J Autism Dev Disord DOI 10.1007/s10803-013-2030-5
LETTER TO THE EDITOR
Why Autism Must be Taken Apart Lynn Waterhouse • Christopher Gillberg
Ó Springer Science+Business Media New York 2014
Abstract Although accumulated evidence has demonstrated that autism is found with many varied brain dysfunctions, researchers have tried to find a single brain dysfunction that would provide neurobiological validity for autism. However, unitary models of autism brain dysfunction have not adequately addressed conflicting evidence, and efforts to find a single unifying brain dysfunction have led the field away from research to explore individual variation and micro-subgroups. Autism must be taken apart in order to find neurobiological treatment targets. Three research changes are needed. The belief that there is a single defining autism spectrum disorder brain dysfunction must be relinquished. The noise caused by the thorny brain-symptom inference problem must be reduced. Researchers must explore individual variation in brain measures within autism. Keywords Autism ASD Brain dysfunction DSM-5 Pathophysiology RDoC
Accumulated evidence has demonstrated that autism is found with many varied brain dysfunctions (Allely et al.
L. Waterhouse (&) Global Graduate Programs, Child Behavior Study, The College of New Jersey, Ewing, NJ 08628, USA e-mail: [email protected] L. Waterhouse 73-4434 Aniani St, Kailua Kona, HI 96740, USA C. Gillberg Gillberg Neuropsychiatry Centre, Gothenburg University, Go¨teborg, Sweden e-mail: [email protected]
2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011; Vasa et al. 2012). Moreover, autism has been found with hundreds of risk factors and a myriad of non-diagnostic symptoms (Coleman and Gillberg 2012; Waterhouse 2013). In the face of this heterogeneity, researchers have tried to find a single brain dysfunction that would provide neurobiological validity for autism (Unwin et al. 2013). Recent claims have asserted that DSM-5 Autism Spectrum Disorder (ASD) (American Psychiatric Association 2013) results from early brain overgrowth (Shen et al. 2013), impaired detection of biological motion (Jones and Klin 2013), aberrant connectivity (Aoki et al. 2013), and/or atypical resting state brain activity (Washington et al. 2013). However, conflicting evidence has been reported for typical brain volumes (Chaste et al. 2013; Raznahan et al. 2013), typical regional brain structure and function for biological motion detection (Kell et al. 2013; Saygin et al. 2010), typical connectivity (Redcay et al. 2013), and for typical default mode function in ASD (Tyszka et al. 2013). In addition, the four unitary models have not adequately accounted for the evidence for one another, and have not accounted for other isolated regional ASD brain dysfunctions (Allely et al. 2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011). The underconnectivity model has been preeminent (Di Martino et al. 2013) and has taken many forms. Notably, Peters et al. (2013) reported finding the same pattern of long-range underconnectivity coupled with local cortical over-connection in both idiopathic ASD and syndromic ASD with Tuberous Sclerosis (TSC), but not in TSC without ASD. Idiopathic ASD is defined as having no known etiology, and brain dysfunctions found in idiopathic ASD are presumed to cause ASD symptoms. In syndromic ASD, symptoms are presumed to result from the brain
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dysfunctions specific to the known genetic or medical disorder, such as the tubers and micro-malformations that appear in Tuberous Sclerosis (Marcotte et al. 2012). However, the distinction between idiopathic and syndromic ASD has been blurred by evidence that various brain dysfunctions have been found to overlap or be shared by both idiopathic and syndromic ASD (Murdoch and State 2013). Finding the same underconnectivity for both idiopathic and syndromic ASD (Peters et al. 2013) added evidence to further blur the boundary, and the fact that this pattern of underconnectivity was not found for TSC without ASD was viewed as strong support for the underconnectivity model of ASD (Tye and Bolton 2013). However, Peters et al. (2013) did not explain how this underconnectivity pattern was related to the atypically small cerebellar volumes and reduced number of Purkinje cells often found in idiopathic ASD, in syndromic ASD with TSC, and in TSC without ASD (Fatemi 2013; Schumann and Nordahl 2011; Tsai et al. 2012; Weisenfeld et al. 2013). Postmortem studies and imaging studies have found a wide range of cerebellar abnormalities in idiopathic ASD (Courchesne et al. 1988; Fatemi 2013; Lee et al. 2002; Schumann and Nordahl 2011), and cerebellar abnormalities have been reported in syndromic ASD diagnosed with the 22q11DS (deletion syndrome) (Moreno-de-luca et al. 2013), and