Topic 5 Methods

Evaluating methods

• What question does method X answer?
• What are we measuring?
  • Structure
  • Activity
• Strengths & Weaknesses
  • Cost (time/$)
  • Invasiveness (surgery vs. no)
  • Spatial/temporal resolution
    • high/fine (small details, fast events)
    • low/poor (big picture, slow events)

Figure 5.1: (Sejnowski, Churchland, and Movshon 2014)

Types of methods

• Structural
  • What are the parts?
  • How do they connect?
• Functional (next time)
  • What do the parts do?

Structural methods

Cellular methods

Golgi stain

• Camillo Golgi
• Complete nerve cells, but only 1-5% of total
• Soak tissue in Potassium Dichromate (\(K_2Cr_2O_7\)) then apply Silver Nitrate (\(AgNO_3\))
• Santiago Ramon y Cajal argued for neuron doctrine, shared 1906 Nobel Prize with Golgi

Figure 5.2: source: http://connectomethebook.com/wp-content/uploads/2011/11/Brainforest17_1119.jpg

Nissl stain

• Franz Nissl
• Only cell bodies
• Cellular distribution, concentration, microanatomy
• Density of staining ~ cell density/number

Histochemical tracers

• Neuron information flow polarized–flows in one direction
  • ≠ electronic wires, but like pipes
• Tracers are substances that flow one direction down the neuron, allow starting/ending points to be traced

Figure 5.3: http://openi.nlm.nih.gov/imgs/512/348/3176268/3176268_1471-2105-12-351-2.png

• Retrograde (from axon terminal to cell body)
• Anterograde (from cell body to axon terminal)

Two-photon microscopy

Figure 5.4: https://www.brainfacts.org/in-the-lab/tools-and-techniques/2021/meet-the-inventors-of-two-photon-microscopy-120721

Large-scale cellular techniques

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

Figure 5.5: (National Geographic 2014)

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2023-01-17 start

Clarity

[Video2013-bj]

Evaluating cellular techniques

• Pros:
  • High spatial resolution (resolve small details)
• Cons:
  • Poor temporal resolution
  • Invasive

Whole-brain imaging

Computed axial tomography (CAT)

• Computed tomography CT
• X-ray based

Figure 5.6: CT scanner: http://img.tfd.com/mk/T/X2604-T-22.png

Figure 5.7: How tomography works: https://cdn.hswstatic.com/gif/cat-scan-pineapple.jpg

Figure 5.8: CT scan of stroke: http://1.bp.blogspot.com/-I5AIwDp1jJM/UF9gqPEw4vI/AAAAAAAB77M/VfLRw2JDEiY/s1600/mca+inferior+division+infarct+ct+brain.JPG

Magnetic Resonance Imaging (MRI)

• Magnetic resonance
• Some common isotopes (e.g., H) & complex molecules have a magnetic dipole
• Axes align with strong magnetic field
• When alignment perturbed by radio frequency (RF) pulse, speed of realignment varies by tissue
• Realignment emits RF signals
• How MRI works
• Types
  • Structural
  • Functional

Figure 5.9: https://www.brainfacts.org/in-the-lab/tools-and-techniques/2020/patient-sm-120920

• Reveals tissue density/type differences
• Gray matter (neurons & dendrites & axons & glia) vs. white matter (mostly axons)

Diffusion tensor imaging (DTI)

• Type of structural MRI
• Measures patterns of movement/diffusion of \(H_{2}O\)
• Reveals integrity/density of axon fibers
• Measure of connectivity

MR Spectroscopy

Figure 5.10: https://radiopaedia.org/cases/glioma-mr-spectroscopy

• Some complex molecules generate distinctive signals that MR detects

Voxel-based morphometry (VBM)

Figure 5.11: (Williamson and Allman 2012)

• Voxels (volume-based elements)
  • like pixels in an image, but volumes of tissue
• Morphometry, measure (“metry”) form/morphology
• How does brain size or thickness vary by age, disease status, etc.?

