Quantifying dynamic facial expressions under naturalistic conditions

  1. Jayson Jeganathan  Is a corresponding author
  2. Megan Campbell
  3. Matthew Hyett
  4. Gordon Parker
  5. Michael Breakspear
  1. University of Newcastle Australia, Australia
  2. University of Western Australia, Australia
  3. University of New South Wales, Australia

Abstract

Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterization of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across individuals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.

Data availability

The DISFA dataset is publically available at http://mohammadmahoor.com/disfa/, and can be accessed by application at http://mohammadmahoor.com/disfa-contact-form/. The melancholia dataset is not publically available due to ethical and privacy considerations for patients, and because the original ethics approval does not permit sharing this data.

The following previously published data sets were used

Article and author information

Author details

  1. Jayson Jeganathan

    School of Psychology, University of Newcastle Australia, Newcastle, Australia
    For correspondence
    jayson.jeganathan@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4175-918X
  2. Megan Campbell

    School of Psychology, University of Newcastle Australia, Newcastle, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4051-1529
  3. Matthew Hyett

    School of Psychological Sciences, University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Gordon Parker

    School of Psychiatry, University of New South Wales, Kensington, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Michael Breakspear

    School of Psychology, University of Newcastle Australia, Newcastle, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4943-3969

Funding

Health Education and Training Institute Award in Psychiatry and Mental Health

  • Jayson Jeganathan

Rainbow Foundation

  • Jayson Jeganathan
  • Michael Breakspear

National Health and Medical Research Council (1118153,10371296,1095227)

  • Michael Breakspear

Australian Research Council (CE140100007)

  • Michael Breakspear

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Participants provided informed consent for the study. Ethics approval was obtained from the University of New South Wales (HREC-08077) and the University of Newcastle (H-2020-0137). Figure 1a shows images of a person's face from the DISFA dataset. Consent to reproduce their image in publications was obtained by the original DISFA authors, and is detailed in the dataset agreement (http://mohammadmahoor.com/disfa-contact-form/) and the original paper (https://ieeexplore.ieee.org/document/6475933).

Reviewing Editor

  1. Alexander Shackman, University of Maryland, United States

Version history

  1. Received: April 19, 2022
  2. Preprint posted: May 10, 2022 (view preprint)
  3. Accepted: August 24, 2022
  4. Accepted Manuscript published: August 31, 2022 (version 1)
  5. Version of Record published: September 2, 2022 (version 2)

Copyright

© 2022, Jeganathan et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,193
    Page views
  • 231
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jayson Jeganathan
  2. Megan Campbell
  3. Matthew Hyett
  4. Gordon Parker
  5. Michael Breakspear
(2022)
Quantifying dynamic facial expressions under naturalistic conditions
eLife 11:e79581.
https://doi.org/10.7554/eLife.79581

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Ian R Outhwaite, Sukrit Singh ... Markus A Seeliger
    Research Article

    Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicates the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Jennifer K Briggs, Anne Gresch ... Richard KP Benninger
    Research Article

    Diabetes is caused by the inability of electrically coupled, functionally heterogeneous -cells within the pancreatic islet to provide adequate insulin secretion. Functional networks have been used to represent synchronized oscillatory [Ca2+] dynamics and to study -cell subpopulations, which play an important role in driving islet function. The mechanism by which highly synchronized -cell subpopulations drive islet function is unclear. We used experimental and computational techniques to investigate the relationship between functional networks, structural (gap-junction) networks, and intrinsic -cell dynamics in slow and fast oscillating islets. Highly synchronized subpopulations in the functional network were differentiated by intrinsic dynamics, including metabolic activity and KATP channel conductance, more than structural coupling. Consistent with this, intrinsic dynamics were more predictive of high synchronization in the islet functional network as compared to high levels of structural coupling. Finally, dysfunction of gap junctions, which can occur in diabetes, caused decreases in the efficiency and clustering of the functional network. These results indicate that intrinsic dynamics rather than structure drive connections in the functional network and highly synchronized subpopulations, but gap junctions are still essential for overall network efficiency. These findings deepen our interpretation of functional networks and the formation of functional sub-populations in dynamic tissues such as the islet.