Cognition, mood disorders, and sex hormones

Macaques (Macaca spp.) are useful models to evaluate effects of ovarian sex steroids and selective estrogen receptor modulators (SERMs) on mood and cognitive function due to similarities to women in their reproductive and central nervous systems. The results of nonhuman primate studies support the hypothesis that estrogen mediates specific aspects of attention and memory, yet much work is needed to understand which cognitive processes are affected, whether natural versus surgical menopause effects are different, and the interaction of age and ovarian senescence on cognitive function. This knowledge is necessary to determine whether to support the cognitive function of women in the menopausal phase of life and, if so, to determine efficacious therapeutic interventions. Mood disorders are prevalent in women and are associated with reproductive function in women and macaques. Exogenous steroid therapies, including oral contraceptives and postmenopausal hormone replacement therapies, have behavioral effects in women and appear to affect the behavior and underlying neural substrates of monkeys. Additional research is necessary to confirm and extend these observations. Ovarian steroids have multiple effects on serotonin synthesis, reuptake, and degradation, on neural activity that drives serotonin release, and on receptor activation in primates. This system modulates cognitive function and mood and is the target of a broad class of antidepressant therapies. Understanding the effects of ovarian steroids on the neural serotonergic system is necessary to understand depression in women. These studies are best carried out in primate models, which are more similar to humans in neural serotonergic function than other animal models.     

Biophysical models of fMRI responses

Functional magnetic resonance imaging (fMRI) is used to investigate where the neural implementation of specific cognitive processes occurs. The standard approach uses linear convolution models that relate experimentally designed inputs, through a haemodynamic response function, to observed blood oxygen level dependent (BOLD) signals. Such models are, however, blind to the causal mechanisms that underlie observed BOLD responses. Recent developments have focused on how BOLD responses are generated and include biophysical input-state-output models with neural and haemodynamic state equations and models of functional integration that explain local dynamics through interactions with remote areas. Forward models with parameters at the neural level, such as dynamic causal modelling, combine both approaches, modelling the whole causal chain from external stimuli, via induced neural dynamics, to observed BOLD responses.     

Neural circuit models for computations in early visual cortex

Some cortical circuit models study the mechanisms of the transforms from visual inputs to neural responses. They model neural properties such as feature tunings, pattern sensitivities, and how they depend on intracortical connections and contextual inputs. Other cortical circuit models are more concerned with computational goals of the transform from visual inputs to neural responses, or the roles of the neural responses in the visual behavior. The appropriate complexity of a cortical circuit model depends on the question asked. Modeling neural circuits of many interacting hypercolumns is a necessary challenge, which is providing insights to cortical computations, such as visual saliency computation, and linking physiology with global visual cognitive behavior such as bottom-up attentional selection.     

Mathematical modeling and optimization of cellulase protein production using Trichoderma reesei RL-P37

The enzyme cellulase, a multienzyme complex made up of several proteins, catalyzes the conversion of cellulose to glucose in an enzymatic hydrolysis-based biomass-to-ethanol process. Production of cellulase enzyme proteins in large quantities using the fungus Trichoderma reesei requires understanding the dynamics of growth and enzyme production. The method of neural network parameter function modeling, which combines the approximation capabilities of neural networks with fundamental process knowledge, is utilized to develop a mathematical model of this dynamic system. In addition, kinetic models are also developed. Laboratory data from bench-scale fermentations involving growth and protein production by T. reesei on lactose and xylose are used to estimate the parameters in these models. The relative performances of the various models and the results of optimizing these models on two different performance measures are presented. An approximately 33% lower root-mean-squared error (RMSE) in protein predictions and about 40% lower total RMSE is obtained with the neural network-based model as opposed to kinetic models. Using the neural network-based model, the RMSE in predicting optimal conditions for two performance indices, is about 67% and 40% lower, respectively, when compared with the kinetic models. Thus, both model predictions and optimization results from the neural network-based model are found to be closer to the experimental data than the kinetic models developed in this work. It is shown that the neural network parameter function modeling method can be useful as a "macromodeling" technique to rapidly develop dynamic models of a process.     

Flexible structure multiple modeling using irregular self-organizing maps neural network

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.     

Flexible structure multiple modeling using irregular self-organizing maps neural network

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.     