Functional methods

Types of functional methods

• Recording from the brain
• Interfering with the brain
• Stimulating the brain
• Simulating the brain

Recording from the brain

• Single/multi unit recording
  • Microelectrodes
  • Units -> Small numbers of nerve cells

Single/multi-unit Recording

Figure 5.12: (Maren and Quirk 2004)

Single/multi-unit recording

• What does neuron X respond to?
• High temporal (ms) & spatial resolution (um)
• Invasive
• Used in non-human animals for purely research purposes

Electrocorticography (ECoG)

Figure 5.13: ECoG array: https://sites.uci.edu/alns/files/2015/03/eCOG-stock-photo-22578962-brain-surgery.jpg

• Used in human neurosurgery

(AANSNeurosurgery 2019)

Positron Emission Tomography (PET)

(Gov 2013)

• Radioactive tracers (glucose, oxygen) delivered intravenously
• Positron decay
• Experimental condition - control
• Average across individuals

Figure 5.14: Data from PET study on language processing: https://www.d.umn.edu/~jfitzake/Lectures/DMED/SpeechLanguage/CorticalS_LAreas/PosnerRaichlePETLanguageAreas.jpg

• Temporal (~ s) and spatial (mm-cm) resolution worse than fMRI
• Radioactive exposures + mildly invasive
• Dose < airline crew exposure in 1 yr

Functional Magnetic Resonance Imaging (fMRI)

• Neural activity -> local \(O_2\) consumption increase
• Blood Oxygen Level Dependent (BOLD) response
  • Oxygenated vs. deoxygenated hemoglobin creates magnetic contrast
  • Do regional blood \(O_2\) volumes (and flow) vary with behavior X?

Figure 5.15: fMRI data on emotion processing: https://www.cmu.edu/news/stories/archives/2013/june/images/happysadbrainactivity_400x200.jpg

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Figure 5.16: fMRI data about retinotopy in V1 from (Dougherty et al. 2003)

What participants viewed

(Charting 2020a)

(Charting 2020b)

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• Non-invasive, but expensive
• Moderate but improving (mm) spatial, temporal (~sec) resolution
• Indirect measure of brain activity
• Hemodynamic Response Function (HRF)
  • 1s delay plus 3-6 s ‘initial-dip’

Figure 5.17: Hemodynamic Response Function (HRF): https://openi.nlm.nih.gov/imgs/512/236/3109590/3109590_TONIJ-5-24_F1.png

Electroencephalography (EEG)

• How does it work?
  • Electrodes on scalp or brain surface
• What do we measure?
  • Combined activity of huge # of neurons
• High/fine temporal resolution (detect fast changes) but poor spatial resolution

Figure 5.18: https://upload.wikimedia.org/wikipedia/commons/2/26/Spike-waves.png

Frequency analysis of EEG

• Analyze frequency bands
  • LOW: deep sleep
  • MIDDLE: Quiet, alert state
  • HIGH:“Binding” information across senses

Figure 5.19: Source: https://i.stack.imgur.com/epLsO.png

Event-related potentials (ERPs)

• EEGs time-locked to some event
• Averaged over many trials

Figure 5.20: Source: https://2.bp.blogspot.com/_2ob-1_LsjJs/TAUjw9i_dYI/AAAAAAAAAQQ/9AfiHsnD-P8/s1600/ERP_technique.gif

Brain Computer Interface (BCI)

• Often based on EEG.

Figure 5.21: https://cdn.hswstatic.com/gif/brain-computer-interface-3.gif

Magneto-encephalography (MEG)

• Like EEG, but measures magnetic fields
• High temporal resolution, low spatial resolution
• Magnetic field propagates with minimal distortion from brain/skull, unlike electric field

Figure 5.22: https://upload.wikimedia.org/wikipedia/commons/thumb/e/e6/NIMH_MEG.jpg/580px-NIMH_MEG.jpg

Manipulating the brain

• Nature’s “experiments”
  • Stroke, head injury, tumor
  • Neuropsychology
• If damage to X impairs performance on Y -> X critical for/controls Y
• Poor spatial/temporal resolution, limited experimental control

The case of Phineas Gage

Figure 5.23: Phineas Gage: http://www.doctorsimpossible.com/the-curious-case-of-phineas-gage/

