Circuit Elements of the Nervous System Ed Boyden Synthetic - - PowerPoint PPT Presentation

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Circuit Elements of the Nervous System Ed Boyden Synthetic Neurobiology Laboratory Massachusetts Institute of Technology Invited lecture at Stanford in CS379C on April 8, 2013 Todays outline PARTS Cells as engineerable circuit elements

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Circuit Elements of the Nervous System

Ed Boyden

Synthetic Neurobiology Laboratory Massachusetts Institute of Technology Invited lecture at Stanford in CS379C on April 8, 2013

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Today’s outline

PARTS

  • Cells as engineerable circuit elements
  • Molecules in the context of cells
  • Brain regions in the context of cells

THEMES

  • Implications for neuroengineering
  • What properties of cells make them observable?
  • What properties of cells make them amenable to

being controlled?

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Why Neurotechnology?

  • Economic Costs of

Neuro-disorders: $1.3 trillion/yr in the U.S alone

  • People: 2 billion people

worldwide, >100 million Americans suffer from a brain or nervous system illness

Source: Neurotechnology Industry Organization, Oct 2008 http://www.neurotechindustry.org/

PERSISTENT, ENORMOUS, UNMET NEEDS Orange font = top 7 (greater than 200 million people worldwide) Alzheimer's Addiction ALS Anxiety Blindness Chronic Pain Depression Epilepsy Headache Hearing loss Huntington’s Insomnia Migraine Multiple sclerosis Obesity Paralysis Parkinson's Schizophrenia Sleep disorders Spinal Cord Injury Stroke

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The Numbers

Cost (US alone, 2008)

  • Addiction: $366b
  • Alzheimer's: $146b
  • Obesity: $123b
  • Chronic pain: $95b
  • Depression: $83b
  • Attention deficits: $77b
  • Sleep: $75b
  • Stroke: $57b
  • TBI: $56b
  • Vision loss: $52b
  • Hearing loss: $50b
  • Anxiety: $47b

People (global, 2008)

  • Addiction: 790m
  • Anxiety: 400m
  • Obesity: 300m
  • Chronic pain: 290m
  • Migraine: 240m
  • Depression: 240m
  • Sleep: 238m
  • Hearing loss: 140m
  • Attention deficits: 120m
  • Alzheimer's: 90m
  • Stroke: 60m
  • Epilepsy: 50m

Source: Neurotechnology Industry Organization, Oct 2008 http://www.neurotechindustry.org/

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What is the right abstraction layer for engineering the brain?

‘Population’ microcircuitry in tissue/slices 1890-present Brain regions 1700s-present

Ramon y Cajal 1899

‘Precise’ microcircuitry? 1990s-present

Denk and Horstmann 2004 1 µm Lewis et al. 2005

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Goals for a good technology

  • Make the most meaningful measurements, and

interpret them accurately

  • Make the most precise perturbations of specific

substrates, and test the necessity and sufficiency of those substrates

  • Understand the mechanisms behind symptoms

in the most precise way possible

  • Control a neural circuit in the most powerful way,

with the fewest side effects

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Today’s outline

PARTS

  • Cells as engineerable circuit elements
  • Molecules in the context of cells
  • Brain regions in the context of cells

THEMES

  • Implications for neuroengineering
  • What properties of cells make them observable?
  • What properties of cells make them amenable to

being controlled?

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Cells as engineerable circuit elements

  • Cell types are defined by geometry, genome

state/gene expression, physiology, connectivity, and protein state

  • Self-contained computational units

– Maintain stable resting potential; contain genome; survive autonomously – Go smaller: vastly more incomplete picture; go larger: heterogeneity

  • Molecules only make sense in the context of the

specific cell type they’re in

  • Brain regions only make sense in the context of

the cell types found therein

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The Neuron

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Molecular components of the neuron

  • Postsynapse and dendrites

– Receptors for neurotransmitters (excitatory - glutamate, inhibitory - GABA, modulatory – DA, ACh, 5-HT, NE; slow vs. fast forms of each) – Integrate signals passively on the membrane, as well as with active conductances (potassium, calcium, sodium) – Prominent plastic elements (kinases, receptors, mRNA)

  • Cell bodies

– Nonlinearity towards a spike

  • Axons

– Sodium and potassium channels: conduction of spike

  • Presynapse

– Calcium channels admit calcium; then, release of neurotransmitter (excitatory, inhibitory, modulatory; short-range vs. long-range) – Prominent plastic elements (release machinery, kinases, cytoskeleton)

  • Throughout the neuron

– Pumps and exchangers to maintain resting potential (Na+ out, K+ in, Cl-

  • ut)

– Receptors for neurotransmitters – Ion channels – Gap junctions

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Cell classes: shapes, location

  • Unlike many other fields
  • f biology, in

neuroscience, “Anatomy is function”

  • Shapes (Markram et al., 2004)
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Molecular markers that correlate with shape/location

