Materiały źródłowe

• #1 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. […] Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. […] This suggests that useful digital biomarkers might be extracted from EEG measurements. […] Because atypical brain development that leads to ASD symptoms is likely to precede atypical behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis or screening is based solely on behavioral features. […] A recent fMRI study of 59 6-month-old infants demonstrated significant differences in the brains of children who would develop a diagnosis of ASD at 24 months of age.

• #2 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Prognosis for Autism […] If your loved one has been diagnosed with autism spectrum disorder, you may wonder about the likely course of their condition. Will they improve? Will challenging behaviors stop or increase? What can you do to support their development and growth over time? Because every individual with autism is different, there are no universal answers to these questions. However, the prognosis for people with autism has improved over the last two decades as more treatments have been identified and tested. […] The prognosis for a child with autism depends on the severity of their initial symptoms but can be influenced by early intervention and treatment. […] While autism is a lifelong condition, there are now evidence-based treatments that can help and support people with autism.

• #3 Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data | Scientific Reports

  https://www.nature.com/articles/s41598-025-90216-8

  Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). […] This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. […] The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. […] The results of this work demonstrate feasibility and potential to identify young children with increased likelihood of developing ASD using a ML model applied to population-based and routinely collected data. […] Our models reported sensitivity of 70.9%, specificity of 56.9%, and AUROC of 69.6% suggest that our ensemble transformer model is a promising candidate for population-based ASD screening.

• #4 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  According to a recent influential proposal, several phenotypic features of autism spectrum disorder (ASD) may be accounted for by differences in predictive skills between individuals with ASD and neurotypical individuals. […] These studies suggest that ASD may be associated with differences in the learning of predictive pairings (e.g., learning cause and effect) and in low-level predictive processing in the brain (e.g., processing repeated sounds). […] An important goal for future research will be to better define and constrain the broad domain-general hypothesis by testing multiple types of prediction within the same individuals. […] A vulnerability of any hypothesis that attempts to unify diverse aspects of ASD in cognitive terms lies in its tendency toward reductionism: the ASD diagnosis is shaped by the pragmatic needs of individuals spanning a wide range of genotypes and phenotypes, their families, and the medical professionals who serve them.

• #5 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  According to a recent influential proposal, several phenotypic features of autism spectrum disorder (ASD) may be accounted for by differences in predictive skills between individuals with ASD and neurotypical individuals. […] These studies suggest that ASD may be associated with differences in the learning of predictive pairings (e.g., learning cause and effect) and in low-level predictive processing in the brain (e.g., processing repeated sounds). […] An important goal for future research will be to better define and constrain the broad domain-general hypothesis by testing multiple types of prediction within the same individuals. […] A vulnerability of any hypothesis that attempts to unify diverse aspects of ASD in cognitive terms lies in its tendency toward reductionism: the ASD diagnosis is shaped by the pragmatic needs of individuals spanning a wide range of genotypes and phenotypes, their families, and the medical professionals who serve them.

• #6 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  The results further show reduced perceptual adaptation, which can be interpreted as reduced use of the expectation of stimulus repetition to improve stimulus discrimination. […] Overall, the results of our review suggest reduced habituation of neural activity and behavior (gaze and response time) over stimulus repetitions in the ASD group compared to the NT controls. […] These differences likely have downstream consequences for higher-level prediction skills and may underlie social manifestations of ASD.

• #7 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  The results further show reduced perceptual adaptation, which can be interpreted as reduced use of the expectation of stimulus repetition to improve stimulus discrimination. […] Overall, the results of our review suggest reduced habituation of neural activity and behavior (gaze and response time) over stimulus repetitions in the ASD group compared to the NT controls. […] These differences likely have downstream consequences for higher-level prediction skills and may underlie social manifestations of ASD.

• #8 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  The results further show reduced perceptual adaptation, which can be interpreted as reduced use of the expectation of stimulus repetition to improve stimulus discrimination. […] Overall, the results of our review suggest reduced habituation of neural activity and behavior (gaze and response time) over stimulus repetitions in the ASD group compared to the NT controls. […] These differences likely have downstream consequences for higher-level prediction skills and may underlie social manifestations of ASD.

