Dendrites and Disease Daniel Johnston, Andreas Frick, and Nicholas Poolos
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Reframing Psychiatry for Precision Medicine
Reframing Psychiatry for Precision Medicine Elizabeth B Torres 1,2,3* 1 Rutgers University Department of Psychology; [email protected] 2 Rutgers University Center for Cognitive Science (RUCCS) 3 Rutgers University Computer Science, Center for Biomedicine Imaging and Modelling (CBIM) * Correspondence: [email protected]; Tel.: (011) +858-445-8909 (E.B.T) Supplementary Material Sample Psychological criteria that sidelines sensory motor issues in autism: The ADOS-2 manual [1, 2], under the “Guidelines for Selecting a Module” section states (emphasis added): “Note that the ADOS-2 was developed for and standardized using populations of children and adults without significant sensory and motor impairments. Standardized use of any ADOS-2 module presumes that the individual can walk independently and is free of visual or hearing impairments that could potentially interfere with use of the materials or participation in specific tasks.” Sample Psychiatric criteria from the DSM-5 [3] that does not include sensory-motor issues: A. Persistent deficits in social communication and social interaction across multiple contexts, as manifested by the following, currently or by history (examples are illustrative, not exhaustive, see text): 1. Deficits in social-emotional reciprocity, ranging, for example, from abnormal social approach and failure of normal back-and-forth conversation; to reduced sharing of interests, emotions, or affect; to failure to initiate or respond to social interactions. 2. Deficits in nonverbal communicative behaviors used for social interaction, ranging, for example, from poorly integrated verbal and nonverbal communication; to abnormalities in eye contact and body language or deficits in understanding and use of gestures; to a total lack of facial expressions and nonverbal communication. -
Crayfish Escape Behavior and Central Synapses
Crayfish Escape Behavior and Central Synapses. III. Electrical Junctions and Dendrite Spikes in Fast Flexor Motoneurons ROBERT S. ZUCKER Department of Biological Sciences and Program in the Neurological Sciences, Stanford University, Stanford, California 94305 THE LATERAL giant fiber is the decision fiber and fast flexor motoneurons are located in for a behavioral response in crayfish in- the dorsal neuropil of each abdominal gan- volving rapid tail flexion in response to glion. Dye injections and anatomical recon- abdominal tactile stimulation (27). Each structions of ‘identified motoneurons reveal lateral giant impulse is followed by a rapid that only those motoneurons which have tail flexion or tail flip, and when the giant dentritic branches in contact with a giant fiber does not fire in response to such stim- fiber are activated by that giant fiber (12, uli, the movement does not occur. 21). All of tl ie fast flexor motoneurons are The afferent limb of the reflex exciting excited by the ipsilateral giant; all but F4, the lateral giant has been described (28), F5 and F7 are also excited by the contra- and the habituation of the behavior has latkral giant (21 a). been explained in terms of the properties The excitation of these motoneurons, of this part of the neural circuit (29). seen as depolarizing potentials in their The properties of the neuromuscular somata, does not always reach threshold for junctions between the fast flexor motoneu- generating a spike which propagates out the rons and the phasic flexor muscles have also axon to the periphery (14). Indeed, only t,he been described (13). -
Long-Term Adult Human Brain Slice Cultures As a Model System to Study
