2019-08-29 13:31:05

Prelude

“If understanding everything we need to know about the brain is a mile, how far have we walked?”

PSY 511

Foundations of Cognitive and Affective Neuroscience

Rick O. Gilmore, Ph.D.
Professor of Psychology

Today’s topics

  • Why neuroscience is harder than physics
  • Course overview
  • Methods in neuroscience

Why neuroscience is harder than physics

What do we need to know to answer the question?

  • What is the state…
    • Of the world (\(W\))
    • Of the organism
      • Body (\(B\))
      • Nervous system (\(N\))
      • Mind (\(M\))

Some states are more easily measured than others

  • \(W\), \(B\), \(N\) more or less directly

Measure mental states (\(M\))

  • indirectly
  • Via \(N\), \(B\), \(W\) (+ prior beliefs/knowledge)
  • Examples?

Brain & behavior are complex, dynamic systems with

  • Components
  • Interactions
  • Forces/influences
  • Boundaries
  • Inputs/outputs/processes

Systems…

  • “Behave” or change state across time
  • Return to starting state
  • Appear to be regulated, controlled, influenced by feedback loops
  • \(B(t+1) = f(W(t), B(t), N(t), M(t))\)

May be thought of as networks

At multiple levels of organization…

Studying systems is hard because…

  • Single parts -> multiple functions
  • Single functions -> multiple parts

Studying systems is hard because…

  • Change structure/function over time
  • Biological systems not “designed” like human-engineered ones
  • Hard to measure what is being exchanged, what is being controlled

Course overview

PSY 511.001 Goals

  • Master fundamentals of neuroscientific concepts and facts
  • Prepare to read primary source literature in behavioral, cognitive, affective, and clinical neuroscience

Structure

Questions

  • What is the basic organizational plan of the nervous system?
  • How do neurons work?
  • How do neurons connected in networks achieve behavioral goals?
  • How does the nervous system develop? How has it evolved?
  • How do disorders of the mind reveal themselves in the nervous system?

Approach

  • Brain architecture (neuroanatomy)
  • Brain function (neurophysiology)
  • Brain communication (neurochemistry)
  • Changes over evolutionary and developmental time

Approach

  • The nervous system as an information processing system

Inputs

  • From environment, body, brain

Processing

  • Current inputs + brain state + body state + possible future states…
  • Stored information
  • Physiological & behavioral goals

Outputs

  • To brain, body, environment

Cajal/Swanson Architecture

Why neuroscience needs behavior

Neuroscience methods

Evaluating methods

What are we measuring?

  • Structure
  • Activity
    • Why not function?

What is the question?

  • Structure X -> Structure Y
  • Structure X -> Function Y

Evaluating methods

Strengths & Weaknesses

  • Cost
  • Invasiveness
  • Spatial/temporal resolution

Spatial resolution

…and temporal resolution

Types of methods

Structural

  • Anatomy
  • Connectivity/connectome

Functional (next time)

  • What does it do?
  • Physiology/Activity

Mapping structures

  • Cell/axon stains
  • Cellular distribution, concentration, microanatomy

Golgi stain – whole cells, but small %

Camillo Golgi

Nissl stain: Only cell bodies

Franz Nissl

Brainbow

Brainbow

Clarity

Evaluating cellular techniques

  • Invasive (in humans post-mortem only)
  • High spatial resolution, but poor/coarse temporal
    • Why?

Mapping structures

  • Computed axial tomography (CAT), CT
  • X-ray based

Tomography

Magnetic Resonance Imaging (MRI)

  • Magnetic resonance a property of some isotopes and complex molecules
  • Hydrogen (\(H\)), common in water & fat, is one
  • In magnetic field, \(H\) atoms absorb and release radio frequency (RF) energy
  • \(H\) atoms align with strong magnetic field

  • Applying RF pulse perturbs alignment
  • Rate/timing of realignment varies by tissue
  • Realignment gives off radio frequency (RF) signals
  • Strength of RF ~ density of \(H\) (or other target)
  • K-space (frequency/phase) -> anatomical space

MRI

Structural MRI

  • Tissue density/type differences
  • Gray matter (nerve cells & dendrites) vs. white matter (axon fibers)
  • Spectroscopy (specific metabolites)
  • Region sizes/volumes

Voxel-based morphometry (VBM)

Volume differences in schizophrenic patients vs. controls

(Pomarol-Clotet et al., 2010)

What is the wiring diagram (“connectome”)?

Retrograde (output -> input) vs. anterograde (input -> output) tracers

Diffusion Tensor Imaging (DTI)

  • Structural MRI technique
  • Diffusion tensor: measurement of spatial pattern of \(H_2O\) diffusion in small volume
  • Uniform (“isotropic”) vs. non-uniform (“anisotropic”)
  • Strong anisotropy suggests large # of axons with similar orientations (fiber tracts)

Connectome as matrix

Main points

  • Psychology is harder than physics
  • Understanding brain/behavior relations requires a diverse toolkit
    • Structural vs. functional methods
    • Spatial and temporal resolution
    • Invasive vs. non-

Your turn

1. Pick two papers you want to read and (better) understand

  • Email me APA formatted citation (with DOIs)
  • Indicate three concepts/terms you are especially interested in understanding

2. Choose a behavior or mental state you want to (better) understand

  • Take an information processing perspective and briefly sketch out (in no more than a short paragraph) the main inputs, outputs, and computations involved.
  • When thinking about outputs make sure to distinguish between behaviors (e.g., movements, facial expressions, vocalizations) and physiological states (e.g., changes in heart rate, hormone concentrations in the blood, etc.)

References

Calabrese, R. L. (2018). Inconvenient truth to principle of neuroscience. Trends in Neurosciences, 41(8), 488–491. https://doi.org/10.1016/j.tins.2018.05.006

Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017). Neuroscience needs behavior: Correcting a reductionist bias. Neuron, 93(3), 480–490. https://doi.org/10.1016/j.neuron.2016.12.041

Lichtman, J. W., Livet, J., & Sanes, J. R. (2008). A technicolour approach to the connectome. Nature Reviews Neuroscience, 9(6), 417–422. https://doi.org/10.1038/nrn2391

Pomarol-Clotet, E., Canales-Rodrı'guez, E. J., Salvador, R., Sarró, S., Gomar, J. J., Vila, F., … McKenna, P. J. (2010). Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging. Mol. Psychiatry, 15(8), 823–830. https://doi.org/10.1038/mp.2009.146

Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014). Putting big data to good use in neuroscience. Nature Neuroscience, 17(11), 1440–1441. https://doi.org/10.1038/nn.3839