• #9 All-or-None Evaluation of Prediction Certainty in Autism | bioRxiv

  https://www.biorxiv.org/content/10.1101/2022.11.17.516919.full

  The brain generates predictions to prepare for upcoming events. As life is not always 100% predictable, it also estimates a level of certainty for these predictions. Given that autistic individuals resist even small changes in everyday life, we hypothesized impaired tuning of prediction certainty in autism. […] This study reveals that individuals with autism are less efficient in adjusting the certainty of their predictions to the level of predictability of events. Instead, they may adopt an all-or-none evaluation of certainty. Our findings reveal novel insights into the processes underlying impaired predictive processing in autism, which may open the door to developing targeted behavioral interventions and/or non-invasive brain stimulation therapies that help autistic individuals make more accurate predictions to ease social- and rigidity-based symptoms.

• #10 All-or-None Evaluation of Prediction Certainty in Autism | bioRxiv

  https://www.biorxiv.org/content/10.1101/2022.11.17.516919.full

  Our data suggest that autistic individuals adopted an all-or-none evaluation of certainty of their environment, rather than adjusting certainty of predictions to different levels of environmental statistics. Social responsiveness scores were associated with flexibility in representing prediction certainty, suggesting that impaired representation and updating of prediction certainty may contribute to social difficulties in autism.

• #11 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Treating underlying conditions and related disorders can improve the prognosis for individuals with autism. […] Age at intervention can impact long-term outcomes—research has shown that the earlier a child is treated, the better the prognosis will be. […] However, the majority of people with autism remain affected to some degree in their ability to communicate and socialize. […] […] […] Many researchers, clinicians, and parents have investigated and tracked the efficacy of autism treatments over time. While each individual with autism is different, some treatments have shown positive effects for people with autism. […] […] […] Several co-occurring conditions—called comorbidities by clinicians—have been identified.

• #12 Yale researchers find key to predicting outcomes of autism treatment | Yale News

  https://news.yale.edu/2016/11/15/yale-researchers-find-key-predicting-outcomes-autism-treatment

  Treatments for autism spectrum disorders are varied and costly, and selecting the right one is crucial to long-term outcomes. […] The ability to predict treatment outcomes during a childs preschool years is crucial, say the researchers, because early intervention with effective treatments can dramatically improve outcomes. […] But these neuroimaging biomarkers may help us quickly identify individuals for whom the costly treatments will not work so we can start a more appropriate therapy. […] By studying patterns of activity in an area of the orbitofrontal cortex involved in social processing, the researchers were able to predict which children would respond to therapy. […] Eliminating ineffective treatment can have huge cost savings.

• #13 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Prognosis for Autism […] If your loved one has been diagnosed with autism spectrum disorder, you may wonder about the likely course of their condition. Will they improve? Will challenging behaviors stop or increase? What can you do to support their development and growth over time? Because every individual with autism is different, there are no universal answers to these questions. However, the prognosis for people with autism has improved over the last two decades as more treatments have been identified and tested. […] The prognosis for a child with autism depends on the severity of their initial symptoms but can be influenced by early intervention and treatment. […] While autism is a lifelong condition, there are now evidence-based treatments that can help and support people with autism.

• #14 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Treating underlying conditions and related disorders can improve the prognosis for individuals with autism. […] Age at intervention can impact long-term outcomes—research has shown that the earlier a child is treated, the better the prognosis will be. […] However, the majority of people with autism remain affected to some degree in their ability to communicate and socialize. […] […] […] Many researchers, clinicians, and parents have investigated and tracked the efficacy of autism treatments over time. While each individual with autism is different, some treatments have shown positive effects for people with autism. […] […] […] Several co-occurring conditions—called comorbidities by clinicians—have been identified.

• #15 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  In Aspergers Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. […] In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. […] Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. […] The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

• #16 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Treating underlying conditions and related disorders can improve the prognosis for individuals with autism. […] Age at intervention can impact long-term outcomes—research has shown that the earlier a child is treated, the better the prognosis will be. […] However, the majority of people with autism remain affected to some degree in their ability to communicate and socialize. […] […] […] Many researchers, clinicians, and parents have investigated and tracked the efficacy of autism treatments over time. While each individual with autism is different, some treatments have shown positive effects for people with autism. […] […] […] Several co-occurring conditions—called comorbidities by clinicians—have been identified.

