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Scholtens LH, Pijnenburg R, de Lange SC, Huitinga I, van den Heuvel MP. Common Microscale and Macroscale Principles of Connectivity in the Human Brain. J Neurosci 2022; 42:4147-4163. [PMID: 35422441 PMCID: PMC9121834 DOI: 10.1523/jneurosci.1572-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/21/2022] Open
Abstract
The brain requires efficient information transfer between neurons and large-scale brain regions. Brain connectivity follows predictable organizational principles. At the cellular level, larger supragranular pyramidal neurons have larger, more branched dendritic trees, more synapses, and perform more complex computations; at the macroscale, region-to-region connections display a diverse architecture with highly connected hub areas facilitating complex information integration and computation. Here, we explore the hypothesis that the branching structure of large-scale region-to-region connectivity follows similar organizational principles as the neuronal scale. We examine microscale connectivity of basal dendritic trees of supragranular pyramidal neurons (300+) across 10 cortical areas in five human donor brains (1 male, 4 female). Dendritic complexity was quantified as the number of branch points, tree length, spine count, spine density, and overall branching complexity. High-resolution diffusion-weighted MRI was used to construct white matter trees of corticocortical wiring. Examining complexity of the resulting white matter trees using the same measures as for dendritic trees shows heteromodal association areas to have larger, more complex white matter trees than primary areas (p < 0.0001) and macroscale complexity to run in parallel with microscale measures, in terms of number of inputs (r = 0.677, p = 0.032), branch points (r = 0.797, p = 0.006), tree length (r = 0.664, p = 0.036), and branching complexity (r = 0.724, p = 0.018). Our findings support the integrative theory that brain connectivity follows similar principles of connectivity at neuronal and macroscale levels and provide a framework to study connectivity changes in brain conditions at multiple levels of organization.SIGNIFICANCE STATEMENT Within the human brain, cortical areas are involved in a wide range of processes, requiring different levels of information integration and local computation. At the cellular level, these regional differences reflect a predictable organizational principle with larger, more complexly branched supragranular pyramidal neurons in higher order regions. We hypothesized that the 3D branching structure of macroscale corticocortical connections follows the same organizational principles as the cellular scale. Comparing branching complexity of dendritic trees of supragranular pyramidal neurons and of MRI-based regional white matter trees of macroscale connectivity, we show that macroscale branching complexity is larger in higher order areas and that microscale and macroscale complexity go hand in hand. Our findings contribute to a multiscale integrative theory of brain connectivity.
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Affiliation(s)
- Lianne H Scholtens
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Siemon C de Lange
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Inge Huitinga
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
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2
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Harkin EF, Shen PR, Goel A, Richards BA, Naud R. Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Sub-cellular Computation. Neuroscience 2021; 489:200-215. [PMID: 34358629 DOI: 10.1016/j.neuroscience.2021.07.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/06/2021] [Accepted: 07/25/2021] [Indexed: 11/15/2022]
Abstract
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
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Affiliation(s)
- Emerson F Harkin
- uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Peter R Shen
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Anish Goel
- Lisgar Collegiate Institute, Ottawa, ON, Canada
| | - Blake A Richards
- Mila, Montréal, QC, Canada; Montreal Neurological Institute, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada; School of Computer Science, McGill University, Montréal, QC, Canada.
| | - Richard Naud
- uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada; Department of Physics, University of Ottawa, Ottawa, ON, Canada.
