmental processes that shape early neural wiring patterns and make possible higher-order social, emotional, and communication functions.     

Molecular Bases of Caloric Restriction Regulation of Neuronal Synaptic Plasticity

Aging is associated with the decline of cognitive properties. This situation is magnified when neurodegenerative processes associated with aging appear in human patients. Neuronal synaptic plasticity events underlie cognitive properties in the central nervous system. Caloric restriction (CR; either a decrease in food intake or an intermittent fasting diet) can extend life span and increase disease resistance. Recent studies have shown that CR can have profound effects on brain function and vulnerability to injury and disease. Moreover, CR can stimulate the production of new neurons from stem cells (neurogenesis) and can enhance synaptic plasticity, which modulate pain sensation, enhance cognitive function, and may increase the ability of the brain to resist aging. The beneficial effects of CR appear to be the result of a cellular stress response stimulating the production of proteins that enhance neuronal plasticity and resistance to oxidative and metabolic insults; they include neurotrophic factors, neurotransmitter receptors, protein chaperones, and mitochondrial biosynthesis regulators. In this review, we will present and discuss the effect of CR in synaptic processes underlying analgesia and cognitive improvement in healthy, sick, and aging animals. We will also discuss the possible role of mitochondrial biogenesis induced by CR in regulation of neuronal synaptic plasticity.     

Representation of Cognitive Reappraisal Goals in Frontal Gamma Oscillations

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35–55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals. Our study may provide the basis for an electroencephalogram-based neurofeedback system for the cognitive regulation of emotion.     

Astrocytes as Perspective Targets of Exercise- and Caloric Restriction‐Mimetics

Enhanced mental and physical activity can have positive effects on the function of aging brain, both in the experimental animals and human patients, although cellular mechanisms underlying these effects are currently unclear. There is a growing evidence that pre-clinical stage of many neurodegenerative diseases involves changes in interactions between astrocytes and neurons. Conversely, astrocytes are strategically positioned to mediate the positive influence of physical activity and diet on neuronal function. Thus, development of therapeutic agents which could improve the astroglia-neuron communications in ageing brain is of crucial importance. Recent advances in studies of cellular mechanisms of brain longevity suggest that astrocyte-neuron communications have a vital role in the beneficial effects of caloric restriction, physical exercise and their pharmacological mimetics on synaptic homeostasis and cognitive function. In particular, our recent data indicate that noradrenaline uptake inhibitor atomoxetine can enhance astrocytic Ca²⁺-signaling and astroglia-driven modulation of synaptic plasticity. Similar effects were exhibited by caloric restriction-mimetics metformin and resveratrol. The emerged data also suggest that astrocytes could be involved in the modulatory action of caloric restriction and its mimetics on neuronal autophagy. Still, the efficiency of astrocyte-targeting compounds in preventing age-related cognitive decline is yet to be fully explored, in particular in the animal models of neurodegenerative diseases and autophagy impairment.

     

Leptin regulation of hippocampal synaptic function in health and disease

The endocrine hormone leptin plays a key role in regulating food intake and body weight via its actions in the hypothalamus. However, leptin receptors are highly expressed in many extra-hypothalamic brain regions and evidence is growing that leptin influences many central processes including cognition. Indeed, recent studies indicate that leptin is a potential cognitive enhancer as it markedly facilitates the cellular events underlying hippocampal-dependent learning and memory, including effects on glutamate receptor trafficking, neuronal morphology and activity-dependent synaptic plasticity. However, the ability of leptin to regulate hippocampal synaptic function markedly declines with age and aberrant leptin function has been linked to neurodegenerative disorders such as Alzheimer''s disease (AD). Here, we review the evidence supporting a cognitive enhancing role for the hormone leptin and discuss the therapeutic potential of using leptin-based agents to treat AD.     

Separating vascular and neuronal effects of age on fMRI BOLD signals

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.     

Neuronal interactions between neuropeptide W- and orexin- or melanin-concentrating hormone-containing neurons in the rat hypothalamus

Neuropeptide W (NPW) was recently discovered as the endogenous ligand for GPR7 and GPR8, which are orphan G protein-coupled receptors isolated from the porcine brain. These receptors are assumed to be involved in feeding regulation and/or energy homeostasis. Recent anatomical studies have revealed that high levels of GPR7 mRNA are distributed in the brain, including the hypothalamus and amygdala. However immunohistochemical studies on the distribution and localization of NPW have revealed differing results concerning whether or not NPW-containing cell bodies and their processes are present in the hypothalamus. Only a few immunohistochemical reports have been published concerning the presence of NPW-containing neurons in the brains of rodents, while there have been no anatomical studies of the co-localization of this neuropeptide with other transmitters. On this basis, we used a specific antiserum against NPW to determine immunohistochemically the presence of NPW-containing neurons in the rat hypothalamus. Many NPW-like immunoreactive cell bodies and their processes could be detected in the caudal region of the lateral hypothalamus but not in its anterior or middle regions. Given this positive identification of NPW-containing neurons in the lateral hypothalamus, we further studied the nature of interaction between NPW-containing neurons and neurons containing feeding regulating peptides such as orexin- and melanin-concentrating hormone (MCH). Very close interactions between NPW-containing nerve processes and orexin- and MCH-containing neuronal cell bodies and processes could be observed. These morphological findings strongly suggest that NPW is involved in the regulation of feeding and/or sleep/arousal behavior through orexin- and/or MCH-mediated neuronal pathways.     