for syndromic ASD diagnosed with the valproate syndrome (Roullet et al. 2013). Although cerebellar dysfunction alone has been claimed to be the single unifying idiopathic autism brain deficit (Courchesne et al. 1988; Lee et al. 2002), it is not yet established whether cerebellar dysfunction causes autism symptoms (Courchesne et al. 1988; Fatemi 2013; Lee et al. 2002), or is a more general marker of dysfunction in brain development (Ciesielski and Knight 1994; Ciesielski et al. 1997; Ziats and Rennert 2013). In fact, although ASD is often found with several cooccurring brain dysfunctions, unitary models make claims that address only one dysfunction. For example, ASD with 22q11DS has been found with three significant brain dysfunctions: atypically small brain volume; atypically small cerebellar volume; and impaired cortical connectivity (Jonas et al. 2013). Thus, for ASD with 22q11DS the underconnectivity model ignores global brain volume and atypical cerebellum volume as potential causes of ASD symptoms. Counter to both the unitary cerebellar and unitary underconnectivity theories, for ASD with 22q11DS, atypically small brain volume, atypically small cerebellar volume, and impaired cortical connectivity may all be interlinked contributors to ASD symptoms. Even where a brain dysfunction’s contribution to ASD symptoms has not been elucidated, the dysfunction may not be epiphenomenal. Models of one unifying ASD brain dysfunction also often fail to address the fact that many brain dysfunctions
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found for ASD are not unique to ASD. Underconnectivity and white matter deficits have been reported for schizophrenia, bipolar disorder, and other neuropsychiatric diagnoses (Moreno-de-luca et al. 2013; Skudlarski et al. 2013). Thus underconnectivity per se, is not a brain dysfunction specific to ASD. Licinio and Wong (2013) proposed that because psychiatric diagnoses are defined by behaviors, no diagnosis should be expected to have a unitary brain dysfunction, given that many different brain dysfunctions can yield the same psychiatric symptom, and, conversely, that a single genetic variant or single brain dysfunction may yield many varied behavioral symptoms. Accepting that a psychiatric diagnosis will not map to a single brain dysfunction, however, does not resolve the problem of finding neurobiological treatment targets. Cuthbert and Insel (2013) claimed that no psychiatric diagnosis had neurobiological validity because no diagnosis had been successfully mapped onto a unitary brain dysfunction. They proposed that reconceiving psychiatric disorders as dysfunctions of brain circuits regulating emotion, cognition, and behavior would enhance productivity in the quest for specific neurobiological treatment targets (Cuthbert and Insel 2013). In effect, Cuthbert and Insel (2013) argued for taking apart psychiatric diagnoses to better isolate contributing brain dysfunctions. Insel (2013) announced that the Research Domain Criteria project goal was to conduct research independent of DSM-5 diagnostic categories to find malfunctioning brain circuits. Insel’s translational research goal (2013) has come at a time when the staggering increase in ASD prevalence (Blumberg et al. 2013) has intensified the call for medical treatments for ASD. Unfortunately, with few exceptions, such as hopes for oxytocin (Gordon et al. 2013), or hopes for customized treatments (Delorme et al. 2013), most ASD pharmacotherapy has treated non-diagnostic symptoms of agitation, anxiety, epilepsy, and untoward behaviors (Dove et al. 2012; Doyle and McDougle 2012). Although the ASD diagnosis has clear clinical value, researchers must take ASD apart to identify the many varied single and aggregate brain dysfunctions in order that effective translational research can be conducted. Unfortunately, many researchers continue to describe and study ASD as a unitary syndrome, and those attempting to find a single unifying brain dysfunction have led the field away from research that could explore individual variation and micro-subgroups (Coleman and Gillberg 2012; Waterhouse 2008, 2013). Taking ASD apart requires three changes in research. The first change is relinquishing the belief that a single defining ASD brain dysfunction exists. Kupfer and Regier (2011) claimed that moving from four diagnoses to one ASD diagnosis in DSM-5 increased ASD neurobiological
J Autism Dev Disord