TOOLS AND RESOURCES Long-term adult human brain slice cultures as a model system to study human CNS circuitry and disease Niklas Schwarz1, Betu¨ l Uysal1, Marc Welzer2,3, Jacqueline C Bahr1, Nikolas Layer1, Heidi Lo¨ ffler1, Kornelijus Stanaitis1, Harshad PA1, Yvonne G Weber1,4, Ulrike BS Hedrich1, Ju¨ rgen B Honegger4, Angelos Skodras2,3, Albert J Becker5, Thomas V Wuttke1,4†*, Henner Koch1†* 1Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tu¨ bingen, Tu¨ bingen, Germany; 2Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tu¨ bingen, Tu¨ bingen, Germany; 3German Center for Neurodegenerative Diseases (DZNE), Tu¨ bingen, Germany; 4Department of Neurosurgery, University of Tu¨ bingen, Tu¨ bingen, Germany; 5Department of Neuropathology, Section for Translational Epilepsy Research, University Bonn Medical Center, Bonn, Germany Abstract Most of our knowledge on human CNS circuitry and related disorders originates from model organisms. How well such data translate to the human CNS remains largely to be determined. Human brain slice cultures derived from neurosurgical resections may offer novel avenues to approach this translational gap. We now demonstrate robust preservation of the complex neuronal cytoarchitecture and electrophysiological properties of human pyramidal neurons *For correspondence: in long-term brain slice cultures. Further experiments delineate the optimal conditions for efficient [email protected] viral transduction of cultures, enabling ‘high throughput’ fluorescence-mediated 3D reconstruction tuebingen.de (TVW); of genetically targeted neurons at comparable quality to state-of-the-art biocytin fillings, and [email protected] demonstrate feasibility of long term live cell imaging of human cells in vitro. -
The Narrowing of Dendrite Branches Across Nodes Follows a Well-Defined Scaling Law
The narrowing of dendrite branches across nodes follows a well-defined scaling law Maijia Liaoa, Xin Liangb, and Jonathon Howarda,1 aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520; and bTsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, 100084 Beijing, China Edited by Yuh Nung Jan, Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, and approved May 17, 2021 (received for review October 28, 2020) The systematic variation of diameters in branched networks has minimized. Da Vinci’s law has been reformulated as the “pipe tantalized biologists since the discovery of da Vinci’s rule for trees. model” for plants, in which a fixed cross-section of stems and Da Vinci’s rule can be formulated as a power law with exponent branches is required to support each unit amount of leaves (13). two: The square of the mother branch’s diameter is equal to the Experimental support for scaling laws, however, is scarce because sum of the squares of those of the daughters. Power laws, with of the difficulties of imaging entire branched networks and because different exponents, have been proposed for branching in circula- intrinsic anatomical variability may obscure precise laws (14). Thus, tory systems (Murray’s law with exponent 3) and in neurons (Rall’s it is an open question whether scaling laws such as Eq. 1 apply in law with exponent 3/2). The laws have been derived theoretically, biological systems. based on optimality arguments, but, for the most part, have not To quantitatively test diameter-scaling laws in neurons, it is been tested rigorously. -
Prevalence and Incidence of Rare Diseases: Bibliographic Data
Number 1 | January 2019 Prevalence and incidence of rare diseases: Bibliographic data Prevalence, incidence or number of published cases listed by diseases (in alphabetical order) www.orpha.net www.orphadata.org If a range of national data is available, the average is Methodology calculated to estimate the worldwide or European prevalence or incidence. When a range of data sources is available, the most Orphanet carries out a systematic survey of literature in recent data source that meets a certain number of quality order to estimate the prevalence and incidence of rare criteria is favoured (registries, meta-analyses, diseases. This study aims to collect new data regarding population-based studies, large cohorts studies). point prevalence, birth prevalence and incidence, and to update already published data according to new For congenital diseases, the prevalence is estimated, so scientific studies or other available data. that: Prevalence = birth prevalence x (patient life This data is presented in the following reports published expectancy/general population life expectancy). biannually: When only incidence data