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Valle-Bautista R, Márquez-Valadez B, Fragoso-Cabrera AD, García-López G, Díaz NF, Herrera-López G, Griego E, Galván EJ, Arias-Montaño JA, Molina-Hernández A. Impaired Cortical Cytoarchitecture and Reduced Excitability of Deep-Layer Neurons in the Offspring of Diabetic Rats. Front Cell Dev Biol 2020; 8:564561. [PMID: 33042999 PMCID: PMC7527606 DOI: 10.3389/fcell.2020.564561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/17/2020] [Indexed: 12/26/2022] Open
Abstract
Maternal diabetes has been related to low verbal task scores, impaired fine and gross motor skills, and poor performance in graphic and visuospatial tasks during childhood. The primary motor cortex is important for controlling motor functions, and embryos exposed to high glucose show changes in cell proliferation, migration, and differentiation during corticogenesis. However, the existing studies do not discriminate between embryos with or without neural tube defects, making it difficult to conclude whether the reported changes are related to neural tube defects or other anomalies. Furthermore, postnatal effects on central nervous system cytoarchitecture and function have been scarcely addressed. Through molecular, biochemical, morphological, and electrophysiological approaches, we provide evidence of impaired primary motor cerebral cortex lamination and neuronal function in pups from diabetic rats, showing an altered distribution of SATB2, FOXP2, and TBR1, impaired cell migration and polarity, and decreased excitability of deep-layer cortical neurons, suggesting abnormalities in cortico-cortical and extra-cortical innervation. Furthermore, phase-plot analysis of action potentials suggests changes in the activity of potassium channels. These results indicate that high-glucose insult during development promotes complex changes in migration, neurogenesis, cell polarity establishment, and dendritic arborization, which in turn lead to reduced excitability of deep-layer cortical neurons.
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Affiliation(s)
- Rocío Valle-Bautista
- Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico.,Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
| | - Berenice Márquez-Valadez
- Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
| | - América D Fragoso-Cabrera
- Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
| | - Guadalupe García-López
- Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
| | - Néstor Fabián Díaz
- Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
| | - Gabriel Herrera-López
- Departamento de Farmacobiología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico
| | - Ernesto Griego
- Departamento de Farmacobiología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico
| | - Emilio J Galván
- Departamento de Farmacobiología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico
| | - José-Antonio Arias-Montaño
- Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico
| | - Anayansi Molina-Hernández
- Laboratorio de Investigación en Células Troncales y Biología del Desarrollo, Departamento de Fisiología y Desarrollo Celular, Subdirección de Investigación Biomédica, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico
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Poirazi P, Papoutsi A. Illuminating dendritic function with computational models. Nat Rev Neurosci 2020; 21:303-321. [PMID: 32393820 DOI: 10.1038/s41583-020-0301-7] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2020] [Indexed: 02/06/2023]
Abstract
Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.
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Affiliation(s)
- Panayiota Poirazi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
| | - Athanasia Papoutsi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece
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Ofer N, Shefi O, Yaari G. Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification. Neuroinformatics 2020; 18:581-590. [PMID: 32346847 DOI: 10.1007/s12021-020-09466-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.
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Affiliation(s)
- Netanel Ofer
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel
| | - Orit Shefi
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel. .,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel.
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.