  • Shapes correlate with

markers, but not perfectly

(Markram et al., 2004)

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Cell types: spiking

  • Physiology (Markram et al., 2004)
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Shape, molecules, physiology

  • Not perfect

correspondence (Markram et

al., 2004)

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Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

Whole cell patch clamp: enables simultaneous measurement of electrophysiology, morphology, and gene expression in single cells in living brain

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A robot that can automatically patch clamp neurons in living brain

Commercialized by Neuromatic Devices, Inc. (ESB has no financial affiliation) Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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The patch algorithm: robotic assessment of sequences of pipette resistances, followed by fast action

Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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In vivo robotics: converting an art form to software

Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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Derived an algorithm: high-performance recording, with high yields

Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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Derived for the cortex, the algorithm works in the hippocampus as well

Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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Good access resistances, holding currents, resting potentials, and holding times – independent of depth

Kodandaramaiah et al. (2012) Nature Methods 9:585–587.

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Integrative analysis of cell types of the brain: molecule to morphology to physiology

+ + gene expression

Suhasa Kodandaramaiah, Ian Wickersham, Craig Forest, Hongkui Zeng and Allen Institute for Brain Science

Ragan et al., Nature Methods 2012

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How do the properties of a cell give us a handle on controlling or reading it?

  • Given a cell, can isolate a promoter – DNA in front of

the gene – to target a gene to it

– Small promoter – can put in virus – BAC transgenics

  • Big promoter

– knock-ins

  • Insert your gene into that locus
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Labeling something by activity?

  • Fos-driven feedback loop

– Fos = immediate early gene (IEG), turned on by activity (and perhaps plasticity) – Only on for a short time, though. – Solution: turn on a nonlinear switch (Reijmers et al., 2007)

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Glial cells

  • Long thought to be

“metabolic supporting” cells, but found to release transmitters and communicate

  • >90% of cells in human

brain

  • Connected by gap

junctions, have similar receptors and release machinery to neurons

  • Don’t fire spikes
  • Calcium waves can

spread throughout glial networks, via gap junctions and ATP (Guthrie et

al., 1999)

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Glial cells

  • Glial cells release ATP

and glutamate (Innocenti et al.,

2000)

  • Block release: impairs

synaptic function and plasticity (Pascual et al., 2005)

  • Adenosine:

– If you block release of ATP, then animals do not develop sleep debt (normally you sleep more on night 2, if deprived on night 1) – Also, cognitive consequences

  • f sleep loss are ameliorated

– Glial release of adenosine to A1A receptors important for effects of DBS? (Bekar et al., 2008)

  • Glutamate release implicated

in neural activity-blood flow coupling (Schummers et al., 2008)

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Astrocytes and blood flow

  • Neuronal activity

releases glutamate à astrocyte activation (calcium waves) à release vasoactive substances (prostaglandins) à vasodilation (Zonta et al., 2003)

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Astrocytes and blood flow

  • Neuronal activity

releases glutamate à astrocyte activation (calcium waves) à release vasoactive substances (prostaglandins) à vasodilation (Zonta et al., 2003)

  • Astrocytes are the

‘invisible cell’ in between

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Today’s outline

PARTS

  • Cells as engineerable circuit elements
  • Molecules in the context of cells
  • Brain regions in the context of cells

THEMES

  • Implications for neuroengineering
  • What properties of cells make them observable?
  • What properties of cells make them amenable to

being controlled?

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Molecules make sense in the context of the specific cell type they’re in: leptin

  • Leptin released by fat cells, regulates energy

homeostasis by action in brain

– Potential target for obesity and diabetes

  • But, leptin receptor expressed in multiple cell classes in

the hypothalamus – which is the site?

  • Delete leptin receptor in one subclass, the steroidogenic

factor-1 neurons – neurons are not activated by leptin

  • Potential brain stimulation target? Clustered in

ventromedial hypothalamus…

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Channelopathies

  • Rare, but instructive

– Can link molecule to phenotype – Mutations in a single channel can result in many different results

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Synapsopathies

  • A model of obsessive compulsive disorder:

molecular deletion of Sapap3, scaffolding protein important for glutamate receptor presence, and synaptic strength (Welch et al., 2007)

  • Leads to excessive grooming
  • Striatal intervention reduces phenotype
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Genes are multifunctional

  • Different mutations of a

single gene can cause completely different disorders

– Example: two different mutations in mouse Disc1 result in different behaviors reminiscent of different psychiatric symptoms (Clapcote

et al., 2007)

  • In the same cells, have

different function that can cause different symptoms to emerge?

  • Different functions in

different cells?

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Molecules make sense in the context of the specific cell type they’re in: schizophrenia

  • NMDA receptors

– Ketamine and MK801, NMDAR blockers, can simulate many symptoms of schizophrenia in humans – What does this mean? How does NMDA ßà symptoms of schizophrenia?

