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Brain and Cognition Week 2 Parts of Neurons Dendrites and spines Synapses Action potentials Synaptic transmission 1 The Neuron 2 Dendrites Dendrites of a single neuron = dendritic tree. Dendrites are covered with thousands of synapses (see previous picture). Postsynaptic membrane (part of dendrite) contains receptors. Many (not all) dendrites are covered in spines. 3D reconstruction of dendritic spines 3 Spines Electron micrograph of a synapse on a dendritic spine Light microscope image and 3-D reconstruction of spines on a neuron in the hippocampus 4 Spines Copyright © 2002 Cell Press. Neuron, Vol 35, 1019-1027, September 2002 Spine Motility: Phenomenology, Mechanisms, and Function Tobias Bonhoeffer and Rafael Yuste Max Planck Institut für Neurobiologie, Martinsried, Munich, Germany; Department of Biological Sciences, Columbia University, New York. 5 Spines In live neurons, spines are constantly in moving and changing shape. QuickTime™ and a YUV420 codec decompressor are needed to see this picture. QuickTime™ and a YUV420 codec decompressor are needed to see this picture. Videos from Maria Fischer, Stefanie Kaech, Darko Knutti & Andrew Matus, 1998, "Rapid Actin-Based Plasticity in Dendritic Spines". Neuron 20, p. 847-854. 6 The Neuron Neurons are polarized cells: Dendrite (post-synaptic) Soma (cell body) Axon (pre-synaptic) Axons and dendrites contain quite different sets of molecules and organelles. Sizes of neuronal components: Cell body 20 m Nucleus 5-10 m Axonal diameter <1m-25m Synapse 800 nm Spines 200-500 nm Synaptic Vesicle 50 nm 7 Classifying Neurons pyramidal neuron (cortex) Purkinje neuron (cerebellum) 8 An Engineering Approach 9 Neuronal Morphology Neuronal shape is highly variable and complex! CA3 CA1 DG 10 Glia Astrocytes: involved in regulating extracellular space around neurons, regeneration Myelinating Glia: oligodendrocytes, nerve conduction There are more glial cells than neurons in the brain … 11 The Membrane Potential Cell membrane, extracellular and intracellular space 12 The Membrane Potential 13 The Potassium Channel The Structure of the Potassium Channel: Molecular Basis of K+ Conduction and Selectivity Declan A. Doyle, João Morais Cabral, Richard A. Pfuetzner, Anling Kuo, Jacqueline M. Gulbis, Steven L. Cohen, Brian T. Chait, Roderick MacKinnon – Science 280, 5360, 1998. 14 The Potassium Channel 15 The Resting Potential Ek = (RT/zF) loge([K+]o/[K+]i) Where T is temp in kevin, z is valence (+ is 1) F is faraday's constant, [K+]o and [K+]i are concentrations outside and inside the axon. Ek = 59.8 log10(20/400) = -75 mV K+ Most important: Na+ There is more K+ inside the cell than outside (20×) Ca++ There is less Na+ inside the cell than outside (10×) Cl- 16 The Resting Potential The voltage difference across a neuronal membrane is called the membrane potential. The membrane potential is measured by using a microelectrode. At rest, the membrane potential of a typical neuron is about: -65 mV (millivolts) This is called the resting potential of the cell. 17 Passive Decay of the Membrane Potential 18 Action Potential 19 Recording Action Potentials 20 The Patch Clamp Method 21 The patch clamp consists of an electrode inside a glass pipette. The pipette, which contains a salt solution resembling the fluid normally found within the cell, is lowered to the cell membrane where a tight seal is formed. When a little suction is applied to the pipette, the "patch" of membrane within the pipette ruptures, permitting access to the whole cell. The electrode, which is connected to specialized circuitry, can then be used to measure the currents passing through the ion channels of the cell. Furthermore, we can use our electrical circuitry to "clamp" the membrane potential to any voltage that we desire: very handy when measuring the activity of voltage-dependent channels. Recording Action Potentials Intracellular recording: measures the potential difference between the tip of the electrode (inside the cell, i.e. intracellular) and another electrode outside the cell (ground). Extracellular recording: electrode is close to the cell, but does not impale 23 it. The Shape of the Action Potential ≈ 2 ms Phases of the action potential: ≈ 1 ms Rising phase → Overshoot → Falling phase → Undershoot 24 Triggering the Action Potential What causes the start of an action potential? Analogy: pressing the shutter button on a camera Action potentials are caused by depolarization of the membrane beyond a threshold. This depolarization can be the result of: Na+ entering the cell after a neurotransmitter has been released by another neuron Injecting current through a microelectrode in an experiment After the depolarization crosses the threshold, the action potential unfolds automatically, “all-or-none”. 