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Girolamo T, Butler L, Canale R, Aslin RN, Eigsti IM. fNIRS Studies of Individuals with Speech and Language Impairment Underreport Sociodemographics: A Systematic Review. Neuropsychol Rev 2023:10.1007/s11065-023-09618-y. [PMID: 37747652 PMCID: PMC10961255 DOI: 10.1007/s11065-023-09618-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 09/08/2023] [Indexed: 09/26/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a promising tool for scientific discovery and clinical application. However, its utility depends upon replicable reporting. We evaluate reporting of sociodemographics in fNIRS studies of speech and language impairment and asked the following: (1) Do refereed fNIRS publications report participant sociodemographics? (2) For what reasons are participants excluded from analysis? This systematic review was preregistered with PROSPERO (CRD42022342959) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Searches in August 2022 included the terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Searches yielded 38 qualifying studies from 1997 to present. Eight studies (5%) reported at least partial information on race or ethnicity. Few studies reported SES (26%) or language background (47%). Most studies reported geographic location (100%) and gender/sex (89%). Underreporting of sociodemographics in fNIRS studies of speech and language impairment hinders the generalizability of findings. Replicable reporting is imperative for advancing the utility of fNIRS.
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Affiliation(s)
- Teresa Girolamo
- School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, CA, USA.
- Institute for the Brain and Cognitive Sciences, Storrs, CT, USA.
| | - Lindsay Butler
- Institute for the Brain and Cognitive Sciences, Storrs, CT, USA
- Department of Speech, Language, and Hearing Sciences, University of Connecticut, Storrs, CT, USA
| | - Rebecca Canale
- Institute for the Brain and Cognitive Sciences, Storrs, CT, USA
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Richard N Aslin
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Child Study Center and Department of Psychology, Yale University, New Haven, CT, USA
| | - Inge-Marie Eigsti
- Institute for the Brain and Cognitive Sciences, Storrs, CT, USA
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
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Morrel J, Singapuri K, Landa RJ, Reetzke R. Neural correlates and predictors of speech and language development in infants at elevated likelihood for autism: a systematic review. Front Hum Neurosci 2023; 17:1211676. [PMID: 37662636 PMCID: PMC10469683 DOI: 10.3389/fnhum.2023.1211676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is an increasingly prevalent and heterogeneous neurodevelopmental condition, characterized by social communicative differences, and a combination of repetitive behaviors, focused interests, and sensory sensitivities. Early speech and language delays are characteristic of young autistic children and are one of the first concerns reported by parents; often before their child's second birthday. Elucidating the neural mechanisms underlying these delays has the potential to improve early detection and intervention efforts. To fill this gap, this systematic review aimed to synthesize evidence on early neurobiological correlates and predictors of speech and language development across different neuroimaging modalities in infants with and without a family history of autism [at an elevated (EL infants) and low likelihood (LL infants) for developing autism, respectively]. A comprehensive, systematic review identified 24 peer-reviewed articles published between 2012 and 2023, utilizing structural magnetic resonance imaging (MRI; n = 2), functional MRI (fMRI; n = 4), functional near-infrared spectroscopy (fNIRS; n = 4), and electroencephalography (EEG; n = 14). Three main themes in results emerged: compared to LL infants, EL infants exhibited (1) atypical language-related neural lateralization; (2) alterations in structural and functional connectivity; and (3) mixed profiles of neural sensitivity to speech and non-speech stimuli, with some differences detected as early as 6 weeks of age. These findings suggest that neuroimaging techniques may be sensitive to early indicators of speech and language delays well before overt behavioral delays emerge. Future research should aim to harmonize experimental paradigms both within and across neuroimaging modalities and additionally address the feasibility, acceptability, and scalability of implementing such methodologies in non-academic, community-based settings.
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Affiliation(s)
- Jessica Morrel
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kripi Singapuri
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Rebecca J. Landa
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rachel Reetzke
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Dawson G, Rieder AD, Johnson MH. Prediction of autism in infants: progress and challenges. Lancet Neurol 2023; 22:244-254. [PMID: 36427512 PMCID: PMC10100853 DOI: 10.1016/s1474-4422(22)00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/17/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.
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Affiliation(s)
- Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Amber D Rieder
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
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Ayoub MJ, Keegan L, Tager-Flusberg H, Gill SV. Neuroimaging Techniques as Descriptive and Diagnostic Tools for Infants at Risk for Autism Spectrum Disorder: A Systematic Review. Brain Sci 2022; 12:602. [PMID: 35624989 PMCID: PMC9139416 DOI: 10.3390/brainsci12050602] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 02/04/2023] Open
Abstract
Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.
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Affiliation(s)
- Maria J. Ayoub
- College of Health and Rehabilitation Sciences: Sargent College, Boston University, 635 Commonwealth Avenue, Boston, MA 02215, USA; (M.J.A.); (L.K.)
| | - Laura Keegan
- College of Health and Rehabilitation Sciences: Sargent College, Boston University, 635 Commonwealth Avenue, Boston, MA 02215, USA; (M.J.A.); (L.K.)
| | - Helen Tager-Flusberg
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA;
| | - Simone V. Gill
- College of Health and Rehabilitation Sciences: Sargent College, Boston University, 635 Commonwealth Avenue, Boston, MA 02215, USA; (M.J.A.); (L.K.)
