Structural and functional magnetic resonance imaging of autism spectrum disorders

Stigler, Kimberly A.; McDonald, Brenna C.; Anand, Amit; Saykin, Andrew J.; McDougle, Christopher J.

doi:10.1016/j.brainres.2010.11.076

Cited by 169 publications

(150 citation statements)

References 205 publications

(262 reference statements)

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Section: Does Asd Have Neurobiological Validity?mentioning

confidence: 99%

“…Studies of the amygdala in ASD have reported significantly Bincreased, decreased and preserved volumes^of the amygdala (Bellani et al 2013, p. 3). Similarly, Stigler et al (2011) noted that studies have reported increased, decreased, and typical (preserved) volumes for the fusiform gyrus in ASD.D 'Mello et al (2015) reported that ASD was characterized by reduced gray matter in the cerebellum in lobule VII. However, a consensus paper on the cerebellum in ASD (Fatemi et al 2012) concluded that only a subgroup of ASD had atypical cerebellar anatomy, and this was a smaller cerebellar vermis volume and fewer Purkinje cells.…”

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confidence: 99%

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ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

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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...

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Section: Does Asd Have Neurobiological Validity?mentioning

confidence: 99%

mentioning

confidence: 99%

ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

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“…This technique is commonly referred to as functional connectivity MRI (fcMRI) and it has been used in concert with diffusion imaging to provide new insights into the relationship between function and structure in the human brain (Greicius et al, 2009;Honey et al, 2009;Wang et al, 2013). Further, alterations in functional connectivity are under ongoing investigation for their potential use as a biomarker of a number of diseases, including but not limited to Alzheimer's disease (AD) (Vemuri et al, 2011), schizophrenia (Woodward et al, 2012), and autism (Stigler et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Neuronal and Physiological Correlation to Hemodynamic Resting-State Fluctuations in Health and Disease

Vazquez

Murphy²,

Kim

2014

Brain Connectivity

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Low-frequency, spatially coherent fluctuations present in functional magnetic resonance imaging time series have had a tremendous impact on brain connectomics. This work aims to explore the degree with which hemodynamic connectivity is associated with neuronal, metabolic, and vascular connectivity measures. For this purpose, GCaMP and nontransgenic mice were used to image neuronal activity and oxidative metabolism activity, respectively, along with blood-oxygenation-and cerebral blood volume (CBV)-sensitive hemodynamic changes from the same animals. Although network clusters calculated using either GCaMP (neuronal activity) or optical imaging of intrinsic signal (OIS)-BOLD (blood oxygenation) data did not exhibit strong spatial similarity, the strengths of node-to-node connectivity measured with these modalities were strongly correlated with one another. This finding suggests that hemodynamic connectivity as measured by blood oxygenation measurements, such as functional connectivity magnetic resonance imaging, is a valuable surrogate for the underlying neuronal connectivity. In nontransgenic animals, greater connectivity correlation was observed between tissue oxidative metabolism (flavoprotein autofluorescence imaging [FAI]) and blood oxygenation measurements, suggesting that metabolic contributions to hemodynamic signals are likely responsible for its significant correlation with neuronal connectivity. Lastly, a mouse model of Alzheimer's disease was used to explore the source of decreases in connectivity reported in these mice, a finding that is thought to be associated with amyloid load-driven metabolic decline. The intercluster connectivity measured by metabolic-sensitive measurements (FAI and OIS-BOLD) was maintained while vascular-only signals (OIS-CBV) provided negligible correlation. Therefore, metabolism-sensitive measurements as used in this work are better positioned to capture changes in neuronal connectivity, such that decreases in hemodynamic connectivity likely reflect decreases in oxidative metabolic function.

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“…They also noted that the inconsistencies in the literature might relate to differences in age and IQ, as well as different regions showing abnormal growth trajectories. A review that summarizes the findings from structural MRI studies of human autism has recently been published by Stigler et al [57].…”

Section: Humansmentioning

confidence: 99%

Magnetic Resonance Imaging as a Tool for the Study of Mouse Models of Autism

Ellegood¹,

Henkelman²,

Lerch³

2012

Autism

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Autism is a heterogeneous disorder, in both its behaviour and genetics. This heterogeneity has led to inconsistencies in the neuroanatomical findings in human autistic patients. The benefit of a model system, such as the mouse, is that there could be a decrease in the heterogeneity of the genetics and standardization of the environment could be done, in order to determine a specific anatomical phenotype, which is representative of a specific genotype. Magnetic Resonance Imaging (MRI) has been used quite extensively to examine morphological changes in the mouse brain; however, examining volume and tissue microstructure changes in mouse models of autism with MRI, is just in its infancy. This review will discuss the current research on anatomical phenotyping in mouse models of autism. AutismAnimal Models in Autism

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Structural and functional magnetic resonance imaging of autism spectrum disorders

Cited by 169 publications

References 205 publications

ASD Validity

ASD Validity

Neuronal and Physiological Correlation to Hemodynamic Resting-State Fluctuations in Health and Disease

Magnetic Resonance Imaging as a Tool for the Study of Mouse Models of Autism

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