What is auditory imagery and how does it activate the brain?

Auditory imagery is the experience of hearing sounds without external auditory stimuli present. When you imagine a sound like a ringing phone, your brain activates the same regions involved in actual sound perception. Specifically, the auditory cortex becomes engaged during auditory imagery, demonstrating that imagination and perception share similar neural mechanisms.

Why is multivoxel pattern analysis better than traditional univariate fMRI analysis?

Univariate analysis collapses individual voxels into a single average, often failing to detect significant differences across sounds. Multivoxel pattern analysis examines activation patterns across multiple voxels collectively, revealing unique overall patterns for each imagined sound. This multivariate approach is sensitive to content-specific activity that univariate methods miss, enabling studying brain activation and motor maps using fMRI principles across sensory domains.

How does sparse temporal sampling improve fMRI data collection during auditory imagery tasks?

Sparse temporal sampling acquires a single fMRI volume 4-5 seconds after each stimulus, capturing the peak of the hemodynamic response. This timing reduces signal masking by scanner noise, which is critical for auditory imagery studies where external sounds must not interfere with imagined auditory content. The approach allows cleaner detection of neural activity patterns.

What role does the Support Vector Machine algorithm play in predicting imagined sounds?

The Support Vector Machine is a machine-learning classifier trained on labeled fMRI data to recognize neural features distinguishing different sounds. After learning these features from training data, it predicts unlabeled test data by identifying which sound pattern matches the neural activity. Classification accuracy reveals whether the auditory cortex encodes sound-specific information.

Why is the planum temporale used as the region of interest in auditory imagery studies?

The planum temporale, located on the temporal lobe surface, is the early auditory cortex where sound processing occurs. Researchers trace voxels in this region to measure neural patterns during auditory imagery. The frontal pole serves as a control region to demonstrate that classifier accuracy is specific to auditory cortex rather than global brain activity.

What does cross-validation accomplish in MVPA analysis of auditory imagery data?

Cross-validation tests classifier performance by leaving out one functional scan as testing data while training on the remaining scans, repeated four times. This procedure prevents overfitting and provides robust accuracy estimates across different data subsets. The averaged accuracies across folds reveal whether the classifier reliably predicts imagined sounds based on auditory cortex patterns.

How can MVPA techniques extend beyond auditory imagery to other neuroscience applications?

MVPA classifiers have decoded visual objects from ventral temporal cortex, predicted consumer preferences, and identified emotional states from brain activity patterns. Brain-computer interfaces could convert mental states into signals for speech therapy or prosthetic control. These applications demonstrate that multivariate pattern analysis reveals information content across sensory and cognitive domains.

Decoding Auditory Imagery with Multivoxel Pattern Analysis

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1. Participant recruitment

Recruit 20 participants.
1. Participants should be right-handed and have no history of neurological or psychological disorders.
2. Participants should have normal or corrected-to-normal vision to ensure that they will be able to see the visual cues properly.
3. Participants should not have metal in their body. This is an important safety requirement due to the high magnetic field involved in fMRI.
4. Participants should not suffer from claustrophobia, since the fMRI requires lying in the small space of the scanner bore.

2. Pre-scan procedures

Fill out pre-scan paperwork.
When participants come in for their fMRI scan, instruct them to first fill out a metal screen form to make sure they have no counter-indications for MRI, an incidental-findings form giving consent for their scan to be looked at by a radiologist, and a consent form detailing the risks and benefits of the study.
Prepare participants to go in the scanner by removing all metal from their body, including belts, wallets, phones, hair clips, coins, and all jewelry.

3. Provide instructions for the participant.

Tell the participants that they will see a series of several short videos inside the scanner. These videos will be silent, but they may evoke a sound in their "mind's ear." Ask the participant to focus on and encourage this auditory imagery, to try to "hear" the sound as best as they can.
Stress to the participant the importance of keeping their head still throughout the scan.

4. Put the participant in the scanner.

Give the participant ear plugs to protect their ears from the noise of the scanner and ear phones to wear so they can hear the experimenter during the scan, and have them lie down on the bed with their head in the coil.
Give the participant the emergency squeeze ball and instruct them to squeeze it in case of emergency during the scan.
Use foam pads to secure the participants head in the coil to avoid excess movement during the scan, and remind the participant that it is very important to stay as still as possible during the scan, as even the smallest movements blur the images.

5. Data collection

Collect high-resolution anatomical scan.
Begin functional scanning.
1. Synchronize the start of stimulus presentation with the start of the scanner.
2. To achieve the sparse temporal sampling, set the acquisition time of an MRI volume to 2 s, with a 9-s delay between volume acquisitions.
3. Present the silent videos via a laptop connected to a projector. The participant has a mirror above their eyes, reflecting a screen at the back of the scanner bore.
4. Synchronize the start of each 5-s video clip to begin 4 s after the previous MRI acquisition starts. This will ensure that the next MRI volume is acquired 7 s after the start of the video clip, to capture the hemodynamic activity that corresponds to the middle of the movie.
5. Present three different silent videos that evoke vivid auditory imagery: a bell swinging back and forth, a chainsaw cutting through a tree, and a person playing a violin.
6. In each functional scan, present each video 10 times, in random order. With each trial lasting 11 s, this will result in a scan 330 s (5.5 min) long.
7. Perform 4 functional scans.

6. Data analysis

Define a Region of Interest (ROI).
1. Use the high-resolution anatomical scan of each participant to trace the voxels that correspond to the early auditory cortex (Figure 1). This corresponds to the surface of the temporal lobe, called the planum temporale. Use the anatomical features of each person's brain to create a mask specific to their auditory cortex.

Brain MRI; axial, coronal, sagittal views; highlighted regions; neurological study; anatomy diagram.
Figure 1: Region of interest tracing. The surface of the planum temporale has been traced on this participant's high-resolution anatomical image, and is shown here in blue. In green is the control mask of the frontal pole. These voxels will be used for MVPA analysis.

Pre-process the data.
1. Perform motion correction to reduce motion artifacts.
2. Perform temporal filtering to remove signal drifts.
Train and test the classifier algorithm.
1. Divide the data into training and testing sets. Training data will be used to train the classifier, and the left-out testing data will be used to assess what it has learned. To maximize the independence of training and testing data, leave out data from one functional scan as the testing set.
2. Train a Support Vector Machine algorithm on the labeled training data from auditory cortex in each subject. Test the classifier's ability to correctly guess the identity of the unlabeled testing set, and record the classifier's accuracy.
3. Repeat this procedure 4 times, leaving out each scan as testing data each time. This type of procedure, in which each section of the data is left out once, is called cross-validation.
4. Combine classifier accuracies across the 4 cross-validation folds by averaging.
Statistical testing
1. To determine if the classifier is performing better than chance (33%), we can compare results at the group level to chance. To do this, gather the accuracies for each subject, and test that the distribution is different from chance using a non-parametric Wilcoxon Signed-Rank test.
2. We can also ask whether the classifier is performing better than chance for each individual. To determine the probability of a given accuracy level in chance data, create a null distribution by training and testing the MVPA algorithm on data whose labels have been randomly shuffled. Permute the labels 10,000 times to create a null distribution of accuracy values, and then compare the actual accuracy value to this distribution.
3. To demonstrate the specificity of the information within the auditory cortex, we can train and test the classifier on voxels from a different location in the brain. Here, we will use a mask of the frontal pole, taken from a probabilistic atlas and warped to fit each subject's individual brain.