validity because the four prior autism spectrum disorders shared ‘‘a pathophysiological substrate’’ (p. 673). However, decades of autism research have yielded an ever-increasing number of varied brain dysfunctions (Allely et al. 2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011; Vasa et al. 2012), and there is no evidence that the new unitary DSM-5 ASD diagnosis has one pathophysiological substrate (Pina-Camacho et al. 2012). When researchers assume there is a unitary ASD brain dysfunction and conduct ASD-control group comparisons, individual variation within ASD is ignored. The second required research change is to reduce the noise caused by the thorny brain-symptom inference problem (Licinio and Wong 2013): multiple brain dysfunctions can converge on a single symptom, and one risk factor or single brain dysfunction can yield varied symptoms. This difficult reality will not be addressed by largescale subgrouping of ASD, whether by diagnostic severity levels (Horder et al. 2013), or by presence vs. absence of intellectual disability (Vasa et al. 2012) because these subgroups will still include varied brain dysfunctions. The brain-symptom inference problem might be addressed by exploring very narrowly partitioned subgroups. Lai et al. (2013) proposed adding specifiers to DSM-5 ASD identifying individual differences in development, sex, intelligence, language skill, genetic or chromosomal disorders, and comorbid/co-occurring conditions (Lai et al. 2013). Groups formed by shared specifiers within the ASD diagnosis could be studied for a possible shared brain dysfunction. Adding prevalent comorbid symptoms to the DSM-5 ASD criteria might also allow for significantly narrower subgrouping within the diagnosis and increase the likelihood of discovering subgroups with shared brain dysfunctions. A detailed examination, ESSENCE, which would allow clinicians to record many symptoms across a range of neurodevelopmental disorders, would also help identify narrower subgroups within and beyond the ASD diagnosis (Gillberg 2010). The third required research change is to conduct analyses of individual variation in brain measures. Existing large ASD brain structure and function datasets should be explored with non-parametric exploratory data analyses, and with parametric methods that identify individual and micro-group variation. Recently, ASD gene expression datasets were analyzed by a novel pathway outlier method that required researchers to assume a priori the existence of varied ASD brain dysfunctions that arose from separate genetic expression ‘‘pathways for which individuals are outliers’’ (Campbell et al. 2013, p. 1). This data analysis led to evidence for three distinct ASD neuromolecular dysfunction subgroups: ASD resulting from disrupted neuron development; ASD resulting from impaired nitric oxide
signaling; and, ASD resulting from impaired skeletal development pathways (Campbell et al. 2013). While the complex and heterogeneous brain dysfunction basis of ASD guarantees that these three research changes will not bring quick or clear findings, discovery of neurobiological treatment targets in ASD is a research imperative that requires exploring all the varied ASD brain dysfunctions.
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J Autism Dev Disord Tsai, P. T., Chu, Y., Greene-Colozzi, E., Sadowski, A. R., Leech, J. M., Steinberg, J., et al. (2012). Autistic-like behaviour and cerebellar dysfunction in Purkinje cell Tsc1 mutant mice. Nature, 488(7413), 647–651. doi:10.1038/nature11310. Tye, C., & Bolton, P. (2013). Neural connectivity abnormalities in autism: Insights from the tuberous sclerosis model. BMC Medicine, 11, 55. doi:10.1186/1741-7015-11-55. Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2013). Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. Cerebral Cortex,. doi:10. 1093/cercor/bht040. Unwin, L. M., Maybery, M. T., Wray, J. A., & Whitehouse, A. J. (2013). A ‘‘bottom-up’’ approach to aetiological research in autism spectrum disorders. Frontiers in human neuroscience, 7(606), 2013. doi:10.3389%2Ffnhum.00606. Vasa, R. A., Ranta, M., Huisman, T. A., Pinto, P. S., Tillman, R. M., & Mostofsky, S. H. (2012). Normal rates of neuroradiological findings in children with high functioning autism. Journal of
Autism and Developmental Disorders, 42(8), 1662–1670. doi:10. 1007/s10803-001-1407-6. Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., et al. (2013). Dysmaturation of the default mode network in autism. Human Brain Mapping,. doi:10.1002/hbm. 22252. Waterhouse, L. (2008). Autism overflows: Increasing prevalence and proliferating theories. Neuropsychology Review, 18(4), 273–286. doi:10.1007/s11065-008-9074-x. Waterhouse, L. (2013). Rethinking autism: Variation and complexity. Waltham: Academic Press. Weisenfeld, N. I., Peters, J. M., Tsai, P. T., Prabhu, S. P., Dies, K. A., Sahin, M., et al. (2013). A magnetic resonance imaging study of cerebellar volume in tuberous sclerosis complex. Pediatric Neurology, 48(2), 105–110. doi:10.1016/j.pediatrneurol.2012.10.011. Ziats, M. N., & Rennert, O. M. (2013). The cerebellum in autism: Pathogenic or an anatomical beacon? Cerebellum, 12(5), 776–777. doi:10.1007/s12311-013-0483-x.
