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Itir Onal Ertugrul <[log in to unmask]>
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Itir Onal Ertugrul <[log in to unmask]>
Fri, 6 Aug 2021 10:18:13 +0200
text/plain (88 lines)
**Apologies for cross-posting**

We invite your submissions to our Frontiers Research Topic: Multimodal
approaches for automated assessment, monitoring, and treatment of

New abstract submission deadline is *September 15th, 2021* and the paper
submission deadline is *December 15th, 2021*.

*Call for papers:*
Millions of people worldwide are affected by mental disorders. Examples
include depression, bipolar disorder, obsessive-compulsive disorder, autism
spectrum disorder, schizophrenia, and related neurological diseases, such
as Parkinson’s and Alzheimer’s. Reliable assessment, monitoring, and
evaluation are important to identify individuals in need of treatment,
evaluate treatment response, and achieve remission or moderate impact. Many
indicators of presence or severity of mental disorders are observable.
Indicators include psychomotor agitation (inability to sit still, pacing,
hand wringing) or retardation (slowed speech and body movements, speech
that is decreased in volume or vocal quality), changes in facial
expression, gaze, body movements, and cognition. As mental disorders have a
heterogenous nature, there are many possible combinations of such

Attempts at diagnosis, screening and evaluation of treatment response from
behavioral indicators to date have focused primarily on the individual
alone and individual modalities. Yet, disorders are multimodal and
heterogeneous, changes in facial movements, body movements, gestures,
speech activity can be observed in many combinations. Moreover, disorders
strongly impact social interaction and relationships in family members,
work settings, and on social media. For these reasons, it is critical to
use multimodal indicators in a variety of social contexts.

This Research Topic aims to investigate how the advances in computer
vision, signal processing, and machine learning (especially deep learning)
contribute to automated diagnosis, monitoring, and treatment of mental
disorders especially in interpersonal contexts. Conventional machine
learning approaches, convolutional neural networks (CNNs) or recurrent
neural networks (RNNs) could be used for multimodal investigation of mental
disorders in several contexts. Emerging machine learning techniques
including attention-based approaches for multimodal data fusion,
transformers for speech and language processing, vision transformers for
facial representation, generative adversarial approaches for data
augmentation, and adversarial training for domain transfer and cross-domain
generalizability could be explored within the context of multimodal
investigation of psychopathology.

We are soliciting original contributions that address advancements and
challenges in multimodal approaches for automated assessment, monitoring,
and treatment of psychopathology including but not limited to the following

• Multimodal behavioral indicators of psychopathology occurrence and
severity, especially those concerned with change over time
• Speech and language processing for psychopathology
• Wearable sensors for monitoring psychopathology
• Assessment or monitoring of psychopathology (detection of depression,
obsessive-compulsive disorder, dementia, autism, suicidal ideation or
behavior, and other conditions)
• Evaluation of treatment response
• Interpersonal indicators and mechanisms
• Patient-clinician interaction
• Family interaction
• Group therapy
• Human interaction on social media (e.g., detection of early signals of
psychopathology, suicidal behavior)

*Important dates:*
- Abstract submission deadline: *September 15th, 2021*
- Paper submission deadline: *December 15th, 2021*

Itir Onal Ertugrul, Tilburg University, The Netherlands
Jeffrey Cohn, University of Pittsburgh, USA
Hamdi Dibeklioglu, Bilkent University, Turkey
Nicholas Cummins, King's College London, UK
Sergio Escalera, University of Barcelona, Spain

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