• #17 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  In Aspergers Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. […] In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. […] Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. […] The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

• #18 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #19 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #20 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #21 2015 International Meeting for Autism Research: Parents’ Concerns Predict a Later Autism Spectrum Disorder Outcome: A Prospective Study of High-Risk Siblings from 6 to 36 Months

  https://insar.confex.com/imfar/2015/webprogram/Paper20060.html

  20060 Parents’ Concerns Predict a Later Autism Spectrum Disorder Outcome: A Prospective Study of High-Risk Siblings from 6 to 36 Months […] The results suggest that the presence or absence of parental concerns provides valuable predictive information to aid in differentiating between HR infants who will and will not receive an ASD diagnosis. […] The sequencing of parent concerns reflects the developmental course of ASD, with early concerns mirroring the prodrome of ASD, and later concerns reflecting the core domains of the disorder.

• #22 2015 International Meeting for Autism Research: Parents’ Concerns Predict a Later Autism Spectrum Disorder Outcome: A Prospective Study of High-Risk Siblings from 6 to 36 Months

  https://insar.confex.com/imfar/2015/webprogram/Paper20060.html

  20060 Parents’ Concerns Predict a Later Autism Spectrum Disorder Outcome: A Prospective Study of High-Risk Siblings from 6 to 36 Months […] The results suggest that the presence or absence of parental concerns provides valuable predictive information to aid in differentiating between HR infants who will and will not receive an ASD diagnosis. […] The sequencing of parent concerns reflects the developmental course of ASD, with early concerns mirroring the prodrome of ASD, and later concerns reflecting the core domains of the disorder.

• #23 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #24 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #25 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #26 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #27 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #28 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #29 2014 International Meeting for Autism Research: Early Characteristics of Children Who Lose Their Autism Diagnosis Between Age 2 and 4

  https://insar.confex.com/insar/2014/webprogram/Paper15857.html

  Emerging literature has indicated that a subset of children with a documented ASD lose their diagnosis and function in the average range of cognition and behavior. […] Multiple factors including intervention, symptom severity, adaptive functioning, motor skills and language abilities may help to predict positive outcomes. […] Nine percent of children diagnosed with an ASD attained optimal outcome by age four. At age two, these children showed fewer RRBs, lower symptom severity, more diagnoses of PDD-NOS as opposed to Autistic Disorder, and higher daily living and motor skills than those who retained their diagnosis. RRBs have been found in other studies to be a negative prognostic factor, and may reflect the fact that these children are less attentive to intervention and natural social interactions. Early motor skills have also previously been found to predict outcome and may function as a better indication of CNS integrity than social or language skills in children with impaired motivation to interact.

• #30 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Treating underlying conditions and related disorders can improve the prognosis for individuals with autism. […] Age at intervention can impact long-term outcomes—research has shown that the earlier a child is treated, the better the prognosis will be. […] However, the majority of people with autism remain affected to some degree in their ability to communicate and socialize. […] […] […] Many researchers, clinicians, and parents have investigated and tracked the efficacy of autism treatments over time. While each individual with autism is different, some treatments have shown positive effects for people with autism. […] […] […] Several co-occurring conditions—called comorbidities by clinicians—have been identified.

• #31 Childhood neurodivergent traits, inflammation and chronic disabling fatigue in adolescence: a longitudinal case–control study | BMJ Open

  https://bmjopen.bmj.com/content/14/7/e084203

  Children with neurodivergent traits at ages 7 and 9 years were two times as likely to experience chronic disabling fatigue at age 18 years (likely ADHD OR=2.18 (95% CI=1.33 to 3.56); p=0.002; likely autism OR=1.78 (95% CI=1.17 to 2.72); p=0.004). […] Our results indicate higher risk of chronic disabling fatigue for children with neurodivergent traits, likely linked to higher levels of inflammation. […] We aimed to investigate the relationship between increased experiences of chronic fatigue and neurodivergent traits in a longitudinal birth cohort. […] We found a link between neurodivergent traits in childhood and increased frequency of experiencing chronic disabling fatigue in adolescence, mediated by levels of childhood inflammation. […] Increased inflammation, as indicated by IL-6 levels at age 9 years, provides a potential mechanistic explanation for this link, even when controlling for depression. […] Our findings call for transdiagnostic screening practices to support health and well-being during development and adolescence.

