Levels Of Brain Organization Research Articles

Ghrelin-dependent mechanisms of food reward. Part 1. Ghrelin and dopamine

Feeding behavior includes both the satisfaction of metabolic nutrient requirements and the hedonic aspects of pleasure. The orexigenic hormone ghrelin increases motivation to food intake. In addition, by modulating the reward system, ghrelin enhances the rewarding properties of non-food stimuli. The dopaminergic mesolimbic system plays a key role in the mechanisms of reward. Ghrelin regulates the activity of this system by acting on several levels of brain organization. The hierarchy of ghrelin-sensitive neural networks, as well as afferent connections and reciprocal interactions between its components, mediating the effect of ghrelin on the process of motivation in metabolic and hedonic type of feeding are considered.

A neurocomputational model for the processing of conflicting information in context-dependent decision tasks.

Context-dependent computation is a relevant characteristic of neural systems, endowing them with the capacity of adaptively modifying behavioral responses and flexibly discriminating between relevant and irrelevant information in a stimulus. This ability is particularly highlighted in solving conflicting tasks. A long-standing problem in computational neuroscience, flexible routing of information, is also closely linked with the ability to perform context-dependent associations. Here we present an extension of a context-dependent associative memory model to achieve context-dependent decision-making in the presence of conflicting and noisy multi-attribute stimuli. In these models, the input vectors are multiplied by context vectors via the Kronecker tensor product. To outfit the model with a noisy dynamic, we embedded the context-dependent associative memory in a leaky competing accumulator model, and, finally, we proved the power of the model in the reproduction of a behavioral experiment with monkeys in a context-dependent conflicting decision-making task. At the end, we discuss the neural feasibility of the tensor product and made the suggestive observation that the capacities of tensor context models are surprisingly in alignment with the more recent experimental findings about functional flexibility at different levels of brain organization.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches.

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain.

The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.

Individual-Specific Analysis for Psychoradiology.

In neuroimaging research, averaging data at the level of the group results in blurring of potentially meaningful individual differences. A more widespread use of an individual-specific approach is advocated for, which involves a more thorough investigation of each individual in a group, and characterization of idiosyncrasies at the level of behavior, cognition, and symptoms, as well as at the level of brain organization. It is hoped that such an approach, focused on individuals, will provide convergent findings that will help identify the underlying pathologic condition in various psychiatric disorders and help in the development of treatments individualized for each patient.

Traits of criticality in membrane potential fluctuations of pyramidal neurons in the CA1 region of rat hippocampus.

Evidence that neural circuits are operating near criticality has been provided at various levels of brain organisation with a presumed role in maximising information processing and multiscale activity association. Criticality has been linked to excitation at both the single-cell and network levels, as action potential generation marks an obvious phase transition from a resting to an excitable state. Using in vitro intracellular recordings, we examine irregular, small amplitude membrane potential fluctuations from CA1 pyramidal neurons of Wistar male rats. We show that these fluctuations, confounded with noise, carry information relevant to the neuronal state. The underlying dynamics exhibit intermittent characteristics shown to describe the temporal fluctuations of the order parameter of a macroscopic system at its critical point even in the absence of firing. An externally applied stimulus serves as the control parameter, driving the system in and out of its critical state. Based on our experimental observations we calculate the equivalent of the isothermal critical exponent δh finding a value which depends on the applied stimulus. For each neuron there is a stimulus amplitude for which the critical behaviour becomes most pronounced. The corresponding mean value of δh in the considered ensemble of neurons is δh ≈1.89, close to theoretical predictions for critical networks. Finally, we show that the firing rate of a neuron decreases exponentially with δh .

A robust biomarker of large-scale network failure in Alzheimer's disease

IntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large-scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process. MethodsWe developed and optimized the NFQ using our recently published analyses of task-free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218). ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort. DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large-scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large-scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.

In search of the human engram

Abstract Despite abundant knowledge on the neural basis of memory functions in the human brain, stimulus-specific memory traces- engrams-have long remained elusive. In this article, recent developments that start to shed light on the mechanisms underlying the formation, modification and potential degradation of engrams are reviewed. Stimulus-specific memory representations appear to occur at different levels of brain organization, from spike rates of individual cells via time-frequency signatures of small-scale neural networks to distributed activity patterns. However, memories undergo transformation whenever they are recalled. Thus, novel methodological approaches need to be employed in order to identify considerably modified engrams. Furthermore, engrams are impaired in a number of diseases involving memory dysfunction. This article is concluded with a description of recent translational work on altered content-specific memory representations in the context of Alzheimer’s disease.

