Speech Signals in Stuttering

Primary outcomes

1. Speech Sound Identification

   Time frame: 1 Session (up to 2 hours)

   Behavioral responses will be measured for the syllable identification task in quiet and in the presence of background noise. Children will respond as quickly as possible to identify which speech sound they heard. Within and between group analyses will be conducted between children who stutter and control subjects. Drift diffusion models (DDMs) will be used to aggregate the behavioral responses of accuracy and reaction time to evaluate bias toward more accurate or faster responses as well as change in response behaviors over time in each group.

2. Frequency Following Responses (EEG)

   Time frame: 1 Session (up to 30 minutes)

   Frequency following responses (FFRs) will be collected and used to quantify neural encoding of fast temporal cues in auditory stimuli, including speech sounds. FFRs (70-1500 Hz) will be elicited by syllables. FFRs will be elicited in quiet conditions and in the presence of a competing background story. FFRs will be measured for magnitude. Decoding of FFRs elicited by syllables using support vector machine classifiers will be analyzed. Within and between group analyses will be conducted between children who stutter and control subjects.

3. Temporal Response Functions (EEG)

   Time frame: 1 Session (up to 1 hour)

   Temporal response function (TRFs) analysis directly compares a continuously varying stimulus, such as continuous speech, to EEG data. The relationship between the continuous speech and EEG signals will be estimated as a continuous wave describing how a change in a continuous speech feature relates to changes in the EEG signal. The EEG data predicted by the TRF are compared to the real, observed EEG data via correlation, resulting in a measure of fitness (Pearson's r) for how well the stimulus explains the observed neural activity. Multivariate linear ridge regression using leave-one-out-cross validation method, to prevent over-fitting the data, will be utilized to compare the predicted and obtained EEG. Higher correlations between the predicted and obtained EEG reflect better cortical encoding of the speech envelope. Within and between group analyses will be conducted between children who stutter and control subjects.

4. Blood-oxygen level dependent activation (functional magnetic resonance imaging)

   Time frame: 1 Session (up to 2 hours)

   Brain activation patterns indexed by blood-oxygen level dependent (BOLD) fMRI signals will be analyzed. BOLD responses will be estimated separately for each participant for each functional task. Study-level outcomes include main effects of group (children who stutter vs. controls), group by region interactions, and group by network (auditory, speech motor, and attention) interactions. Drift diffusion models (DDMs) will be used to aggregate the behavioral responses of accuracy and reaction time (e.g. during the categorization the sounds such as /ba/ or /da/) to evaluate bias toward more accurate or faster responses as well as change in response behaviors over time in each group.

5. Multi-voxel pattern analysis (functional magnetic resonance imaging)

   Time frame: 1 Session (up to 2 hours)

   Multi-voxel pattern analysis (MVPA) is a machine learning analysis technique that aims to quantify spatially distributed neural representations across ensembles of voxels. MVPA will be used to determine the neural activity patterns that contain predictive information about the syllables (e.g. /ba/, da/) in the tasks in quiet and with background noise. Extracted BOLD parameter estimates for each syllable will be entered into the analysis. Participant specific classification cross-validation accuracies (per pre-determined regions of interest) will be contrasted between conditions to determine regions of interest in which representations are enhanced or degraded by increasing task demands. Regions with significant group-level classification accuracies in each task, as well as regions of interest showing task-dependent changes in classification accuracies, will be established by permutation testing for each region of interest for each participant.

6. Psychophysiological Interactions

   Time frame: 1 Session (up to 2 hours)

   Psychophysiological interaction (PPI) analyses evaluate task-dependent interactions between brain regions. Each pre-determined region of interest will serve as a seed region. For each target region (all other regions of interest), a general linear model will be used to estimate the interaction of task-related hemodynamic effects and the effects that are linearly related to the time-series of the seed region. Significant interactions reflect regions for which the effective connectivity with the seed-region changes as a function of task condition (i.e., indicating regions that are preferentially coupled for a specific task). Study-level outcomes will assess main effects of group (children who stutter vs controls), group by region interactions, and group by network (auditory, speech motor, attention) interactions.