Selecting the signals for a brain-machine interface

Brain-machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.     

Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task

Since the discovery of steady-state visually evoked potential (SSVEP), it has been used in many fields. Numerous studies suggest that there exist three SSVEP neural networks in different frequency bands. An obvious phenomenon has been observed, that the amplitude and phase of SSVEP can be modulated by a cognitive task. Previous works have studied this modulation on separately activated SSVEP neural networks by a cognitive task. If two or more SSVEP neural networks are activated simultaneously in the process of a cognitive task, is the modulation on different SSVEP neural networks the same? In this study, two different SSVEP neural networks were activated simultaneously by two different frequency flickers, with a working memory task irrelevant to the flickers being conducted at the same time. The modulated SSVEP waves were compared with each other and to those only under one flicker in previous studies. The comparison results show that the cognitive task can modulate different SSVEP neural networks with a similar style.     

Brain imaging and memory systems in humans: the contribution of PET methods

The development of neuroimaging methods such as PET, has provided a new impulse to the study of the neural basis of cognitive functions, and has extended the field of inquiry from the analysis of the consequences of brain lesions to the functional investigations of brain activity, either in patients with selective neuropsychological deficits or in normal subjects engaged in cognitive tasks. Specific patterns of hypometabolism in neurological patients are associated with different profiles of memory deficits. [¹⁸F]FDG PET studies have confirmed the association of episodic memory with the structures of Papez's circuit and have shown correlations between short-term and semantic memory and the language areas. The identification of anatomo-functional networks involved in specific components of memory function in normal subjects is the aim of several PET activation studies. The results are in agreement with ‘neural network’ models of the neural basis of memory, as complex functions subserved by multiple interconnected cortical and subcortical structures.     

The stressed hippocampus,synaptic plasticity and lost memories

Stress is a biologically significant factor that, by altering brain cell properties, can disturb cognitive processes such as learning and memory, and consequently limit the quality of human life. Extensive rodent and human research has shown that the hippocampus is not only crucially involved in memory formation, but is also highly sensitive to stress. So, the study of stress-induced cognitive and neurobiological sequelae in animal models might provide valuable insight into the mnemonic mechanisms that are vulnerable to stress. Here, we provide an overview of the neurobiology of stress memory interactions, and present a neural endocrine model to explain how stress modifies hippocampal functioning.     

Mindspan: lessons from rat models of neurocognitive aging

Research on the biology of aging seeks to enhance understanding of basic mechanisms and thus support improvements in outcomes throughout the lifespan, including longevity itself, susceptibility to disease, and life-long adaptive capacities. The focus of this review is the use of rats as an animal model of cognitive change during aging, and specifically lessons learned from aging rats in behavioral studies of cognitive processes mediated by specialized neural circuitry. An advantage of this approach is the ability to compare brain aging across species where functional homology exists for specific neural systems; in this article we focus on behavioral assessments that target the functions of the medial temporal lobe and prefrontal cortex. We also take a critical look at studies using calorie restriction (CR) as a well-defined experimental approach to manipulating biological aging. We conclude that the effects of CR on cognitive aging in rats are less well established than commonly assumed, with much less supportive evidence relative to its benefits on longevity and susceptibility to disease, and that more research in this area is necessary.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Early development of visual recognition

The most important ability of the human vision is object recognition, yet it is exactly the less understood aspect of the vision system. Computational models have been helpful in progressing towards an explanation of this obscure cognitive ability, and today it is possible to conceive more refined models, thanks to the new availability of neuroscientific data about the human visual cortex. This work proposes a model of the development of the object recognition capability, under a different perspective with respect to the most common approaches, with a precise theoretical epistemology. It is assumed that the main processing functions involved in recognition are not genetically determined and hardwired in the neural circuits, but are the result of interactions between epigenetic influences and the basic neural plasticity mechanisms. The model is organized in modules related with the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent self-organizing algorithm closely reflecting the essential behavior of cortical circuits.     

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.     

A Realistic Neural Mass Model of the Cortex with Laminar-Specific Connections and Synaptic Plasticity – Evaluation with Auditory Habituation

In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel “short-cut” pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.     