Figure 5.24: Sacks, O. The Man Who Mistook His Wife for a Hat

Stimulating the brain

• Pharmacological
• Electrical (transcranial Direct Current Stimulation - tDCS)
• Magnetic (Transcranial magnetic stimulation - TMS)
• Optically (optogenetics)

Trans-cranial Direct Current Stimulation (tDCS)

Figure 5.25: (Dayan et al. 2013)

Trans-cranial Magnetic Stimulation (TMS)

Figure 5.26: (Dayan et al. 2013)

Optogenetic stimulation

Evaluating stimulation methods

• Spatial/temporal resolution?
  • Does stimulation mimic natural activity?
  • Optogenetic stimulation highly similar, others less so
• Deep brain stimulation as therapy
  • Parkinson’s Disease
  • Depression
  • Epilepsy

Deep brain stimulation (DBS)

Figure 5.27: https://www.nimh.nih.gov/health/topics/brain-stimulation-therapies/brain-stimulation-therapies

Treating Parkinson’s Disease with DBS

(Understanding Animal Research 2009)

2023-01-19 start

Simulating the brain

• Computer/mathematical models of brain function
• Example: neural networks
• Cheap, noninvasive, can be stimulated or “lesioned”

Application: AI

Figure 5.28: https://viso.ai/deep-learning/deep-neural-network-three-popular-types/

(Redmon 2018)

Spatial and Temporal Resolution revisited

Figure 5.29: (Sejnowski, Churchland, and Movshon 2014)

References

AANSNeurosurgery. 2019. “Intraop Awake Brain Mapping & Multimodal Image-Guided Resection of Dominant Side Glioma.” Youtube. https://www.youtube.com/watch?v=gFky09ekmzw.

Charting, Individual Brain. 2020a. “Retinotopy Task – Ring-Expanding Run.” Youtube. https://www.youtube.com/watch?v=DcgHJIlwQCo.

———. 2020b. “Retinotopy Task – Wedge-Clockwise Run.” Youtube. https://www.youtube.com/watch?v=rsykP-9-moA.

Dayan, Eran, Nitzan Censor, Ethan R Buch, Marco Sandrini, and Leonardo G Cohen. 2013. “Noninvasive Brain Stimulation: From Physiology to Network Dynamics and Back.” Nature Neuroscience 16 (7): 838–44. https://doi.org/10.1038/nn.3422.

Dougherty, R. F., V. M. Koch, A. A. Brewer, B. Fischer, J. Modersitzki, and B. A. Wandell. 2003. “Visual Field Representations and Locations of Visual Areas V1/2/3 in Human Visual Cortex.” Journal of Vision 3 (10): 1–1. https://doi.org/10.1167/3.10.1.

Gov, Nibib. 2013. “How Does a PET Scan Work?” Youtube. https://www.youtube.com/watch?v=GHLBcCv4rqk.

Maren, Stephen, and Gregory J Quirk. 2004. “Neuronal Signalling of Fear Memory.” Nature Reviews. Neuroscience 5 (11): 844–52. https://doi.org/10.1038/nrn1535.

National Geographic. 2014. “Beautiful 3-D Brain Scans Show Every Synapse | National Geographic.” Youtube. https://www.youtube.com/watch?v=nvXuq9jRWKE.

Redmon, Joseph. 2018. “YOLOv3.” Youtube. https://www.youtube.com/watch?v=MPU2HistivI.

Sejnowski, Terrence J, Patricia S Churchland, and J Anthony Movshon. 2014. “Putting Big Data to Good Use in Neuroscience.” Nature Neuroscience 17 (11): 1440–41. https://doi.org/10.1038/nn.3839.

Understanding Animal Research. 2009. “Parkinson’s Disease.” Youtube. https://www.youtube.com/watch?v=KDjWdtDyz5I.

Williamson, Peter C, and John M Allman. 2012. “A Framework for Interpreting Functional Networks in Schizophrenia.” Frontiers in Human Neuroscience 6 (June): 184. https://doi.org/10.3389/fnhum.2012.00184.