is documented, the prevalence is estimated when possible, so that : • Prevalence, incidence or number of published cases listed by diseases (in alphabetical order); Prevalence = incidence x disease mean duration. • Diseases listed by decreasing prevalence, incidence When neither prevalence nor incidence data is available, or number of published cases; which is the case for very rare diseases, the number of cases or families documented in the medical literature is Data collection provided. A number of different sources are used : Limitations of the study • Registries (RARECARE, EUROCAT, etc) ; The prevalence and incidence data presented in this report are only estimations and cannot be considered to • National/international health institutes and agencies be absolutely correct. -
Orphanet Report Series Rare Diseases Collection
Marche des Maladies Rares – Alliance Maladies Rares Orphanet Report Series Rare Diseases collection DecemberOctober 2013 2009 List of rare diseases and synonyms Listed in alphabetical order www.orpha.net 20102206 Rare diseases listed in alphabetical order ORPHA ORPHA ORPHA Disease name Disease name Disease name Number Number Number 289157 1-alpha-hydroxylase deficiency 309127 3-hydroxyacyl-CoA dehydrogenase 228384 5q14.3 microdeletion syndrome deficiency 293948 1p21.3 microdeletion syndrome 314655 5q31.3 microdeletion syndrome 939 3-hydroxyisobutyric aciduria 1606 1p36 deletion syndrome 228415 5q35 microduplication syndrome 2616 3M syndrome 250989 1q21.1 microdeletion syndrome 96125 6p subtelomeric deletion syndrome 2616 3-M syndrome 250994 1q21.1 microduplication syndrome 251046 6p22 microdeletion syndrome 293843 3MC syndrome 250999 1q41q42 microdeletion syndrome 96125 6p25 microdeletion syndrome 6 3-methylcrotonylglycinuria 250999 1q41-q42 microdeletion syndrome 99135 6-phosphogluconate dehydrogenase 67046 3-methylglutaconic aciduria type 1 deficiency 238769 1q44 microdeletion syndrome 111 3-methylglutaconic aciduria type 2 13 6-pyruvoyl-tetrahydropterin synthase 976 2,8 dihydroxyadenine urolithiasis deficiency 67047 3-methylglutaconic aciduria type 3 869 2A syndrome 75857 6q terminal deletion 67048 3-methylglutaconic aciduria type 4 79154 2-aminoadipic 2-oxoadipic aciduria 171829 6q16 deletion syndrome 66634 3-methylglutaconic aciduria type 5 19 2-hydroxyglutaric acidemia 251056 6q25 microdeletion syndrome 352328 3-methylglutaconic -
NEURAL CONNECTIONS: Some You Use, Some You Lose
NEURAL CONNECTIONS: Some You Use, Some You Lose by JOHN T. BRUER SOURCE: Phi Delta Kappan 81 no4 264-77 D 1999 . The magazine publisher is the copyright holder of this article and it is reproduced with permission. Further reproduction of this article in violation of the copyright is prohibited JOHN T. BRUER is president of the James S. McDonnell Foundation, St. Louis. This article is adapted from his new book, The Myth of the First Three Years (Free Press, 1999), and is reprinted by arrangement with The Free Press, a division of Simon Schuster Inc. ©1999, John T. Bruer . OVER 20 YEARS AGO, neuroscientists discovered that humans and other animals experience a rapid increase in brain connectivity -- an exuberant burst of synapse formation -- early in development. They have studied this process most carefully in the brain's outer layer, or cortex, which is essentially our gray matter. In these studies, neuroscientists have documented that over our life spans the number of synapses per unit area or unit volume of cortical tissue changes, as does the number of synapses per neuron. Neuroscientists refer to the number of synapses per unit of cortical tissue as the brain's synaptic density. Over our lifetimes, our brain's synaptic density changes in an interesting, patterned way. This pattern of synaptic change and what it might mean is the first neurobiological strand of the Myth of the First Three Years. (The second strand of the Myth deals with the notion of critical periods, and the third takes up the matter of enriched, or complex, environments.) Popular discussions of the new brain science trade heavily on what happens to synapses during infancy and childhood. -
How Do Dendrites Take Their Shape?
