L9.5 - Techniques Flashcards
When are invasive perturbations used?
in clinical cases
Name two types of non-invasive perturbations
~ Transcranial magnetic stimulation (TMS)
~ Transcranial direct-current stimulation (tDCS)
TMS
Transcranial magnetic stimulation
~ electric current in coil generates magnetic field
~ magnetic field generates focal electric field in brain
~ depolarises neurons locally
~ briefly interferes with cognitive processing
~ stimulus needs to follow pulse by 70-130ms for participant to be unable to recognise it
~ devise looks like number 8
TMS pros
~ non-invasive
~ good temporal and spacial resolution
~ harmless
~ subjects act as their own control
TMS cons
~ cortical effect - doesn’t reach deep brain areas
~ neurophysiological effect not clear
tDCS
Transcranial direct-current stimulation
~ low-level current that results in action potentials under the anodes
~ hyperpolarisation (inhibited activity) under cathodes
tDCS pros
~ non-invasive ~ poor spacial resolution ~ cheap + portable ~ subjects act as their won controls ~ clinical applications (depression)
tDCS cons
~ Cortical effect - dines’t reach deeper brain areas
~ neurophysiological effect not clear
Computed Tomography (CT)
~ 3D construction computed from multiple 2D X-ray images from all angles
~ low resolution, low precision but good at finding tumours
Magnetic resonance imaging (MRI)
~ measures presence of water (73%) in brain - specifically protons within the water
~ combination of strong magnetic field + radio pulses leads to protons emitting radio signal
~ MRI picks ups he radio waves
~ high resolution
Diffusion tensor imaging
~ MRI variant
~ MRI equipment can be ‘tuned’ to detect water diffusion
~ diffusion in the bran is anisotropic
~ water diffusion is highest within axons (water pipes)
~ myelin sheath creates a tight lipid boundary
~ high resolution
anisotropic
restricted
Problems with real data
~ invasive recordings –> only record activity in small samples, no access to whole system
~ non-invasive recording –> no access to individual neurones
~ no access to many variables e.g. synaptic strength
General scheme of modelling
~ experiment ~ data (link to S&A) ~ define variables and parameters ~ build model ~ simulate and analyse (computers) ~ repeat
What are the markers of model success? (3)
- does the model explain existing data?
- does it produce correct predictions?
- does it give us new insight into what is going on?