Special Issue "Precision Medicine in Neurodevelopmental Disorders: Personalized Characterization of Autism from Molecules to Behavior"

A special issue of Journal of Personalized Medicine (ISSN 2075-4426).

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 21483

Special Issue Editor

Dr. Elizabeth B. Torres
E-Mail Website
Guest Editor
Department of Psychology, Rutgers State University New Brunswick, New Brunswick, NJ 08903, USA.
Interests: digital biomarkers; machine learning; artificial intelligence; precision medicine; computational neuroscience; objective behavioral analyses sensory-motor integration; autism; schizophrenia; Parkinson's disease; algorithms for transcriptome interrogation; data mining; signal processing

Special Issue Information

Dear Colleagues,

The precision medicine (PM) platform has emerged as a powerful model for the development of personalized targeted treatments in cancer research. It may be advantageous to adapt this model to psychiatric and psychological disorders that are now defined within the realm of mental illness, without reference to their underlying neurology.

Among such disorders are autism, currently defined through observation and description of behaviors, with an emphasis on social inappropriateness. One of the barriers to translating the PM model to autism has been the subjective nature of its current definition of behaviors. The current criteria (problems with social communication and repetitive ritualistic behaviors) defined by observation, have led to a highly heterogeneous phenomenology. There is now a consensus that there may be different autism subtypes. However, in view of such heterogeneity, it has proven difficult to advance basic scientific research to develop personalized targeted treatments tailored to each person within a sub-phenotypic group.
In this Special Issue, we redefine the layer of behaviors of the PM model by leveraging the wearable sensors revolution and considering the neurological underpinnings of currently defined autistic behaviors. We welcome work that considers these issues as they evolve from infancy throughout a person’s lifespan, across different layers of the knowledge network of PM. By redefining autism as a problem of nervous system development, and pairing new objective criteria with physical data from biosensors we will be able to stratify autism into different subtypes according to the structure and function of the nervous systems, thus leveraging the phylogenetic order of maturation that neurobiology already defines from molecules to complex social interactions.

Dr. Elizabeth B. Torres
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autism
  • clinical reports
  • wearable biosensors
  • digital behavioral data
  • transcriptomic data
  • targeted treatments
  • data science
  • microbiome
  • metabolomics

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Editorial
Special Issue “Precision Medicine in Neurodevelopmental Disorders: Personalized Characterization of Autism from Molecules to Behavior”
J. Pers. Med. 2022, 12(6), 918; https://doi.org/10.3390/jpm12060918 - 01 Jun 2022
Viewed by 533
Abstract
The Precision Medicine (PM) platform [...] Full article
Show Figures

Figure 1

Research

Jump to: Editorial, Review

Article
Precision Autism: Genomic Stratification of Disorders Making Up the Broad Spectrum May Demystify Its “Epidemic Rates”
J. Pers. Med. 2021, 11(11), 1119; https://doi.org/10.3390/jpm11111119 - 30 Oct 2021
Cited by 3 | Viewed by 1640
Abstract
In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to [...] Read more.
In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNET portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes’ expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth. Full article
Show Figures

Figure 1

Article
Personalized Biometrics of Physical Pain Agree with Psychophysics by Participants with Sensory over Responsivity
J. Pers. Med. 2021, 11(2), 93; https://doi.org/10.3390/jpm11020093 - 02 Feb 2021
Cited by 5 | Viewed by 1137
Abstract
The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work [...] Read more.
The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population. Full article
Show Figures

Figure 1

Article
A Child’s Perception of Their Developmental Difficulties in Relation to Their Adult Assessment. Analysis of the INPP Questionnaire
J. Pers. Med. 2020, 10(4), 156; https://doi.org/10.3390/jpm10040156 - 05 Oct 2020
Cited by 4 | Viewed by 1199
Abstract
This study involved a comparison of the perception of developmental difficulties in a child by the parents, the teacher, and through the child’s self-assessment. Based on the Institute for Neuro-Psychological Psychology (INPP) questionnaire according to S. Goddard Blythe, three groups were examined: schoolchildren, [...] Read more.
This study involved a comparison of the perception of developmental difficulties in a child by the parents, the teacher, and through the child’s self-assessment. Based on the Institute for Neuro-Psychological Psychology (INPP) questionnaire according to S. Goddard Blythe, three groups were examined: schoolchildren, parents, and teachers. Each of them answered a set of 21 questions and assessed the degree of occurrence of a given difficulty for the child on a scale from 0 to 4. The questions concerned psychomotor problems related to balance, motor coordination and concentration, as well as school skills. In total, 49 questionnaires from children and parents and 46 from teachers were used for the study. The mean answer to each question was calculated within the following groups: child–parent, child–teacher, and parent–teacher. The sum of the children’s answer points was significantly higher than the sum of the parents’ answer points (p = 0.037). Children assessed their developmental difficulties more strongly than teachers, but this difference was not statistically significant. The individual difficulties of the children were assessed significantly more seriously or more gently than by the National Scientific Conference “Human health problems—causes, present state, ways for the future” speeches by 44 teacher participants on 5 June 2020. Parents and teachers also assessed the children’s difficulties significantly differently (p = 0.044). The biggest difference in answers concerned the question of maintaining attention. The obtained results indicate a significant difference in the perception of difficulties occurring in the same child by the teacher and the parent. The child’s behavior in school and home environments may be different and, depending on the requirements, assessed differently. Children perceive their difficulties much more seriously than adults. Talking and the support of adults can make it easier for a child to overcome developmental difficulties. Full article
Show Figures

