A systematic review of molecular imaging (PET and SPECT) in autism spectrum disorder: Current state and future research opportunities

Zürcher, Nicole R.; Bhanot, Anisha; McDougle, Christopher J.; Hooker, Jacob M.

doi:10.1016/j.neubiorev.2015.02.002

Cited by 74 publications

(57 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, noninvasive positron emission tomography and single-photon emission computed tomography using radioligands could be used to determine the regional specificity, if any, of CNS immune activation, including activation of microglial cells. 35 Radioligandssuch as [11C]PK11195 and the more specific [11C]PBR-28 -binding to 18 kDa translocator protein (TSPO), which is highly expressed by activated microglia, provide a unique opportunity to trace microglial activation in the brain. 35 However, just as performing lumbar puncture has associated risks, so does the use of radioactive ligands, especially in children.…”

Section: Discussionmentioning

confidence: 99%

“…35 Radioligandssuch as [11C]PK11195 and the more specific [11C]PBR-28 -binding to 18 kDa translocator protein (TSPO), which is highly expressed by activated microglia, provide a unique opportunity to trace microglial activation in the brain. 35 However, just as performing lumbar puncture has associated risks, so does the use of radioactive ligands, especially in children.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Elevated plasma concentrations of S100 calcium-binding protein B and tumor necrosis factor alpha in children with autism spectrum disorders

Gülöksüz

Abali

Çetin

et al. 2017

Rev. Bras. Psiquiatr.

View full text Add to dashboard Cite

Objective: To investigate plasma concentrations of S100B (a calcium-binding protein derived primarily from the glia) and inflammatory cytokines in children with autism and the relationship between S100B and cytokine concentrations. Methods: Plasma levels of S100B, tumor necrosis factor alpha (TNF-a), interferon gamma, interleukin (IL)-1b, IL-4, IL-6, IL-10, and IL-17A were measured in 40 unmedicated children with autism and 35 normally developing healthy children. The severity of autism was assessed using the Childhood Autism Rating Scale (CARS). Results: Concentrations of both S100B and TNF-a were higher in children with autism before and after adjusting for a priori-selected confounders (age, sex, and body mass index). S100B concentrations were higher in children with severe autism compared to children with mild-moderate autism. However, this association remained as a trend after adjusting for confounders. S100B concentrations correlated positively with TNF-a concentrations. Conclusion: Our findings showing an increase in peripheral concentrations of S100B and TNF-a provide limited support to the hypothesis about the roles of altered immune function and S100B in autism spectrum disorder (ASD). Studies of larger numbers of well-characterized individuals with ASD are needed to clarify the potential role of the immune system in the pathophysiology of this disorder.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Elevated plasma concentrations of S100 calcium-binding protein B and tumor necrosis factor alpha in children with autism spectrum disorders

Gülöksüz

Abali

Çetin

et al. 2017

Rev. Bras. Psiquiatr.

View full text Add to dashboard Cite

show abstract

“…Equally important, varied ASD electrophysiology findings (Billeci et al 2013) and varied ASD molecular neurochemistry findings (Zürcher et al 2015) stand against the idea that there is any shared single ASD brain impairment in electrophysiology or molecular neurochemistry.…”

Section: Does Asd Have Neurobiological Validity?mentioning

confidence: 99%

“…Moreover, none of these models has accounted for the variation in ASD global and regional brain structures, or the variation in ASD electrophysiology findings (Billeci et al 2013), or the variation in neurochemistry findings (Zürcher et al 2015).…”