25 Modeling the Action Potential In the 1950s Alan Hodgkin and Andrew Huxley proposed an ionic model for the action potential and conducted experiments to test it. They received the Nobel Prize in 1963. (a) The first action potential ever recorded (from squid giant axon). (b) Voltages and conductances according to Hodgkin/Huxley, 1952. 26 The Chemical Synapse 27 Chemical Neurotransmission “At rest”, the synapse (presynaptic side) contains numerous synaptic vesicles filled with neurotransmitter, intracellular calcium levels are very low (1). Arrival of an action potential: voltage-gated calcium channels open, calcium enters the synapse (2). Calcium triggers exocytosis and release of neurotransmitter (3). Vesicle is recycled by endocytosis (4). 28 Chemical Neurotransmission Once released, the neurotransmitter molecules diffuse across the synaptic cleft (about 20-50 nm wide). When they “arrive” at the postsynaptic membrane, they bind to neurotransmitter receptors (“lock-and-key” mechanism). Two main classes of receptors: Transmitter-gated ion channels G-protein-coupled receptors Transmitter-gated ion channels: transmitter molecules bind on the outside, cause the channel to open and become permeable to either Na+ (depolarizing, excitatory effect) or Cl– (hyperpolarizing, inhibitory effect). G-protein-coupled receptors have slower, longer-lasting and diverse postsynaptic effects. They can have effects that change an entire cell’s metabolism. 29 Excitatory Effects of Neurotransmitters EPSP = Excitatory PostSynaptic Potential 30 Inhibitory Effects of Neurotransmitters IPSP = Inhibitory PostSynaptic Potential 31 Integration of Synaptic Inputs In the CNS, many EPSP’s are needed to generate an AP in a single neuron. A single EPSP has, in general, very little effect on the state of a neuron (this makes computational sense). On average, the dendrite of a cortical pyramidal cell receives ~10000 synaptic contacts, of which several hundred to a thousand are active at any given time. The adding together of many EPSP’s in both space and time is called synaptic integration. 32 Synaptic Integration (a)Single input → single EPSP. (b)Three APs arriving simultaneously at different parts of the dendrite add together to produce a larger response (spatial summation). (c)Three APs arriving in quick succession in the same fiber can also result in a larger response (temporal summation). 33 Integration of Synaptic Inputs Distal and proximal synaptic inputs: 34 Synaptic Plasticity Synaptic efficacy (strength) is changing with time. Many of these changes are activity-dependent, i.e. the magnitude and direction of change depend on the activity of pre- and post-synaptic neuron. Some of the mechanisms involved: - Changes in the amount of neurotransmitter released. Biophysical changes in ion channels. Morphological alterations of spines or dendritic branches. Modulatory action of other transmitters. Changes in gene transcription. Synaptic loss or sprouting. 35 Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly and persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” Donald Hebb, “Organization of Behavior”, 1949 36 Animal Models of Plasticity Long-Term Potentiation (LTP) Cross-section of the hippocampus: Cajal’s drawing 37 Animal Models of Plasticity Brain slice preparation of the hippocampus: 38 LTP Typical LTP experiment: record from cell in hippocampus area CA1 (receives Schaffer collaterals from area CA3). In addition, stimulate two sets of input fibers. 39 LTP Typical LTP experiment: record EPSP’s in CA1 cells (magnitude) Step 1: weakly stimulate input 1 to establish baseline Step 2: give strong stimulus (tetanus) in same fibers (arrow) Step 3: continue weak stimulation to record increased responses Step 4: throughout, check for responses in control fibers (input 2) 40 LTP LTP is input specific. LTP is long-lasting (hours, days, weeks). LTP results when synaptic stimulation coincides with postsynaptic depolarization (achieved by cooperativity of many coactive synapses during tetanus). The timing of the postsynaptic response relative to the synaptic inputs is critical. LTP has Hebbian characteristics (“what fires together wires together”, or, in this case, connects together more strongly). LTP may produce synaptic “sprouting”. 41 The NMDA Receptor (a)At the resting potential (postsynaptic neuron), glutamate binds to the NMDA channel, the channel opens, but is “plugged” by a magnesium ion (Mg2+). (b)Depolarization of the postsynaptic membrane relieves the magnesium block and the channel open to allow passage of sodium, potassium and calcium. 