Neuromolecular computing: a new approach to human brain evolution

Evolutionary approaches in human cognitive neurobiology traditionally emphasize macroscopic structures. It may soon be possible to supplement these studies with models of human information-processing of the molecular level. Thin-film, simulation, fluorescence microscopy, and high-resolution X-ray crystallographic studies provide evidence for transiently organized neural membrane molecular systems with possible computational properties. This review article examines evidence for hydrophobic-mismatch molecular interactions within phospholipid microdomains of a neural membrane bilayer. It is proposed that these interactions are a massively parallel algorithm which can rapidly compute near-optimal solutions to complex cognitive and physiological problems. Coupling of microdomain activity to permenant ion movements at ligand-gated and voltage-gated channels permits the conversion of molecular computations into neuron frequency codes. Evidence for microdomain transport of proteins to specific locations within the bilayer suggests that neuromolecular computation may be under some genetic control and thus modifiable by natural selection. A possible experimental approach for examining evolutionary changes in neuromolecular computation is briefly discussed. Received: 2 October 1998 / Accepted in revised form: 19 March 1999     

Caspase-3-Dependent Proteolytic Cleavage of Tau Causes Neurofibrillary Tangles and Results in Cognitive Impairment During Normal Aging

Mouse models of neurodegenerative diseases such as Alzheimer’s disease (AD) are important for understanding how pathological signaling cascades change neural circuitry and with time interrupt cognitive function. Here, we introduce a non-genetic preclinical model for aging and show that it exhibits cleaved tau protein, active caspases and neurofibrillary tangles, hallmarks of AD, causing behavioral deficits measuring cognitive impairment. To our knowledge this is the first report of a non-transgenic, non-interventional mouse model displaying structural, functional and molecular aging deficits associated with AD and other tauopathies in humans with potentially high impact on both new basic research into pathogenic mechanisms and new translational research efforts. Tau aggregation is a hallmark of tauopathies, including AD. Recent studies have indicated that cleavage of tau plays an important role in both tau aggregation and disease. In this study we use wild type mice as a model for normal aging and resulting age-related cognitive impairment. We provide evidence that aged mice have increased levels of activated caspases, which significantly correlates with increased levels of truncated tau and formation of neurofibrillary tangles. In addition, cognitive decline was significantly correlated with increased levels of caspase activity and tau truncated by caspase-3. Experimentally induced inhibition of caspases prevented this proteolytic cleavage of tau and the associated formation of neurofibrillary tangles. Our study shows the strength of using a non-transgenic model to study structure, function and molecular mechanisms in aging and age related diseases of the brain.     

Neural integration by short term potentiation

Neurophysiological studies in the oculomotor system suggest that an integrative operation is required in order to derive an eye position signal from a command signal which usually correlates with eye velocity. Several proposed models for a neural integrator are examined. All these models incorporate some form of positive feedback as a basic mechanism. Based on the performance of the models, we argue that such a scheme require extreme high precision in order to work properly. A new model based on potentiation phenomena in synaptic transmission is proposed and is shown to be free from the deficits of most previous models. The proposed model also accounts for various neural behaviors in a very natural way. A possible implementation of the model is also discussed in the context of the vestibulo-ocular reflex (VOR).     

Optimal path-finding through mental exploration based on neural energy field gradients

Rodent animal can accomplish self-locating and path-finding task by forming a cognitive map in the hippocampus representing the environment. In the classical model of the cognitive map, the system (artificial animal) needs large amounts of physical exploration to study spatial environment to solve path-finding problems, which costs too much time and energy. Although Hopfield’s mental exploration model makes up for the deficiency mentioned above, the path is still not efficient enough. Moreover, his model mainly focused on the artificial neural network, and clear physiological meanings has not been addressed. In this work, based on the concept of mental exploration, neural energy coding theory has been applied to the novel calculation model to solve the path-finding problem. Energy field is constructed on the basis of the firing power of place cell clusters, and the energy field gradient can be used in mental exploration to solve path-finding problems. The study shows that the new mental exploration model can efficiently find the optimal path, and present the learning process with biophysical meaning as well. We also analyzed the parameters of the model which affect the path efficiency. This new idea verifies the importance of place cell and synapse in spatial memory and proves that energy coding is effective to study cognitive activities. This may provide the theoretical basis for the neural dynamics mechanism of spatial memory.