Chronic stress alters synaptic terminal structure in hippocampus

Repeated psychosocial or restraint stress causes atrophy of apical dendrites in CA3 pyramidal neurons of the hippocampus, accompanied by specific cognitive deficits in spatial learning and memory. Excitatory amino acids mediate this atrophy together with adrenal steroids and the neurotransmitter serotonin. Because the mossy fibers from dentate granule neurons provide a major excitatory input to the CA3 proximal apical dendrites, we measured ultrastructural parameters associated with the mossy fiber–CA3 synapses in control and 21-day restraint-stressed rats in an effort to find additional morphological consequences of stress that could help elucidate the underlying anatomical as well as cellular and molecular mechanisms. Although mossy fiber terminals of control rats were packed with small, clear synaptic vesicles, terminals from stressed animals showed a marked rearrangement of vesicles, with more densely packed clusters localized in the vicinity of active zones. Moreover, compared with controls, restraint stress increased the area of the mossy fiber terminal occupied by mitochondrial profiles and consequently, a larger, localized energy-generating capacity. A single stress session did not produce these changes either immediately after or the next day following the restraint session. These findings provide a morphological marker of the effects of chronic stress on the hippocampus that points to possible underlying neuroanatomical as well as cellular and molecular mechanisms for the ability of repeated stress to cause structural changes within the hippocampus.

Deficits in memory and hippocampal long-term potentiation in mice with reduced calbindin D28K expression.

The influx of calcium into the postsynaptic neuron is likely to be an important event in memory formation. Among the mechanisms that nerve cells may use to alter the time course or size of a spike of intracellular calcium are cytosolic calcium binding or "buffering" proteins. To consider the role in memory formation of one of these proteins, calbindin D28K, which is abundant in many neurons, including the CA1 pyramidal cells of the hippocampus, transgenic mice deficient in calbindin D28K have been created. These mice show selective impairments in spatial learning paradigms and fail to maintain long-term potentiation. These results suggest a role for calbindin D28K protein in temporally extending a neuronal calcium signal, allowing the activation of calcium-dependent intracellular signaling pathways underlying memory function.

Neuroprotective strategy for Alzheimer disease: intranasal administration of a fatty neuropeptide.

Neurodegenerative diseases, in which neuronal cell disintegrate, bring about deteriorations in cognitive functions as is evidenced in millions of Alzheimer patients. A major neuropeptide, vasoactive intestinal peptide (VIP), has been shown to be neuroprotective and to play an important role in the acquisition of learning and memory. A potent lipophilic analogue to VIP now has been synthesized, [stearyl-norleucine17]VIP ([St-Nle17]VIP), that exhibited neuroprotection in model systems related to Alzheimer disease. The beta-amyloid peptide is a major component of the cerebral amyloid plaque in Alzheimer disease and has been shown to be neurotoxic. We have found a 70% loss in the number of neurons in rat cerebral cortical cultures treated with the beta-amyloid peptide (amino acids 25-35) in comparison to controls. This cell death was completely prevented by cotreatment with 0.1 pM [St-Nle17]VIP. Furthermore, characteristic deficiencies in Alzheimer disease result from death of cholinergic neurons. Rats treated with a cholinergic blocker (ethylcholine aziridium) have been used as a model for cholinergic deficits. St-Nle-VIP injected intracerebroventricularly or delivered intranasally prevented impairments in spatial learning and memory associated with cholinergic blockade. These studies suggest both an unusual therapeutic strategy for treatment of Alzheimer deficiencies and a means for noninvasive peptide administration to the brain.

Estudo da participação do receptor P2X7 no desempenho cognitivo de roedores: abordagens farmacológicas e genéticas

A memória é um fenômeno decorrente de um conjunto de processos fisiológicos denominado plasticidade. Várias formas de plasticidade são necessárias no processo de formação da memória e também são responsáveis pelo gerenciamento do comportamento. O fenômeno eletrofisiológico chamado potencialização de longa duração (PLD), cuja ocorrência no hipocampo merece destaque, foi proposto como sendo o mecanismo de plasticidade constitutivo das bases da consolidação da memória nesta região encefálica. A importância da plasticidade na região CA1 do hipocampo se manifesta em diversas formas de aprendizado, como espacial e condicionamento clássico. Os eventos bioquímicos que culminam na plasticidade e formação da memória sofrem influência de diversos sistemas de neurotransmissores e evidências indicam também a participação do sistema purinérgico, provavelmente através dos receptores ionotrópicos P2X. Receptores purinérgicos do subtipo P2X7 (P2X7R), largamente distribuídos no sistema nervoso central (SNC), além de possuírem várias características que os distinguem de outros subtipos de receptores P2X, estão envolvidos na regulação da liberação de neurotransmissores cruciais para a promoção da PLD na região hipocampal e formação da memória. Assim, este trabalho objetivou avaliar a participação dos P2X7R em camundongos geneticamente modificados (KO), que não expressam o receptor P2X7, e ratos através da exposição destes a diferentes tarefas comportamentais, bem como avaliar o efeito do enriquecimento ambiental sobre possíveis déficits mnemônicos resultantes da supressão gênica sobre o receptor P2X7. Os resultados sugerem que os P2X7R participam tanto da memória aversiva como da memória espacial: o bloqueio farmacológico com o antagonista específico de P2X7R A-740003 em diferentes janelas temporais causou prejuízos mnemônicos em ratos submetidos à tarefa do medo condicionado contextual (MCC), enquanto a deleção do P2X7R causou déficits mnemônicos a camundongos nas tarefas do labirinto aquático de Morris e no MCC, indicando prejuízos nas memórias espacial e aversiva, respectivamente. Experimentos com enriquecimento ambiental sugerem que esta forma de estimulação contribui na reversão dos déficits mnemônicos causado pela ausência do P2X7R. Por fim, nenhuma alteração na memória de habituação foi observada em animais com deleção gênica para o P2X7R.