42 The Associative Nature of LTP Old(er) view: Associative requirement is mediated by the voltage-dependent characteristics of the NMDA receptor. New discovery (1994): Active conductances in dendrites mediate back-propagation of AP’s into the dendritic tree. 43 Spike-Timing Dependent Plasticity Basic Idea: Change in synaptic strength depends on the precise temporal difference between pre- and post-synaptic neuronal firing (causality!). 44 The Neuron: Integrator or Coincidence Detector? Synchronous inputs really matter! 45 Data Analysis in Neurophysiology Spike train data sets: Neuron in MT Colby and Duhamel, 1991 46 Data Analysis in Neurophysiology Neuron in IT (object selective) Desimone et al., 1984 47 Data Analysis in Neurophysiology Neurons in V1 (orientation selective) PSTH (firing rate) Cross-Correlation Auto-Correlation Shift Predictor Engel et al., 1991 48 Neural Coding Rate coding versus temporal coding One major mechanism of how neurons encode information is through their firing rate (number of AP’s per second). – Example: orientation selectivity. Another major mechanism is synchronization (AP’s occurring together in time). – Example: perceptual grouping. Synchrony could affect other neurons (e.g. through spatial summation – see unit 1). 49 Computational Neuroscience Components of (most) neural models: - Units and connections Inputs and outputs Activation function Learning rule 50 The McCullogh-Pitts Neuron 51 The McCullogh-Pitts Neuron 52 “Why the Mind is in the Head” “Why is the mind in the head? Because there, and only there, are hosts of possible connections to be formed as time and circumstance demand. Each new connection, serves to set the stage for others yet to come and better fitted to adapt us to the world, for through the cortex pass the greatest inverse feedbacks whose function is the purposive life of the human intellect.” Warren S. McCullogh, Hixon Symposium 1951. 53 54 55 gyrus sulcus Cortical Anatomy Motor Cortex Broca’s Area Representation of Imaging Data Sets: - 3-D based - Surface based Auditory Cortex Visual Cortex 56 Cortical Anatomy The macaque monkey cortex - unfolded. Felleman and Van Essen, 1991. 57 Cortical Anatomy 58 “Brain Flattening” 59 Source: Marty Sereno, UCSD 60 Source: Marty Sereno, UCSD 61 “Brain Averaging” 62 Standardized Brain Atlas 63 Cortical Anatomy macaque, chimpanzee, human 64 Cortical Anatomy 65 Methods of Cognitive Neuroscience Neurobiology: Neuroanatomy Neurophysiology Neuroimaging Techniques PET MRI/fMRI EEG MEG Evidence from Dysfunction Lesions Diseases of the CNS Cognitive Psychology Computational Approaches 66 Methods of Cognitive Neuroscience 67 Neurobiology Neuroanatomy and neurophysiology are often conducted in animal systems (monkey, cat, etc.). Neuroanatomy: - Large-scale anatomy of the brain Subdivisions of the cortex Neuronal subtypes, layers Morphology of single neurons 68 Neurobiology Neurophysiology: - Recording of neural activity, often in the context of a stimulus or task (single-cell recording, local field potential recording, multi-electrode recording). - Electrical stimulation of neurons to study their role in perception or movement (micro-stimulation, surface stimulation) 69 Methods Comparison Method Space Time Neural Correlate ------------------------------------------------------------PET coarse coarse “brain activation”, metabolic (5 mm) (sec.) rate of tissue, incorporation of glucose, oxygen utiliz., receptor distribution, 2D-3D capable. fMRI coarse (2 mm) coarse (1 sec.) “blood oxygenation state”, “regional blood flow”, oxygenated/deoxygenated hemoglobin, 2D-3D, in register with struct. Scan. EEG coarse (?) fine (msec) “electrical (field) potentials”, surface electrodes arranged on skull, limited depth, inverse problem, unknown source locations, allows correlation measures. MEG coarse (?) fine (msec) “magnetic (field) potentials”, SQUIDs arranged around head, sensitive to noise, similar advantages and drawbacks as EEG. 70 A New Method… Transcranial Magnetic Stimulation (TMS) 71 EEG The electroencephalogram (EEG) measures the activity of large numbers (populations) of neurons. First recorded by Hans Berger in 1929. EEG recordings are noninvasive, painless, do not interfere much with a human subject’s ability to move or perceive stimuli, are relatively low-cost. Electrodes measure voltage-differences at the scalp in the microvolt (μV) range. Voltage-traces are recorded with millisecond resolution – great advantage over brain imaging (fMRI or PET). 72 EEG Standard placements of electrodes on the human scalp: A, auricle; C, central; F, frontal; Fp, frontal pole; O, occipital; P, parietal; T, temporal. 73 EEG 74 EEG 75 EEG Many neurons need to sum their activity in order to be detected by EEG electrodes. The timing of their activity is crucial. Synchronized neural activity produces larger signals. 