Research ArticleNEUROSCIENCE

Assessing bimanual motor skills with optical neuroimaging

See allHide authors and affiliations

Science Advances  03 Oct 2018:
Vol. 4, no. 10, eaat3807
DOI: 10.1126/sciadv.aat3807
  • Fig. 1 fNIRS probe placement design for PFC, M1, and SMA measurements.

    (A) Schematic depicting the FLS box simulator where trainees perform the bimanual dexterity task. A continuous-wave spectrometer is used to measure functional brain activation via raw fNIRS signals in real time. (B) Optode positions for coverage over the PFC, M1, and SMA. Red dots indicate infrared sources, blue dots indicate long separation detectors, and light blue dots indicate short separation detectors. The PFC has three sources (1 to 3), three short separation detectors (S1 to S3), and four long separation detectors (1 to 4). The M1 has 4 sources (4 to 7), 4 short separation detectors (S4 to S7), and 10 long detectors (5 to 14). The SMA has one source (8), one short separation detector (S8), and three long separation detectors (9, 15, and 16). Illustration by Nicolás Fernández.

  • Fig. 2 Bimanual motor task performance scores.

    (A) FLS performance scores for Novice surgeons (green) and Expert surgeons (red), where Expert surgeons significantly outperformed Novice surgeons. (B) FLS performance scores for all training subjects (black) with respect to days trained compared to untrained Control subjects (orange). Two-sample t tests were used for statistical differentiation. n.s., not significant. Red+ signs indicate outliers. *P < 0.05. (C) CUSUM scores for each trained subject with respect to trials. The H0 threshold indicates that the probability of any given trained subject is mislabeled as a “Skilled trainee” is less than 0.05 and is subsequently labeled as a “Skilled trainee” subject. Results indicate that three trained subjects, FLS 2, FLS 3, and FLS 5, are labeled as “Skilled trainees.” The remaining trained subjects that do not cross the H0 line are labeled “Unskilled trainees.”

  • Fig. 3 Differentiation and classification of motor skill between Novice and Expert surgeons.

    (A) Brain region labels are shown for PFC, M1, and SMA regions. Average functional activation for all subjects in the Novice and Expert surgeon groups are shown as spatial maps while subjects perform the FLS task. (B) Average changes in hemoglobin concentration (conc.) during the FLS task duration with respect to specific brain regions for Novice (green) and Expert (red) surgeons. Two sample t tests were used for statistical tests. *P < 0.05. (C) LDA classification results for FLS scores and all combinations of fNIRS metrics. (D) Leave-one-out cross-validation results show the ratio of samples that are below MCE rates of 0.05 for FLS scores and all other combinations of fNIRS metrics.

  • Fig. 4 Differentiation of motor skill between Control, Skilled, and Unskilled trainees.

    (A) Spatial maps of average functional activation for all subjects in each respective group during the FLS training task on the posttest day. (B) Average changes in hemoglobin concentration during stimulus duration with respect to specific brain regions for untrained Control subjects (orange) and all FLS training students (black). Two-sample t tests were used for statistical differentiation. *P < 0.05. Type I error is defined as 0.05 for all cases.

  • Fig. 5 Classification of motor skill between Control, Skilled, and Unskilled trainees.

    (A) Inter- and intragroup MCEs for each subject population (Control, Skilled, and Unskilled trainees) with respect to training days. MCE12 and MCE21 values significantly decrease below 5% when classifying pretest Skilled and Unskilled trainees on the final training day. Furthermore, MCEs are also low when classifying Skilled and Unskilled trainees on the final training day, along with Skilled trainees and untrained Control subjects. (B) MCEs are reported for each combination of training groups (Control, Skilled, and Unskilled trainees) with respect to pretest, posttest, and final training days. MCEs are substantially low when classifying Skilled trainees and Control subjects along with inter-Skilled trainee group classification. Unskilled trainees, however, showed high MCEs even when compared to Unskilled trainees and Control subjects during the posttest. As a measure of skill retention, classification models were also applied for all subject groups from the final training day to the posttest.

  • Fig. 6 Cross-validation results for classification across all subjects with varying degree of motor skills.

    Each box represents one trial per expertise group during the posttest, where the shaded regions indicate the MCE if that given trial is removed from the classification model. Cross-validation results with their respective ratio of samples that are below MCE rates of 5% for Expert surgeons versus Skilled trainees (28 of 35 samples), Expert surgeons versus Unskilled trainees (29 of 38), Expert versus Novice surgeons (43 of 43), Expert surgeons versus untrained Control subjects (34 of 38), Skilled trainees versus Unskilled trainees (15 of 21), Skilled trainees versus Novice surgeons (24 of 26), Skilled trainees versus untrained Control subjects (18 of 21), Unskilled trainees versus Novice surgeons (16 of 29 samples), Unskilled trainees versus untrained Control subjects (11 of 24), and finally, Novice surgeons versus untrained Control subjects (9 of 29).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/10/eaat3807/DC1

    Table S1. Subject demographics and descriptive data.

    Table S2. Theoretical Montreal Neurological Institute coordinates for each source detector channel.

    Table S3. FLS task trial completion times for Novices.

    Table S4. Expert versus Novice classification results for fNIRS (with and without short separation regression) and FLS metrics.

    Fig. S1. Experimental protocol design.

    Fig. S2. Subjects performing FLS PC task with fNIRS measurements.

    Fig. S3. Group average HRFs with respect to cortical regions.

    Fig. S4. Quadratic SVM classification of Expert and Novice surgeons.

    Fig. S5. Weighted quadratic SVM classification of Expert and Novice surgeons.

    Fig. S6. Quadratic SVM classification of Skilled versus Unskilled trainees.

    Fig. S7. Weighted quadratic SVM classification of Skilled versus Unskilled trainees.

    Fig. S8. Probability density functions for projected LDA classification models.

    Fig. S9. Differentiation and classification of motor skill between Novice and Expert surgeons without short separation regression.

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Subject demographics and descriptive data.
    • Table S2. Theoretical Montreal Neurological Institute coordinates for each source detector channel.
    • Table S3. FLS task trial completion times for Novices.
    • Table S4. Expert versus Novice classification results for fNIRS (with and without short separation regression) and FLS metrics.
    • Fig. S1. Experimental protocol design.
    • Fig. S2. Subjects performing FLS PC task with fNIRS measurements.
    • Fig. S3. Group average HRFs with respect to cortical regions.
    • Fig. S4. Quadratic SVM classification of Expert and Novice surgeons.
    • Fig. S5. Weighted quadratic SVM classification of Expert and Novice surgeons.
    • Fig. S6. Quadratic SVM classification of Skilled versus Unskilled trainees.
    • Fig. S7. Weighted quadratic SVM classification of Skilled versus Unskilled trainees.
    • Fig. S8. Probability density functions for projected LDA classification models.
    • Fig. S9. Differentiation and classification of motor skill between Novice and Expert surgeons without short separation regression.

    Download PDF

    Files in this Data Supplement:

Stay Connected to Science Advances

Navigate This Article