• #32 Childhood neurodivergent traits, inflammation and chronic disabling fatigue in adolescence: a longitudinal case–control study | BMJ Open

  https://bmjopen.bmj.com/content/14/7/e084203

  Children with neurodivergent traits at ages 7 and 9 years were two times as likely to experience chronic disabling fatigue at age 18 years (likely ADHD OR=2.18 (95% CI=1.33 to 3.56); p=0.002; likely autism OR=1.78 (95% CI=1.17 to 2.72); p=0.004). […] Our results indicate higher risk of chronic disabling fatigue for children with neurodivergent traits, likely linked to higher levels of inflammation. […] We aimed to investigate the relationship between increased experiences of chronic fatigue and neurodivergent traits in a longitudinal birth cohort. […] We found a link between neurodivergent traits in childhood and increased frequency of experiencing chronic disabling fatigue in adolescence, mediated by levels of childhood inflammation. […] Increased inflammation, as indicated by IL-6 levels at age 9 years, provides a potential mechanistic explanation for this link, even when controlling for depression. […] Our findings call for transdiagnostic screening practices to support health and well-being during development and adolescence.

• #33 Yale researchers find key to predicting outcomes of autism treatment | Yale News

  https://news.yale.edu/2016/11/15/yale-researchers-find-key-predicting-outcomes-autism-treatment

  Treatments for autism spectrum disorders are varied and costly, and selecting the right one is crucial to long-term outcomes. […] The ability to predict treatment outcomes during a childs preschool years is crucial, say the researchers, because early intervention with effective treatments can dramatically improve outcomes. […] But these neuroimaging biomarkers may help us quickly identify individuals for whom the costly treatments will not work so we can start a more appropriate therapy. […] By studying patterns of activity in an area of the orbitofrontal cortex involved in social processing, the researchers were able to predict which children would respond to therapy. […] Eliminating ineffective treatment can have huge cost savings.

• #34 Yale researchers find key to predicting outcomes of autism treatment | Yale News

  https://news.yale.edu/2016/11/15/yale-researchers-find-key-predicting-outcomes-autism-treatment

  Treatments for autism spectrum disorders are varied and costly, and selecting the right one is crucial to long-term outcomes. […] The ability to predict treatment outcomes during a childs preschool years is crucial, say the researchers, because early intervention with effective treatments can dramatically improve outcomes. […] But these neuroimaging biomarkers may help us quickly identify individuals for whom the costly treatments will not work so we can start a more appropriate therapy. […] By studying patterns of activity in an area of the orbitofrontal cortex involved in social processing, the researchers were able to predict which children would respond to therapy. […] Eliminating ineffective treatment can have huge cost savings.

• #35 Prediction learning in adults with autism and its molecular correlates | Molecular Autism | Full Text

  https://molecularautism.biomedcentral.com/articles/10.1186/s13229-021-00470-6

  Autistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. […] Yet, higher ratios of Glx/GABA+ in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts. […] Overall, these behavioural studies tend to indicate that priors can be learned in ASD, but maybe with a different dynamic, more inflexibility or with decreased abilities in people with ASD or high autistic traits. […] As higher glutamate/GABA ratios in the IFG were associated with worse predictive abilities and that autistic individuals had more glutamate in this region, we can hypothesize the encoding of priors and prediction errors might be altered in the IFG in ASD.

• #36 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. […] Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. […] This suggests that useful digital biomarkers might be extracted from EEG measurements. […] Because atypical brain development that leads to ASD symptoms is likely to precede atypical behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis or screening is based solely on behavioral features. […] A recent fMRI study of 59 6-month-old infants demonstrated significant differences in the brains of children who would develop a diagnosis of ASD at 24 months of age.