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Molecules make sense in the context of the specific cell type they’re in: schizophrenia

  • Blocking NMDA reduces

gamma oscillations in a slice model of such

  • scillations (Cunningham et al.,

2006)

  • Less gamma oscillations

in schizophrenic patients

(Spencer et al., 2004)

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Gamma oscillations involve inhibitory interneurons

(Cunningham Et al., 2003)

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Molecules in the context of cells: Example: GABA receptor and NMDA receptor

  • In patients: histological changes in subclasses of

interneurons (Lewis and Volk 2005)

– See reductions in GAT-1 staining, the GABA transporter – In, amongst other cells, PV+ neurons

  • “chandelier cell”
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A possible pipeline from molecule to macroscopic phenotype

  • NMDA dysfunction à problems with

interneurons à less gamma oscillations

  • Brain stimulation is done pretty ad hoc right now
  • By moving beyond the molecule, we can start to

think about whether brain stimulation could correct deficits directly

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A plot twist?

  • GABA receptor lets

chloride into cell: classical inhibitory transmitter

  • Chandelier cells: powerful

GABA input to pyramidal cell axon initial segment

– Often considered a “veto power” (Lewis and Volk, 2005)

  • But, chandelier cells

synapse upon a part of pyramidal cells that has a low density of chloride extrusion transporters

  • Assumptions about role of

molecules in absence of circuit context can be flawed! (Szabadics et al., 2006)

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Thus

  • The recording and stimulation of brain regions is

highly dependent on the neuron types at hand in the circuit

  • The geometry and other properties of specific

cells can govern what in a brain region is

  • bservable and/or manipulable
  • Observations and manipulations are often

indirect, the more noninvasive you get, and depend on more and more properties of the cells being observed (and on cells that are invisible)

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Conclusions

  • Technologies to interface and record from the

brain are molecules or utilize laws of physics to measure or deliver specific things

  • We are limited by our knowledge of how specific

computational elements couple to molecules and physical phenomena

  • What we do know has taken us a long way, but

few people have tried to use what we know in a principled way to develop neurotechnologies

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Papers

READINGS Motor behavior activates Bergmann glial networks. Nimmerjahn A, Mukamel EA, Schnitzer MJ.

  • Neuron. 2009 May 14;62(3):400-12.

http://linkinghub.elsevier.com/retrieve/pii/ S0896-6273%2809%2900244-X Marowsky A, Yanagawa Y, Obata K, Vogt KE. A specialized subclass of interneurons mediates dopaminergic facilitation of amygdala function.

  • Neuron. 2005 Dec 22;48(6):1025-37.

http://linkinghub.elsevier.com/retrieve/pii/ S0896-6273(05)00940-2 Bernard C, Anderson A, Becker A, Poolos NP, Beck H, Johnston D. Acquired dendritic channelopathy in temporal lobe epilepsy.

  • Science. 2004 Jul 23;305(5683):532-5.

http://www.sciencemag.org/cgi/content/ abstract/305/5683/532

D'Ascenzo M, Fellin T, Terunuma M, Revilla-Sanchez R, Meaney DF, Auberson YP, Moss SJ, Haydon PG. mGluR5 stimulates gliotransmission in the nucleus accumbens. Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1995-2000. http://www.pnas.org/cgi/content/full/104/6/1995 Astrocytic modulation of sleep homeostasis and cognitive consequences of sleep loss. Halassa MM, Florian C, Fellin T, Munoz JR, Lee SY, Abel T, Haydon PG, Frank MG.

  • Neuron. 2009 Jan 29;61(2):213-9.

http://linkinghub.elsevier.com/retrieve/pii/S0896-6273%2808%2901017-9 Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C. Interneurons of the neocortical inhibitory system. Nat Rev Neurosci. 2004 Oct;5(10):793-807. http://www.nature.com/nrn/journal/v5/n10/abs/nrn1519.html Nimmerjahn A, Kirchhoff F, Helmchen F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo.

  • Science. 2005 May 27;308(5726):1314-8.

http://www.sciencemag.org/cgi/content/full/308/5726/1314 Lin L, Faraco J, Li R, Kadotani H, Rogers W, Lin X, Qiu X, de Jong PJ, Nishino S, Mignot E. The sleep disorder canine narcolepsy is caused by a mutation in the hypocretin (orexin) receptor 2 gene.

  • Cell. 1999 Aug 6;98(3):365-76.

http://linkinghub.elsevier.com/retrieve/pii/S0092-8674(00)81965-0 Wang X, Lou N, Xu Q, Tian GF, Peng WG, Han X, Kang J, Takano T, Nedergaard M. Astrocytic Ca2+ signaling evoked by sensory stimulation in vivo. Nat Neurosci. 2006 Jun;9(6):816-23. Epub 2006 May 14. http://www.nature.com/neuro/journal/v9/n6/abs/nn1703.html Skim: Chapters 4-16, Principles of Neural Science, by Eric R. Kandel, James H. Schwartz, Thomas M. Jessell