Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

Available online at www.sciencedirect.com

Physics of Life Reviews 8 (2011) 410–437 www.elsevier.com/locate/plrev

Review

Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders Rajesh K. Kana a,∗ , Lauren E. Libero a , Marie S. Moore b a Department of Psychology, University of Alabama at Birmingham, CIRC 235G, 1719 6th Avenue South, Birmingham, AL 35294, United States b Department of Psychology, University of Alabama, Box 870348, Tuscaloosa, AL 35487-0348, United States

Received 28 September 2011; accepted 9 October 2011 Available online 13 October 2011 Communicated by L. Perlovsky

Abstract Recent findings of neurological functioning in autism spectrum disorder (ASD) point to altered brain connectivity as a key feature of its pathophysiology. The cortical underconnectivity theory of ASD (Just et al., 2004) provides an integrated framework for addressing these new findings. This theory suggests that weaker functional connections among brain areas in those with ASD hamper their ability to accomplish complex cognitive and social tasks successfully. We will discuss this theory, but will modify the term underconnectivity to ‘disrupted cortical connectivity’ to capture patterns of both under- and over-connectivity in the brain. In this paper, we will review the existing literature on ASD to marshal supporting evidence for hypotheses formulated on the disrupted cortical connectivity theory. These hypotheses are: 1) underconnectivity in ASD is manifested mainly in long-distance cortical as well as subcortical connections rather than in short-distance cortical connections; 2) underconnectivity in ASD is manifested only in complex cognitive and social functions and not in low-level sensory and perceptual tasks; 3) functional underconnectivity in ASD may be the result of underlying anatomical abnormalities, such as problems in the integrity of white matter; 4) the ASD brain adapts to underconnectivity through compensatory strategies such as overconnectivity mainly in frontal and in posterior brain areas. This may be manifested as deficits in tasks that require frontal–parietal integration. While overconnectivity can be tested by examining the cortical minicolumn organization, long-distance underconnectivity can be tested by cognitively demanding tasks; and 5) functional underconnectivity in brain areas in ASD will be seen not only during complex tasks but also during task-free resting states. We will also discuss some empirical predictions that can be tested in future studies, such as: 1) how disrupted connectivity relates to cognitive impairments in skills such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD. Published by Elsevier B.V. Keywords: Functional connectivity; Autism; fMRI; Theory-of-Mind; Cognitive flexibility; Processing speed; Triad of impairments

Abbreviations: ASD, Autism Spectrum Disorder; fMRI, functional Magnetic Resonance Imaging; ToM, Theory-of-Mind; DTI, Diffusion Tensor Imaging; PET, Positron Emission Tomography; FA, Fractional Anisotropy; ACC, Anterior Cingulate Cortex; PCC, Posterior Cingulate Cortex; EEG, Electroencephalogram; MEG, Magnetoencephalogram; MPFC, Medial Prefrontal Cortex; DLPFC, Dorsolateral Prefrontal Cortex; GABA, Gamma Aminobutyric Acid; TPJ, Temporoparietal Junction; STS, Superior Temporal Sulcus. * Corresponding author. Tel.: +1 205 934 3171; fax: +1 205 975 6330. E-mail address: [email protected] (R.K. Kana). 1571-0645/$ – see front matter Published by Elsevier B.V. doi:10.1016/j.plrev.2011.10.001

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

411

1. Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impairments in social interaction, language and communication, and restricted interests and repetitive stereotyped behaviors [1]. The Center for Disease Control (CDC) estimates the prevalence of ASD to be 1 in 110 children, affecting all racial, ethnic, and socioeconomic groups equally, with 4–5 times greater prevalence in boys than in girls [2]. Individuals with ASD often display challenging problem behaviors, such as self-injury, aggression, tantrums, stereotypies (repetitive, purposeless movements or utterances), anxiety, sleep disturbances, problems with feeding, and non-compliance with directions [3]. They may also display odd, repetitive behaviors, such as hand flapping, tip-toe walking, body rocking, echolalia (automatic repetition of others’ speech), or spinning objects [4,5]. It is estimated that around 15% of individuals diagnosed with ASD will become reasonably self-sufficient by adulthood, while another 15–20% will function well with sporadic support [6]. ASD is often co-morbid with other disorders, such as attention deficit hyperactivity disorder (ADHD) and seizures, and 50–75% of individuals with ASD also have an intellectual disability [7,8]. The autism spectrum refers to several disorders including classic autism, high-functioning autism, Asperger Syndrome, Rett Syndrome, Childhood Disintegrative Disorder, and Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). While all of these disorders share similar developmental characteristics, the discussion in this paper will mostly focus on classic autism, high-functioning autism, and Asperger Syndrome. Autism was first described by Leo Kanner in 1943, in a case description of 11 children who demonstrated a lack of social engagement, absence of language, and a strong need for sameness [9]. By 1978, the criteria for ASD were refined to include the triad of impairments currently used today: 1) impairments in social interaction, 2) impaired communication, and 3) restricted and repetitive behaviors and interests [10,11]. ASD is currently diagnosed based on this behavioral phenotype according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-R) [12], with patients meeting criteria by demonstrating symptoms in all three areas by the age of 3 years. The most commonly used tools for diagnosis are the Autism Diagnostic Observation Schedule-Generic (ADOS-G) [13] and the Autism Diagnostic Interview-Revised (ADI-R) [14]. While the behavioral manifestation of ASD spans social, communicative, and cognitive domains, making it quite an intriguing disorder, such breadth of symptoms has made it difficult to discover a unifying causal explanation. Although it has been established that ASD is a disorder with genetic and neurobiological origin, the quest for a single gene or a focal brain area to explain its origin has been elusive. Instead, genetic research has pointed to hundreds of genes with links to ASD [15], and neurobiological investigations have implicated several brain areas as abnormal in ASD [16]. As the quest for causal explanations of ASD continues, designing effective and early interventions to improve the lives of affected individuals is equally or more important. Empirical evidence suggests that children with ASD who enter programs at younger ages make greater gains than those who enter programs at older ages [17,18]. At present, behavioral therapy is used to improve functioning and independent living skills, and language interventions are implemented to improve communication. On the pharmacological front, psychotropic medications and other drugs are often prescribed to those with ASD to reduce anxiety and aggression toward the self or others. While these medications may reduce negative behaviors, they do not treat the core symptoms of ASD. The difficulties that people with ASD experience are usually manifested in interpersonal interactions and complex cognition. The triad of impairments that defines ASD is a good starting point for studying behavioral differences, tracing autism back to its causes, and finding appropriate solutions. Social impairment is the hallmark of ASD and may include issues with social referencing, difficulty initiating and responding to social cues, difficulty initiating and maintaining eye contact, failing to engage in joint attention, or displaying inappropriate emotional reactions [19–23]. As a result of their difficulties with social interactions, children with ASD are often rejected by peers, have difficulty achieving success in school, and may develop other mental health problems [24,25]. While social difficulties in ASD are striking, such difficulties are also intertwined with problems in language and communication. It is estimated that about 20–50% of children with ASD may not develop functional speech [26,27]. Of those who do, some children with ASD will develop speech late in childhood [28]. For individuals who have reasonably good language, the problem lies in using language in social communication or in dealing with the pragmatic aspects of language [27,29,30]. A relatively under-researched component of the triad of impairments in ASD is the one involving repetitive and restricted behaviors and interests. Persons with ASD may have restricted, narrow interests, form odd object attachments (such as with a household broom instead of a teddy bear), or have an unusual preoccupation with objects (e.g., repeatedly spinning the wheel of a toy car) [31]. They may also display

412

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

sensory processing issues such as hyper- or hyposensitivity [32,33]. For example, children with ASD may engage in self-injurious behavior with a seemingly high pain threshold, or they may become extremely upset in a crowded, noisy room. Individuals with ASD may also engage in sensory-seeking behaviors [32,33]. In sum, the constellation of behavioral symptoms featured under the triad of impairments constitutes ASD. Throughout this paper, we will revisit the “triad” from behavioral, cognitive, and neuroscience perspectives. While it is important to “connect the dots” between behavioral and cognitive variables in ASD, perhaps even more interesting is the complex nature of the disorder, which in turn makes it so challenging to study. For instance, heterogeneity is a marked feature in the manifestation of ASD. Individuals with ASD exhibit unique behaviors and have varied symptom severity in relation to the three distinct areas of impairment. The heterogeneous manifestation of the disorder poses a challenge to researchers, clinicians, and educators. Another element of complexity is the high rate of comorbidity of ASD with intellectual disability, ADHD, and seizures, making it difficult to tease apart the sole contributors to ASD as opposed to the effects of other disorders. Yet another element of complexity in ASD pertains to the absence of a biological marker for the disorder, despite a rather well established biological origin. Although modern neuroimaging techniques have presented a promising avenue for discovering a neurological marker for ASD, many questions remain unanswered. One possible reason for this is that these approaches have relied heavily on finding a focal brain abnormality to explain ASD. Given the breadth and depth of symptoms of ASD, it may be more intuitive to expect a systems-level neural system abnormality, rather than a focal one. Following this reasoning, in this paper, we will use the cortical underconnectivity theory of ASD [34] as the framework for addressing the problems in ASD. 1.1. Cortical underconnectivity theory of ASD Functional specialization and functional integration are said to be the two fundamental principles of brain organization [35]. Areas, regions and subregions of brain, individually, may have vital roles in mediating certain functions or aspects of certain functions suggesting functional specialization. In addition, certain functions may also be mediated by the collective effort of different areas, regions, and subregions, suggesting functional integration. A delicate balance between specialization and integration during brain development may prove critical in brain organization and brain functioning. One of the ways to quantify functional integration among brain areas is to examine the synchronization of activation between two brain areas through correlation. This measure is known as functional connectivity, which refers to the temporal correlation between spatially remote neurophysiological events [35]. In other words, functional connectivity is the mechanism for the synchronization of the time course of brain activation in order to accomplish complex cognitive tasks. Since it provides a systems-level approach to study brain functioning, the applicability of functional connectivity methods to study disorders is quite significant. For example, connection abnormalities, as opposed to focal regional abnormalities, may provide valuable insights into explaining the widespread symptomatology of developmental disorders like ASD. Evidence for disrupted connectivity of neural networks in ASD was first presented in a Positron Emission Tomography (PET) study [36]. They found that during resting state, a group of individuals with ASD exhibited weaker correlations between glucose metabolic levels in frontal and posterior brain regions than the comparison group of typically developing adults. Another study [37] used Single Photon Emission Computed Tomography (SPECT) and found delayed maturation of frontal lobe circuitry in young children with ASD, relative to control participants. While the importance of these findings cannot be neglected, it should be noted that these are coarser measures to look at brain connectivity. With the advent of functional MRI, measurements of connectivity became more refined, and the first influential formulation of connection abnormalities in ASD was provided by Just and colleagues in 2004 in their account of the cortical underconnectivity theory of ASD. Cortical underconnectivity theory [34], proposed to explain the brain bases of cognitive functioning in ASD, mainly addresses how complex cognitive functions are accomplished by the coordination and communication among cortical areas, and how such communication may be compromised in individuals with ASD. Since the initial description of widespread underconnectivity in a language comprehension task in ASD [34], there have been many reports of similar findings suggesting that connection abnormalities may help explain cognition in ASD. Recent reports point to cortical underconnectivity in ASD between: dorsolateral prefrontal cortex and inferior parietal lobule during problemsolving [38], frontal cortex and fusiform gyrus during working memory for faces [39], inferior frontal gyrus and inferior parietal lobule during sentence imagery [40], medial frontal and temporoparietal regions during Theory-of-

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

413

Fig. 1. Representation of selected results (from previous studies) of underconnectivity among a set of regions in individuals with ASD. The dashed lines between regions indicate underconnectivity (MPFC: Medial Prefrontal Cortex; ACC: Anterior Cingulate Cortex; LIFG: Left Inferior Frontal Gyrus; RIFG: Right Inferior Frontal Gyrus; LTPJ: Left Temporoparietal Junction; RTPJ: Right Temporoparietal Junction; LIPL: Left Inferior Parietal Lobule; RIPL: Right Inferior Parietal Lobule).

Mind [41,42], cingulate cortex and parietal areas during response inhibition [43], and between frontal and temporal areas during discourse processing [44] (see Fig. 1). Although these studies report underconnectivity among many brain areas, a relatively consistent finding is the underconnectivity between frontal and parietal areas (see [45,46] for reviews), perhaps suggesting that the coordination between frontal and parietal areas may be critical in accomplishing many higher cognitive functions. Several other recent studies also provide supporting evidence for underconnectivity in ASD [47–54]. In addition, the underconnectivity theory provides striking parallel to several social cognitive theories of ASD (see [38]). Yet another line of supporting evidence for cortical underconnectivity in ASD comes from anatomical studies of volume, density, and cellular organization of the brain. It should also be noted that, of late, studies have provided some evidence of overconnectivity also in individuals with autism, suggesting widespread alterations in brain connectivity. Taking recent findings into consideration, in the sections below, we will incorporate weaker as well as stronger and intact connectivity in the underconnectivity model. Therefore, the phrase we use to refer to the underconnectivity account in this paper is “disrupted cortical connectivity”. We propose a multimodal investigation of the brain, focusing on the disrupted connectivity model for a more comprehensive explanation of ASD. Because of the widespread symptomatology of ASD, it is not feasible to discuss every single symptom at the cognitive and neural level in this paper. Thus, our focus will be on three critical cognitive skills, and the problems associated with them, which, we believe, may drive many behavioral impairments noted in people with ASD. These cognitive skills are: 1) Theory-of-Mind (ToM, the ability to attribute mental states to oneself and to others), 2) Cognitive flexibility, and 3) Processing speed. Focusing on these skills, we will discuss cognitive functioning in ASD with the goal of predicting the implications of cognitive deficits at the behavioral level as well as at the neural level. As previously mentioned, disrupted cortical connectivity and the triad of impairments will be unifying themes throughout the paper. Using these themes, we will describe hypotheses and discuss supporting evidence for each, as well as make predictions to be experimentally verified by future research. 2. Theory-of-Mind Successfully navigating the social world involves understanding, reacting to, and predicting one’s own and others’ actions. This is accomplished by making inferences about thoughts, beliefs, desires, and intentions of others. ToM