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LETTER TO THE EDITOR
Why Autism Must be Taken Apart Lynn Waterhouse • Christopher Gillberg
Ó Springer Science+Business Media New York 2014
Abstract Although accumulated evidence has demonstrated that autism is found with many varied brain dysfunctions, researchers have tried to find a single brain dysfunction that would provide neurobiological validity for autism. However, unitary models of autism brain dysfunction have not adequately addressed conflicting evidence, and efforts to find a single unifying brain dysfunction have led the field away from research to explore individual variation and micro-subgroups. Autism must be taken apart in order to find neurobiological treatment targets. Three research changes are needed. The belief that there is a single defining autism spectrum disorder brain dysfunction must be relinquished. The noise caused by the thorny brain-symptom inference problem must be reduced. Researchers must explore individual variation in brain measures within autism. Keywords Autism ASD Brain dysfunction DSM-5 Pathophysiology RDoC
Accumulated evidence has demonstrated that autism is found with many varied brain dysfunctions (Allely et al.
L. Waterhouse (&) Global Graduate Programs, Child Behavior Study, The College of New Jersey, Ewing, NJ 08628, USA e-mail: [email protected] L. Waterhouse 73-4434 Aniani St, Kailua Kona, HI 96740, USA C. Gillberg Gillberg Neuropsychiatry Centre, Gothenburg University, Go¨teborg, Sweden e-mail: [email protected]
2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011; Vasa et al. 2012). Moreover, autism has been found with hundreds of risk factors and a myriad of non-diagnostic symptoms (Coleman and Gillberg 2012; Waterhouse 2013). In the face of this heterogeneity, researchers have tried to find a single brain dysfunction that would provide neurobiological validity for autism (Unwin et al. 2013). Recent claims have asserted that DSM-5 Autism Spectrum Disorder (ASD) (American Psychiatric Association 2013) results from early brain overgrowth (Shen et al. 2013), impaired detection of biological motion (Jones and Klin 2013), aberrant connectivity (Aoki et al. 2013), and/or atypical resting state brain activity (Washington et al. 2013). However, conflicting evidence has been reported for typical brain volumes (Chaste et al. 2013; Raznahan et al. 2013), typical regional brain structure and function for biological motion detection (Kell et al. 2013; Saygin et al. 2010), typical connectivity (Redcay et al. 2013), and for typical default mode function in ASD (Tyszka et al. 2013). In addition, the four unitary models have not adequately accounted for the evidence for one another, and have not accounted for other isolated regional ASD brain dysfunctions (Allely et al. 2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011). The underconnectivity model has been preeminent (Di Martino et al. 2013) and has taken many forms. Notably, Peters et al. (2013) reported finding the same pattern of long-range underconnectivity coupled with local cortical over-connection in both idiopathic ASD and syndromic ASD with Tuberous Sclerosis (TSC), but not in TSC without ASD. Idiopathic ASD is defined as having no known etiology, and brain dysfunctions found in idiopathic ASD are presumed to cause ASD symptoms. In syndromic ASD, symptoms are presumed to result from the brain
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dysfunctions specific to the known genetic or medical disorder, such as the tubers and micro-malformations that appear in Tuberous Sclerosis (Marcotte et al. 2012). However, the distinction between idiopathic and syndromic ASD has been blurred by evidence that various brain dysfunctions have been found to overlap or be shared by both idiopathic and syndromic ASD (Murdoch and State 2013). Finding the same underconnectivity for both idiopathic and syndromic ASD (Peters et al. 2013) added evidence to further blur the boundary, and the fact that this pattern of underconnectivity was not found for TSC without ASD was viewed as strong support for the underconnectivity model of ASD (Tye and Bolton 2013). However, Peters et al. (2013) did not explain