Autistic traits, ADHD symptoms, neurological soft signs and regional cerebral blood flow in adults with autism spectrum disorders

Manouilenko, Irina; Pagani, Marco; Stone‐Elander, Sharon; Odh, Richard; Brolin, Fredrik; Hatherly, Robert; Jacobsson, Hans; Larsson, Stig; Bejerot, Susanne

doi:10.1016/j.rasd.2012.12.008

Cited by 9 publications

(4 citation statements)

References 106 publications

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“…A larger sample will increase statistical power to substantiate results, and measurements in lower functioning ASD children may reveal more significant changes in metabolic levels and associations with symptomatology. In addition, longitudinal measurement may reveal abnormalities in the developmental trajectory of these metabolites, since studies in adults with ASD with typical intelligence showed involvement of the same deep brain structures in the pathophysiology of ASD (Manouilenko, Pagani, Stone-Elander, Odh, & Brolin, 2013). Our investigation was limited to deep brain structures captured in the axial plane where measurements were acquired.…”

Section: Discussionmentioning

confidence: 99%

Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders

Doyle‐Thomas

Card

Soorya

et al. 2014

Research in Autism Spectrum Disorders

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Structural neuroimaging studies in autism report atypical volume in deep brain structures which are related to symptomatology. Little is known about metabolic changes in these regions, and how they vary with age and sex, and/or relate to clinical behaviors. Using magnetic resonance spectroscopy we measured N-acetylaspartate, choline, creatine, myoinositol and glutamate in the caudate, putamen, and thalamus of 20 children with autism and 16 typically developing controls (7-18 years). Relative to controls, individuals with autism had elevated glutamate/creatine in the putamen. In addition, both groups showed age-related increases in glutamate in this region. Boys, relative to girls had increased choline/creatine in the thalamus. Lastly, there were correlations between glutamate, choline, and myoinositol in all three regions, and behavioral scores in the ASD group. These findings suggest changes in deep gray matter neurochemistry, which are sensitive to diagnosis, age and sex, and are associated with behavioral differences.

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Section: Discussionmentioning

confidence: 99%

Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders

Doyle‐Thomas

Card

Soorya

et al. 2014

Research in Autism Spectrum Disorders

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“…Following from early detection, are early intervention programs aimed at remediating this broad array of deficits (Birnbrauer & Leach, 1993;Matson, Mahan, & LoVullo, 2009;Matson & Smiroldo, 1997;Smith, Groen, & Wynn, 2000). However, ASD and the comorbid symptoms that accompany it are quite heterogeneous across individuals (Manouilenko et al, 2013;Vasa et al, 2013). As a result, it is not probable or efficacious to develop a one-size fits all intervention.…”

mentioning

confidence: 99%

Early intervention for autism: Who provides treatment and in what settings

Matson

Konst

2014

Research in Autism Spectrum Disorders

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“…An emerging literature underscores the role of neurological soft signs as markers of brain immaturity across multiple psychopathologies ( Martins et al, 2008 ). Indeed, NSS are frequent in both children with ASD, those with ADHD, and other neurodevelopmental conditions ( Patankar et al, 2012 , Manouilenko et al, 2013 ). Our study did not include a measure of NSS, e.g., Physical And Neurological Examination of Soft Signs ( Camp et al, 1977 ) and to date, the relation between such a measure and RRB metrics is unknown.…”

Section: Discussionmentioning

confidence: 99%

Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Simhal

Filho

Segura

et al. 2021

Developmental Cognitive Neuroscience

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Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6–12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following functional scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.

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Autistic traits, ADHD symptoms, neurological soft signs and regional cerebral blood flow in adults with autism spectrum disorders

Cited by 9 publications

References 106 publications

Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders

Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders

Early intervention for autism: Who provides treatment and in what settings

Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

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