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Perturbed Ca2+-dependent signaling of DYT2 hippocalcin mutant as mechanism of autosomal recessive dystonia. Neurobiol Dis 2019; 132:104529. [DOI: 10.1016/j.nbd.2019.104529] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/22/2019] [Accepted: 07/09/2019] [Indexed: 11/23/2022] Open
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Petanjek Z, Sedmak D, Džaja D, Hladnik A, Rašin MR, Jovanov-Milosevic N. The Protracted Maturation of Associative Layer IIIC Pyramidal Neurons in the Human Prefrontal Cortex During Childhood: A Major Role in Cognitive Development and Selective Alteration in Autism. Front Psychiatry 2019; 10:122. [PMID: 30923504 PMCID: PMC6426783 DOI: 10.3389/fpsyt.2019.00122] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 02/18/2019] [Indexed: 12/12/2022] Open
Abstract
The human specific cognitive shift starts around the age of 2 years with the onset of self-awareness, and continues with extraordinary increase in cognitive capacities during early childhood. Diffuse changes in functional connectivity in children aged 2-6 years indicate an increase in the capacity of cortical network. Interestingly, structural network complexity does not increase during this time and, thus, it is likely to be induced by selective maturation of a specific neuronal subclass. Here, we provide an overview of a subclass of cortico-cortical neurons, the associative layer IIIC pyramids of the human prefrontal cortex. Their local axonal collaterals are in control of the prefrontal cortico-cortical output, while their long projections modulate inter-areal processing. In this way, layer IIIC pyramids are the major integrative element of cortical processing, and changes in their connectivity patterns will affect global cortical functioning. Layer IIIC neurons have a unique pattern of dendritic maturation. In contrast to other classes of principal neurons, they undergo an additional phase of extensive dendritic growth during early childhood, and show characteristic molecular changes. Taken together, circuits associated with layer IIIC neurons have the most protracted period of developmental plasticity. This unique feature is advanced but also provides a window of opportunity for pathological events to disrupt normal formation of cognitive circuits involving layer IIIC neurons. In this manuscript, we discuss how disrupted dendritic and axonal maturation of layer IIIC neurons may lead into global cortical disconnectivity, affecting development of complex communication and social abilities. We also propose a model that developmentally dictated incorporation of layer IIIC neurons into maturing cortico-cortical circuits between 2 to 6 years will reveal a previous (perinatal) lesion affecting other classes of principal neurons. This "disclosure" of pre-existing functionally silent lesions of other neuronal classes induced by development of layer IIIC associative neurons, or their direct alteration, could be found in different forms of autism spectrum disorders. Understanding the gene-environment interaction in shaping cognitive microcircuitries may be fundamental for developing rehabilitation and prevention strategies in autism spectrum and other cognitive disorders.
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Affiliation(s)
- Zdravko Petanjek
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Dora Sedmak
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Domagoj Džaja
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ana Hladnik
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Mladen Roko Rašin
- Department of Neuroscience and Cell Biology, Rutgers University, Robert Wood Johnson Medical School, Piscataway, NJ, United States
| | - Nataša Jovanov-Milosevic
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Medical Biology, School of Medicine, University of Zagreb, Zagreb, Croatia
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Patterns of Dendritic Basal Field Orientation of Pyramidal Neurons in the Rat Somatosensory Cortex. eNeuro 2019; 5:eN-NWR-0142-18. [PMID: 30656209 PMCID: PMC6335082 DOI: 10.1523/eneuro.0142-18.2018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/16/2018] [Accepted: 11/20/2018] [Indexed: 11/23/2022] Open
Abstract
The study of neuronal dendritic orientation is of interest because it is related to how neurons grow dendrites to establish the synaptic input that neurons receive. The dendritic orientations of neurons in the nervous system vary, ranging from rather heterogeneously distributed (asymmetric) to homogeneously distributed (symmetric) dendritic arbors. Here, we analyze the dendritic orientation of the basal dendrites of intracellularly labeled pyramidal neurons from horizontal sections of Layers II–VI of the hindlimb somatosensory (S1HL) cortex of 14-d-old (P14) rats. We used circular statistics and proposed two new graphical descriptive representations of the neuron. We found that the dendritic pattern of most neurons was asymmetric. Furthermore, we found that there is a mixture of different types of orientations within any given group of neurons in any cortical layer. In addition, we investigated whether dendritic orientation was related to the physical location within the brain with respect to the anterior, dorsal, posterior and ventral directions. Generally, there was a preference towards the anterior orientation. A comparison between layers revealed that the preference for the anterior orientation was more pronounced in neurons located in Layers II, III, IV, and Va than for the neurons located in Layers Vb and VI. The dorsal orientation was the least preferred orientation in all layers, except for Layers IV and Va, where the ventral orientation had the lowest preference. Therefore, the orientation of basal dendritic arbors of pyramidal cells is variable and asymmetric, although a majority has a single orientation with a preference for the anterior direction in P14 rats.
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