© 2001 Nature Publishing Group http://neurosci.nature.com review How do dendrites take their shape? Ethan K. Scott1 and Liqun Luo1,2 1 Department of Biological Sciences, Stanford University, Stanford, California 94305, USA 2 Neurosciences Program, Stanford University, Stanford, California 94305, USA Correspondence should be addressed to L.L. ([email protected]) Recent technical advances have made possible the visualization and genetic manipulation of individual dendritic trees. These studies have led to the identification and characterization of molecules that are important for different aspects of dendritic development. Although much remains to be learned, the existing knowledge has allowed us to take initial steps toward a comprehensive understanding of how complex dendritic trees are built. In this review, we describe recent advances in our understanding of the molecular mechanisms underlying dendritic morphogenesis, and discuss their cell-biological implications. With their great complexity and variety, dendrites (Fig. 1) are won- added advantage, can be used to study loss-of-function muta- ders of nature’s design. Built to receive and integrate inputs to neu- tions. Much of the progress reviewed in this article would not rons, dendrites occupy much of the brain’s volume and have been have been possible without these technological advances. the subject of studies since the days of Golgi and Cajal1. Over the course of much of the twentieth century, the prevailing belief that Breaking down complex dendritic trees axons take the more active role in wiring the brain and in estab- Although the processes used to build dendritic trees are complex lishing synaptic specificity led researchers to focus on the develop- and diverse (Fig. -
MODULE 2: HISTOLOGY II Muscle and Nervous Tissue
MODULE 2: HISTOLOGY II Muscle and Nervous Tissue Muscle Tissue General Characteristics of Muscle Elongated cells in direction of contraction Appearance depends on direction of cut (longitudinal or cross section) General Functions of Muscle Contracts in response to a stimulus to generate movement o Movement of one part with respect to another o Movement of materials through the body o Movement of the body through space (locomotion) Skeletal Muscle A. Skeletal muscle makes up the majority of the tissue that we think of when we think of muscles. It is under voluntary control and is located throughout the body. Skeletal muscle is generally attached to bones by tendons and uses these bones as levers to accomplish work. However, not all skeletal muscle is anchored to bones. Some, like those in the face, are attached to fibrous connective tissue. Skeletal muscle occurs in bundles. Each skeletal muscle cell or fiber is elongated and has multiple nuclei that are located on the periphery of the cell. The cells are very large and can be several centimeters long, often running the entire length of the muscle (note: when referring to muscles we use the terms cell and fiber interchangeably. This is not to be confused with the fibers found in the matrix of connective tissue). Skeletal muscle is also striated with alternating light and dark bands running the entire length of the cell. These striations are caused by the regular overlapping arrangement of the muscle proteins, actin and myosin. The dark band is called the “A Band” and the light band is the “I Band.” If you look carefully, you will be able to identify a very thin, dark line running down the middle of the “I Band.” This represents the backbone of the actin molecule and is called the “Z-disk”. -
Can Single Neurons Solve MNIST? the Computational Power of Biological Dendritic Trees
Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees Ilenna Simone Jones1 and Konrad Kording2 1Department of Neuroscience, University of Pennsylvania 2Departments of Neuroscience and Bioengineering, University of Pennsylvania September 4, 2020 Abstract Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artificial neural networks that are generally linear apart from an output nonlinearity. If dendritic trees can be nonlinear, biological neurons may have far more computational power than their artificial counterparts. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We find that such dendrites can readily solve machine learning problems, such as MNIST or CIFAR-10, and that they benefit from having the same input onto several branches of the dendritic tree. This dendrite arXiv:2009.01269v1 [q-bio.NC] 2 Sep 2020 model is a special case of sparse network. This work suggests that popular neuron models may severely underestimate the computational power enabled by the biological fact of nonlinear dendrites and multiple synapses per pair of neurons. The next generation of artificial neural networks may significantly benefit from these biologically inspired dendritic architectures. 