Figure 1

Article
Reframing Psychiatry for Precision Medicine
J. Pers. Med. 2020, 10(4), 144; https://doi.org/10.3390/jpm10040144 - 25 Sep 2020
Cited by 7 | Viewed by 2192
Abstract
The art of observing and describing behaviors has driven diagnosis and informed basic science in psychiatry. In recent times, studies of mental illness are focused on understanding the brain’s neurobiology but there is a paucity of information on the potential contributions from peripheral [...] Read more.
The art of observing and describing behaviors has driven diagnosis and informed basic science in psychiatry. In recent times, studies of mental illness are focused on understanding the brain’s neurobiology but there is a paucity of information on the potential contributions from peripheral activity to mental health. In precision medicine, this common practice leaves a gap between bodily behaviors and genomics that we here propose to address with a new layer of inquiry that includes gene expression on tissues inclusive of brain, heart, muscle-skeletal and organs for vital bodily functions. We interrogate gene expression on human tissue as a function of disease-associated genes. By removing genes linked to disease from the typical human set, and recomputing gene expression on the tissues, we can compare the outcomes across mental illnesses, well-known neurological conditions, and non-neurological conditions. We find that major neuropsychiatric conditions that are behaviorally defined today (e.g., autism, schizophrenia, and depression) through DSM-observation criteria have strong convergence with well-known neurological conditions (e.g., ataxias and Parkinson’s disease), but less overlap with non-neurological conditions. Surprisingly, tissues majorly involved in the central control, coordination, adaptation and learning of movements, emotion and memory are maximally affected in psychiatric diagnoses along with peripheral heart and muscle-skeletal tissues. Our results underscore the importance of considering both the brain–body connection and the contributions of the peripheral nervous systems to mental health. Full article
Show Figures

Graphical abstract

Article
Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition
J. Pers. Med. 2020, 10(3), 86; https://doi.org/10.3390/jpm10030086 - 13 Aug 2020
Cited by 12 | Viewed by 2559
Abstract
Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers—defined as vetted members of popular crowdsourcing platforms—to aid in the task [...] Read more.
Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers—defined as vetted members of popular crowdsourcing platforms—to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine. Full article
Show Figures

Figure 1

Article
The Autonomic Nervous System Differentiates between Levels of Motor Intent and End Effector
J. Pers. Med. 2020, 10(3), 76; https://doi.org/10.3390/jpm10030076 - 31 Jul 2020
Cited by 7 | Viewed by 2727
Abstract
While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of [...] Read more.
While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We conclude that the autonomic nervous system can differentiate levels of intent. Our results are discussed while considering their potential translational value. Full article
Show Figures

Graphical abstract

Review

Jump to: Editorial, Research

Review
A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder
J. Pers. Med. 2021, 11(4), 299; https://doi.org/10.3390/jpm11040299 - 14 Apr 2021
Cited by 2 | Viewed by 2265
Abstract
Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies [...] Read more.
Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML. Full article
Show Figures

Graphical abstract

Review
Autism Spectrum Disorder from the Womb to Adulthood: Suggestions for a Paradigm Shift
J. Pers. Med. 2021, 11(2), 70; https://doi.org/10.3390/jpm11020070 - 25 Jan 2021
Cited by 22 | Viewed by 6287
Abstract
The wide spectrum of unique needs and strengths of Autism Spectrum Disorders (ASD) is a challenge for the worldwide healthcare system. With the plethora of information from research, a common thread is required to conceptualize an exhaustive pathogenetic paradigm. The epidemiological and clinical [...] Read more.
The wide spectrum of unique needs and strengths of Autism Spectrum Disorders (ASD) is a challenge for the worldwide healthcare system. With the plethora of information from research, a common thread is required to conceptualize an exhaustive pathogenetic paradigm. The epidemiological and clinical findings in ASD cannot be explained by the traditional linear genetic model, hence the need to move towards a more fluid conception, integrating genetics, environment, and epigenetics as a whole. The embryo-fetal period and the first two years of life (the so-called ‘First 1000 Days’) are the crucial time window for neurodevelopment. In particular, the interplay and the vicious loop between immune activation, gut dysbiosis, and mitochondrial impairment/oxidative stress significantly affects neurodevelopment during pregnancy and undermines the health of ASD people throughout life. Consequently, the most effective intervention in ASD is expected by primary prevention aimed at pregnancy and at early control of the main effector molecular pathways. We will reason here on a comprehensive and exhaustive pathogenetic paradigm in ASD, viewed not just as a theoretical issue, but as a tool to provide suggestions for effective preventive strategies and personalized, dynamic (from womb to adulthood), systemic, and interdisciplinary healthcare approach. Full article
Show Figures

Graphical abstract

Back to TopTop