An fNIRS-Based Feature Learning and Classification Framework to Distinguish Hemodynamic Patterns in Children Who Stutter

Abstract

Stuttering is a communication disorder that affects approximately 1% of the population. Although 5-8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering while others persist and stutter throughout their lives. Detecting neurophysiological biomarkers of stuttering persistence is a critical objective of this paper. In this paper, we developed a novel supervised sparse feature learning approach to discover discriminative biomarkers from functional near infrared spectroscopy (fNIRS) brain imaging data recorded during a speech production experiment from 46 children in three groups: children who stutter ( ); children who do not stutter ( ); and children who recovered from stuttering ( ). We made an extensive feature analysis of the cerebral hemodynamics from fNIRS signals and selected a small number of important discriminative features using the proposed sparse feature learning framework. The selected features are capable of differentiating neural activation patterns between children who do and do not stutter with an accuracy of 87.5% based on a five-fold cross-validation procedure. The discovered set cerebral hemodynamics features are presented as a set of promising biomarkers to elucidate the underlying neurophysiology in children who have recovered or persisted in stuttering and to facilitate future data-driven diagnostics in these children.

Fig. 1:

Approximate positions of emitters (orange circles) and detectors (purple circles) are shown on a standard brain atlas (ICBM 152). The probes were placed symmetrically over the left and right hemisphere, with channels 1–5 spanning inferior frontal gyrus, channels 6–7 over superior temporal gyrus, and channels 8–9 over precentral gyrus/premotor cortex.

Fig. 2:

Oxy-Hb hemodynamic responses averaged over all 18 channels for each subject. Controls are plotted on the left (cyan curves) and stutterers on the right (magenta curves). The grand average hemodynamic response across all channels and subjects is represented by the black dashed curve.

Fig. 3:

Deoxy-Hb hemodynamic responses averaged over all 18 channels for each subject. Controls are plotted on the left (cyan curves) and children who stutter on the right (magenta curves). The grand average hemodynamic response across all channels and subjects is represented by the black dashed curve.

Fig. 4:

The process of feature engineering: pre-process input data, features extraction, post-process the features

Fig. 5:

Feature selection and tuning the regularization parameters via N-fold cross-validation in order to introduce the promising features (biomarkers).

Fig. 6:

The process of choosing the most accurate ML classification algorithm with N-fold cross-validation and parameter tuning

Fig. 7:

Statistical summary of the selected feature groups and channels with MILASSO and MISGL in N-fold cross validation. In each fold, there was 11 to 14 selected features, from different channels and feature-groups. The pie charts illustrate the group that selected features most frequently came from. The histograms summarize the channel selection with MISGL and MILASSO. For example, from approximately 60 total features selected from 5 folds, 6 features were selected from channel 1, and 9 features from channel 4 (either based on MI ranking (yellow bar) or LASSO coefficients (blue bar) which are stacked for each channel).

Fig. 8:

Box-plot of top 5 significant features from talk-phase and source Oxy-Hb, ch: channel, (S: stutterer , C: control).