1 1 Introduction 1.1 Dendritic Nonlinearities Though the role of biological neurons as the mediators of sensory integration and behavioral output is clear, the computations performed within neurons has been a point of investigation for decades (McCulloch and Pitts, 1943; Hodgkin and Huxley, 1952; FitzHugh, 1961; Poirazi et al., 2003a; Mel, 2016). For example, the McCulloch and Pitts (M&P) neuron model is based on an approximation that a neuron linearly sums its input and maps this through a nonlinear threshold function, allowing it to carry out a selection of logic- gate-like functions, which can be expanded to create logic-based circuits (McCulloch and Pitts, 1943). -
Microtubule Destablizers Control Axon and Dendrite
The Pennsylvania State University The Graduate School Eberly College of Science MICROTUBULE DESTABLIZERS CONTROL AXON AND DENDRITE DISASSEMBLY AFTER INJURY AND DURING PRUNING A Dissertation in Biochemistry, Microbiology, and Molecular Biology by Juan Tao © 2014 Juan Tao Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2014 ii The dissertation of Juan Tao was reviewed and approved* by the following: Melissa M. Rolls Associate Professor of Biochemistry and Molecular Biology Dissertation Advisor Chair of Committee Lorraine Santy Associate Professor of Biochemistry and Molecular Biology Zhi-Chun Lai Professor of Biology Professor of Biochemistry and Molecular Biology Richard W. Ordway Associate Professor of Biology Scott Selleck Professor of Biochemistry and Molecular Biology Head of the Department of Biochemistry and Molecular Biology *Signatures are on file in the Graduate School iii Abstract A neuron extends one axon and several dendrites far from its cell body, leaving these extremely thin processes subject to various insults, including physical damages, strokes, and neurodegenerative diseases during aging. Axon degeneration after injury, called Wallerian degeneration, was discovered in mice two decades ago, yet few molecular mechanisms that control the degeneration process have been identified. To study injury-induced and developmental degeneration, our lab used Drosophila dendritic arborization (da) neurons. These neurons are classified into 4 types based on their dendrite branch complexity. I used the most simple ddaE classI and most complex ddaC classIV neurons in the experiments. DdaC neurons go through dendrite remodeling during the pupae stage. This characteristic of ddaC neurons allows me to compare three types of degeneration, including injury-induced axon degeneration, injury-induced dendrite degeneration and developmental pruning, in the same cell type on a single cell level of a living animal. -
Epilepsy, Ataxia, Sensorineural Deafness
SCO0010.1177/2050313X17723549SAGE Open Medical Case ReportsPapavasiliou et al. 723549case-report2017 SAGE Open Medical Case Reports Case Report SAGE Open Medical Case Reports Volume 5: 1 –6 Epilepsy, ataxia, sensorineural deafness, © The Author(s) 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav tubulopathy syndrome in a European child https://doi.org/10.1177/2050313X17723549DOI: 10.1177/2050313X17723549 with KCNJ10 mutations: A case report journals.sagepub.com/home/sco Antigone Papavasiliou1, Katerina Foska1, John Ioannou1 and Mato Nagel2 Abstract Background: Epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome is a multi-organ disorder that links to autosomal recessive mutations in the KCNJ10 gene, which encodes for the Kir4.1 potassium channel. It is mostly described in consanguineous, non-European families. Case Report: A European male of non-consanguineous birth, with early-onset, static ataxic motor disorder, intellectual disability and epilepsy, imitating cerebral palsy, presented with additional findings of renal tubulopathy, sensorineural deafness and normal neuroimaging leading to the diagnosis of epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome. The patient was heterozygous for two KCNJ10 mutations: a missense mutation (p.R65C) that is already published and a not yet published duplication (p.F119GfsX25) that creates a premature truncation of the protein. Both mutations are likely damaging. Parental testing has not been performed, and therefore, we do not know for certain whether the mutations are on different alleles. This young man presents some clinical and laboratory features that differ from previously reported patients with epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome. Conclusion: The necessity of accurate diagnosis through genetic testing in patients with static motor disorders resembling cerebral palsy phenotypes, atypical clinical features and noncontributory neuroimaging is emphasized.