414

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

refers to the ability to attribute such mental states to oneself and others. It enables us to decipher the emotions, beliefs, and goals of others, and it has a significant impact on our social interactions and communications. However, individuals with ASD have impaired or limited ToM skills [55–62]. In tests of ToM abilities, such as false belief tasks, Strange Stories, and Faux-Pas, children and adults with ASD show significantly worse performance than their typically-developing peers [63–65]. In addition, it has been found that individuals with ASD are developmentally delayed in acquiring mentalizing abilities [66–69]. ToM impairment in ASD is so salient that one of the leading theoretical models of ASD, the Mindblindness theory, is based on impairment in ToM in this population [55,70–73]. While the significance of the ToM model in explaining all symptoms of ASD is debatable, impairment in ToM does have a detrimental effect on social cognition, and it carries its effects into other domains of cognitive functioning for persons with ASD. In the subsections below, we discuss the impact of ToM impairment on behavior in individuals with ASD as well as the potential sources of such problems. 2.1. Behavioral manifestations One of the classic behavioral symptoms that almost anyone would notice when talking to a person with ASD is his/her difficulty in maintaining eye contact. Usually there is hardly any eye contact, but in some instances there is a piercing look through the eyes. Research has demonstrated that infants with ASD have impaired eye contact, specifically demonstrating poor quality of eye contact and differential timing of eye contact [74,75]. In addition, children with ASD make more lateral glances than typically developing children [76]. Lack of eye contact may lead to a cascade of issues related to gaze, joint attention, face processing, reading emotions, and interpreting what others are thinking, ultimately affecting social interaction as a whole. It can be argued that ToM is essential for the appropriate functioning of many of these skills. For instance, from a social motivational point of view, people with ASD may lack the motivation for initiating or participating in social interaction, hence no desire to maintain eye contact [77]. People often convey information about their thoughts and feelings through eye contact, eye movements, and eye gaze. Failure to read these cues may impede one’s ability to mentalize and engage in successful interpersonal interactions. Perhaps as a consequence of their poor eye contact and altered glances, children with ASD also fail to use the eye gaze of others as a source of information. Directed and averted eye gaze provide valuable clues to infer another person’s potentially different intentions or goals. However, spontaneous gaze following is impaired in ASD [78,79], and individuals with ASD fail to interpret gaze and head movements as an indicator of someone’s attention to a specific location [55]. Moreover, individuals with ASD display reduced interest toward aspects of their social environment as compared to typical peers, and they have a low tendency to follow others’ gaze [80]. Children with ASD fail to orient to social cues and attention-seeking cues [81], and two-year-old infants with ASD demonstrate less frequent joint attention behaviors when compared with typically developing peers [82]. Because of the deficits in ToM skills in children with ASD, it is possible that these children have limited, if any, interest in attending to social cues, from the start. With impaired ToM, it may be no surprise that children with ASD also show poor imitation of goal-oriented behaviors [83,20,84–87]. It is evident that individuals with ASD exhibit many differences in their social behavior through altered eye contact and glances, failure to respond to join attention, deviant face-processing, and so on. The common factor underlying these behaviors is an impaired ToM. With an inability to process what others are thinking and feeling, individuals with ASD may cease to look for clues from social behaviors such as eye gaze or joint attention. 2.2. Deficits in ToM and the triad of impairments in ASD Problems with social interactions form one piece of the triad of impairments that defines ASD, and most of these social difficulties are the result of an underlying deficit in ToM. For example, the ability to understand others’ mental states and respond to them in an appropriate way is critical in social behaviors, such as empathizing. Children with ASD often lack empathy for others or display inappropriate emotional expression in response to others. A robust ToM is also critical in making causal attributions, especially intentional, to human actions. We rely on reading others’ cues to determine their thoughts, plans, and feelings so that we can respond to their actions appropriately. Because there are problems with this ability in children with ASD, it comes as no surprise that individuals with ASD often fail in complex interpersonal interactions. An impaired ToM may pose difficulty for reading whether other children are interested in playing with them or are enjoying a shared game. Missing the social cues their peers display, children with ASD may pester other children when it should be obvious that they are uninterested in playing with them, or they

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

415

may completely ignore other children’s attempts at sharing. In addition, without a well-functioning ToM, individuals with ASD may make inappropriate social responses to others’ queries. For example, when a first-time mother asks whether a person with ASD would like to see her baby pictures, the person may reply with “no”, whereas the socially appropriate behavior would be to agree in order to avoid offending the mother. Impairment in ToM likely affects other areas of functioning as well, such as language and communication [88,89], another element of the triad in ASD. Such problems are pronounced in the social use of language and in understanding figures of speech such as idioms, metaphors, irony, puns, and sarcasm. The difficulty here lies in the ability to understand that what is uttered by a speaker is not what is intended. It has been found that individuals with ASD perform poorly in recognizing mental state terms [90], and they seldom utilize mental state verbs in their spontaneous speech [91]. One study indicated that ToM skills in children with ASD were related to everyday social and conversational applications [92]. In addition, ToM scores of children were found to be related to their level of socialization (a measure on the Vineland Adaptive Behavior Scale), as well as social and communication symptom severity scores on the ADOS [93]. So, those children who are less adept at reading the minds of others may be more severely impaired on the social and communication areas of the triad. In addition, ToM predicts ASD children’s social maturity [94], and children with ASD who have better ToM skills also display improved eye-reading behavior [95]. Non-literal, or figurative, language is that which involves an intended meaning that is usually different from the literal meaning of the words. Figurative language can be used socially to express humor or sarcasm. Since the understanding of non-literal language requires one to understand both reality as well as the speaker’s intent, this is a form of language which requires ToM. It has been found that children’s ability to understand figurative language is related to their ability to pass a false belief ToM task [96]. Development of false belief, an understanding that people can have beliefs about the world that are different from reality, is a critical step in the development of ToM. Thus, it comes as no surprise that people with ASD have difficulty understanding non-literal language; specifically, irony, metaphors, indirect requests, deception, faux pas, and jokes [97–104,67,105–111]. In addition, individuals with ASD are more likely to enjoy simple jokes and slapstick humor [112], which may not require ToM. Meanwhile, they have difficulty understanding social humor and the social functions of figurative language [113,114], laugh less in response to socially inappropriate acts [115], and miss a speaker’s intent to make a joke [116]. ToM requires the maintenance of an event within working memory, inhibition of one’s own knowledge of reality or beliefs about a situation, and planning [117], all skills that are part of our higher cognitive or executive functions. The relationship between executive functions (especially inhibition, mental flexibility, and working memory) and ToM has been well demonstrated in typically-developing children [118,119,118,120–123]. However, children with ASD are noted to have impairments in some components of executive functions (for review, see [124]). Since solving ToM tasks requires the participant to mentally switch to think from the mental perspective of another person, it is logical that persons with ASD, who have dysfunctional executive functions, will have difficulty passing these tests. Research has indicated that ToM is significantly correlated with inhibitory control in children with ASD [125], and executive function skills are predictive of ToM skills in children with ASD [126]. Another one of the triad of impairments, repetitive behavior and restricted interests, may be the result of a faulty cognitive filter (a mechanism that allows one to select responses, switch from a task, and monitor one’s performance) in ASD. As a result, a person with ASD may engage in repetition of actions or phrases, adhere to strict routines, and have restricted interests. While ToM may not be directly related to these behaviors, it plays an indirect role through executive functions. Individuals with ASD commonly engage in a conversation about a topic of their interest with total disregard for their listener’s lack of attention. This disregard may result from a lack of understanding of the listener’s mind due to ToM deficits. It may also come as a consequence of executive dysfunction. Without a good grasp of what others are thinking, it is easy for these individuals to miss the meaning of their listener’s gestures, such as checking his/her watch or becoming restless, and instead continue talking about the same thing. It is also common for the individual with ASD to perseverate on a topic, even when the listener verbally expresses his/her distaste for the topic at hand. 2.3. Neural differences and ToM in persons with ASD Several brain areas have been implicated in ToM processing, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and the posterior superior temporal sulcus (pSTS) at the temporoparietal junction (TPJ) [127–131]. Recent functional magnetic resonance imaging (MRI) studies have found that individuals with ASD fail

416

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

to activate these functionally specialized regions during ToM tasks [99,57,42,132,41,48]. Although the MPFC and TPJ have been implicated primarily in processing ToM, the relative role of each region is a topic of debate. Adults with ASD have atypical MPFC activation patterns for judging the emotional states of others [133]. In addition, adults with ASD do not differentially activate the pSTS for others’ gaze shifts [132], and, compared to typically developing peers, they have decreased activation in frontal areas when judging others’ emotion states based on images of their eyes [99]. Also, studies using electroencephalography (EEG) and magnetoencephalography (MEG) methods have found abnormal brain responses in children and adults with ASD while they observed others’ actions [83,134,135]. While most of these studies suggest altered brain responses to ToM tasks in individuals with ASD, another topic that needs discussion is the problem of communication and coordination of these brain areas. Functional MRI studies of ToM have shown reduced functional connectivity between frontal and posterior brain regions in ASD [41,44]. Specifically, adults with ASD show lower synchronization between frontal and posterior regions [41] and reduced connectivity between the STS and extrastriate cortex [42] during a ToM task (extrastriate cortex is a part of the occipital cortex comprising Brodmann areas 18 and 19; STS is a long sulcus of the temporal lobe extending from anterior to posterior part). Complex tasks like ToM may require participating brain areas to collaborate, and these results suggest a problem with the integration of necessary information across brain areas for persons with ASD. In addition to altered brain activation and functional connectivity, it appears that structural and white matter abnormalities may also play a role in the ToM deficit. The MPFC is anatomically connected to the temporal poles, STS, parietotemporal cortex, and PCC [136,137] (MPFC and PCC are medial to midline cortical structures usually involved in self-reflective thoughts; the posterior part of the STS where it meets the parietal lobe known as temporoparietal junction is critical in inferring others’ mental states). Therefore, anatomical abnormalities in the connections of these areas could have serious consequences for ToM functioning. Morphometric studies have shown abnormalities in ASD in the frontal lobe, including delayed maturation [138], increased cortical folding [139], cortical shape abnormalities [140], and abnormal development of cortical minicolumns [141,142]. Research has also found deficits in white matter volume in frontal areas [143–148] and white matter abnormalities in the connections between brain regions [149–151] in individuals with ASD. In particular, lower fractional anisotropy (FA), an index of the diffusion of water in the brain (and thus the underlying white matter structure), for white matter has been found in the STS and TPJ, which mediate ToM [150]. Reduced FA in these regions is suggestive of abnormalities in white matter structure, such as altered myelination, alterations in fiber orientation, shape and density. A disruption to the anatomical inputs to these regions may affect information transfer and may be reflected in complex social cognitive tasks like ToM. Alterations in functional connectivity, along with underlying white matter abnormalities in regions that mediate ToM, provide a larger, yet complicated picture of the neural bases of ToM in individuals with ASD. One of the theoretical models of reading others’ minds, the Simulation Theory, proposes that we attribute mental states to others by taking their perspective through a mechanism of simulation [152–154]. At the neural level, the discovery of mirror neurons in the F5 area of the primate brain has provided clues to a possible mirror mechanism mediating this simulation process [155]. Studies have suggested that the mirror neuron system (MNS) in humans is involved in motor simulation of others’ actions, allowing us to understand what other people are doing [156–159]. Other studies have linked MNS response with social cognitive functions such as empathy, shared attention, and understanding others’ mental states, implicating simulation mediated by the MNS as a mechanism for mentalizing [62, 134,160,161]. So, is the MNS another component of the mentalizing system? Based on recent studies, it appears that the MNS serves as a mediator, simulating an action and passing the information to regions associated with ToM processing [162–164]. Some have implicated the MNS and STS as parts of a network for action understanding, and suggested that this network will be activated for understanding actions involving widely varied sensory stimuli [165– 169]. Recent studies have indicated reduced activation [135,170,171,83] as well as anatomical abnormalities [160] in the MNS in those with ASD. Despite such abnormalities, there is no direct evidence of MNS dysfunction and its role in mentalizing deficits in people with ASD. Future studies should examine the validity of the MNS dysfunction hypothesis, especially in the context of the Simulation Theory of mindreading. With structural and functional neuroimaging studies pointing to the roles of the MPFC and TPJ in ToM, and potentially a dysfunctional MNS, as areas of concern in ASD, it is difficult to determine the root of the mentalizing impairment in ASD. It could be that each of these regions contributes to the problems in simulation and ToM, or that the weaker communication among these systems causes this breakdown in ASD (see [172] for a review). Future studies should examine early development, organization, functional specialization, and connectivity of these regions in children with ASD.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

417

The development of ToM is a gradual process, with children learning the ability to distinguish appearance from reality [173], separate mental events from physical ones [174], and engage in pretend play [175]. It has been found that children with ASD have difficulty in each of these steps, which may impact the development of ToM [176]. However, the cause of the ToM deficit in individuals with ASD is still under debate. One theory is that a lack of social motivation causes individuals with ASD to ignore social cues, providing insufficient interactive feedback to the brain to process [177]. This deprives the developing child from learning appropriate social-cognitive skills. A contrasting theory contends that it is not a lack of social motivation but perceptual issues, such as lateral glances or impaired perceptual direction in face processing, that provide the brain with the wrong feedback and affect many social skills such as joint attention and ToM (see [178] for review). Individuals with ASD have difficulty in properly applying mental state information, even when the information is given to them [179]. Thus, despite bypassing lower level aspects, people with ASD still may face difficulty in this domain. Another perspective points to the problems with executive functioning [180], such as impaired inhibition, that can affect an individual’s ability to suppress the reality that he/she sees before him/her and think from others’ perspectives. Yet another possibility pertains to weak central coherence [71] (a weakness in the operation of central systems that are normally responsible for drawing together or integrating individual pieces of information to establish meaning), causing those with ASD to process individual details at the expense of a holistic picture. This affects their ability to see a social situation holistically, failing to read the overall meaning of social cues. Finally, it may be that ToM simply involves information too complex to process [181], thus too demanding for the ASD brain to integrate. While these ideas provide possible explanations, previous findings on altered activity and disrupted connectivity of brain areas functionally specialized for ToM point to a neural basis to this impairment. 3. Cognitive flexibility Flexible thinking is an important aspect of cognition that helps people make decisions, solve problems, and tackle the challenges posed by novel situations. A particularly striking aspect of the behavior of individuals with ASD is their extreme preference for structure and routine. Such preference for structure may be the consequence of their difficulty in cognitive flexibility, sometimes referred to as set shifting. This may translate to difficulty in switching attention between tasks and adapting to new situations, as well as modulating coordinated actions. These difficulties are often manifested in tasks that measure general executive functions, higher cognitive abilities that are commonly associated with the frontal lobe such as planning, working memory, inhibition, impulse control, and cognitive flexibility [181– 183]. There is a great deal of behavioral evidence supporting executive dysfunction in individuals with ASD [124, 184], suggesting frontal lobe and, more specifically, orbitofrontal cortex abnormalities in this population [185]. The most commonly used behavioral measure of cognitive flexibility in ASD is the Wisconsin Card Sorting Test (WCST) [186] or some variant of it. In the classic WCST, participants are required to match response cards to stimulus cards based on category (e.g., same color, shape, number of shapes, etc.). During administration, the target category changes without warning, and participants must incorporate feedback to notice any change and adapt to it. Thus, the task requires cognitive flexibility to shift sets. Over the years, a myriad of studies has found that, compared to participants without ASD, people with ASD struggle during the WCST, as they tend to perseverate on one category or rule [187–192]. Recently, Sanders, Johnson, Garavan, Gill, and Gallagher [193] reviewed many of these studies and concluded that individuals with ASD do show perseverative responding, with no evidence of problems in attention driving these results. Nevertheless, other factors, such as language ability or working memory, may be a confounding factor for some of the differences seen in the WCST. For example, Guerts and colleagues [194] suggest that other tasks of cognitive flexibility, by lowering additional task demands, have more closely isolated the measure of cognitive flexibility. One such task is the intra-dimensional/extra-dimensional shift task (ID/ED). It requires participants to shift within one dimension as well as shift across multiple dimensions. Similar to the WCST, participants are not given the specific rules or told when the task is being shifted, but, unlike the WCST, they are told that the rules will change during the task. Evidence from the ID/ED task is inconclusive, as some studies have found no significant differences in performance between individuals with and without ASD [195,196], whereas others have found group differences [197,198]. Although behavioral evidence points in the direction of impaired cognitive flexibility in individuals with ASD, the supporting neurological evidence is limited. This discussion is included in Section 3.2.

