The Evolution of the Brain, the Human Nature of Cortical Circuits, and Intellectual Creativity

The tremendous expansion and the differentiation of the neocortex constitute two major events in the evolution of the mammalian brain. The increase in size and complexity of our brains opened the way to a spectacular development of cognitive and mental skills. This expansion during evolution facilitated the addition of microcircuits with a similar basic structure, which increased the complexity of the human brain and contributed to its uniqueness. However, fundamental differences even exist between distinct mammalian species. Here, we shall discuss the issue of our humanity from a neurobiological and historical perspective

Selective alterations of neurons and circuits related to early memory loss in Alzheimers's disease

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permissionAprogressive loss of episodic memory is awell-known clinical symptom tha tcharacterizes Alzheimer’s disease(AD). The beginning of this loss of memory has been associated with the very early, pathological accumulation of tau and neuronal degeneration observed in the entorhinal lcortex(EC). Tau-related pathology is thought to then spread progressively to the hippocampal formation and other brain areas as the disease progresses.The major cortical afferent source of the hippocampus and dentate gyrus is the EC through the performant pathway. At least two main circuits participate in the connection between EC and the hippocampus; one originating in layer II and the other in layer III of the EC giving rise to the classical trisynaptic (ECII→dentategyrus→CA3→CA1) and mono synaptic (ECIII→CA1) circuits. Thus, the study of the early pathological changes in these circuitsis of great interest. In this review,we will discuss mainly the alterations of the granule cell neurons of the dentate gyrus and the atrophy of CA1 pyramidal neurons that occu rin AD in relation to the possible differential alterations of these two main circuitsThis work wa ssupported by grants from the following entities: CIBERNED,Comunidad de Madrid (grant S2010/BMD-2331), Fundación CIEN, Fundación Ramón Areces and the Spanish Ministry of Economy and Competitiveness (SAF2011-24841, BFU2012-34963 and the Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL

Espina: A Tool for the Automated Segmentation and Counting of Synapses in Large Stacks of Electron Microscopy Images

The synapses in the cerebral cortex can be classified into two main types, Gray's type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes

The dendritic spine story: an intriguing process of discovery

Dendritic spines are key components of a variety of microcircuits and they represent the majority of postsynaptic targets of glutamatergic axon terminals in the brain. The present article will focus on the discovery of dendritic spines, which was possible thanks to the application of the Golgi technique to the study of the nervous system, and will also explore the early interpretation of these elements. This discovery represents an interesting chapter in the history of neuroscience as it show us that progress in the study of the structure of the nervous system is based not only on the emergence of new techniques but also on our ability to exploit the methods already available and correctly interpret their microscopic images

The anatomical problem posed by brain complexity and size: a potential solution

Over the years the field of neuroanatomy has evolved considerably but unraveling the extraordinary structural and functional complexity of the brain seems to be an unattainable goal, partly due to the fact that it is only possible to obtain an imprecise connection matrix of the brain. The reasons why reaching such a goal appears almost impossible to date is discussed here, together with suggestions of how we could overcome this anatomical problem by establishing new methodologies to study the brain and by promoting interdisciplinary collaboration. Generating a realistic computational model seems to be the solution rather than attempting to fully reconstruct the whole brain or a particular brain region

A univocal definition of the neuronal soma morphology using Gaussian mixture models

The definition of the soma is fuzzy, as there is no clear line demarcating the soma of the labeled neurons and the origin of the dendrites and axon. Thus, the morphometric analysis of the neuronal soma is highly subjective. In this paper, we provide a mathematical definition and an automatic segmentation method to delimit the neuronal soma. We applied this method to the characterization of pyramidal cells, which are the most abundant neurons in the cerebral cortex. Since there are no benchmarks with which to compare the proposed procedure, we validated the goodness of this automatic segmentation method against manual segmentation by experts in neuroanatomy to set up a framework for comparison. We concluded that there were no significant differences between automatically and manually segmented somata, i.e., the proposed procedure segments the neurons more or less as an expert does. It also provides univocal, justifiable and objective cutoffs. Thus, this study is a means of characterizing pyramidal neurons in order to objectively compare the morphometry of the somata of these neurons in different cortical areas and species

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neurocientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neurocientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts a LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels and that the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and therefore might serve as objective counterparts to the subjective, categorical axonal features

Changes in the Golgi apparatus of neocortical and hippocampal neurons in the hibernating hamster

Hibernating animals have been used as models to study several aspects of the plastic changes that occur in the metabolism and physiology of neurons. These models are also of interest in the study of Alzheimer’s disease because the microtubule-associated protein tau is hyperphosphorylated during the hibernation state known as torpor, similar to the pretangle stage of Alzheimer’s disease. Hibernating animals undergo torpor periods with drops in body temperature and metabolic rate, and a virtual cessation of neural activity. These processes are accompanied by morphological and neurochemical changes in neurons, which reverse a few hours after coming out of the torpor state. Since tau has been implicated in the structural regulation of the neuronal Golgi apparatus (GA) we have used Western Blot and immunocytochemistry to analyze whether the GA is modified in cortical neurons of the Syrian hamster at different hibernation stages. The results show that, during the hibernation cycle, the GA undergo important structural changes along with differential modifications in expression levels and distribution patterns of Golgi structural proteins. These changes were accompanied by significant transitory reductions in the volume and surface area of the GA elements during torpor and arousal stages as compared with euthermic animal

Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis.

Geometrical features of chemical synapses are relevant to their function. Two critical components of the synaptic junction are the active zone and the postsynaptic density, as they are related to the probability of synaptic release and the number of postsynaptic receptors, respectively. Morphological studies of these structures are greatly facilitated by the use of recent electron microscopy techniques, such as combined focused ion beam milling and scanning electron microscopy (FIB/SEM), and software tools that permit reconstruction of large numbers of synapses in three dimensions. Since the active zone and the postsynaptic density are in close apposition and have a similar surface area, they can be represented by a single surface — the synaptic apposition surface (SAS). We have developed an efficient computational technique to automatically extract this surface from synaptic junctions that have previously been three-dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM. Given its relationship with the release probability and the number of postsynaptic receptors, the surface area of the SAS is a functionally relevant measure of the size of a synapse that can complement other geometrical features like the volume of the reconstructed synaptic junction, the equivalent ellipsoid size and the Feret’s diameter