In search of the human engram

Despite abundant knowledge on the neural basis of memory functions in the human brain, stimulus-specific memory traces — engrams — have long remained elusive. In this article, recent developments that start to shed light on the mechanisms underlying the formation, modification and potential degradation of engrams are reviewed. Stimulus-specific memory representations appear to occur at different levels of brain organization, from spike rates of individual cells via time-frequency signatures of small-scale neural networks to distributed activity patterns. However, memories undergo transformation whenever they are recalled. Thus, novel methodological approaches need to be employed in order to identify considerably modified engrams. Furthermore, engrams are impaired in a number of diseases involving memory dysfunction. This article is concluded with a description of recent translational work on altered content-specific memory representations in the context of Alzheimer’s disease.

Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization

Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous interareal coactivation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across the dorsal aspect of several visual areas in one hemisphere in each of two awake monkeys to investigate spatial patterns of local and interareal synchronization. We show that stimulation-induced patterns of interareal coactivation were reactivated in the absence of stimulation for the visual quadrant covered. Reactivation occurred through both interareal cofluctuation of local activity and interareal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of interareal coupling observed during stimulation, i.e., the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha, and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.

Introduction to special topic – Resting-state brain activity: implications for systems neuroscience

EDITORIAL article Front. Syst. Neurosci., 11 August 2010 https://doi.org/10.3389/fnsys.2010.00037

Impact of Alzheimers Disease on the Functional Connectivity of Spontaneous Brain Activity

Alzheimer's disease (AD) prominently affects the structure and function of cerebral networks. Reflecting the complex network structure of the brain, spontaneous brain activity is organized by synchronized activity across distinct temporal and spatial scales. Temporal correlations of the functional MRI (fMRI) signal during rest have been used to characterize the impact of AD on the functional connectivity of spontaneous brain activity. Here we review studies using resting-state fMRI (rs-fMRI) to explore AD-induced changes of synchronized intrinsic activity at three levels of brain organization: the regional, inter-regional and large-scale level. Changes in posterior areas of the default network (DN) and the medial temporal lobes seem to be central to AD. These areas show remarkable disturbances in neuronal communication at all spatial levels and in very early stages of the disease. Finally, rs-fMRI seems to have the potential to produce connectivity-related biomarkers that distinguish AD and healthy aging.

Cetacean Brain Evolution: Multiplication Generates Complexity

Over the past 55-60 million years cetacean (dolphin, whale, and porpoise) brains have become hyperexpanded so that modern cetacean encephalization levels are second only to modern humans. At the same time, brain expansion proceeded along very different lines than in other large-brained mammals so that substantial differences between modern cetacean brains and other mammalian brains exist at every level of brain organization. Perhaps the most profound difference between cetacean and other mammalian brains is in the architecture of the neocortex. Cetaceans possess a unique underlying neocortical organizational scheme that is particularly intriguing in light of the fact that cetaceans exhibit cognitive and behavioral complexity at least on a par with our closest phylogenetic relatives, the great apes. The neurobiological complexity underlying these cognitive capacities may involve the extreme multiplication of vertical structural units in the cetacean neocortex.

Coexisting Spatio-Temporal Scales In Neuroscience

In this study I propose an epistemological discussion of multiple spatio-temporal scales in neuroscience. Are such scales merely convenient levels of description of structure and function, or do they correspond to irreducible levels of brain organization? What criteria should we employ in order to reduce one level to another, or to identify levels that are not reducible to others? Should we think of these criteria as based on empirical and/or theoretical reasons? Beginning with an empirical criterion – the necessity of different experimental methodologies for the measurement of different phenomena in the same system – I summarize spatial and temporal scales currently used in neuroscience and discuss the possibility of a more general theoretical criterion. I conclude that multiscaling should be recognized as a central concept in the epistemology of neuroscience.

New Perspectives on the Clinical Neurobiology of Treatment Response in Schizophrenia

A structured approach is used to review the rapid progress in neurobiology related to treatment response in schizophrenia. Findings are presented and discussed according to the molecular, cellular and regional levels of brain organization. The genotype-phenotype structure is used to consider potential interactions between genes, clinical manifestations of the illness and treatment response. The integration of neurobiological research into the future development of new treatment strategies for schizophrenia holds significant promise.