418

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

3.1. Cognitive inflexibility and the triad of impairments in ASD Difficulty in flexible cognition can lead to perseveration and inertia to switch from one task to another. Direct consequences of this may be repetitive behaviors and an extreme preference for structure and routine. Evidence for the relationship between cognitive inflexibility and ASD symptomatology of restricted and repetitive behaviors has been found in studies using the WCST along with trail-making test [199]. This pattern was found for measures of working memory and response inhibition, but not for measures of other aspects of executive function (such as planning and fluency), further highlighting that the impairment found in cognitive flexibility is unique. Another study found significant positive correlations between repetitive behaviors (as reported on two common ASD diagnostic measures) and perseverative responding on the WCST in high-functioning adolescents with ASD [200]. This again supports the link between cognitive inflexibility and repetitive behavior in this population. While lack of cognitive flexibility may explain the mechanisms underlying perseverative behaviors found in individuals with ASD, it can also impact the other two core symptoms of the triad. Social interactions are rich and involve interpreting information at multiple levels, and cognitive inflexibility can have a detrimental effect here. For instance, Dichter and colleagues [201] examined cognitive flexibility in high-functioning adults with ASD using an oddball detection paradigm that required participants to shift pre-potent behavioral responses in reaction to social and non-social events. In the task, participants rapidly viewed images of shapes and faces, and were given a “target” (either shapes or faces) category at the beginning of each run. Participants were told to make a button press for each image indicating whether the image was a member of the target category or not. Functional MRI data revealed that adults with ASD showed increased activation compared to the control group in many areas, including the dorsal anterior cingulate region in response to face (social) vs. shape (non-social) targets. The dorsal anterior cingulate is thought to utilize cognitive control to mediate performance on such a task. Again, this supports the notion that the neural substrates of cognitive control may be atypical in individuals with ASD. The hyperactivation also suggests inefficiency, in which the brain must work harder to use cognitive control in response to social events. If individuals with ASD do have problems with switching cognitive and behavioral responses to stimuli, then it follows that they would have trouble monitoring feedback in social situations and incorporating this feedback to appropriately change their behavior. In other words, they could be “stuck in set” in many complex social situations. Another problem of inflexible cognition in the social domain is difficulty inferring the intentions of other people. Oftentimes, the mental state of a social partner is not explicitly apparent, and a person must read between the lines to decode this information. To figure out another person’s intentions, one may need to suppress the reality in front of him/her and think from the other person’s perspective. As we know from the section on ToM, this also can be difficult for an individual with ASD. In sum, social interactions are unpredictable, reciprocal, and interactive, which require a person to be both flexible and resourceful, and this may pose a challenge for those affected by ASD. The final core symptom of the triad, impaired language and communication in ASD, could also potentially be explained by cognitive inflexibility. We know that individuals with ASD have particular difficulty with complex language processing in rich semantic interpretations such as metaphors, puns, and ambiguous sentences. In order to successfully decode this type of language, one must take into account the context of the word, phrase, or sentence and integrate it into the relevant meaning of the word(s). For example, if one sees the word bank, one must use context to decipher whether it refers to the financial institution or the side of a river. Another example is the homographic word tear, which changes meaning depending upon the contextual phrase in which it is imbedded (“There was a tear in her eye” vs. “There was a tear in her dress”; [202]). In both of these cases, one must utilize flexible thinking in order to extract the appropriate meaning given the current context. If individuals with ASD show cognitive inflexibility and perseverative tendencies, then it follows that they would have difficulty in adapting their thoughts to a different meaning of a word or phrase. Thus, cognitive inflexibility may be an important aspect that affects information processing in social, linguistic, and other modalities in individuals with ASD. 3.2. Neural differences in cognitive flexibility in persons with ASD Only a few research studies have directly investigated cognitive flexibility in ASD. While the frontal lobe plays a vital role in mediating executive functions, its connections may prove decisive in determining the robustness of executive functions. In addition to findings of excessive connections within the frontal lobe, the pathways from frontal to parietal and subcortical regions in the brain seem underdeveloped in individuals with ASD [203,204]. One of the few

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

419

functional neuroimaging studies to investigate cognitive flexibility was conducted by Schmitz and colleagues [205]. They examined high-functioning adults with ASD compared to typically developing adults during a set-shifting task. The participants with ASD showed increased activation in the left inferior and right medial parietal cortices during set-shifting. Furthermore, these increases in activation were mapped onto the same anatomical areas with gray matter abnormalities. Interestingly, in this study, the two groups showed no statistically significant difference in behavioral measures of the cognitive flexibility task. Thus, it is possible that increased activation in these regions may be evidence that successful completion of this task requires more effortful processing in the group with ASD. In another fMRI study of cognitive flexibility that used a task designed to differentiate shifts [in response (changing the behavioral response) from shifts in cognitive set (shifting from one mental state to another. For example, sorting cards by color for a while and shifting to sort by shape)], Shafritz et al. [206] found decreased activation in frontal (the dorsolateral prefrontal cortex, premotor cortex, anterior cingulate cortex), basal ganglia (striatal), and parietal (intraparietal sulcus) brain areas in the group with ASD, along with poorer behavioral responses than the control group. Interestingly, these poor behavioral responses were found specifically on the trials requiring response shifting, regardless of whether they also required cognitive set shifting. Furthermore, the severity of restricted and repetitive behavior symptomatology was correlated with decreased activation in the anterior cingulate and posterior parietal area for the group with ASD. The anterior cingulate cortex (ACC) plays a vital role in set shifting, as it is an area responsible for response monitoring and attention shifting (see [207] for a review). It is interesting to note that repetitive behaviors may be a natural consequence of cognitive inflexibility. Functional and anatomical abnormalities associated with the ACC have been found to contribute to repetitive behaviors [206,208]. Although there are only two studies that found this effect in ASD, a relationship between ACC and repetitive behaviors has been well established in obsessive compulsive disorder [209]. Another area of interest is the basal ganglia, and abnormalities in the basal ganglia have been found associated with repetitive behaviors in individuals with ASD (see [210]). Larger volume of the right caudate nucleus as well as left and right putamen in adults with ASD has been positively correlated with repetitive behaviors in this group [211]. These findings have been further supported by another study that examined basal ganglia volumes in individuals with ASD who were not on neuroleptic medication that could potentially cause structural neural changes [212]. They found increased volume in the caudate nucleus in ASD individuals, compared to their typically-developing counterparts. Additional attempts to explain executive dysfunction in ASD have offered the idea of medial temporal lobe (amygdala, hippocampus, and parahippocampus) functional abnormalities playing a causal role [177]. This view suggests that medial temporal lobe dysfunction causes the deficits seen in the prefrontal cortex, and in turn, executive functions via the frontal lobe, which develops much later in life. In other words, early developmental problems in the medial temporal lobe in ASD may cause a breakdown in connections in the brain that are normally recruited during complex cognitive tasks, and this breakdown in connectivity may trigger abnormal development of the prefrontal cortex. Others posit that atypical frontal lobe development is central to executive dysfunction in ASD [213]. According to functional underconnectivity theory [34,40], individuals with ASD have weaker connections between the prefrontal cortex and more posterior brain regions, and it is this lack of connectivity that causes cognitive inflexibility and difficulty in executive functioning tasks. 4. Information processing speed Differences in cognitive abilities across tasks and across individuals may depend on the speed with which mental operations can be executed. The maximum rate at which cognitive operations can be executed is termed processing speed [214]. Two distinct mechanisms may be responsible for the relation between speed and cognition [215]. The first is the limited time mechanism, which is based on the assumption that the time required for early operations limits the time available for later operations. Thus, if processing is slow, operations that need to be completed may not be accomplished. Second, the simultaneity mechanism proposes that slow early processing reduces the amount of simultaneous available information for higher level processing. The later operations may not receive the necessary information for subsequent processing. In other words, slower processing speed may lead to not all of the relevant information being available when needed. This may further lead to impairments of critical operations that could result in either a high rate of errors or time-consuming repetitions of critical operations [216]. The amount of information to process and the time available to do so are critical in deciding the effectiveness of cognitive processing. The limited time availability may be related to the amount of information to be processed,

420

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

which in turn may depend on factors such as selecting relevant information and inhibiting unwanted information. Children and adults with ASD tend to have difficulties in both of these areas. The Weak Central Coherence theory posits a weakness in the operation of central systems that are normally responsible for drawing together or integrating individual pieces of information to establish meaning. This weakness, according to the theory, results in a cognitive bias toward processing local parts of information rather than the overall context in individuals with ASD [71]. For example, focusing on peripheral details of a face can result in the inability to read the most salient information or emotion conveyed by that face. Such a piecemeal approach to information processing may result in processing every single detail involved in a stimulus without integrating them to infer the global meaning. In other words, one attempts to comprehend too many details, leading to information overload and, ultimately, failure. 4.1. Cognitive aspects of information processing in ASD Cognitive processes are executed and accomplished through the collaborative work of allocating, organizing, and integrating resources. Such mechanisms are subserved by large-scale cortical networks consisting of spatially separate components, each with its own set of relative specializations that connect extensively to accomplish cognitive functions [217]. Problems in any of these functions or processes can affect information processing. Uneven cognitive skills have been repeatedly documented in people with ASD [57,218], as individuals with ASD can show significantly differential performance across subsections of psychological assessments [the Wechsler Intelligence Scale-Third Edition (WISC-III; [219])]. For example, the mean group scores for children with ASD have been found to be low on the Freedom from Distractibility and Processing Speed relative to Verbal Comprehension and Perceptual Organization [220–222]. If processing speed is too slow, it may be difficult to keep up with incoming information, resulting in information overload. Slower processing speed has been found to predict problems with reading, math, and written expression achievement in ASD [223]. Additionally, impaired semantic and phonemic fluency in ASD has been attributed to differences in processing speed [224]. Again, it may be that an inefficient filtering (or inhibition) mechanism, along with slower processing speed, causes information overload in ASD. Thus, at the cognitive level, inefficient information processing in ASD can result from a variety of reasons, such as: 1) inefficient inhibition; 2) complexity of information; 3) slow processing speed or late processing; and 4) misinterpretation. 4.1.1. Inefficient inhibition Suppressing irrelevant information is a hallmark of cognitive control and efficient information processing. Inhibitory processes develop hand in hand with cognition throughout childhood [225–227]. Several studies have indicated no difficulty for high-functioning individuals with ASD with simple inhibition tasks, such as Stroop tasks [228,229,205], “go/no-go” tasks [205,230,231], stop-signal tasks [230], negative priming tasks [232,230], and switch tasks [205]. Nevertheless, people with ASD have shown impairments when the complexity of task and hence the need for inhibitory control increases, as in tasks that involve working memory and/or set shifting components along with inhibition [43,233–235,230]. According to Temple Grandin, an individual with high-functioning ASD who has a doctorate, holds a professorial job at a university, and is the author of several critically acclaimed books on her experiences of being a person with ASD, making decisions can become difficult in those with ASD due to the overload of incoming information [236]. In people with ASD, all outside stimuli are given the same amount of attention and evaluation. When unimportant stimuli are processed at the same level as important stimuli, a processing “bottleneck” may occur, ultimately slowing down performance [237]. 4.1.2. Complexity of information As mentioned above, while simple inhibition is not difficult for people with ASD, they do show impairments as the complexity of the task and cognitive demand increases. Information processing in ASD appears to be intact in tasks with low cognitive demand, but it is impaired in tasks with increased cognitive demand [238]. Intact and impaired performances have been recorded in ASD on different neuropsychological tests. In a study involving adults with ASD, Minshew et al. [239] found intact or even enhanced performance in certain domains, including attention, sensory perception, elementary motor ability, simple memory, formal language, rule learning, and visuospatial processing. However, they also found deficits in their participants with ASD in complex motor ability, complex memory, complex language, and concept formation. Thus, deficits become more prominent as the complexity of the tasks increased. The findings from this study led to the characterization of ASD as a disorder of complex information processing [181].

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

421

Another example of an increase in complexity is dual tasking. A study involving digit recall and motor tracking found that performance of the group with ASD was similar to that of the control group when the tasks were completed individually. However, when the tasks were carried out simultaneously (dual tasking), the performance of participants with ASD declined about 40% for each task, perhaps suggesting that cognitive demand imposes constraints on information processing in ASD [240]. Given this, the speed of information processing and allocation of available resources may prove critical in accomplishing dual tasks. 4.1.3. Slow processing speed or late processing Generally, slower processing speed predicts slower rates of learning, performance, and comprehension in young children [241]. Studies examining processing speed index (PSI) have found that children with ASD show slower processing speeds than typically developing children [242–244]. However, they do well when accuracy, and not speed, is the only factor involved [188]. On the other hand, when the instructions required children with ASD to execute a function as fast as possible, they tended to perform poorly [245]. Completing timed tasks requires an individual to rapidly integrate attention, memory, response preparation, and inhibition [246], and individuals with ASD perform poorly on tests that require cognitive flexibility and fast psychomotor speed. Another recent study of information processing showed that individuals with ASD had similar global–local processing skills as well as inhibition responses when compared to typically developing controls, but the group with ASD was seriously impaired on measures of cognitive flexibility, which can result in poor response time [247]. Thus, time demand may be another significant factor in determining how well an individual with ASD will process information. 4.1.4. Misinterpretation of information Misinterpretation of information refers to errors in information processing, the failure to detect them, and the failure to comprehend the global meaning. For instance, we apply meanings, rules, and plans to novel situations, and a robust application of this would involve integrating available information within context. A request such as “Can you please pass the salt?” to a person with ASD who is sitting across the dinner table may elicit the response “Yes, I can” without actually passing the salt. In this situation, the person with ASD misinterprets the request and makes a literal response. This is a typical example of not understanding the contextual significance of the information. Such misinterpretations can also arise from the nature and quality of an individual’s attention. People with ASD tend to have slowed attentional processes, such as orienting spatial attention or attention shifting [248,249]. In addition to slower attentional processes, the focus of attention within a given field of view is important. People with ASD tend to pay attention to the details while often failing to integrate those details. Thus, individuals with ASD may misinterpret cues by failing to attend to them, failing to integrate them with the current context, and/or missing the global meaning that should emerge from the details. 4.2. Differences in processing speed and its impact on the triad of impairments Slower processing speed can have a significant impact on cognitive processes and, subsequently, on behavior. In social interaction, such difficulty can lead to unsuccessful or awkward interpersonal moments. For example, skills such as joint attention (sharing the experience of observing an object or event using gaze or gesture) are vital in early social and cognitive development. Initiating and especially responding to joint attention requires quick processing of the target stimulus as well as the participating agents. On a different and higher level, social interaction involves an interpersonal pact that guides the speaker and listener through a cooperative enterprise. Quick processing of subtle signals, such as gaze, grimaces, and facial expressions, is crucial in these kinds of interactions, and people with ASD may have trouble with social interaction behavior due to slower information processing. In an event-related potential (ERP) study of face processing, McPartland et al. [250] found that individuals with high-functioning ASD had slower N170 latencies to faces than to furniture. This study suggests that encoding faces, which is critical in social interaction, is a disrupted process in ASD, possibly due to slower information processing speed. The relationship between speed of processing and social impairments in ASD was also established by another ERP study [251]. These researchers found that children who displayed faster N300 latency to fearful faces exhibited better joint attention and fewer social orienting errors, suggesting that slower information processing speed for emotional stimuli is associated with more severe social attention impairments in children with ASD.