Influence of Acute and Chronic Exercise on Markers of Hippocampal Plasticity

Exercise and physical activity are lifestyle behaviors associated with enriched mental health. Understanding the mechanisms by which exercise and physical activity improve mental health may provide insight for novel therapeutic approaches for numerous mental health disorders. This dissertation reports the findings from three studies investigating the influence of acute and chronic exercise on behavioral and mechanistic markers of hippocampal plasticity and delves into the potential role of noradrenergic signaling in the hippocampal adaptations with exercise. The first study assessed the effects of long-term voluntary wheel running on hippocampal expression of plasticity-associated genes and proteins in adult male and female C57BL/6J mice, highlighting sex differences in the adaptations to long-term voluntary wheel running. The second study examined the influence of acute exercise intensity on AMPA receptor phosphorylation, a mechanism essential for hippocampal plasticity, plasticity- associated gene expression, spatial learning and memory, and anxiety-like behavior. The unexpected finding that acute exercise increased anxiety-like behavior encouraged investigation into the role of central noradrenergic signaling in acute exercise-induced anxiety. The third study determined how previous exposure to voluntary wheel running modulates the response to an acute bout of exercise, focusing primarily on transcription of the important plasticity-promoting gene, brain-derived neurotrophic factor. Using a pharmacological approach to compromise the locus coeruleus noradrenergic system, a system that is implicated in age-related mental health disorders such as Alzheimer’s Disease, the third study also investigated the influence and interaction of the noradrenergic system and acute exercise on expression of multiple brain-derived neurotrophic factor transcripts. Together, this dissertation reports the findings from a series of experiments that explored similarities, differences, and interactions between the effects of acute and chronic exercise on markers of hippocampal plasticity and behavior. Further, this work provides insight into the role of the noradrenergic system in exercise-induced hippocampal plasticity.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging.

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learning-induced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to approximately 75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted <6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Effects of age on learning a spatial motor task in younger and older adults individualizing their knowledge of results schedule

Self-controlled KR practice has revealed that providing participants the opportunity to control their KR is superior for motor learning compared to participants replicating the KR schedule of a self-control participant, without the choice (e.g., yoked). The purpose of the present experiment was two-fold. First, to examine the utility of a self-controlled KR schedule for learning a spatial motor task in younger and older adults and second, to determine whether a self-controlled KR schedule facilitates an increased ability to estimate one’s performance in retention and transfer. Twenty younger adults and 20 older adults practiced in either the self-control or yoked condition and were required to push and release a slide along a confined pathway using their non-dominant hand to a target distance. The retention data revealed that as a function of age, a self-controlled KR schedule facilitated superior retention performance and performance estimations in younger adults compared to their yoked counterparts.

Deep learning and spatial transcriptomics to investigate thyroid tumors

There are many diseases that affect the thyroid gland, and among them are carcinoma. Thyroid cancer is the most common endocrine neoplasm and the second most frequent cancer in the 0-49 age group. This thesis deals with two studies I conducted during my PhD. The first concerns the development of a Deep Learning model to be able to assist the pathologist in screening of thyroid cytology smears. This tool created in collaboration with Prof. Diciotti, affiliated with the DEI-UNIBO "Guglielmo Marconi" Department of Electrical Energy and Information Engineering, has an important clinical implication in that it allows patients to be stratified between those who should undergo surgery and those who should not. The second concerns the application of spatial transcriptomics on well-differentiated thyroid carcinomas to better understand their invasion mechanisms and thus to better comprehend which genes may be involved in the proliferation of these tumors. This project specifically was made possible through a fruitful collaboration with the Gustave Roussy Institute in Paris. Studying thyroid carcinoma deeply is essential to improve patient care, increase survival rates, and enhance the overall understanding of this prevalent cancer. It can lead to more effective prevention, early detection, and treatment strategies that benefit both patients and the healthcare system.