• #37 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. […] Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. […] This suggests that useful digital biomarkers might be extracted from EEG measurements. […] Because atypical brain development that leads to ASD symptoms is likely to precede atypical behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis or screening is based solely on behavioral features. […] A recent fMRI study of 59 6-month-old infants demonstrated significant differences in the brains of children who would develop a diagnosis of ASD at 24 months of age.

• #38 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. […] Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. […] This suggests that useful digital biomarkers might be extracted from EEG measurements. […] Because atypical brain development that leads to ASD symptoms is likely to precede atypical behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis or screening is based solely on behavioral features. […] A recent fMRI study of 59 6-month-old infants demonstrated significant differences in the brains of children who would develop a diagnosis of ASD at 24 months of age.

• #39 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. […] Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. […] This suggests that useful digital biomarkers might be extracted from EEG measurements. […] Because atypical brain development that leads to ASD symptoms is likely to precede atypical behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis or screening is based solely on behavioral features. […] A recent fMRI study of 59 6-month-old infants demonstrated significant differences in the brains of children who would develop a diagnosis of ASD at 24 months of age.

• #40 EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach | Scientific Reports

  https://www.nature.com/articles/s41598-018-24318-x

  The goal of this study is to demonstrate that nonlinear analysis of EEG signals, together with pattern classification methods, can extract information as early as 3 months of age that predicts an infant will develop a clinical diagnosis of ASD. […] Furthermore, the severity of ASD symptoms, as measured by the Autism Diagnostic Observation Scale (ADOS), can also be predicted from EEG data taken as early as 3 months of age, with strong correlation to the real ADOS scores that the child has at three years of age. […] The predicted severity scores were significantly correlated with actual scores using EEG measurements taken at 3 months of age and older.

• #41

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. […] Evidence suggests that the best prognosis for ASD currently lies in early targeted intervention aimed to improve later outcome by modifying emergent atypical developmental trajectories. […] Thus, individual prediction of later development of ASD as soon as early signs emerge could help to better target early intervention strategies. […] Prediction of autism may require a multi-measure approach. […] The aim of the present study was to investigate predictive longitudinal differences from 8 to 36 months between infants at low and high familial risk for autism with different developmental outcomes (typical, ASD, atypical).

• #42

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. […] Evidence suggests that the best prognosis for ASD currently lies in early targeted intervention aimed to improve later outcome by modifying emergent atypical developmental trajectories. […] Thus, individual prediction of later development of ASD as soon as early signs emerge could help to better target early intervention strategies. […] Prediction of autism may require a multi-measure approach. […] The aim of the present study was to investigate predictive longitudinal differences from 8 to 36 months between infants at low and high familial risk for autism with different developmental outcomes (typical, ASD, atypical).

• #43

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #44

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #45

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #46

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #47 Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data | Scientific Reports

  https://www.nature.com/articles/s41598-025-90216-8

  Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). […] This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. […] The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. […] The results of this work demonstrate feasibility and potential to identify young children with increased likelihood of developing ASD using a ML model applied to population-based and routinely collected data. […] Our models reported sensitivity of 70.9%, specificity of 56.9%, and AUROC of 69.6% suggest that our ensemble transformer model is a promising candidate for population-based ASD screening.

• #48 Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data | Scientific Reports

  https://www.nature.com/articles/s41598-025-90216-8

  Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). […] This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. […] The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. […] The results of this work demonstrate feasibility and potential to identify young children with increased likelihood of developing ASD using a ML model applied to population-based and routinely collected data. […] Our models reported sensitivity of 70.9%, specificity of 56.9%, and AUROC of 69.6% suggest that our ensemble transformer model is a promising candidate for population-based ASD screening.

• #49 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. […] However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. […] We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. […] Our machine learning model in T3 showed that diagnosis group affected the prognosis. […] In the autism group, older father and mother age; in PDD-NOS group, MR comorbidity, less birth weight and older age at diagnosis have a worse outcome.

• #50 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. […] However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. […] We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. […] Our machine learning model in T3 showed that diagnosis group affected the prognosis. […] In the autism group, older father and mother age; in PDD-NOS group, MR comorbidity, less birth weight and older age at diagnosis have a worse outcome.