422

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

Processing speed can also affect problems associated with language and communication in ASD. A recent study [252] examined the profile of children with ASD using the Wechsler Intelligence Scale for Children-IV (WISCIV; [253]). The authors found that processing speed was the most significant area of weakness in children with ASD, and processing speed performance was related to communicative symptoms and adaptive communication abilities in ASD. Speed of processing can also be critical during interpretation of figurative speech and jokes, which are commonly used in social communication. In real time social communication, interpreting a joke and responding to it must occur rather quickly, and people with ASD tend to have difficulty interpreting this type of speech. Slower speed of processing can also affect the accuracy of interpretation of complex information, especially when it is presented at a rapid rate. Such difficulty can be manifested in giving up on a task or it can lead to repetitive behaviors or repetitive strategies in solving a task, both often seen in people with ASD. Thus, these stereotyped behaviors may be a set of coping mechanisms utilized when the complexity and demands of the incoming information are too difficult to manage. Such behaviors in some individuals with ASD may also be a strategic approach to buy more time to collect one’s thoughts before solving the problem at hand. 4.3. Neural mechanisms of information processing in ASD Successful information processing is accomplished by drawing the right kind of brain resources at the right time and in appropriate proportions. Recruitment of brain areas for cognitive performance is dynamically configured and allocated as the computational demands vary [217]. Appropriate and optimal recruitment of a given brain area to accomplish a certain task may depend on many factors, including the structural integrity of that area, its functional specialization, and its connectivity with other brain areas. In people with ASD, impairments in one or more of these factors can affect information processing. As the cognitive demand increases, multiple centers in the brain may need to participate and communicate with each other as a team. Such situations usually result in functional disruption in people with ASD, leading them to resort to compensatory mechanisms to accomplish the task at hand. It is also possible that the functional disruption and the associated cognitive changes may be the result of altered brain organization. For instance, a relationship between frontal lobe volume and processing speed in ASD has been reported [254]. Functional brain activation and connectional differences in ASD, relative to typical individuals, have been widely documented through functional neuroimaging studies (for reviews, see [255,46]). Such functional differences may be triggered by many of the above-mentioned factors along with processing speed and task complexity. For example, adults with Asperger’s Syndrome have delayed cortical activation from the occipital cortex to the STS, inferior parietal lobule, and inferior frontal cortex during tasks of imitation [135]. Evidence from electrophysiological studies also points to slowed neural speed of processing in face processing tasks in individuals with ASD [250]. In addition, a DTI study by Alexander et al. [149] found a link between low FA values and slower processing speed in ASD. Whereas level of activation and timing of activation are critical in accomplishing a cognitive task, perhaps even more important is the synchronization of activation across different brain areas. Functional connectivity refers to the synchronization of the time course of activated brain areas that are spatially distant [35]. Such connections would be crucial as the cognitive demand increases and a collaborative effort from more areas is needed. Consensus has grown over recent years that the functional brain characteristics in ASD cannot be fully understood in terms of local dysfunction (e.g., abnormality in the amygdala or the cerebellum), but are better viewed as impairments of functional networks [34,40]. The topic of connectivity is, therefore, both worthy and important. Henceforth, this paper will focus on brain connectivity and methods to explain the symptoms of ASD through altered brain connectivity. 5. The impact of disrupted cortical connectivity on ToM, cognitive flexibility, and information processing Since anatomical and functional abnormalities associated with specific regions have not resulted in a consensus about the brain in ASD, a better approach might be to focus on how the communication among different areas, or the lack of it, produces the constellation of symptoms in ASD. Alterations in brain activation and connectivity have been reported in ASD in ToM tasks. For example, reduced activation in frontal (e.g., MPFC) and temporoparietal (e.g., TPJ) regions in individuals with ASD while performing ToM tasks could imply disruption in them working as a network. This has been supported by a couple of recent studies that found lower synchronization between frontal and parietal regions [41,44] and reduced connectivity between the STS and extrastriate cortex (while the extrastriate body

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

423

area, a part of the human extrastriate cortex responds to visual appearance of human bodies, the nearby TPJ responds to intentionality) [42] during ToM tasks in adults with ASD. As suggested in Section 2, it could be that early problems in mentalizing and other processes involved in ToM (e.g., eye contact, self–other representation, or joint attention), over time, lead to atypical functional specialization and integration of brain areas in individuals with ASD. If this is the case, then it follows that brain regions under-utilized for ToM in ASD would not form the same strong connections as seen in typically developing individuals, thus leading to what we have termed disrupted cortical connectivity. In other words, weaker functional communication among brain areas seen in ASD may be the result of altered functional and anatomical developmental trajectory. For this reason, longitudinal studies of functional connectivity among these regions would prove enlightening. We previously discussed cognitive inflexibility as a characteristic feature of cognition in people with ASD. In attempting to explain cognitive inflexibility, researchers have often found evidence of dysfunction in the medial temporal lobe (primarily associated with memory) in people with ASD (e.g., [177]). Dawson and colleagues have suggested that the medial temporal dysfunction causes a breakdown in its connections with the frontal lobe, which are necessary for successful and flexible cognition and for other executive functions. Additionally, as the frontal lobe develops later in life, it has been suggested that the disruptions in connectivity between medial temporal and frontal areas may contribute to independently functioning frontal lobes, which have been found in individuals with ASD [256,257]. Alternatively, it could be that early frontal lobe disorganization leads to its inability to connect with lower-level brain areas and utilize a feedback loop necessary to complete many executive function tasks (see [257]). Regardless of directionality, it does seem that disrupted cortical connectivity and explanations of cognitive inflexibility are compatible in explaining ASD, but additional research is needed for more conclusive evidence. Finally, disruptions in connectivity between frontal and parietal regions in individuals with ASD would make information processing more difficult, particularly as cognitive demand increases, either with complexity or speed of processing. It is possible that, with lower demands, individuals with ASD can process information via these networks, but with information overload, the networks are not equipped to manage such a level of processing. Again, more research is needed to disentangle this task complexity/processing speed interaction at the neural level, and developmentally sensitive studies are necessary to show directionality of dysfunction. 5.1. Disrupted cortical connectivity and other theories of ASD The disrupted cortical connectivity model is a compelling and comprehensive explanation of ASD at the systems neuroscience level, and its compatibility with some of the theories at the cognitive level is particularly striking. Disrupted connectivity would affect higher level cognitive functions, especially the ones that require the integration of information, which would likely involve many areas across the brain working together for processing. Meanwhile, simpler functions that require processing by one area or areas in close proximity would be relatively spared. For example, the Weak Central Coherence theory [71,258–260] explains ASD as characterized by processing information in constituent parts instead of holistically. This means that processing information at a global level, such as processing faces, understanding language, and ToM, may be impaired in people with ASD. Nevertheless, the processing of low-level tasks is generally found to be intact. While central coherence refers to integrating information to form a larger whole, weak central coherence may entail a lack of such integration. At the neural level, this may translate into underconnectivity or altered connectivity among brain areas. For example, underconnectivity between occipital and frontal brain areas may result in atypical top-down and bottom-up processing, which in turn may result in less information integration. It is also possible that overconnectivity among local networks can lead to spatially proximate regions functioning as islands, without communicating with distant brain areas. In this way, weak central coherence can be seen, at the neural level, as due to individual brain areas working separately and not as a team in the brains of people with ASD. The mindblindness theory [261], on the other hand, deals with impairments in ToM in ASD. This explains ASD as a failure to process the mental states of the self and others, resulting in an inability to understand social information, conceptualize what others are thinking, and empathize. As discussed earlier, since ToM is a complex skill, it may require the communication and coordination of different subprocesses such as perspective-taking and simulation. Therefore, accomplishing ToM may involve robust connectivity between several different brain areas. In ASD, there has been evidence of functional underconnectivity among the regions mediating ToM [42,41]. Another cognitive theory attempting to explain ASD is the Theory of Executive Dysfunction [235,180], suggesting that impaired executive functions account for the difficulties faced by individuals with ASD. As executive functions

424

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

are higher cognitive functions encompassing working memory, response inhibition, processing speed, set shifting, problem-solving, and decision-making, the complexity associated with such functions may be the reason for impairment in ASD. Another related theory is the Complex Information Processing Impairment in ASD [181]. Complex cognitive functions, especially executive functions, are mediated by frontal lobe areas in communication with other centers. For instance, a difficult Tower of London (TOL) problem-solving task is accomplished cognitively by the coordination of visuospatial and executive components. At the neural level, this demands the integrative functioning of the dorsolateral prefrontal cortex (DLPFC) and inferior parietal areas [262]. In ASD, underconnectivity between these frontal and parietal regions have been found in a TOL task [38]. In brief, complex cognitive and social tasks may require the communication among several brain areas, and the underconnectivity among those key areas may hamper task performance in people with ASD. 6. Hypotheses and specific predictions In the sections below, we will discuss disrupted cortical connectivity theory in terms of specific predictions and implications for the triad of impairments in ASD. With each hypothesis, we will discuss existing evidence as well as gaps in research that should be filled, in order to better understand the neural architecture of this disorder. 6.1. Hypothesis 1. Underconnectivity in ASD is manifested mainly in long-distance connections involving the frontal lobe, rather than in short-distance local connections. Earlier reports of connectional abnormalities in ASD described underconnectivity throughout the brain, suggesting it to be a widespread and global issue. However, subsequent studies focused on examining the generality along with the specificity of this problem, and those reports indicated the epicenter of altered connectivity to be the frontal lobe in ASD and the relatively long-distance connections to and from it. If Hypothesis 1 is supported, then at the behavioral level, we should see greater impairment during tasks that are complex and mediated by long-distance over short-distance neural networks. Current evidence at the neural level, from studies of ToM [41], cognitive flexibility/inhibition [43], and other executive functions [38,263,39] seems to support this notion of frontal lobe (dys)functioning at the heart of disrupted connectivity in individuals with ASD. Problems associated with relatively long-distance functional connectivity involving the frontal lobe would result in impaired information processing at the whole-brain level, or in top-down processing [264]. At the cognitive level, problems with top-down communication would subsequently be reflected by less integration of information, resulting in a piecemeal style of processing. This, in turn, could cause information overload in a person with ASD, because the individual may attend to too many details, resulting in too much information to process at once. Behaviorally, we see that individuals with ASD tend to focus on and process excessive amounts of low-level visuospatial details, suggesting a more bottom-up processing approach. The question that remains is whether this excessive bottom-up processing is impeding top-down processing, or whether impaired top-down processing causes compensatory strategies reliant on bottom-up processing. This same cause-effect question also remains valid at the neural level. Is increased functional specialization of brain areas (hyper-specialization) in ASD decreases the need for larger, whole-brain networks, or are disrupted long-distance connections the root of the impairment in ASD? Great strides have been made in exploring impairments associated with ASD at the neural level, and functional connectivity studies, we believe, are critical to this endeavor. However, to further our knowledge of ASD and work toward a more explanatory model, we must find ways to establish causal relations or directionality that is missing in functional connectivity measurements. One possibility for future research is to undertake effective connectivity analyses that allow for predicting cause-effect relationships. Unlike functional connectivity, the strength of effective connectivity is in providing the directionality of communication between two brain areas. Based on our current review of the evidence, we would predict that the top-down feedback from the frontal to posterior brain regions may be compromised in ASD, rather than the bottom-up occipital to frontal connections. 6.2. Hypothesis 2. Underconnectivity in ASD is manifested only in complex cognitive and social functions and not in lower-level sensory or perceptual tasks.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

425

Whereas spatial distance was a factor in the first hypothesis, task complexity is at the core here. These two aspects are interrelated, in that accomplishing complex tasks may require the integration of widespread brain areas. Thus, task complexity is a test not only for the availability of resources, but also for the appropriate allocation and integration of them. If this hypothesis is supported, then higher-order cognitive and social tasks may pose problems for people with ASD. Evidence from previous neuroimaging studies suggests underconnectivity in ASD between regions that are critical in solving a complex task, such as between inferior frontal and inferior parietal areas in sentence imagery [40], between dorsolateral prefrontal cortex and inferior parietal areas in problem-solving [38], between cingulate cortex and parietal areas in response inhibition [43], and between medial/orbital prefrontal and temporoparietal areas in ToM [41]. On the other hand, in less demanding visuospatial tasks, individuals with ASD perform relatively well and show intact connectivity or overconnectivity in posterior networks, such as occipital or parietal lobes. In addition, individuals with ASD may use the occipital/parietal networks as default areas when accomplishing any task. Recently, Sahyoun and colleagues [265] found that individuals with high-functioning ASD, relative to typically developing controls, showed underconnectivity between frontal and posterior regions during pictorial reasoning tasks. The authors suggested that those with ASD may rely on visual mediation in tasks requiring higher cognition. Similar findings have been reported in social and linguistic tasks as well [40]. In one task that required participants to process photographs of faces within a working memory paradigm, adults with ASD showed greater local activation of posterior, visual networks, with underconnectivity to frontal areas, compared to typically developing controls [39]. Taken together, findings from these studies support Hypothesis 2. In low-level perceptual tasks, such as visual search, a bottom-up feature-based processing strategy would allow one to more efficiently find the target, while a topdown integrative strategy may not fare so well. If individuals with ASD tend to over-rely on a bottom-up processing approach, then we would expect them to be successful in performing these types of perceptual tasks. As suggested, we may also expect them to overuse such a strategy, along with the recruitment of associated brain regions, even in situations in which such an approach is not pragmatic. 6.3. Hypothesis 3. Functional Underconnectivity in ASD may be the result of underlying anatomical abnormalities, such as problems in the integrity of white matter. Supporting evidence for Hypothesis 3 would show a link between anatomical and functional connectivity through functional MRI, neuroanatomical, and DTI studies. Neuroanatomical studies have consistently found abnormalities associated with total brain volume as well as gray and white matter volumes in children with ASD [266,267]. Such enlargement in gray and white matter has been found significantly more in the frontal lobe in children with ASD [143,203]. Regarding white matter volume, Herbert et al. [144] reported overall greater white matter volume in 7- to 11-year-old children with ASD. In a later study, they also found that this altered pattern was greatest in the radiate compartment of white matter in the frontal lobe [145]. While functional connectivity findings point to the connections to and from the frontal lobes being altered in people with ASD, the volumetric abnormalities mentioned above, along with white matter connections, may provide a comprehensive picture of the brain organization in ASD. Larger brain volume in ASD, along with findings of a smaller corpus callosum, may cause conduction delays and long-distance underconnectivity, mainly across hemispheres [268]. DTI studies have provided evidence for reduced FA in individuals with ASD, suggesting problems in white matter integrity and connectivity [149,151,150,269,270]. White matter plays a vital role in brain connectivity because it serves as the route by which neural networks communicate. While functional connectivity provides information about the synchronization of functional MRI time-courses, DTI data help understand the underlying anatomical connections. Together, functional neuroimaging and water diffusion measures in brain suggest that disrupted white matter organization (anatomical connectivity) may result in aberrant functional synchronization. The evidence pertaining to anatomical and functional connectivity is currently coming from separate DTI and functional MRI studies. The structure–function relationship and its nature in individuals with ASD should be tested using combined DTI and functional MRI studies that focus on examining the functional connectivity between two regions and the underlying white matter that connects those very regions. Such characterization of the brain at a neural systems level is critical in approaching any complex disorder, especially ASD. 6.4. Hypothesis 4. The ASD brain adapts to disrupted connectivity through compensatory strategies such as overconnectivity in frontal and/or relatively posterior brain areas.