how this underconnectivity pattern was related to the atypically small cerebellar volumes and reduced number of Purkinje cells often found in idiopathic ASD, in syndromic ASD with TSC, and in TSC without ASD (Fatemi 2013; Schumann and Nordahl 2011; Tsai et al. 2012; Weisenfeld et al. 2013). Postmortem studies and imaging studies have found a wide range of cerebellar abnormalities in idiopathic ASD (Courchesne et al. 1988; Fatemi 2013; Lee et al. 2002; Schumann and Nordahl 2011), and cerebellar abnormalities have been reported in syndromic ASD diagnosed with the 22q11DS (deletion syndrome) (Moreno-de-luca et al. 2013), and for syndromic ASD diagnosed with the valproate syndrome (Roullet et al. 2013). Although cerebellar dysfunction alone has been claimed to be the single unifying idiopathic autism brain deficit (Courchesne et al. 1988; Lee et al. 2002), it is not yet established whether cerebellar dysfunction causes autism symptoms (Courchesne et al. 1988; Fatemi 2013; Lee et al. 2002), or is a more general marker of dysfunction in brain development (Ciesielski and Knight 1994; Ciesielski et al. 1997; Ziats and Rennert 2013). In fact, although ASD is often found with several cooccurring brain dysfunctions, unitary models make claims that address only one dysfunction. For example, ASD with 22q11DS has been found with three significant brain dysfunctions: atypically small brain volume; atypically small cerebellar volume; and impaired cortical connectivity (Jonas et al. 2013). Thus, for ASD with 22q11DS the underconnectivity model ignores global brain volume and atypical cerebellum volume as potential causes of ASD symptoms. Counter to both the unitary cerebellar and unitary underconnectivity theories, for ASD with 22q11DS, atypically small brain volume, atypically small cerebellar volume, and impaired cortical connectivity may all be interlinked contributors to ASD symptoms. Even where a brain dysfunction’s contribution to ASD symptoms has not been elucidated, the dysfunction may not be epiphenomenal. Models of one unifying ASD brain dysfunction also often fail to address the fact that many brain dysfunctions
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found for ASD are not unique to ASD. Underconnectivity and white matter deficits have been reported for schizophrenia, bipolar disorder, and other neuropsychiatric diagnoses (Moreno-de-luca et al. 2013; Skudlarski et al. 2013). Thus underconnectivity per se, is not a brain dysfunction specific to ASD. Licinio and Wong (2013) proposed that because psychiatric diagnoses are defined by behaviors, no diagnosis should be expected to have a unitary brain dysfunction, given that many different brain dysfunctions can yield the same psychiatric symptom, and, conversely, that a single genetic variant or single brain dysfunction may yield many varied behavioral symptoms. Accepting that a psychiatric diagnosis will not map to a single brain dysfunction, however, does not resolve the problem of finding neurobiological treatment targets. Cuthbert and Insel (2013) claimed that no psychiatric diagnosis had neurobiological validity because no diagnosis had been successfully mapped onto a unitary brain dysfunction. They proposed that reconceiving psychiatric disorders as dysfunctions of brain circuits regulating emotion, cognition, and behavior would enhance productivity in the quest for specific neurobiological treatment targets (Cuthbert and Insel 2013). In effect, Cuthbert and Insel (2013) argued for taking apart psychiatric diagnoses to better isolate contributing brain dysfunctions. Insel (2013) announced that the Research Domain Criteria project goal was to conduct research independent of DSM-5 diagnostic categories to find malfunctioning brain circuits. Insel’s translational research goal (2013) has come at a time when the staggering increase in ASD prevalence (Blumberg et al. 2013) has intensified the call for medical treatments for ASD. Unfortunately, with few exceptions, such as hopes for oxytocin (Gordon et al. 2013), or hopes for customized treatments (Delorme et al. 2013), most ASD pharmacotherapy has treated non-diagnostic symptoms of agitation, anxiety, epilepsy, and untoward behaviors (Dove et al. 2012; Doyle and McDougle 2012). Although the ASD diagnosis has clear clinical value, researchers must take ASD apart to identify the many varied single and aggregate brain dysfunctions in order that effective translational research can be conducted. Unfortunately, many researchers continue to describe and study ASD as a unitary syndrome, and those attempting to find a single unifying brain dysfunction have led the field away from research that could explore individual variation and micro-subgroups (Coleman and Gillberg 2012; Waterhouse 2008, 2013). Taking ASD apart requires three changes in research. The first change is relinquishing the belief that a single defining ASD brain dysfunction exists. Kupfer and Regier (2011) claimed that moving from four diagnoses to one ASD diagnosis in DSM-5 increased ASD neurobiological
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validity because the four prior autism spectrum disorders shared ‘‘a pathophysiological substrate’’ (p. 673). However, decades of autism research have yielded an ever-increasing number of varied brain dysfunctions (Allely et al. 2013; Levitt et al. 2013; Philip et al. 2012; Silver and Rapin 2012; Stigler et al. 2011; Vasa et al. 2012), and there is no evidence that the new unitary DSM-5 ASD diagnosis has one pathophysiological substrate (Pina-Camacho et al. 2012). When researchers assume there is a unitary ASD brain dysfunction and conduct ASD-control group comparisons, individual variation within ASD is ignored. The second required research change is to reduce the noise caused by the thorny brain-symptom inference problem (Licinio and Wong 2013): multiple brain dysfunctions can converge on a single symptom, and one risk factor or single brain dysfunction can yield varied symptoms. This difficult reality will not be addressed by largescale subgrouping of ASD, whether by diagnostic severity levels (Horder et al. 2013), or by presence vs. absence of intellectual disability (Vasa et al. 2012) because these subgroups will still include varied brain dysfunctions. The brain-symptom inference problem might be addressed by exploring very narrowly partitioned subgroups. Lai et al. (2013) proposed adding specifiers to DSM-5 ASD identifying individual differences in development, sex, intelligence, language skill, genetic or chromosomal disorders, and comorbid/co-occurring conditions (Lai et al. 2013). Groups formed by shared specifiers within the ASD diagnosis could be studied for a possible shared brain dysfunction. Adding prevalent comorbid symptoms to the DSM-5 ASD criteria might also allow for significantly narrower subgrouping within the diagnosis and increase the likelihood of discovering subgroups with shared brain dysfunctions. A detailed examination, ESSENCE, which would allow clinicians to record many symptoms across a range of neurodevelopmental disorders, would also help identify narrower subgroups within and beyond the ASD diagnosis (Gillberg 2010). The third required research change is to conduct analyses of individual variation in brain measures. Existing large ASD brain structure and function datasets should be explored with non-parametric exploratory data analyses, and with parametric methods that identify individual and micro-group variation. Recently, ASD gene expression datasets were analyzed by a novel pathway outlier method that required researchers to assume a priori the existence of varied ASD brain dysfunctions that arose from separate genetic expression ‘‘pathways for which individuals are outliers’’ (Campbell et al. 2013, p. 1). This data analysis led to evidence for three distinct ASD neuromolecular dysfunction subgroups: ASD resulting from disrupted neuron development; ASD resulting from impaired nitric oxide
signaling; and, ASD resulting from impaired skeletal development pathways (Campbell et al. 2013). While the complex and heterogeneous brain dysfunction basis of ASD guarantees that these three research changes will not bring quick or clear findings, discovery of neurobiological treatment targets in ASD is a research imperative that requires exploring all the varied ASD brain dysfunctions.
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