• #51 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  In Aspergers Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. […] In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. […] Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. […] The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

• #52 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  In Aspergers Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. […] In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. […] Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. […] The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

• #53 Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

  https://psychiatry-psychopharmacology.com/en/use-of-machine-learning-methods-in-prediction-of-short-term-outcome-in-autism-spectrum-disorders-13366

  In Aspergers Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. […] In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. […] Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. […] The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

• #54 Autism Prediction using Machine Learning | GeeksforGeeks

  https://www.geeksforgeeks.org/autism-prediction-using-machine-learning/

  Here we can conclude that the Young and Toddler group of people have lower chances of having Autism. […] Another amazing observation is that higher the sum score higher the chances of having autism and similarly sum scores less than 5 means person has rare chance of autism. […] From the above accuracies we can say that Logistic Regression and SVC() classifier perform better on the validation data with less difference between the validation and training data. The disease for which there are no diagnostics methods machine learning models are able to predict whether the person has Autism or not. This is where machine learning helps in real-world problems and solving them.

• #55 Autism Prediction using Machine Learning | GeeksforGeeks

  https://www.geeksforgeeks.org/autism-prediction-using-machine-learning/

  Here we can conclude that the Young and Toddler group of people have lower chances of having Autism. […] Another amazing observation is that higher the sum score higher the chances of having autism and similarly sum scores less than 5 means person has rare chance of autism. […] From the above accuracies we can say that Logistic Regression and SVC() classifier perform better on the validation data with less difference between the validation and training data. The disease for which there are no diagnostics methods machine learning models are able to predict whether the person has Autism or not. This is where machine learning helps in real-world problems and solving them.

• #56 Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

  https://pmc.ncbi.nlm.nih.gov/articles/PMC4494609/

  At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). […] The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism. […] The semantic connectivity map obtained by means of the Auto-CM system has identified parent involvement as the main variable that influences the positive outcome of children under treatment; on the other side, no parent involvement is the main factor predicting negative outcomes. […] The identification of these variables represents a core step to respond to the key question what works for whom and thus to pave the way for treatment personalization.

• #57 Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

  https://pmc.ncbi.nlm.nih.gov/articles/PMC4494609/

  At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). […] The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism. […] The semantic connectivity map obtained by means of the Auto-CM system has identified parent involvement as the main variable that influences the positive outcome of children under treatment; on the other side, no parent involvement is the main factor predicting negative outcomes. […] The identification of these variables represents a core step to respond to the key question what works for whom and thus to pave the way for treatment personalization.

• #58 Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

  https://pmc.ncbi.nlm.nih.gov/articles/PMC4494609/

  At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). […] The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism. […] The semantic connectivity map obtained by means of the Auto-CM system has identified parent involvement as the main variable that influences the positive outcome of children under treatment; on the other side, no parent involvement is the main factor predicting negative outcomes. […] The identification of these variables represents a core step to respond to the key question what works for whom and thus to pave the way for treatment personalization.

• #59 Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

  https://pmc.ncbi.nlm.nih.gov/articles/PMC4494609/

  At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). […] The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism. […] The semantic connectivity map obtained by means of the Auto-CM system has identified parent involvement as the main variable that influences the positive outcome of children under treatment; on the other side, no parent involvement is the main factor predicting negative outcomes. […] The identification of these variables represents a core step to respond to the key question what works for whom and thus to pave the way for treatment personalization.

• #60

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #61

  https://link.springer.com/article/10.1007/s10803-018-3509-x

  Our main findings were: (1) clear but small size group effects for Mullen and Vineland scores between LR, HR-ASD, HR-Atypical and HR-Typical outcome groups at 8 and 14 months, and larger group effects at 24 and 36 months; (2) individual prediction of ASD from non-ASD outcome at chance level at 8 months, but at moderate and above chance level (AUC=71.3%) at 14 months; (3) individual prediction of broader atypical development from typical outcome with moderate AUC at 8 and 14 months (approximately 70%); (4) added value of combined measures for prediction of broader atypical from typical outcome, but not for prediction of ASD from non-ASD outcome; and (5) added value of combined time points to prediction for some, but not all measures. […] Despite clear group differences at various levels, individual prediction of ASD using different measures at different time points was still far from optimal. […] Further work is needed to allow a more accurate prediction of the minority class, for instance including data more specific to ASD.