426

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

If frontal–parietal underconnectivity is a characteristic feature of brain functioning in ASD in complex tasks, there may be two possibilities of such a finding: cause and consequence. The first possibility is that frontal–parietal underconnectivity may be the result of both these lobes functioning independently due to increased local connectivity. There is evidence of overconnectivity in both frontal [256,257] and posterior [271] regions. The frontal lobe overconnectivity view suggests that an overconnectivity in the frontal lobe (frontal lobe “talking to itself”) may be the major cause of neural dysfunction in ASD. Enhancement of local connectivity formed by neocortical pyramidal neurons has been found in the cortex of rats in a study examining the effects of prenatal exposure to valproic acid (VPA) [272]. The authors suggested that local hyperconnectivity my cause cortical nodes to become more sensitive to stimulation, and thus they may become more autonomous and isolated. This mechanism may be at play in ASD, regardless of the cause, resulting in overconnectivity of local modules and a decrease in global connectivity. There is also evidence of increased amounts of cortical minicolumns in the frontal and temporal lobes in individuals with ASD [141] and smaller minicolumns, especially in BA44 [273], again suggesting localized overconnectivity. Individuals with ASD have narrower minicolumn width, smaller mean neuron and nucleolar cross sections, and greater neuron density (as a direct result of a greater number of minicolumns overall) [274]. According to Casanova and Trippe [273], minicolumns contain GABAergic (Gamma-Aminobutyric Acid) interneurons, and alterations in minicolumns will affect the activity of GABA output, which in turn would disrupt connectivity in the brain overall. Greater numbers of minicolumns, narrowly spaced together, would most likely contain greater numbers of GABA neurons forming tightly packed circuits locally. It is possible local excitation would increase with greater neuron densities, leading to greater levels of noise inside the minicolumns, which would lower the specificity (and ultimately the functioning) of networks. Additionally, there is developmental evidence for localized overconnectivity in the frontal lobe as well, showing overabundant neural networks within the frontal regions over the first four years of life [266]. It is possible that both frontal and parietal lobes are “talking to itself” more than to each other in the ASD brain, causing increased local and compromised global connectivity. The second possibility is the idea that the frontal–parietal underconnectivity forces the ASD brain to adapt in such a way that there is parietal autonomy in brain functioning [45,275]. Therefore, parietal autonomy, or intact posterior brain connectivity, in ASD is a consequence of the frontal–parietal underconnectivity. Behavioral as well as neuroimaging evidence of increased use of visuospatial strategies and increased recruitment of visuospatial brain regions in accomplishing cognitive tasks in those with ASD provide some evidence, although indirect, for this view. There is evidence for compensatory activation in low-level visual and perceptual tasks [276–278]. Taken together, it seems that there is rather strong support for this hypothesis. Frontal–parietal underconnectivity in ASD can also be viewed as a bandwidth (the amount of information that can be transmitted between nodes per unit time) limitation problem [275]. Additionally, support for Hypothesis 4 would be a better performance in those with ASD, compared to typically developing counterparts, on tasks involving the recruitment of areas that are relatively frontal (e.g., problem-solving) or relatively posterior (e.g., visual search). Such overconnectivity may be manifested by increased reliance on visual or visuospatial processing, which has been found in several neuroimaging studies of those with ASD during tasks that do not typically utilize such areas to the same extent [276,47,279,263,39,281,265]. For example, Koshino and colleagues [39] found that high-functioning adults with ASD utilized posterior cortical neural networks to perform a working memory task with photographs of faces as stimuli, while showing underconnectivity with frontal regions that are typically used to perform this type of task. In 2005, Koshino and colleagues [263] also found that adults with ASD use more right parietal regions, while typically developing participants use more left parietal regions, during another working memory task using letters. This suggests that while typical adults code the letters in a more verbal way, the ASD adults use more visual areas. This was also suggested by increased activation in occipital regions in the ASD group compared to their typical counterparts. According to Manjaly and colleagues [280], high-functioning individuals with ASD may not show weak central coherence, but rather they simply have a local processing advantage in visual tasks that require feature-based search techniques. During such a task, the Embedded Figures Task, they found that the ASD group activated more right primary visual cortex and bilateral extrastriate areas, while the typically developing group utilized left parietal and premotor areas. This further supports the idea of increased reliance of lowerlevel visual, posterior regions. Behaviorally, however, both groups performed with similar accuracy and speed on the Embedded Figures Task, suggesting that while both groups were successful, they approached the task differently.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

427

6.5. Hypothesis 5. Functional underconnectivity among brain areas in ASD will be seen not only during complex tasks but also during task-free resting states. Prima facie, this hypothesis may seem contradictory to the basic premise of cortical underconnectivity theory, which is that underconnectivity in ASD will be reflected only in complex tasks. However, previous evidence suggests that underconnectivity in the default mode network, which mediates resting state brain functioning, in individuals with ASD [53,281]. The default mode network comprises the medial frontal gyrus, ACC, PCC, precuneus, angular gyrus, and inferior parietal lobule [282–287]. This network is said to be active during resting state, and will deactivate for a cognitively demanding task. In addition to underconnectivity, individuals with ASD are also found to have functional differences within the default mode network during rest [288–292]. A recent diffusion tensor imaging (DTI) study found long fiber tracts connecting the MPFC to PCC, tracts connecting PCC to medial temporal lobe, with no tracts directly connecting MPFC to medial temporal lobe [293]. Functional underconnectivity has been reported between MPFC and PCC in a recent study [292]. One of the basic premises of underconnectivity theory is that the disruption in cortical connectivity in ASD occurs when the computational demand increases. That the default mode network would be underconnected in ASD would seem unlikely, then, as it is a network active in the absence of complex tasks. However, while resting states are taskfree, such states trigger self-reflection, internally directed thought, and mentalizing. Therefore, resting state involves thought processes that are coordinated by the default mode network, which happen to include brain regions associated with self–other thinking and ToM. Both behavioral and neuroimaging studies have successfully shown that mentalizing and self-referential thinking are atypical in ASD (e.g., [42,294,295,41]). In addition, internal experiences and introspection are qualitatively different in individuals with ASD from typically developing peers [294,295]. So, the deficits in social cognition in individuals with ASD may be pervasive even at resting state. In individuals with ASD, the coordinated and coherent thinking and self-reflection at rest may not be well-coordinated and may be reflected in terms of underconnectivity in the default mode brain areas. It should be noted that weaker connectivity within the default mode network has also been associated with two of the triad of impairments: increased social dysfunction and increased repetitive behaviors and restricted interests [291,49,292]. Altered brain connectivity at rest, thought to be the psychological baseline state, may explain neural and behavioral differences in other processes in ASD. Future studies should investigate a possible link between structure and function of the default mode network and behavior in ASD. 7. Disrupted cortical connectivity framework as an explanatory model of ASD In this paper, we outlined a comprehensive theoretical examination of ASD at behavioral, cognitive, and neural levels using disrupted cortical connectivity as the underlying framework. Perhaps what is most intriguing about ASD is its multidimensional manifestation with widespread symptoms. Attempts to identify a single cognitive explanation, a single genetic basis, or a focal neurobiological marker may be the wrong route to take in explaining this disorder. Instead, approaching the disorder at a global level may not only help in tying together the widespread symptomatology of ASD, but also will confirm the complexity of the disorder. Research has found widely varied symptoms (such as learning difficulties, social issues, intellectual disability, etc.) as well as problems in communication among brain areas in ASD. Therefore, a system-wide manifestation likely explains a large number of symptoms. At the neural level, this calls for a network-level approach, where it is assumed that cognitive processes are mediated and accomplished by the concerted and coordinated efforts of participating brain areas. Another issue to take into account is the heterogeneity of the ASD population, where the affected individuals present widely varied symptoms. Some individuals show severe cognitive impairment, have no functional language, engage in self-injurious behavior, and have poor adaptive living skills. Others are highly intelligent, but interact awkwardly with others, have odd fixations on objects, adhere to strict schedules, and have difficulty maintaining relationships. There are so many differential characteristics in people with ASD, making it highly unlikely that one source explains such a wide variation of phenotypes across so many domains. This also means that the severity of altered cortical connectivity may be different across individuals within the autism spectrum. It is possible that neural connectivity is systematically related to the level of impairment at the behavioral level. However, because most of the published research uses high-functioning individuals with ASD, so there is a need for researchers to explore this idea in widely varying samples.

428

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

It is also important to keep in mind that ASD is a neurodevelopmental disorder, which means that there is impairment in the growth and development of the brain. Early brain development involves a delicate balance between the functional specialization of specific regions as well as the formation of connections across these regions through integration. Disruption, delay, and/or alterations in this process may result in problems at the cognitive, neurobiological, and behavioral levels. Thus, early brain developmental stages are crucial, and there is evidence that key neuropathological changes occur early in development [296]. It is interesting to note that early behavioral anomalies seen in ASD coincide with these deficits. In addition, atypical behavior patterns at young ages, such as lateral glances, can provide wrong feedback to the brain, disrupting the typical feedback loop and resulting in a secondary neurological assault. So, initial altered experiences will cause the brain to develop even more abnormally in ASD. This may result in alterations in pruning, and wrong connections may be formed and maintained. Thus, it is possible that the pattern of structural connectivity itself may be different at early stages of development in people with ASD. While alterations in brain development can have significant impact on brain organization, maintaining the balance between excitatory and inhibitory signals is critical in successful communication. It is possible that an imbalance between inhibition and excitation of neurons could be at play in ASD, serving as mechanisms contributing to altered connectivity. Rubenstein and Merzenich [297] hypothesized that the “noisy” ASD brain is associated with high levels of excitation and/or low levels of inhibition in key neural circuits. Minicolumn (the smallest unit of vertical organization of neurons in the cortex) abnormalities have been reported in ASD with more numerous and abnormally narrow columns [141,298,257]. The increased number and narrow size of the minicolumns in ASD may result in enhancing short or local connective fibers and under use of long-distance connectivity. Volumetric abnormalities in ASD also point to connectivity problems. The most consistent finding in studies of brain structure is a larger brain size in individuals with ASD. Also, some studies have shown increased gray matter in particular regions (e.g., frontal lobes), along with reduced white matter, and/or abnormalities in white matter tracts. In addition, studies have consistently found smaller corpus callosum size in ASD. Larger brains with smaller corpus callosum can cause conduction delays and altered patterns of connectivity in ASD. These are all indicators of abnormal structural connectivity and in turn may cause problems in functional connectivity. In terms of function, it follows that larger cortices and larger overall brains will need to form more long-distance connections via white matter for proper conduction. Taking a micro-level approach, studies have found excessively high neuron counts in ASD, most strikingly in the frontal cortex. This adds to the list of developmental abnormalities associated with the frontal cortex in ASD and may result in local overconnectivity and global underconnectivity. This would have a cascading effect on the connections that the frontal lobe should make with the rest of the brain, in turn isolating the frontal lobes. A recent study examining individual axon integrity and structure in postmortem brain tissue of individuals with ASD found decreased long-range axons and an excess of thin axons connecting neighboring regions in the anterior cingulate cortex. The researchers also found less myelination of axons in the orbitofrontal cortex [299]. This suggests that the structure of individual neurons may also play a role in creating inefficient pathways and disconnection on a global scale. Altered cell numbers, neural structure, and neural connectivity may be creating a disconnected system at large, thus affecting many areas across the brain. With the ultimate goal of moving toward an overarching explanatory model of ASD, we should gather evidence at both the biological (neural) and cognitive levels, relating this evidence to what we know about behavioral manifestations of ASD, such as the triad of Impairments. Given the complexity, heterogeneity, and the developmental nature of ASD, a global explanation or a set of explanations seems optimal. We believe that disrupted cortical connectivity may be one such explanatory model that provides a comprehensive outlook of the disorder from a biological perspective. More research is needed to address alterations in connectivity across individuals along the autism spectrum, but it is promising that as researchers, we may be moving toward more comprehensive, explanatory models of ASD. Since the diagnosis of ASD is still behavior-dependent, one of the goals of present and future research should be to identify a potential biomarker for the disorder; and disrupted brain connectivity may be a possible avenue in this endeavor. Promising attempts in these lines are already underway, especially using techniques, such as machine learning (involves the design of programs that can learn the rules from data; see [300] for review) can provide valuable information, at an individual level, about whether a participant can be classified as having ASD based on morphometric patterns [301] or on the pattern of their brain connectivity [281]. Second, any scientific research in the field of developmental disorders should, ultimately, lead to insights that can help improve the lives of affected individuals.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

429

Therefore, better characterization of connection abnormalities in ASD at an early age can contribute significantly to design specific interventions that can mend faulty connections and form new connections. Acknowledgements This research was supported by the UAB Psychology Department faculty start-up funds and the McNulty-Civitan Scientist Award to R.K. The authors would like to thank Dr. Rosalyn Weller for her valuable comments on an earlier version of this manuscript. The authors would also like to thank Ms. Miranda Morris for help in formatting and proof-reading this manuscript. References [1] American Psychiatric Association, Diagnostic and Statistical Manual. Washington, DC: Am. Psychiatr. Assoc.; 1994. [2] Autism and Developmental Disabilities Monitoring Network Surveillance Year 2006 Principal Investigators. Prevalence of autism spectrum disorders—autism and developmental disabilities monitoring network. MMWR Surveill Summ 2009;58:1–20. [3] Matson J, Nebel-Schwalm M. Assessing of challenging behaviors in persons with autism spectrum disorders. Res Dev Disabil 2006;28:567– 79. [4] Schreibman L, Heyser L, Stahmer A. Autistic disorder: characteristics and behavioral treatment. In: Wieseler NA, Hanson RH, Siperstein GN, editors. Challenging behavior of persons with mental health disorders and severe disabilities. Washington: American Association of Mental Retardation; 1999. p. 39–63. [5] Wing L. The autistic spectrum: a guide for parents and professionals. London: Constable & Robinson Ltd.; 1996. [6] Volkmar FR, Pauls D. Autism. Lancet 2003;362:1133–41. [7] Baird G, Simonoff E, Pickles A, Chandler S, Loucas T, Meldrum D, et al. Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 2006;368:210–5. [8] Rutter M, the English and Romanian Adoptees (ERA) Study Team. Developmental catch-up, and deficit, following adoption after severe early privation. J Child Psychol Psychiatry 1998;39:465–76. [9] Kanner L. Autistic disturbances of affective contact. Nervous Child 1943;2:217–50. [10] Wing L. Mentally retarded children in Camberwell. In: Hafner H, editor. Estimating needs for mental health care. Berlin: Springer; 1978. p. 107–12. [11] Rutter M. Diagnosis and definition. In: Rutter M, Schopler E, editors. Autism: a reappraisal of concepts of treatment. New York: Plenum; 1978. [12] American Psychiatric Association, Diagnostic and statistical manual of mental disorders (DSM-IV). New York: American Psychiatric Association; 2000. [13] Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 2000;30:205–23. [14] Lord C, Rutter M, Le Couteur A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 1994;24:659–85. [15] Wall DP, Esteban FJ, DeLuca TF, Huyck M, Monaghan T, Velez de Mendizabal N, et al. Comparative analysis of neurological disorders focuses genome-wide search for autism genes. Genomics 2009;93:120–9. [16] Anagnostou E, Taylor M. Review of neuroimaging in autism spectrum disorders: what have we learned and where do we go from here? Mol Autism 2011;2:2–9. [17] Harris S, Handleman J. Age and IQ at intake as predictors of placement for young children with autism: a four- to six-year follow-up. J Autism Dev Disord 2000;30:137–42. [18] Sheinkopf S, Siegel B. Home-based behavioral treatment of young children with autism. J Autism Dev Disord 1998;28:15–23. [19] Attwood T. Strategies for improving the social integration of children with Asperger syndrome. Autism 2000;4:85–100. [20] Charman T, Swettenham J, Baron-Cohen S, Cox A, Baird G, Drew A. Infants with autism: an investigation of empathy, pretend play, joint attention, and imitation. Dev Psychol 1997;33:781–9. [21] Mundy P, Sigman M, Kasari C. A longitudinal study of joint attention and language development in autistic children. J Autism Dev Disord 1990;20:115–28. [22] Sigman M, Kasari C. Joint attention across contexts in normal and autistic children. In: Moore C, Dunham P, editors. Joint attention: its origins and role in development. Mahwah, NJ: Erlbaum; 1995. p. 189–204. [23] Weiss MJ, Harris SL. Teaching social skills to people with autism. Behav Modif 2001;25:785–802. [24] Hartup W. Social relationships and their developmental significance. Amer Psychologist 1989;44:120–6. [25] Little L. Peer victimization of children with Asperger syndrome disorder. J Am Acad Child Psychiatry 2001;49:995–6. [26] Lord C, Risi S, Pickles A. Trajectory of language development in autism spectrum disorders. In: Rice M, Warren S, editors. Developmental language disorders: from phenotypes to etiologies. New Jersey: Erlbaum; 2004. p. 7–29. [27] Tager-Flusberg H, Paul R, Lord C. Language and communication in autism. In: Volkmar FR, Paul R, Klin A, Cohen DJ, editors. Handbook of autism and pervasive developmental disorders. 3rd ed. New Jersey: Wiley; 2005. p. 335–64. [28] Johnson CP. Early clinical characteristics of children with autism. In: Gupta VB, editor. Autistic spectrum disorders in children. New York: Marcel Dekker Inc.; 2004. p. 85–123.