76 The Electroencephalogram A simple circuit to generate rhythmic activity 77 The Electroencephalogram Two ways of generating synchronicity: a) pacemaker; b) mutual coordination 1600 oscillators (excitatory cells) un-coordinated coordinated 78 EEG EEG potentials are good indicators of global brain state. They often display rhythmic patterns at characteristic frequencies 79 EEG EEG suffers from poor current source localization and the “inverse problem” 80 EEG EEG rhythms correlate with patterns of behavior (level of attentiveness, sleeping, waking, seizures, coma). Rhythms occur in distinct frequency ranges: Gamma: 20-60 Hz (“cognitive” frequency band) Beta: 14-20 Hz (activated cortex) Alpha: 8-13 Hz (quiet waking) Theta: 4-7 Hz (sleep stages) Delta: less than 4 Hz (sleep stages, especially “deep sleep”) Higher frequencies: active processing, relatively de-synchronized activity (alert wakefulness, dream sleep). Lower frequencies: strongly synchronized activity (nondreaming sleep, coma). 81 EEG Power spectrum: 82 EEG - ERP ERP’s are obtained after averaging EEG signals obtained over multiple trials (trials are aligned by stimulus onset). 83 MEG The MEG laboratory Images courtesy of CTF Systems Inc. 84 MEG Measures changes in magnetic fields that accompany electrical activity. 85 MEG An example (auditory task): 86 87 88 89 MEG Viktor Jirsa (FAU): http://www.ccs.fau.edu/~jirsa/Imaging.html Three task conditions: MEG results 1 2 3 1 -- Listening to tones that were delivered with a delay of about 5s. A random time was added to prevent stimulus prediction. The signal is an average over about 80 stimulus presentations. 2 -- Reacting to acoustic stimuli. The same stimulus presentation as in (1) but now the subject was told to press on an air cushion as soon as possible after the tone was heard. 3 -- Synchronizing with a rhythm. Here the tones were presented regularly with a frequency of 1 Hz. The subject was told to press the air cushion 90 in synchrony with the stimulus. PET Positron Emission Tomography Requires the injection of a positron-emitting radioactive isotope (tracer) Examples: C-11 Glucose analogs (metabolism) O-15 water (blood flow or volume) C-11 or O-15 carbon monoxide PET tracers must have short half-life, e.g. C-11 (20 min.), O-15 (2 min.). Cyclotron! Positron + electron 2 gamma ray beams. Gamma radiation is detected by ring of detectors, source is plotted in 2-D producing an image slice. 91 PET 92 PET - Examples In cognitive studies, a subtraction paradigm is often used. 93 PET - Examples Another example of control and task states, and of averaging over subjects: Marc Raichle 94 PET - Examples (a) Passive viewing of nouns; (b) Hearing of nouns; (c ) Spoken nouns minus viewed or heard nouns; (d) Generating verbs. M. Raichle 95 PET - Examples M. Raichle 96 PET - Examples PET images taken at different times, e.g. during learning, can be compared. M. Raichle 97 PET - Examples PET images are pretty to look at ... … and can be combined with other imaging modalities, here MRI. 98 Functional Magnetic Resonance Imaging Typical MRI Scanner 99 MRI - fMRI The Physics (sort of) ... Subjects are placed in a strong external magnetic field. Spin axes of nuclei orient within the field. External RF pulse is applied. Spin axes reorient, then relax. During relaxation time, nuclei send out pulses, which differ depending on the microenvironment (e.g. water/fat ratio). fMRI – functional MRI Allows fast acquisition of a complete image slice in as little as 20 ms. Several slices are acquired in rapid succession and the data is examined for statistical differences. Hemoglobin is “brighter” than deoxyhemoglobin. Oxygenated blood is “brighter” - active areas are “brighter”. BOLD-fMRI 100 PET - MRI in Comparison 101 fMRI - Examples 102 Stimulus: “checkerboard pattern” V1 responses Jezzard/Friston 1994 103 104 fMRI High-Resolution Mapping BOLD fMRI in cat cortex (level of area 18) Kim et al., 2000 105 fMRI High-Resolution Mapping Kim et al., 2000 106 PET and fMRI Similarities and Differences - Different biological signal. Yet, both pick up a signal related to bulk metabolism (not electricity). - fMRI has better temporal (<100 ms) and spatial resolution (1 mm and less) - fMRI does not involve radioactive tracers and subjects can be measured repeatedly, over many trials. - PET images generally represent “idealized averages”. fMRI images are often registered with structural scans to show individual anatomy. - For both, images can be aligned for multiple subjects. - fMRI is widely available, PET is not. - fMRI does not allow localization of neurotransmitters or receptors etc. - For both, it can be tricky to get stimuli to the subject. 107 Data Analysis Issues Neuroimaging (PET/fMRI): Activation values, spatial resolution, averaging, image alignment and registration. EEG/MEG: Current source localization (inverse problem), time domain data sets, frequency power spectrum, correlation and coherency. 108