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Conti E, Scaffei E, Bosetti C, Marchi V, Costanzo V, Dell’Oste V, Mazziotti R, Dell’Osso L, Carmassi C, Muratori F, Baroncelli L, Calderoni S, Battini R. Looking for “fNIRS Signature” in Autism Spectrum: A Systematic Review Starting From Preschoolers. Front Neurosci 2022; 16:785993. [PMID: 35341016 PMCID: PMC8948464 DOI: 10.3389/fnins.2022.785993] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/08/2022] [Indexed: 01/16/2023] Open
Abstract
Accumulating evidence suggests that functional Near-Infrared Spectroscopy (fNIRS) can provide an essential bridge between our current understanding of neural circuit organization and cortical activity in the developing brain. Indeed, fNIRS allows studying brain functions through the measurement of neurovascular coupling that links neural activity to subsequent changes in cerebral blood flow and hemoglobin oxygenation levels. While the literature offers a multitude of fNIRS applications to typical development, only recently this tool has been extended to the study of neurodevelopmental disorders (NDDs). The exponential rise of scientific publications on this topic during the last years reflects the interest to identify a “fNIRS signature” as a biomarker of high translational value to support both early clinical diagnosis and treatment outcome. The purpose of this systematic review is to describe the updating clinical applications of fNIRS in NDDs, with a specific focus on preschool population. Starting from this rationale, a systematic search was conducted for relevant studies in different scientific databases (Pubmed, Scopus, and Web of Science) resulting in 13 published articles. In these studies, fNIRS was applied in individuals with Autism Spectrum Disorder (ASD) or infants at high risk of developing ASD. Both functional connectivity in resting-state conditions and task-evoked brain activation using multiple experimental paradigms were used in the selected investigations, suggesting that fNIRS might be considered a promising method for identifying early quantitative biomarkers in the autism field.
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Affiliation(s)
- Eugenia Conti
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Elena Scaffei
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Neuroscience, Psychology, Drug Research and Child Health NEUROFARBA, University of Florence, Florence, Italy
- *Correspondence: Elena Scaffei,
| | - Chiara Bosetti
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Viviana Marchi
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Valeria Costanzo
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Valerio Dell’Oste
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Raffaele Mazziotti
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Liliana Dell’Osso
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Claudia Carmassi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Filippo Muratori
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Laura Baroncelli
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Institute of Neuroscience, National Research Council, Pisa, Italy
| | - Sara Calderoni
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Roberta Battini
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children. Transl Psychiatry 2022; 12:53. [PMID: 35136021 PMCID: PMC8826368 DOI: 10.1038/s41398-022-01820-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 12/21/2022] Open
Abstract
Autistic traits represent a continuum dimension across the population, with autism spectrum disorder (ASD) being the extreme end of the distribution. Accumulating evidence shows that neuroanatomical and neurofunctional profiles described in relatives of ASD individuals reflect an intermediate neurobiological pattern between the clinical population and healthy controls. This suggests that quantitative measures detecting autistic traits in the general population represent potential candidates for the development of biomarkers identifying early pathophysiological processes associated with ASD. Functional near-infrared spectroscopy (fNIRS) has been extensively employed to investigate neural development and function. In contrast, the potential of fNIRS to define reliable biomarkers of brain activity has been barely explored. Features of non-invasiveness, portability, ease of administration, and low-operating costs make fNIRS a suitable instrument to assess brain function for differential diagnosis, follow-up, analysis of treatment outcomes, and personalized medicine in several neurological conditions. Here, we introduce a novel standardized procedure with high entertaining value to measure hemodynamic responses (HDR) in the occipital cortex of adult subjects and children. We found that the variability of evoked HDR correlates with the autistic traits of children, assessed by the Autism-Spectrum Quotient. Interestingly, HDR amplitude was especially linked to social and communication features, representing the core symptoms of ASD. These findings establish a quick and easy strategy for measuring visually-evoked cortical activity with fNIRS that optimize the compliance of young subjects, setting the background for testing the diagnostic value of fNIRS visual measurements in the ASD clinical population.
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Clairmont C, Wang J, Tariq S, Sherman HT, Zhao M, Kong XJ. The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review. Front Neurosci 2022; 15:812946. [PMID: 35185452 PMCID: PMC8851356 DOI: 10.3389/fnins.2021.812946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Given the significance of validating reliable tests for the early detection of autism spectrum disorder (ASD), this systematic review aims to summarize available evidence of neuroimaging and neurophysiological changes in high-risk infants to improve ASD early diagnosis. We included peer-reviewed, primary research in English published before May 21, 2021, involving the use of magnetic resonance imaging (MRI), electroencephalogram (EEG), or functional near-infrared spectroscopy (fNIRS) in children with high risk for ASD under 24 months of age. The main exclusion criteria includes diagnosis of a genetic disorder and gestation age of less the 36 weeks. Online research was performed on PubMed, Web of Science, PsycINFO, and CINAHL. Article selection was conducted by two reviewers to minimize bias. This research was funded by Massachusetts General Hospital Sundry funding. IRB approval was not submitted as it was deemed unnecessary. We included 75 primary research articles. Studies showed that high-risk infants had divergent developmental trajectories for fractional anisotropy and regional brain volumes, increased CSF volume, and global connectivity abnormalities on MRI, decreased sensitivity for familiar faces, atypical lateralization during facial and auditory processing, and different spectral powers across multiple band frequencies on EEG, and distinct developmental trajectories in functional connectivity and regional oxyhemoglobin concentrations in fNIRS. These findings in infants were found to be correlated with the core ASD symptoms and diagnosis at toddler age. Despite the lack of quantitative analysis of the research database, neuroimaging and electrophysiological biomarkers have promising value for the screening of ASD as early as infancy with high accuracy, which warrants further investigation.
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Affiliation(s)
- Cullen Clairmont
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Jiuju Wang
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
| | - Samia Tariq
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Hannah Tayla Sherman
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Mingxuan Zhao
- Department of Business Analytics, Bentley University, Waltham, MA, United States
| | - Xue-Jun Kong
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
- *Correspondence: Xue-Jun Kong,
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