430

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

[29] Kjelgaard MM, Tager-Flusberg H. An investigation of language impairment in autism: implications for genetic subgroups. Lang Cogn Proc 2001;16:287–308. [30] Tager-Flusberg H, Joseph RM. Identifying neurocognitive phenotypes in autism. Philos Trans R Soc B 2003;358:303–14. [31] Lam KSL, Bodifsh JW, Piven J. Evidence for three subtypes of repetitive behavior in autism that differ in familiarity and association with other symptoms. J Child Psychol Psychiatry 2008;49:1193–200. [32] Ben-Sasson A, Hen L, Fluss R, Cermak S, Engel-Yeger B, Gal E. A meta-analysis of sensory modulation symptoms in individuals with autism spectrum disorders. J Autism Dev Disord 2009;39:1–11. [33] Liss M, Saulnier C, Fein D, Kinsbourne M. Sensory and attention abnormalities in autism spectrum disorders. Autism 2006;10:155–72. [34] Just M, Cherkassky V, Keller T, Minshew N. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain 2004;127:1811–21. [35] Friston K, Tononi G, Reeke G Jr, Sporns O, Endelman G. Value-dependent selection in the brain: simulation in a synthetic neural model. Neuroscience 1994;59:229–43. [36] Horwitz B, Rumsey J, Grady C, Rapoport S. The cerebral metabolic landscape in autism: intercorrelations of regional glucose utilization. Arch Neurol 1988;45:749–55. [37] Zilbovicius M, Garreau B, Samson Y, Remy P, Barthelemy C, Syrota A, et al. Delayed maturation of the frontal cortex in childhood autism. Am J Psychiatry 1995;152:248–52. [38] Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex 2007;17:951–61. [39] Koshino H, Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA. fMRI investigation of working memory for faces in autism: visual coding and underconnectivity with frontal areas. Cereb Cortex 2008;18:289–300. [40] Kana R, Keller T, Cherkassky V, Minshew N, Just M. Sentence comprehension in autism: thinking in pictures with decreased functional connectivity. Brain 2006;129:2484–93. [41] Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA. Atypical frontal-posterior synchronization of Theory-of-Mind regions in autism during mental state attribution. Soc Neurosci 2009;4:135–52. [42] Castelli F, Frith C, Happé F, Frith U. Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain 2002;125:1839–49. [43] Kana RK, Keller TA, Minshew NJ, Just MA. Inhibitory control in high functioning autism: decreased activation and underconnectivity in inhibition networks. Biol Psychiatry 2007;62:198–206. [44] Mason RA, Williams DL, Kana RK, Minshew N, Just MA. Theory-of-Mind disruption and recruitment of the right hemisphere during narrative comprehension in autism. Neuropsychologia 2008;46:269–80. [45] Kana RK, Just MA. Autism as a disorder of functional brain connectivity. In: Amaral DG, Geschwind D, Dawson G, editors. Handbook of autism spectrum disorders. New York: Oxford University Press; 2011. p. 981–90. [46] Schipul S, Keller T, Just M. Inter-regional brain communication and its disturbance in autism. Front Neurosci 2011;5:1–11. [47] Damarla SR, Keller TA, Kana RK, Cherkassky VL, Williams DL, Minshew N, et al. Cortical underconnectivity coupled with preserved visuospatial cognition in autism: evidence from an fMRI study of an embedded figures task. Autism Res 2010;3:273–9. [48] Lombardo M, Chakrabarti B, Bullmore E, Baron-Cohen S. Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism. NeuroImage 2011;56:1832–8. [49] Weng S, Wiggins J, Peltier S, Carrasco M, Risi S, Lord C, et al. Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Res 2010;1313:202–14. [50] Mostofsky SH, Powell SK, Simmonds DJ, Goldberg MC, Caffo B, Pekar JJ. Decreased connectivity and cerebellar activity in autism during motor task performance. Brain 2009;132:2413–25. [51] Solomon M, Ozonoff SJ, Ursu S, Ravizza S, Cummings N, Ly S, et al. The neural substrates of cognitive control deficits in autism spectrum disorders. Neuropsychologia 2009;47:515–22. [52] Villalobos ME, Mizuno A, Dahl BC, Kemmotsu N, Müller RA. Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. NeuroImage 2005;25:916–25. [53] Cherkassky V, Kana R, Keller T, Just M. Functional connectivity in a baseline resting-state network in autism. NeuroReport 2006;17:1687– 90. [54] Kleinhans NM, Richards T, Sterling L, et al. Abnormal functional connectivity in autism spectrum disorders during face processing. Brain 2008;131:1000–12. [55] Baron-Cohen S. Mindblindness: an essay on autism and Theory-of-Mind. MIT Press/Bradford Books; 1995. [56] Baron-Cohen S, Leslie AM, Frith U. Does the autistic child have a “Theory-of-Mind”? Cognition 1985;21:37–46. [57] Happé F, Frith U. The neuropsychology of autism. Brain 1996;119:1377–400. [58] Leekam S, Perner J. Does the autistic child have a metarepresentational deficit? Cognition 1991;40:203–18. [59] Perner J, Frith U, Leslie AM, Leekam SR. Exploration of the autistic child’s Theory-of-Mind: knowledge, belief, and communication. Child Dev 1989;60:688–700. [60] Reed T, Peterson C. A comparative study of autistic subjects’ performance at two levels of visual and cognitive perspective taking. J Autism Dev Disord 1990;20:555–68. [61] Swettenham J, Baron-Cohen S, Gomez JC, Walsh S. What’s inside a person’s head? Conceiving of the mind as a camera helps children with autism develop an alternative Theory-of-Mind. Cogn Neuropsychiatry 1996;1:73–88. [62] Williams JH, Whiten A, Suddendorf T, Perrett DI. Imitation, mirror neurons and autism. Neurosci Biobehav Rev 2001;25:287–95. [63] Happé F. The role of age and verbal ability in the ToM performance of subjects with autism. Child Dev 1995;66:843–55. [64] Spek AA, Scholte EM, Berckelaer-Onnes I. Theory-of-Mind in adults with HFA and Asperger syndrome. J Autism Dev Disord 2010;40:280– 9.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

431

[65] Yirmiya N, Erel O, Shaked M, Solomonica-Levi D. Meta-analyses comparing Theory-of-Mind abilities of individuals with autism, individuals with mental retardation and typically developing individuals. Psychol Bull 1998;124:283–307. [66] Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I. The “reading the mind in the eyes” test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatry 2001;42:241–51. [67] Happé F. An advanced test of Theory-of-Mind: understanding of story characters’ thoughts and feelings by able autistic, mentally handicapped, and normal children and adults. J Autism Dev Disord 1994;24:129–54. [68] Klin A. Attributing social meaning to ambiguous visual stimuli in higher-functioning autism and Asperger syndrome: the social attribution task. J Child Psychol Psychiatry 2000;41:831–46. [69] Roeyers H, Buysse A, Ponnet K, Pichal B. Advancing advanced mind-reading tests: empathic accuracy in adults with a pervasive developmental disorder. J Child Psychol Psychiatry 2001;42:271–8. [70] Baron-Cohen S. The autistic child’s Theory-of-Mind: a case of specific developmental delay. J Child Psychol Psychiatry 1989;30:285–97. [71] Frith U. Autism: explaining the enigma. Oxford: Blackwell; 1989. [72] Frith U, Morton J, Leslie A. The cognitive basis of a biological disorder: Autism. Trends Neurosci 1991;14:433–8. [73] Leslie A. Pretense and representation: The origins of “Theory-of-Mind”. Psychol Rev 1987;94:412–26. [74] Clifford SM, Dissanayake C. The early development of joint attention in infants with autistic disorder using home video observations and parental interview. J Autism Dev Disord 2008;30:791–805. [75] Phillips W, Baron-Cohen S, Rutter M. The role of eye-contact in the detection of goals: evidence from normal toddlers, and children with autism or mental handicap. Dev Psychopathol 1992;4:375–83. [76] Mottron L, Mineau S, Martel S, St-Charles Bernier C, Berthiaume C, et al. Lateral glances toward moving stimuli among young children with autism: early regulation of locally-oriented perception? Dev Psychopathol 2007;19:23–36. [77] Dawson G, Carver L, Meltzov A, Pagagiotides H, McPartland J, Webb S. Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay and typical development. Child Dev 2002;73:700–17. [78] Leekam S, Hunnisett E, Moore C. Targets and cues: gaze following in children with autism. J Child Psychol Psychiatry 1998;39:951–62. [79] Leekam S, Lopez B, Moore C. Attention and joint attention in preschool children with autism. Dev Psychol 2000;36:261–73. [80] Kuhn G, Tatler B, Cole G. You look where I look! Effect of gaze cues on overt and covert attention in misdirection. Vis Cogn 2009;17:925–44. [81] Dawson G, Meltzoff A, Osterling J, Rinaldi J, Brown E. Children with autism fail to orient to naturally occurring social stimuli. J Autism Dev Disord 1998;28:479–85. [82] Naber FB, Swinkels SH, Buitelaar JK, Dietz C, Van Daalen E, Bakermans-Kraneburg M, et al. Joint attention and attachment in toddlers with autism. J Abnorm Child Psych 2007;35:899–911. [83] Bernier R, Dawson G, Webb S, Murias M. EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder. Brain Cogn 2007;64:228–37. [84] DeMeyer M, Alpern G, Barton S, DeMyer W, Churchill D, Hingtgen J, et al. Imitation in autistic, early schizophrenic, and non-psychotic subnormal children. J Autism Child Schizophr 1972;2:264–87. [85] Rogers SJ, Hepburn SL, Stackhouse T, Wehner E. Imitation performance in toddlers with autism and those with other developmental disorders. J Child Psychol Psychiatry 2003;44:763–81. [86] Stone W, Lemanek KL, Fishel PT, Fernandez MC, Altmeier WA. Play and imitation skills in the diagnosis of young autistic children. Pediatrics 1990;86:267–72. [87] Vivanti G, Nadig A, Ozonoff S, Rogers S. What do children with autism attend to during imitation tasks? J Exp Child Psychol 2008;101:186– 205. [88] Frith U. Autism: explaining the enigma. Oxford: Blackwell Publishing; 2003. [89] Hamilton AF. Goals, intentions and mental states: challenges for theories of autism. J Child Psychol Psychiatry 2009;50:881–92. [90] Baron-Cohen S, Ring H, Moriarty J, Schmitz B, Costa D, Ell P. Recognition of mental state terms: clinical findings in children with autism and a functional neuroimaging study of normal adults. Brit J Psychiatry 1994;165:640–9. [91] Tager-Flusberg H. Autistic children talk about psychological states: deficits in the early acquisition of a Theory-of-Mind. Child Dev 1992;63:161–72. [92] Peterson C, Garnett M, Kelly A, Attwood T. Everyday social and conversation applications of Theory-of-Mind understanding by children with autism-spectrum disorders or typical development. Eur Child Adolesc Psychiatry 2009;18:105–15. [93] Tager-Flusberg H. Exploring the relationships between Theory-of-Mind and social-communicative functioning in children with autism. In: Repacholi B, Slaughter V, editors. Individual differences in Theory-of-Mind: implications for typical and atypical development. London: Psychology Press; 2003. p. 197–212. [94] Peterson C, Slaughter V, Paynter J. Social maturity and Theory-of-Mind in typically developing children and those on the autism spectrum. J Child Psychol Psychiatry 2007;48:1243–50. [95] Peterson CC, Slaughter V. Theory-of-Mind (ToM) in children with autism or typical development: links between eye-reading and false belief understanding. Res Autism Spect Dis 2009;3:462–73. [96] Filippova E, Astington JW. Further development in social reasoning revealed in discourse irony understanding. Child Dev 2008;79:126–38. [97] Adachi T, Koeda T, Hirabayashi S, Maeoka Y, Shiota M, Wright EC. The metaphor and sarcasm scenario test: a new instrument to help differentiate high functioning pervasive developmental disorder from attention deficit/hyperactivity disorder. Brain Dev 2004;26:301–6. [98] Baron-Cohen S. Are children with autism superior at folk physics? In: Wellman H, Inagaki K, editors. The emergence of cre domains of thought: children’s reasoning about physical, psychological and biological phenomena. New Dir Child Adolesc Dev 1997;75:45–54. [99] Baron-Cohen S, Ring HA, Wheelwright S, Bullmore ET, Brammer MJ, Simmons A, et al. Social intelligence in the normal and autistic brain: an fMRI study. Eur J Neurosci 1999;11:1891–8. [100] Dennis M, Lockyer L, Lazenby A. How high-functioning children with autism understand real and deceptive emotion. Autism 2000;4:370– 81.

