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

DeFelipe, Javier

doi:10.3389/fnana.2015.00104

Cited by 56 publications

(63 citation statements)

References 101 publications

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“…Very recently, Markram et al (2015) published a paper describing a reconstruction of a rat brain neocortical column. This model was based on experimental research and probabilistic modeling of various characteristics of the column and its components, as was suggested by DeFelipe (2015). In total, it comprises over 30,000 neurons, which have about 8 million connections through ~36 million synapses.…”

Section: Discussionmentioning

confidence: 99%

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

2016

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Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. “Connectomics” is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network. However, because of the submicron caliber, 3D complexity, and high packing density of most such structures, as well as the fact that axons frequently extend over long distances to make synapses in remote brain regions, creating connectomic maps is technically challenging and requires multi-scale approaches, Such approaches involve the combination of the most sensitive cell labeling and analysis methods available, as well as the development of new ones able to resolve individual cells and synapses with increasing high-throughput. In this review, we provide an overview of recently introduced high-resolution methods, which researchers wanting to enter the field of connectomics may consider. It includes several molecular labeling tools, some of which specifically label synapses, and covers a number of novel imaging tools such as brain clearing protocols and microscopy approaches. Apart from describing the tools, we also provide an assessment of their qualities. The criteria we use assess the qualities that tools need in order to contribute to deciphering the key levels of circuit organization. We conclude with a brief future outlook for neuroanatomic research, computational methods, and network modeling, where we also point out several outstanding issues like structure–function relations and the complexity of neural models.

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Section: Discussionmentioning

confidence: 99%

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

2016

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“…• Big research teams with the resources sufficient to deal with the big scientific problems • Data ladders, interlinked sets of data providing a complete image of single areas of the brain at their different levels of organization • Efficient predictive tools • Novel hardware and software sufficiently powerful to simulate the brain • New ways of classifying and simulating brain diseases, leading to better diagnosis and more effective drug discovery • New brain-inspired technologies, with benefits for industry and for society • Social understanding of neuroscience and its benefits for society [32] Although the assumption that realistic computational models are easier and quicker solutions than reconstruction of the whole brain region or even the whole central nervous system [14] may be true. To build a model of the whole brain we have to take into consideration 1,000 different gray matter regions, 5,000 neuron classes, and up to 100,000 macroconnections between the aforementioned neuron classes, which are not always fully identified [4].…”

Section: Models For Linking Hypotheses and Experimental Studiesmentioning

confidence: 99%

“…• Receptor apparatus: Formed by the dendrites and cell body or soma • Emission apparatus: Axon • Distribution apparatus: Terminal axonal arborization there are many exceptions: bidirectional connections, electrical synapses, various axosomatic and axodendritic synapses, and the role of neuron-glial interaction within information processing [14]. Realistic modeling of brain functions is based on a more detailed biophysical description of neurons and synapses (at the molecular and cellular levels) integrated into microcircuits, and then further integrated in largescale brain networks and even brain systems [11].…”

Section: Models Of a Single Neuronmentioning

confidence: 99%

“…According to the concept by DeFelipe [14] there is a need to identify the general connection matrix of the brain based on three main levels of operation and modeling:…”

Section: Models Of Biologically Relevant Neural Networkmentioning

confidence: 99%

“…• Macroscopic: Providing a map of major tract connectivity (connectome), acquired using medical imaging (e.g., fMRI) • Intermediate: Providing a map of connections, acquired using light microscopy • Ultrastructural: Providing a map of the synaptic connections (synaptome), acquired using electon microscopy [14].…”

Section: Models Of Biologically Relevant Neural Networkmentioning

confidence: 99%

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OFN-Based Brain Function Modeling

Prokopowicz

Mikołajewski

2017

Theory and Applications of Ordered Fuzzy Numbers

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A modeling approach may significantly help to explore the problem of weak understanding of the physiological and pathological central nervous system function in the most noninvasive and comprehensive way. The aim of this chapter is to assess and discuss the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited. IntroductionStructured networks and functional connections of interacting neural populations underlying both physiological and pathological central nervous system (CNS) function are still poorly understood. Interdisciplinarity and independence of research provide a variety of scientific approaches, used tools, and wide coverage and even overlapping of the research fields, especially as regards human research, including neuroscience [1]. The modeling approach may significantly help to explore the aforementioned problem in the most noninvasive way. The aim of this chapter is to assess and discuss the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited.

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Cortical synapses of the world's smallest mammal: An FIB/SEM study in the Etruscan shrew

Alonso‐Nanclares

Toledo-Rodriguez

Merchán-Pérez

et al. 2022

J of Comparative Neurology

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The main aim of the present study was to determine if synapses from the exceptionally small brain of the Etruscan shrew show any peculiarities compared to the much larger human brain. We analyzed the cortical synaptic density and a variety of structural characteristics of 7,239 3D reconstructed synapses, using using Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM). We found that some of the general synaptic characteristics are remarkably similar to those found in the human cerebral cortex. However, the cortical volume of the human brain is about 50,000 times larger than the cortical volume of the Etruscan shrew, while the total number of cortical synapses in human is only 20,000 times the number of synapses in the shrew, and synaptic junctions are 35% smaller in the Etruscan shrew. Thus, the differences in the number and size of synapses cannot be attributed to a brain size scaling effect but rather to adaptations of synaptic circuits to particular functions.

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

Cited by 56 publications

References 101 publications

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

OFN-Based Brain Function Modeling

Cortical synapses of the world's smallest mammal: An FIB/SEM study in the Etruscan shrew

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