Temporal Lobe Cortical Thickness Correlations Differentiate the Migraine Brain from the Healthy Brain

BackgroundInterregional cortical thickness correlations reflect underlying brain structural connectivity and functional connectivity. A few prior studies have shown that migraine is associated with atypical cortical brain structure and atypical functional connectivity amongst cortical regions that participate in sensory processing. However, the specific brain regions that most accurately differentiate the migraine brain from the healthy brain have yet to be determined. The aim of this study was to identify the brain regions that comprised interregional cortical thickness correlations that most differed between migraineurs and healthy controls.MethodsThis was a cross-sectional brain magnetic resonance imaging (MRI) investigation of 64 adults with migraine and 39 healthy control subjects recruited from tertiary-care medical centers and their surrounding communities. All subjects underwent structural brain MRI imaging on a 3T scanner. Cortical thickness was determined for 70 brain regions that cover the cerebral cortex and cortical thickness correlations amongst these regions were calculated. Cortical thickness correlations that best differentiated groups of six migraineurs from controls and vice versa were identified.ResultsA model containing 15 interregional cortical thickness correlations differentiated groups of migraineurs from healthy controls with high accuracy. The right temporal pole was involved in 13 of the 15 interregional correlations while the right middle temporal cortex was involved in the other two.ConclusionsA model consisting of 15 interregional cortical thickness correlations accurately differentiates the brains of small groups of migraineurs from those of healthy controls. Correlations with the right temporal pole were highly represented in this classifier, suggesting that this region plays an important role in migraine pathophysiology.     

Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI) datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly) reflects the activity of the brain structure(s)–located at the corresponding point in space–at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA), a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.     

Neuroprotection in epilepsy

Brain damage and neuronal loss caused by traumatic brain injury, ischemic stroke, and symptomatic status epilepticus can lead to severe long-term consequences, such as impairment in learning and memory and cognitive functions, and development of chronic epilepsy. This can be the result of morphologic and functional changes underlying temporal lobe epilepsy. Epilepsy patients have increased risk of status epilepticus. It is a life-threatening condition when seizures last for more than 30 min and trigger processes leading to neuronal apoptosis and necrosis in various parts of brain. Administration of neuroprotective drugs preventing these pathologic processes could improve the prognosis for such patients. However despite of active research of neuroprotective drugs, the effective ways to prevent brain damage resulting from prolonged seizures are yet to be found. Studies of neuroprotective properties of classic and novel anticonvulsant drugs showed that most of them do not have the sufficient neuroprotective effect and are not able to prevent epileptogenesis. Thus the studies of other potential neuroprotective drugs seem to be promising.     

Intracranial EEG and human brain mapping

This review is an attempt to highlight the value of human intracranial recordings (intracranial electro-encephalography, iEEG) for human brain mapping, based on their technical characteristics and based on the corpus of results they have already yielded. The advantages and limitations of iEEG recordings are introduced in detail, with an estimation of their spatial and temporal resolution for both monopolar and bipolar recordings. The contribution of iEEG studies to the general field of human brain mapping is discussed through a review of the effects observed in the iEEG while patients perform cognitive tasks. Those effects range from the generation of well-localized evoked potentials to the formation of large-scale interactions between distributed brain structures, via long-range synchrony in particular. A framework is introduced to organize those iEEG studies according to the level of complexity of the spatio-temporal patterns of neural activity found to correlate with cognition. This review emphasizes the value of iEEG for the study of large-scale interactions, and describes in detail the few studies that have already addressed this point.     

Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys

Consciousness is an emergent property of the complex brain network. In order to understand how consciousness is constructed, neural interactions within this network must be elucidated. Previous studies have shown that specific neural interactions between the thalamus and frontoparietal cortices; frontal and parietal cortices; and parietal and temporal cortices are correlated with levels of consciousness. However, due to technical limitations, the network underlying consciousness has not been investigated in terms of large-scale interactions with high temporal and spectral resolution. In this study, we recorded neural activity with dense electrocorticogram (ECoG) arrays and used the spectral Granger causality to generate a more comprehensive network that relates to consciousness in monkeys. We found that neural interactions were significantly different between conscious and unconscious states in all combinations of cortical region pairs. Furthermore, the difference in neural interactions between conscious and unconscious states could be represented in 4 frequency-specific large-scale networks with unique interaction patterns: 2 networks were related to consciousness and showed peaks in alpha and beta bands, while the other 2 networks were related to unconsciousness and showed peaks in theta and gamma bands. Moreover, networks in the unconscious state were shared amongst 3 different unconscious conditions, which were induced either by ketamine and medetomidine, propofol, or sleep. Our results provide a novel picture that the difference between conscious and unconscious states is characterized by a switch in frequency-specific modes of large-scale communications across the entire cortex, rather than the cessation of interactions between specific cortical regions.     

The neural bases of cognitive conflict and control in moral judgment

Traditional theories of moral psychology emphasize reasoning and "higher cognition," while more recent work emphasizes the role of emotion. The present fMRI data support a theory of moral judgment according to which both "cognitive" and emotional processes play crucial and sometimes mutually competitive roles. The present results indicate that brain regions associated with abstract reasoning and cognitive control (including dorsolateral prefrontal cortex and anterior cingulate cortex) are recruited to resolve difficult personal moral dilemmas in which utilitarian values require "personal" moral violations, violations that have previously been associated with increased activity in emotion-related brain regions. Several regions of frontal and parietal cortex predict intertrial differences in moral judgment behavior, exhibiting greater activity for utilitarian judgments. We speculate that the controversy surrounding utilitarian moral philosophy reflects an underlying tension between competing subsystems in the brain.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.