432

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

[101] Dennis M, Lazenby AL, Lockyer L. Inferential language in high-function children with autism. J Autism Dev Disord 2001;31:47–54. [102] Gillberg IC, Gillberg C. Asperger syndrome—some epidemiological considerations: a research note. J Child Psychol Psychiatry 1989;30:631–8. [103] Happé F. Theory-of-Mind and communication in autism. Unpublished PhD thesis. University of London; 1991. [104] Happé F. Communicative competence and Theory-of-Mind in autism: a test of relevance theory. Cognition 1993;48:101–19. [105] Jolliffe T, Baron-Cohen S. A test of central coherence theory: linguistic processing in high-functioning adults with autism or Asperger syndrome: is local coherence impaired? Cognition 1999;71:149–85. [106] Jolliffe T, Baron-Cohen S. The strange stories test: a replication with high-functioning adults or Asperger syndrome. J Autism Dev Disord 1999;29:395–404. [107] Kaland N, Moller-Nielsen A, Callesen K, Mortensen EL, Gottlieb D, Smith L. A new ‘advanced’ test of Theory-of-Mind: evidence from children and adolescents with Asperger syndrome. J Child Psychol Psychiatry 2002;43:517–28. [108] MacKay G, Shaw A. A comparative study of figurative language in children with autistic spectrum disorders. Child Lang Teach 2004;20:13– 32. [109] Martin I, McDonald S. An exploration of causes of nonliteral language problems in individuals with Asperger syndrome. J Autism Dev Disord 2004;34:311–28. [110] Ozonoff S, Miller JN. An exploration of right hemisphere contributions to the pragmatic impairments of autism. Brain Lang 1996;52:411–34. [111] Wang A, Lee S, Sigman M, Dapretto M. Neural basis of irony comprehension in children with autism: the role of prosody and context. Brain 2006;129:932–43. [112] Ricks DM, Wing L. Language, communication and the use of symbols in normal and autistic children. J Autism Child Schizophr 1975;5:191– 221. [113] Pexman P, Rostad K, McMorris C, Climie E, Stowkowy J, Glenwright M. Processing of ironic language in children with high-functioning autism spectrum disorder. J Autism Dev Disord 2011;41:1097–112. [114] Samson AC, Hegenloh M. Structural stimulus properties affect humor processing in individuals with Asperger syndrome. J Autism Dev Disord 2010;40:438–47. [115] Reddy V, Williams E, Vaughan A. Sharing humour and laughter in autism and Down’s syndrome. Brit J Psychol 2002;93:219–42. [116] Baron-Cohen S. Hey! It was just a joke! Understanding propositions and propositional attitudes by normally developing children and children with autism. Isr J Psychiatr 1997;34:174–8. [117] Tager-Flusberg H. Evaluating the Theory-of-Mind hypothesis of autism. Curr Dir Psych Sci 2007;16:311–5. [118] Carlson S, Mandell D, Williams L. Executive function and Theory-of-Mind: stability and prediction from ages 2 to 3. Dev Psychol 2004;40:1105–22. [119] Carlson S, Moses L. Individual differences in inhibitory control and children’s Theory-of-Mind. Child Dev 2001;72:1032–53. [120] Frye D, Zelazo PD, Palfai T. Theory-of-Mind and rule-based reasoning. Cogn Dev 1995;10:483–527. [121] Hughes C. Executive function in preschoolers: links with Theory-of-Mind and verbal ability. Brit J Dev Psychol 1998;16:233–53. [122] Hughes C. Finding your marbles: does preschoolers’ strategic behavior predict later understanding of mind? Dev Psychol 1998;34:1326–39. [123] Hughes C, Ensor R. Theory-of-Mind and executive function in 2-year-olds: a family affair? Dev Neuropsychol 2005;28:645–68. [124] Hill EL. Executive dysfunction in autism. Trends Cogn Sci 2004;8:26–32. [125] Yang J, Zhou S, Yao S, Su L, McWhinnie C. The relationship between Theory-of-Mind and executive function in a sample of children from mainland China. Child Psychiatr Hum Dev 2009;40:169–82. [126] Pellicano E. Individual differences in executive function and central coherence predict later understanding of mind in autism. Dev Psychol 2010;46:530–44. [127] Fletcher P, Frith C, Baker S, Shallice T, Frackowiak R, Dolan R. The mind’s eye-precuneus activation in memory-related imagery. NeuroImage 1995;2:195–200. [128] Gallagher H, Happé F, Brunswick N, Fletcher P, Frith U, Frith C. Reading the mind in cartoons and stories: an fMRI study of ‘Theory-ofMind’ in verbal and nonverbal tasks. Neuropsychologia 2000;38:11–21. [129] Vogeley K, Bussfeld P, Newen A, Herrmann S, Happé F, Falkai P, et al. Mind reading: neural mechanisms of Theory-of-Mind and selfperspective. NeuroImage 2001;14:170–81. [130] Ruby P, Decety J. What you believe versus what you think they believe: a neuroimaging study of conceptual perspective-taking. Eur J Neurosci 2003;17:2475–80. [131] Saxe R, Kanwisher N. People thinking about thinking people: fMRI investigations of Theory-of-Mind. NeuroImage 2003;19:1835–42. [132] Pelphrey K, Morris J, McCarthy G. Grasping the intentions of others: the perceived intentionality of an action influences activity in the superior temporal sulcus during social perception. J Cogn Neurosci 2004;16:1706–16. [133] Schulte-Ruther M, Griemel E, Markowitsch H, Kamp-Becker I, Remschmidt H, Fink G, et al. Dysfunctions in brain networks supporting empathy: an fMRI study in adults with autism spectrum disorders. Soc Neurosci 2011;17:1–21. [134] Oberman L, Hubbard E, McCleery J, Altschuler E, Ramachandran V, Pineda J. EEG evidence for mirror neuron dysfunction in autism spectrum disorders. Cogn Brain Res 2005;24:190–8. [135] Nishitani N, Avikainen S, Hari R. Abnormal imitation-related cortical activation sequences in Asperger syndrome. Ann Neurol 2004;55:558– 62. [136] Carmichael S, Price J. Connectional networks within the orbital and medial prefrontal cortex of macaque monkeys. J Comp Neurol 1996;371:179–207. [137] Barbas H, Ghashghaei H, Dombrowski S, Rempel-Clower N. Medial prefrontal cortices are united by common connections with superior temporal cortices and distinguished by input from memory-related areas in the rhesus monkey. J Comp Neurol 1999;410:343–67. [138] Zilbovicius M, Garreau B, Samson Y, Remy P, Barthelemy C, Syrota A, et al. Delayed maturation of the frontal cortex in childhood autism. Am J Psychiatry 1995;152:248–52.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

433

[139] Hardan AY, Jou RJ, Keshavan MS, Varma R, Minshew NJ. Increased frontal cortical folding in autism: a preliminary MRI study. Psychiatry Res 2004;131:263–8. [140] Nordahl C, Dierker D, Mostafavi I, Schumann C, Rivera S, Amaral D, et al. Cortical folding abnormalities in autism revealed by surfacebased morphometry. J Neurosci 2007;24:11725–35. [141] Casanova MF, Buxhoeveden DP, Switala AE, Roy E. Minicolumnar pathology in autism. Neurology 2002;58:428–32. [142] Buxhoeveden D, Semendeferi K, Schenker N, Courchesne E. Decreased cell column spacing in autism [abstract]. Soc Neurosci 2004;40:6. [143] Carper R, Moses P, Tigue Z, Courchesne E. Cerebral lobes in autism: early hyperplasia and abnormal age effects. NeuroImage 2002;16:1038– 51. [144] Herbert M, Ziegler D, Deutsch C, O’Brien L, Lange N, Bakardjiev A, et al. Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain 2003;126:1182–92. [145] Herbert M, Ziegler D, Makris N, Filipek P, Kemper T, Normandin J, et al. Localization of white matter volume increase in autism and developmental language disorder. Ann Neurol 2004;55:530–40. [146] Chung MK, Robbins SM, Dalton KM, Davidson RJ, Alexander AL, Evans AC. Cortical thickness analysis in autism with heat kernel smoothing. NeuroImage 2005;25:1256–65. [147] Waiter G, Williams J, Murray A, Perrett D, Whiten A. Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: a voxel-based investigation. NeuroImage 2005;24:455–61. [148] Hendry J, DeVito T, Gelman N, Densmore M, Rajakumar N, Pavlosky W, et al. White matter abnormalities in autism detected through transverse relaxation time imaging. NeuroImage 2006;29:1049–57. [149] Alexander AL, Lee JE, Lazar M, Boudos R, DuBray MB, Oakes TR, et al. Diffusion tensor imaging of the corpus callosum in autism. NeuroImage 2007;34:61–73. [150] Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss AL. White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 2004;55:323–6. [151] Keller TA, Kana RK, Just MA. A developmental study of the structural integrity of white matter in autism. NeuroReport 2007;18:23–7. [152] Heal J. Replication and functionalism. In: Butterfield J, editor. Language, mind, and logic. Cambridge: Cambridge University Press; 1986. reprinted in: Davies M, Stone T, editors. Folk psychology: the Theory-of-Mind debate. Oxford: Blackwell Publishers; 1995. [153] Gordon R. Folk psychology as simulation. Mind Lang 1986;1:158–71. [154] Goldman A. The mentalizing folk. In: Sperber D, editor. Metarepresentation. London: Oxford; 1998. p. 171–96. [155] Rizzolatti G, Fadiga L, Gallese V, Fogassi L. Premotor cortex and the recognition of motor actions. Brain Res Cogn Brain Res 1996;3:131– 41. [156] Gallese V, Goldman A. Mirror neurons and the simulation Theory-of-Mind-reading. Trends Cogn Sci 1998;2:493–501. [157] Avikainen S, Wohlschlager A, Liuhanen S, Hanninen R, Hari R. Impaired mirror-image imitation in Asperger and high-functioning autistic subjects. Curr Biol 2002;13:339–41. [158] Gazzola V, Aziz-Zadeh L, Keysers C. Empathy and the somatotopic auditory mirror system in humans. Curr Biol 2006;16:1824–9. [159] Gazzola V, Rizzolatti G, Wicker B, Keysers C. The anthropomorphic brain: the mirror neuron system responds to human and robotic actions. NeuroImage 2007;35:1674–84. [160] Hadjikhani N, Joseph R, Snyder J, Tager-Flusberg H. Anatomical differences in the mirror neuron system and social cognition network in autism. Cereb Cortex 2006;16:1276–82. [161] Iacoboni M, Dapretto M. The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci 2006;7:942–51. [162] Keysers C, Gazzola V. Integrating simulation and Theory-of-Mind: from self to social cognition. Trends Cogn Sci 2007;11:194–6. [163] Uddin L, Iacoboni M, Lange C, Keenan J. The self and social cognition: the role of cortical midline structures and mirror neurons. Trends Cogn Sci 2007;11:153–7. [164] De Lange FP, Spronk M, Willems RM, Toni I, Bekkering H. Complementary systems for understanding action intentions. Curr Biol 2008;18:454–7. [165] Rizzolatti G, Fogassi L, Gallese V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci 2001;2:661–70. [166] Kohler E, Keysers C, Umilta M, Fogassi L, Gallese V, Rizzolatti C. Hearing sounds, understanding actions: action representation in mirror neurons. Science 2002;297:846–8. [167] Beauchamp M, Lee K, Argall B, Martin A. Integration of auditory and visual information about objects in superior temporal sulcus. Neuron 2004;41:809–23. [168] Lahav A, Saltzman E, Schlaug G. Action representation of sound: audiomotor recognition network while listening to newly acquired actions. J Neurosci 2007;27:308–14. [169] Kitada R, Johnsrude I, Kochiyama T, Lederman S. Functional specialization and convergence in the occipito-temporal cortex supporting haptic and visual identification of human faces and body parts: an fMRI study. J Cogn Neurosci 2009;21:2027–45. [170] Dapretto M, Davies M, Pfeifer J, Scott A, Sigman M, Bookheimer S, et al. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nat Neurosci 2006;9:28–30. [171] Williams J, Waiter G, Gilchrist A, Perrett D, Murray A, Whiten A. Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder. Neuropsychologia 2006;44:610–21. [172] Kana R, Wadsworth H, Travers B. A systems level analysis of the mirror neuron hypothesis and imitation impairments in autism spectrum disorders. Neurosci Biobehav Rev 2010;35:894–902. [173] Flavell J, Green F, Flavell E. Development of knowledge about the appearance-reality distinction. Monogr Soc Res Child Dev 1986;51:1–87. [174] Wellman H, Estes D. Early understanding of mental entities: a reexamination of childhood realism. Child Dev 1986;57:910–23. [175] Leslie A. Pretense and representation: the origins of ‘Theory-of-Mind’. Psychol Rev 1987;94:412–26.

434

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

[176] Baron-Cohen S. Theory-of-Mind in normal development and autism. Prisme 2001;34:174–83. [177] Dawson G, Webb S, Schellenberg GD, Dager S, Friedman S, Aylward E, et al. Defining the broader phenotype of autism: genetic, brain, and behavioral perspectives. Dev Psychopathol 2002;14:581–611. [178] Behrmann M, Thomas C, Humphreys K. Seeing it differently: visual processing in autism. Trends Cogn Sci 2006;10:258–64. [179] Moran J, Young L, Saxe R, Lee S, O’Young D, Mavros P, et al. Impaired Theory-of-Mind for moral judgment in high-functioning autism. Proc Natl Acad Sci 2011;108:2688–92. [180] Hughes C, Russell J, Robbins T. Evidence for executive dysfunction in autism. Neuropsychologia 1994;32:477–92. [181] Rabbitt P. Methodology of frontal and executive function. Hove: Psychology Press; 1997. [182] Roberts A, Robbins T, Weiskrantz L. The prefrontal cortex: executive and cognitive functions. Oxford: Oxford University Press; 1998. [183] Stuss D, Knight R. Principles of frontal lobe function. Oxford: Oxford University Press; 2002. [184] Pennington BF, Ozonoff S. Executive functions and developmental psychopathology. J Child Psychol Psychiatry 1996;37:51–87. [185] Salmond CH, de Haan M, Friston K, Gadian D, Vargha-Khadem F. In: Frith U, Hill E, editors. Autism: mind and brain. New York: Oxford University Press; 2003. p. 247–65. [186] Heaton RK, Chelune GJ, Talley JL, Kay GG, Curtiss G. Wisconsin card sorting test manual: revised and expanded. Odessa: Psychological Assessment Resources; 1993. [187] Bennetto L, Pennington B, Rogers S. Intact and impaired memory functions in autism. Child Dev 1996;67:1816–35. [188] Goldstein G, Johnson C, Minshew N. Attentional processes in autism. J Autism Dev Disord 2001;31:433–40. [189] Ozonoff S. Reliability and validity of the Wisconsin card sorting test in studies of autism. Neuropsychology 1995;9:491–500. [190] Pellicano E, Mayberry M, Durkin K, Maley A. Multiple cognitive capabilities/deficits in children with an autism spectrum disorder: “Weak” central coherence and its relationship to Theory-of-Mind and executive control. Dev Psychopathol 2006;18:77–98. [191] Tsuchiya E, Oki J, Yahara N, Fujieda K. Computerized version of the Wisconsin card sorting test in children with high-functioning autistic disorder or attention-deficit/hyperactivity disorder. Brain Dev 2005;27:233–6. [192] Willcutt E, Sonuga-Barke E, Nigg J, Sergeant J. Recent developments in neuropsychological models of childhood psychiatric disorders. Recent Adv Biol Psychiatr 2008;24:195–226. [193] Sanders J, Johnson KA, Garavan H, Gill M, Gallagher L. A review of neuropsychological and neuroimaging research in autistic spectrum disorders: attention, inhibition and cognitive flexibility. Res Autism Spect Dis 2008;2:1–16. [194] Guerts HM, Corbett B, Solomon M. The paradox of cognitive flexibility in autism. Trends Cogn Sci 2009;13:74–82. [195] Goldberg MC, Mostofsky SH, Cutting LE, Mahone EM, Astor BC, Kenckla MB, et al. Subtle executive impairment in children with autism and children with ADHD. J Autism Dev Disord 2005;35:279–93. [196] Sinzig J, Morsch D, Bruning N, Schmidt M, Lehmkuhl G. Inhibition, flexibility, working memory and planning in autism spectrum disorders with and without comorbid ADHD-symptoms. Child Adolesc Psychiatry Ment Health 2008;2:1–12. [197] Landa RJ, Goldberg MC. Language, social, and executive functions in high functioning autism: a continuum of performance. J Autism Dev Disord 2005;35:557–73. [198] Ozonoff S, Cook I, Coon H, Dawson G, Joseph R, Klin A, et al. Performance on Cambridge neuropsychological test automated battery subtests sensitive to frontal lobe function in people with autistic disorder: evidence from the collaborative programs of excellence in autism network. J Autism Dev Disord 2004;34:139–50. [199] Lopez B, Lincoln A, Ozonoff S, Lai Z. Examining the relationship between executive functions and restricted repetitive symptoms of autistic disorder. J Autism Dev Disord 2005;35:445–59. [200] South M, Ozonoff S, McMahon W. The relationship between executive functioning, central coherence, and repetitive behaviors in the highfunctioning autism spectrum. Autism 2007;11:437–51. [201] Dichter G, Felder J, Bodfish J. Autism is characterized by dorsal anterior cingulate hyperactivation during social target detection. Soc Cogn Affect Neurosci 2009;4:215–26. [202] Happé F. Central coherence and Theory-of-Mind in autism: reading homographs in context. Brit J Dev Psychol 1997;15:1–12. [203] Carper RA, Courchesne E. Localized enlargement of the frontal cortex in early autism. Biol Psychiatry 2005;57:126–33. [204] McAlonan GM, Cheung V, Cheung C, Suckling J, Lam G, Tai K, et al. Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2002;128:268–76. [205] Schmitz N, Rubia K, Daly E, Smith A, Williams S, Murphy DG. Neural correlates of executive function in autistic spectrum disorders. Biol Psychiatry 2006;59:7–16. [206] Shafritz KM, Dichter GS, Baranek GT, Belger A. The neural circuitry mediating shifts in behavioral response and cognitive set in autism. Biol Psychiatry 2008;63:974–80. [207] Bush G, Luu P, Posner M. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 2000;4:215–22. [208] Thakkar K, Polli F, Joseph R, Tuch D, Hadjikhani N, Barton J, et al. Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain 2008;131:2464–78. [209] Leyfer OT, Folstein SE, Bacalman S, Davis NO, Dinh E. Comorbid psychiatric disorders in children with autism: interview development and rates of disorders. J Aut Dev Disord 2006;36:849–61. [210] Langen M, Durston S, Kas M, van Engeland H, Staal W. The neurobiology of repetitive behavior: . . . and men. Neurosci Biobehav R 2011;35:356–65. [211] Hollander E, Anagnostou E, Chaplin W, Esposito K, Haznedar M, Licalzi E, et al. Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol Psychiatry 2005;58:226–32. [212] Langen M, Durston S, Staal W, Palmen S, van Engeland H. Caudate nucleus is enlarged in high-functioning medication-naïve subjects with autism. Biol Psychiatry 2007;62:262–6. [213] Minshew N, Meyer J, Goldstein G. Abstract reasoning in autism: a dissociation between concept formation and concept identification. Neuropsychology 2002;16:327–34.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

