Sonification of fMRI Data Nik Sawe Music 220C. Overview PhD studies assess decision-making on environmental issues through neuroimaging Neural activation.

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!

[email protected]