sonification of fmri data nik sawe music 220c. overview phd studies assess decision-making on...
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Sonification of fMRI Data
Nik SaweMusic 220C
Overview
• PhD studies assess decision-making on environmental issues through neuroimaging
• Neural activation suggests underlying physiological bases for framing effects, heuristics, affect (emotion) and their impact on decision-making
How We Image the Brain
• Functional MRI allows us to take realtime pictures of the brain’s response to stimuli.
• Using headsets and hand input devices, can present subjects with a wide range of tasks.
The BOLD Signal
• BOLD: Blood Oxygenation Level-Dependent
• fMRI evaluates brain activity indirectly, by measuring changes in the local amount of oxygenated blood
• Complex regressions account for fluctuations due to heart rate, breathing, etc.
• Validity confirmed through optogenetics
Motivations for Sonification
• Can hear patterns of activation that would be less obvious through visualization of time courses
Motivations for Sonification
• May be able to hear “conversations” between different brain regions that would be less obvious through traditional neuroimaging analyses
• Intuitive level of interpretation that may provide clues for further analytic techniques
Limitations of fMRI
• Poor temporal resolution– One pass through each brain region
every 1-2 seconds (most often 2)
Limitations of fMRI
• Poor temporal resolution– One pass through each brain region
every 1-2 seconds (most often 2)
• For most study designs, need many repeated trials in one person to get an accurate read
Translatable fMRI Outputs
Sonification Methodology
Built in R from a simple initial formula– Pitch: 128 * [(Xi – Xl)/(Xh-Xl)]– Velocity: 128*Pi
Xi : signal at timepoint iXh: maximum signalXl: minimum signalPi: a given network's proportional
contribution to the total signal strength of all sampled networks at timepoint i
Sonification Methodology
Use these new values as downstream MIDI values, convert to MIDI file via Java
First trial: utilized data from one subject in my first study (environmental philanthropy to save parks threatened with potentially destructive land use development)
Used 3 networks: attentional, visual, default mode network
Visual Cortex Quartet
Final project: Sampled from the visual cortex as subject undergoes retinotopy
Sonification Methodology
Program had several stages: • Scale converter: created array of
MIDI values based on desired scale • Instrument filter: selected valid (in
scale range) notes for a given instrument
• Signal to MIDI converter: Gated signals below a threshold value (5%) and did not play them
Sonification Methodology
• Velocity based upon relative prominence of the voxel signal given other voxels’ activity
• Duration based on arbitrary equation of:– ((128-note value)+velocity)/20
The Next Step
• Scan whole brain while watching a silent film
• May obtain complementary EEG data
• Will have PCA networks to work with, as well as a wealth of regions
• Signals do not all have to be pitch modulation
Mapping Ideas
• Activity in the attendant PCA network helps define duration and velocity for each region, based on its relative contribution
• Talairach (spatial) coordinates define surround sound mapping
Mapping: Anterior Insula
• Handles “negative arousal” / response to physiologically as well as morally aversive stimuli
• Control how discordant the note selection is in other regions
Nucleus Accumbens
• Handles “positive arousal”/reward/approach behavior
• Control weighting towards major scales
• May be able to make a balancing equation of AI vs Nacc
Mapping: Amygdala
• Fear/apprehension/anxiety region• Control tempo, accelerating at tense
moments• Control percussive
elements• Trigger clusters
Mapping: Fusiform Gyrus
• Recognizes faces: triggering of voice samples?
Parahippocampal Gyrus
• Spatial/landscape encoding• Spatial manipulation of
samples/Doppler?
Incorporation of EEG
• Since temporal resolution is only 2 sec passes, would be good to have variation that decides interleaving of notes
• This interpolation can be decided by activity in relevant EEG signals
Thanks!
sawe@stanford.edu
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