435

[214] Kail R, Salthouse T. Processing speed as a mental capacity. Acta Psychol 1994;86:199–225. [215] Salthouse T. The processing-speed theory of adult age differences in cognition. Psychol Rev 1996;103:403–28. [216] Salthouse T. Age and memory: strategies for localizing the loss. In: Poon L, Fozard J, Cermak L, Arenberg D, Thompson L, editors. New directions in memory and aging: proceedings of the George A. Talland memorial conference. Hillsdale: Erlbaum; 1980. [217] Just M, Carpenter P, Varma S. Computational modeling of high-level cognition and brain function. Hum Brain Mapp 1999;8:128–36. [218] Rumsey J. High-functioning individuals with autism. In: Schopler E, Mesibov GB, editors. Neuropsychological studies of high-level autism. New York: Plenum Press; 1992. p. 41–64. [219] Wechsler D. Wechsler scales of intelligence. 3rd ed. San Antonio, TX: Psychological Corporation; 1991. [220] Mayes S, Calhoun S. Ability profiles in children with autism: influence of age and IQ. Autism 2003;6:65–80. [221] Nydén A, Billstedt E, Hjelmquist E, Gillberg C. Neurocognitive stability in Asperger syndrome, ADHD, and reading and writing disorder: a pilot study. Dev Med Child Neurol 2001;43:165–71. [222] Calhoun S, Mayes S. Processing speed in children with clinical disorders. Psychol Schools 2005;42:333–43. [223] Mayes SD, Calhoun SL. Learning, attention, writing, and processing speed in typical children and children with ADHD, autism, anxiety, depression, and oppositional-defiant disorder. Child Neuropsych 2007;13:469–93. [224] Spek AA, Scholte EM, Berckelaer-Onnes IA. Theory-of-Mind in adults with HFA and Asperger syndrome. J Aut Dev Disord 2009;40:280–9. [225] Case R. Validation of a neo-Piagetian capacity construct. J Exp Child Psychol 1972;14:287–302. [226] Diamond A, Doar B. The performance of human infants on a measure of frontal cortex function, the delayed response task. Dev Psychobiol 1989;22:271–94. [227] Pascual-Leone J. A mathematical model for the transition rule in Piaget’s developmental stages. Acta Psychol 1970;63:301–45. [228] Eskes G, Bryson S, McCormick T. Comprehension of concrete and abstract words in autistic children. J Autism Dev Disord 1990;20:61–73. [229] Ozonoff S, Jensen J. Specific executive function profiles in three neurodevelopmental disorders. J Autism Dev Disord 1999;29:171–7. [230] Ozonoff S, Strayer D. Inhibitory function in nonretarded children with autism. J Autism Dev Disord 1997;27:59–77. [231] Ozonoff S, Strayer D, McMahon W, Filloux F. Executive function abilities in autism and Tourette syndrome: an information processing approach. J Child Psychol Psychiatry 1994;35:1015–32. [232] Brian J, Tipper S, Weaver B, Bryson S. Inhibitory mechanisms in autism spectrum disorders: typical selective inhibition of location versus facilitated perceptual processing. J Child Psychol Psychiatry 2003;44:552–60. [233] Minshew NJ, Luna B, Sweeney JA. Oculomotor evidence for neocortical systems but not cerebellar dysfunction in autism. Neurology 1999;52:917–22. [234] Russell J, editor. Autism as an executive disorder. Oxford: Oxford University Press; 1997. [235] Hughes C, Russell J. Autistic children’s difficulty with mental disengagement from an object: its implications for theories of autism. Dev Psychol 1993;29:498–510. [236] Grandin T. My mind as a web browser—how people with autism think. Cerebrum 2000;9:13–22. [237] Burack J, Enns J, Johannes E. Attention and autism: behavioral and electrophysiological evidence. In: Cohen D, Volkmar F, editors. Handbook of autism and pervasive developmental disorders. New York: J. Wiley; 1997. p. 226–47. [238] Luna B, Doll S, Hegedus S, Minshew N, Sweeney J. Maturation of executive function in autism. Biol Psychiatry 2007;61:474–81. [239] Minshew NJ, Goldstein G, Siegel DJ. Neuropsychologic functioning in autism: profile of a complex information processing disorder. J Int Neuropsych Soc 1997;3:303–16. [240] García-Villamisar D, Della Sala S. Dual-task in adults with autism. Cogn Neuropsychiatry 2002;5:139–56. [241] Prifitera A, Weiss LG, Saklofske DH. The WISC-III in context. In: Prifitera A, Saklofske DH, editors. WISC-III clinical use and interpretation: scientist–practitioner perspectives. New York: Academic Press; 1998. p. 1–38. [242] Goldstein G, Johnson C, Minshew NJ. Attentional processes in autism. J Autism Dev Disord 2001;31(4):433–46. [243] Nyden A, Billstedt E, Hjelmquist E, Gillberg C. Neurocognitive stability in Asperger syndrome, ADHS, and reading and writing disorder: a pilot study. Dev Med Child Neurol 2001;43(3):165–71. [244] Snow J, Sapp G. WISC-III subtest patterns of ADHD and normal samples. Psychol Rep 2000;87:759–65. [245] Noterdaeme M, Mildenberger K, Minow F, Amorosa H. Evaluation of neuromotor deficits in children with autism and children with a specific speech and language disorder. Eur Child Adolesc Psychiatry 2002;11:219–25. [246] Mandelbaum DE, Stevens M, Rosenberg E, Wiznitzer M, Steinschneider M, Filipek P, et al. Sensorimotor performance in school-age children with autism, developmental language disorder, or low IQ. Dev Med Child Neurol 2006;48:33–9. [247] Ozonoff S, Cook I, Coon H, Dawson G, Joseph RM, Klin A, et al. Performance on Cambridge neuropsychological test automated battery subtests sensitive to frontal lobe function in people with autistic disorder: evidence from the collaborative programs of excellence in autism network. J Autism Dev Disord 2004;34:139–50. [248] Wainwright-Sharp J, Bryson S. Visual orienting deficits in high-functioning people with autism. J Autism Dev Disord 1993;23:1–13. [249] Townsend J, Courchesne E, Egaas B. Slowed orienting of covert visual-spatial attention in autism: specific deficits associated with cerebellar and parietal abnormality. Dev Psychopathol 1996;8:563–84. [250] McPartland J, Dawson G, Webb S, Panagiotides H, Carver L. Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder. J Child Psychol Psychiatry 2004;45:1235–45. [251] Dawson G, Webb S, Carver L, Panagiotides H, McPartland J. Young children with autism show atypical brain responses to fearful versus neutral facial expressions. Dev Sci 2004;7:340–59. [252] Oliveras-Rentas R, Kenworthy L, Roberson R, Martin A, Wallace G. WISC-IV profile in high-functioning autism spectrum disorders: impared processing speed is associated with increased autism communication symptoms and decreased adaptive communication abilities. J Autism Dev Disord 2011:1–10. [253] Wechsler D. Wechsler intelligence scale for children. 4th ed. San Antonio, TX: Pearson Education, Inc.; 2003.

436

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

[254] Schmitz N, Daly E, Murphy D. Frontal anatomy and reaction time in autism. Neurosci Lett 2007;412:12–7. [255] Anognostou E, Taylor MJ. Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Mol Autism 2011;18. [256] Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ. Autism and abnormal development of brain connectivity. J Neurosci 2004;24:9228–31. [257] Courchesne E, Pierce K. Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection. Curr Opin Neurobiol 2005;15:225–30. [258] Frith U. Autism: explaining the enigma. Blackwell Publishing; 2003. [259] Frith U, Happé F. Autism: beyond “Theory-of-Mind”. Cognition 1994;50:115–32. [260] Happe F. Autism: cognitive deficit or cognitive style? Trends Cogn Sci 1999;3(6):216–22. [261] Baron-Cohen S. Mindblindness: an essay on autism and Theory-of-Mind. MIT Press/Bradford Books; 1995. [262] Newman S, Carpenter P, Varma S, Just M. Frontal and parietal participation in problem solving in the tower of London: fMRI and computational modeling of planning and high level perception. Neuropsychologia 2003;41:1668–82. [263] Koshino H, Carpenter PA, Minshew NJ, Cherkassy VL, Keller TA, Just MA. Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage 2005;24:810–21. [264] Bar M, Kassam KS, Ghuman AS, Boshyan J, Schmid AM, Dale AM, et al. Top-down facilitation of visual recognition. Proc Natl Acad Sci 2006;103:449–54. [265] Sahyoun CP, Belliveau JW, Soulieres I, Schwartz S, Mody M. Neuroimaging of the functional and structural networks underlying visuospatial versus linguistic reasoning in high-functioning autism. Neuropsychologia 2010;48:86–95. [266] Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, et al. Unusual brain growth patterns in early life in patients with autistic disorder. Neurology 2001;57:245–54. [267] Piven J, Arndt S, Bailey J, Havercamp S, Andreasen NC, Palmer P. An MRI study of brain size in autism. Am J Psychiatry 1995;152:1145–9. [268] Lewis JD, Elman JL. Growth-related neural reorganization and the autism phenotype: a test of the hypothesis that altered brain growth leads to altered connectivity. Dev Sci 2008;11(1):135–55. [269] Jou R, Jackowski A, Papademetris X, Rajeevan N, Staib L, Volkmar F. Diffusion tensor imaging in autism spectrum disorders: preliminary evidence of abnormal neural connectivity. Aust N Z J Psychiatry 2011;45:153–62. [270] Lee JE, Bigler ED, Alexander AL, Lazar M, DuBray MD, Chung MK, et al. Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism. Neurosci Lett 2007;424:127–32. [271] Monk C, Peltier S, Wiggins J, Weng S, Carrasco M, Risi S, et al. Abnormalities of intrinsic functional connectivity in autism spectrum disorders. NeuroImage 2009;15:764–72. [272] Rinaldi T, Silberg G, Markram H. Hyperconnectivity of local neocortical microcircuitry induced by prenatal exposure to valproic acid. Cereb Cortex 2008;18:763–70. [273] Casanova M, Trippe J. Radial cytoarchitecture and patterns of cortical connectivity in autism. Philos Trans R Soc B 2009;364:1433–6. [274] Casanova M, van Kooten I, Switala A, van Engeland H, Heinsen H, Steinbusch H, et al. Minicolumnar abnormalities in autism. Acta Neuropathol 2006;112:287–303. [275] Just MA, Keller TA, Malave V, Kana RK, Varma S. Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity [submitted for publication]. [276] Belmonte MK, Yurgelun-Todd DA. Functional anatomy of impaired selective attention and compensatory processing in autism. Cognitive Brain Res 2003;17:651–64. [277] Ring H, Baron-Cohen S, Williams S, Wheelwright S, Bullmore E, et al. Cerebral correlates of preserved cognitive skills in autism. A functional MRI study of Embedded Figures task performance. Brain 1999;122:1305–15. [278] Baron-Cohen S, Belmonte MK. Autism: a window onto the development of the social and the analytic brain. Annu Rev Neurosci 2005;28:109–26. [279] Gaffrey MS, Kleinhans NM, Haist F, Akshoomoff N, Campbell A, Courchesne E, et al. Atypical participation of visual cortex during word processing in autism: An fMRI study of semantic decision. Neuropsychologia 2007;45:1672–84. [280] Manjaly ZM, Bruning N, Neufang S, Stephan KE, Brieber S, Marshall JC, et al. Neurophysiological correlates of relatively enhanced local visual search in autistic adolescents. NeuroImage 2007;35:283–91. [281] Murdaugh D, Shinkareva S, Deshpande R, Wang J, Kana R. Default mode network in autism: differential deactivation and altered connectivity during mentalizing [submitted for publication]. [282] Buckner R, Andrews-Hanna J, Schacter D. The brain’s default network: anatomy, function, and relevance to disease. Ann NY Acad Sci 2008;1124:1–38. [283] Raichle M, MacLeod A, Snyder A, Powers W, Gusnard D, Shulman G. A default mode of brain function. Proc Natl Acad Sci 2001;98:676– 82. [284] Shulman G, Fiez J, Corbetta M, Buckner R, Miezin F, Raichle M, et al. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J Cogn Neurosci 1997;9:648–63. [285] Greicius M, Krasnow B, Reiss A, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci 2003;100:253–8. [286] Fox M, Snyder A, Vincent J, Corbetta M, Van Essen D, Raichle M. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci 2005;102:9673–8. [287] Broyd S, Demanuele C, Debener S, Helps S, James C, Sonuga Barke E. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 2009;33:279–96. [288] Kennedy D, Courchesne E. The intrinsic functional organization of the brain is altered in autism. NeuroImage 2008;39:1877–85.

R.K. Kana et al. / Physics of Life Reviews 8 (2011) 410–437

437

[289] Kennedy D, Courchesne E. Functional abnormalities of the default network during self- and other-reflection in autism. Soc Cogn Affect Neurosci 2008;3:177–90. [290] Kennedy D, Redcay E, Courchesne E. Failing to deactivate: resting functional abnormalities in autism. Proc Natl Acad Sci 2006;103:8275– 80. [291] Monk CS, Peltier SJ, Wiggins JL, Weng SJ, Carrasco M, Risi S, et al. Abnormalities of intrinsic functional connectivity in autism spectrum disorders. NeuroImage 2009;47:764–72. [292] Assaf M, Jagannathan K, Calhoun V, Miller L, Stevens M, Sahl R, et al. Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage 2010;15:247–56. [293] Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 2009;19:72–8. [294] Frith U, Happé F. Theory-of-Mind and self-consciousness: what is it like to be autistic? Mind Lang 1999;14:1–22. [295] Hurlburt R, Happé F, Frith U. Sampling the form of inner experience in three adults with Asperger syndrome. Psychol Med 1994;24:385–95. [296] Redcay E, Courchesne E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biol Psychiatry 2005;58:1–9. [297] Rubenstein J, Merzenich M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2003;2:255– 67. [298] Buxhoeveden DP, Casanova M. The minicolumn hypothesis in neuroscience. Brain 2002;125:935–51. [299] Zikopoulos B, Barbas H. Changes in prefrontal axons may disrupt the network in autism. J Neurosci 2010;30:14595–609. [300] Haynes J-D, Rees G. Decoding mental states from brain activity in humans. Nat Rev Neurosci 2006;7:523–34. [301] Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly E, Brammer M, et al. Describing the brain in autism in five dimensions: magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci 2010;30:10612–23.