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Brain Sciences
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Biomarker Development in the Early Identification of Autism Spectrum Disorders
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Biomarker Development in the Early Identification of Autism Spectrum Disorders

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Developmental Neuroscience".

Deadline for manuscript submissions: 5 September 2026 | Viewed by 1831

Special Issue Editor

Dr. Elissar Andari

E-Mail Website
Guest Editor
Department of Neurosciences and Psychiatry, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614-2598, USA
Interests: autism deep phenotyping; social and affective neuroscience

Special Issue Information

Dear Colleagues,

Autism Spectrum Disorder (ASD) is a very heterogeneous disorder that is characterized by various phenotypical and biological presentations. Its prevalence is four-fold greater than that in girls. This sex difference in prevalence could be related to biological underpinnings, but could it also be due, in part, to a lack of screening and diagnostic tools that are tailored to girls with ASD? It is possible that women with ASD show different phenotypical presentation and distinctive biological features in comparison to men with ASD. Indeed, among clinicians, it is reported frequently that girls with ASD are often misdiagnosed or diagnosed later in life (even in adulthood). Therefore, identifying behavioral and biological differences between boys and girls with ASD, and uncovering early screeners and diagnostic tools that are tailored to girls with ASD are critical endeavors that could contribute to an increase in early detection and a reduction in the sex ratio differences in prevalence.

In this Special Issue, we aim to cover manuscripts related to (1) differences in behavioral traits of core symptoms and comorbidity, and in biological characteristics between boys and girls with ASD and between girls/women with ASD and controls across the life span; (2) the strengths and limitations of the currently available early screeners and diagnostic tools with regards to the early detection of ASD in girls; (3) the development of new tools that are tailored to detection of ASD in girls; (4) the uncovering of relevant biomarkers (brain function, brain structure, eye-tracking, etc.) beyond behavioral traits that help in identifying ASD in boys and girls across the spectrum early on in life.

These topics will help assess the progress that has been achieved so far on the early identification of ASD, as well as identify the current need to diagnose ASD earlier on and more accurately across the spectrum.

Dr. Elissar Andari
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 250 words) can be sent to the Editorial Office for assessment.

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. Brain Sciences 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 2200 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 deep phenotyping
  • social and affective neuroscience
  • biomarkers

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Published Papers (2 papers)

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Research

22 pages, 10534 KB  
Article
M3ASD: Integrating Multi-Atlas and Multi-Center Data via Multi-View Low-Rank Graph Structure Learning for Autism Spectrum Disorder Diagnosis
by Shuo Yang, Zuohao Yin, Yue Ma, Meiling Wang, Shuo Huang and Li Zhang
Brain Sci. 2025, 15(11), 1136; https://doi.org/10.3390/brainsci15111136 - 23 Oct 2025
Viewed by 612
Abstract
Background: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying [...] Read more.
Background: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying mechanisms. Numerous existing studies using rs-fMRI data have achieved accurate diagnostic performance. However, these methods often rely on a single brain atlas for constructing brain networks and overlook the data heterogeneity caused by variations in imaging devices, acquisition parameters, and processing pipelines across multiple centers. Methods: To address these limitations, this paper proposes a multi-view, low-rank subspace graph structure learning method to integrate multi-atlas and multi-center data for automated ASD diagnosis, termed M3ASD. The proposed framework first constructs functional connectivity matrices from multi-center neuroimaging data using multiple brain atlases. Edge weight filtering is then applied to build multiple brain networks with diverse topological properties, forming several complementary views. Samples from different classes are separately projected into low-rank subspaces within each view to mitigate data heterogeneity. Multi-view consistency regularization is further incorporated to extract more consistent and discriminative features from the low-rank subspaces across views. Results: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 83.21%, outperforming most existing methods and confirming its effectiveness. Conclusions: The proposed method was validated using the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results demonstrate that the M3ASD method not only improves ASD diagnostic accuracy but also identifies common functional brain connections across atlases, thereby enhancing the interpretability of the method. Full article
(This article belongs to the Special Issue Biomarker Development in the Early Identification of Autism Spectrum Disorders)
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17 pages, 919 KB  
Article
Timing of Intervals Between Utterances in Typically Developing Infants and Infants Later Diagnosed with Autism Spectrum Disorder
by Zahra Poursoroush, Gordon Ramsay, Ching-Chi Yang, Eugene H. Buder, Edina R. Bene, Pumpki Lei Su, Hyunjoo Yoo, Helen L. Long, Cheryl Klaiman, Moira L. Pileggi, Natalie Brane and D. Kimbrough Oller
Brain Sci. 2025, 15(8), 819; https://doi.org/10.3390/brainsci15080819 - 30 Jul 2025
Viewed by 893
Abstract
Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: [...] Read more.
Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article
(This article belongs to the Special Issue Biomarker Development in the Early Identification of Autism Spectrum Disorders)
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