Section: Does Asd Have Neurobiological Validity?mentioning

confidence: 99%

ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

View full text Add to dashboard Cite

ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child's condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.Keywords DSM-5 . ASD . Autism . Diagnosis . Validity . Comorbidity . HeterogeneityThe goal of the DSM-3 nosology (American Psychiatric Association 1980) was to create reliable and standard categorical psychiatric diagnoses (Robins and Guze 1970). However, in the past 30 years, clinical, genetics, and neuroscience findings have revealed that the DSM diagnoses are not biologically valid. The National Institutes of Mental Health (NIMH) responded by proposing the Research Domain Criteria (RDoC) framework for a brain-based transdiagnostic psychiatric symptom nosology (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016). Peterson (2015) and Weinberger et al. (2015) argued that the RDoC could not replace the DSM-5 psychiatric nosology (American Psychiatric Association 2013) or the parallel International Classification of Diseases (ICD) psychiatric nosology (World Health Organization 2012). But BFor the foreseeable future, RDoC is not envisioned as a system of psychiatric classification in its own right. Instead, in the near term, RDoC and DSM-ICD are expected to coexist. Nevertheless, RDoC is intended to provide scaffolding for a large-scale research program that will ultimately yield an alternative to DSM-ICD^ (Lilienfeld and Treadway 2016, p. 445).RDoC advocates accept that DSM-5/ICD psychiatric categories remain necessary in clinical practice, but argue that researchers should shift to RDoC study designs immediately. They assert that studying psychiatric categories lacking biological validity blocks the discovery of brain bases for psychopathology and thus cannot lead to effective medical treatments for specific psychiatric symptoms (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016;Yee et al. 2015). Against this RDoC imperative for biological validity, Weinberger et al. (2015) countered that current DSM-5 psychiatric behavioral diagnoses were valid when they yielded effective medical treatment, clear prognosis, and a life course specific to a diagnosis.Autism spectrum disorder (ASD) research has been productive (Dawson 2016;de la Torre-Ubieta et al. 2016;Szatmari et al. 2016), but no ASD research findings have met the validity criteria of Weinberger et al. (2015). DSM-5 ASD research has found no s...

show abstract

“…Single photon emission computed technology (SPECT) is an imaging modality that has been applied to better understand the neuropathology of ASD; an excellent systematic review of this work can be read in Zurcher et al [37]. Since 1992, there have been many studies using SPECT and PET imaging, totaling over 900 ASD patients.…”

Section: Introductionmentioning

confidence: 99%

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

Amen¹,

Sarabi²,

Willeumier³

et al. 2017

J Syst Integr Neurosci

View full text Add to dashboard Cite

The diagnosis of Autism Spectrum Disorder (ASD) relies on history and behavioral observation, lacking reliable biomarkers. We performed a retrospective analysis using machine learning algorithms of 928 persons with ASD (mean age: 17 ± 10.8 years; age range 4-67) obtained from a multisite psychiatric database with rest and on-task brain SPECT scans to investigate whether or not these scans distinguish ASD from healthy controls (HC, n=101; mean age: 43 ± 17.2 years; age range 13-84). Using 128 regions of interest extracts (ROIs), we applied multiple machine learning algorithms for binary classification. Due to an unbalanced sample size between ASD and controls, we then sub-sampled the data prior to feature selection and classification. Using a subsampled dataset, least absolute shrinkage and selection operator (LASSO) feature selection with Random Forest method baseline accuracy results of approximately 81% were achieved, based on optimal classifier settings with the top selected features. We applied machine learning algorithms to ASD adults only, the majority of our sample, and selected subjects in both AD and HC groups with age range of 13-67 years and found the results consistent with the combined data. These machine learning results identified potential diagnostic biomarkers differentiating ASD from HC in the regions of the cerebellum and vermis, anterior cingulate gyrus, amygdala, thalamus, frontal, and temporal lobes.

show abstract

A systematic review of molecular imaging (PET and SPECT) in autism spectrum disorder: Current state and future research opportunities

Cited by 74 publications

References 129 publications

Elevated plasma concentrations of S100 calcium-binding protein B and tumor necrosis factor alpha in children with autism spectrum disorders

Elevated plasma concentrations of S100 calcium-binding protein B and tumor necrosis factor alpha in children with autism spectrum disorders

ASD Validity

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

Contact Info

Product

Resources

About