• #62 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  According to a recent influential proposal, several phenotypic features of autism spectrum disorder (ASD) may be accounted for by differences in predictive skills between individuals with ASD and neurotypical individuals. […] These studies suggest that ASD may be associated with differences in the learning of predictive pairings (e.g., learning cause and effect) and in low-level predictive processing in the brain (e.g., processing repeated sounds). […] An important goal for future research will be to better define and constrain the broad domain-general hypothesis by testing multiple types of prediction within the same individuals. […] A vulnerability of any hypothesis that attempts to unify diverse aspects of ASD in cognitive terms lies in its tendency toward reductionism: the ASD diagnosis is shaped by the pragmatic needs of individuals spanning a wide range of genotypes and phenotypes, their families, and the medical professionals who serve them.

• #63 Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8043993/

  According to a recent influential proposal, several phenotypic features of autism spectrum disorder (ASD) may be accounted for by differences in predictive skills between individuals with ASD and neurotypical individuals. […] These studies suggest that ASD may be associated with differences in the learning of predictive pairings (e.g., learning cause and effect) and in low-level predictive processing in the brain (e.g., processing repeated sounds). […] An important goal for future research will be to better define and constrain the broad domain-general hypothesis by testing multiple types of prediction within the same individuals. […] A vulnerability of any hypothesis that attempts to unify diverse aspects of ASD in cognitive terms lies in its tendency toward reductionism: the ASD diagnosis is shaped by the pragmatic needs of individuals spanning a wide range of genotypes and phenotypes, their families, and the medical professionals who serve them.

• #64 Prediction learning in adults with autism and its molecular correlates | Molecular Autism | Full Text

  https://molecularautism.biomedcentral.com/articles/10.1186/s13229-021-00470-6

  Autistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. […] Yet, higher ratios of Glx/GABA+ in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts. […] Overall, these behavioural studies tend to indicate that priors can be learned in ASD, but maybe with a different dynamic, more inflexibility or with decreased abilities in people with ASD or high autistic traits. […] As higher glutamate/GABA ratios in the IFG were associated with worse predictive abilities and that autistic individuals had more glutamate in this region, we can hypothesize the encoding of priors and prediction errors might be altered in the IFG in ASD.

• #65 Prediction learning in adults with autism and its molecular correlates | Molecular Autism | Full Text

  https://molecularautism.biomedcentral.com/articles/10.1186/s13229-021-00470-6

  Autistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. […] Yet, higher ratios of Glx/GABA+ in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts. […] Overall, these behavioural studies tend to indicate that priors can be learned in ASD, but maybe with a different dynamic, more inflexibility or with decreased abilities in people with ASD or high autistic traits. […] As higher glutamate/GABA ratios in the IFG were associated with worse predictive abilities and that autistic individuals had more glutamate in this region, we can hypothesize the encoding of priors and prediction errors might be altered in the IFG in ASD.

• #66 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Treating underlying conditions and related disorders can improve the prognosis for individuals with autism. […] Age at intervention can impact long-term outcomes—research has shown that the earlier a child is treated, the better the prognosis will be. […] However, the majority of people with autism remain affected to some degree in their ability to communicate and socialize. […] […] […] Many researchers, clinicians, and parents have investigated and tracked the efficacy of autism treatments over time. While each individual with autism is different, some treatments have shown positive effects for people with autism. […] […] […] Several co-occurring conditions—called comorbidities by clinicians—have been identified.

• #67 The Long Term Prognosis for Autism | Autism Research Institute

  https://autism.org/autism-prognosis/

  Prognosis for Autism […] If your loved one has been diagnosed with autism spectrum disorder, you may wonder about the likely course of their condition. Will they improve? Will challenging behaviors stop or increase? What can you do to support their development and growth over time? Because every individual with autism is different, there are no universal answers to these questions. However, the prognosis for people with autism has improved over the last two decades as more treatments have been identified and tested. […] The prognosis for a child with autism depends on the severity of their initial symptoms but can be influenced by early intervention and treatment. […] While autism is a lifelong condition, there are now evidence-based treatments that can help and support people with autism.

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