Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought’s underlying brain activation patterns.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All data on which this paper is based have been uploaded to the National Institutes of Health Database for Autism Research (NDAR) ( https://ndar.nih.gov/access.html ) with accession number P50HD55748-01. NDAR policy requires that qualified investigators follow the procedure described at https://ndar.nih.gov/access.html to request access to these data.

Introduction

Psychiatric disorders of thought are usually characterized and diagnosed on the basis of clinical assessment of an individual’s verbal and physical behavior. This is the conventional way to assess a thought disorder. However, recent advances in brain reading have made it possible to identify neurocognitive representations based on the underlying brain activation patterns assessed with fMRI [1]–[5]. These innovations have advanced from merely associating an activation pattern with a particular thought to decomposing the activation pattern into its neural and psychological components. For example, the activation pattern corresponding to the thought of a banana consists of components representing how one holds a banana (indicated in several premotor areas) and how one eats a banana (represented in eating-related areas). Another example is that the thought of an emotion such as sadness can be identified in terms of the neural representation of its valence, degree of arousal, and sociality [3]. Thus it has become possible to assess the content of a thought in neurotypical populations.

In our study, this approach was applied to characterize the altered neural representation of social concepts in autism, known to be disordered in terms of psychiatric diagnosis. If certain types of social concepts are altered in autism, it may be possible to (a) detect the alterations and possibly interpret them as diagnostic of autism; and (b) understand the biological and psychological nature of the alterations in terms of the underlying dimensions of neural representation; and (c) make use of the understanding to develop therapies that ameliorate the alteration. Furthermore, if the approach is successful with respect to autism, it may hold promise for application to other psychiatric disorders.

One of the largest challenges in autism research is to determine the relation between the psychological alterations in autism (assessed in behavioral and psychiatric studies) and the neural alterations (assessed in neuroscience and particularly brain imaging studies). Because the social alterations are often the most prominent ones in autism, fMRI studies of autism have investigated the relation between brain and behavior with respect to several different types of social processing. One of the earliest-studied social functions investigated with fMRI was face perception, during which it was found that the fusiform face area (a brain region associated with the processing of faces) activated abnormally in autism [6]. A second type of social task in which altered activation was found in autism was in Theory of Mind processing in which participants must understand the mental state of another individual (and in which there is altered activation in autism in the medial frontal and temporoparietal junction regions) [7].

A third type of autism alteration involved in social processing (and arguably the most central one) concerns the altered conception of self (see Uddin [8] for a review). The altered conception of self in autism is at the focus of the current study. Since its first description by Kanner [9], autism has always been prominently associated with a disruption of the social relation between self and others. In fact, the word autism stems from the Greek autos meaning self. Although self representation may have several types of components, such as visual self-recognition and perspective, the facet of self that seems most altered in autism is the relating of oneself socially to others. Individuals with autism exhibit atypical social behavior, manifested as disproportionate self-focus in social interaction with others. Hence the current study investigated a number of social (dyadic) interactions, using a neurosemantic paradigm in which participants are asked to think about a concept such as to insult, while their brain activation was assessed with fMRI.

Several fMRI studies of autism that have involved self-related cognition have found disruption of the brain activation in midline cortical structures (ventromedial prefrontal, middle and posterior cingulate), as summarized in a recent review [10]. One example is that in participants with autism there is a failure to reduce the activity in midline structures during the performance of a cognitive task [11], which has been attributed to a reduction of self-referential processing in the resting state in autism [12]. Another example of unusual self-related disruption in children with autism is the use of the pronoun you to refer to themselves, echoing the use of that pronoun by others to refer to the child, as first noted by Kanner [9]. This language behavior is ascribed to an errorful assessment of the relation between the self and another person. Consistent with Kanner’s observations, an fMRI study of pronoun processing in adult participants found diminished functional connectivity in autism between a frontal region (right anterior insula) and the precuneus (a posterior midline) region as well as altered activation levels in the precuneus [13]. Several other studies have found the precuneus to be involved in the representation of self [14], [15], [16], [17], [18]. Taken together, these types of findings indicate disruption of self-related processing in autism associated with the precuneus and frontal regions.

Findings of mean differences between autism and control groups in brain anatomy or brain activity have led more recently to classification studies in which participants are automatically (i.e. using an algorithmic statistical technique) classified as autistic or control based on such measures [19], [20], [21], [22]. Based on the structural grey matter anatomy measures, it was possible to classify the group membership with 85% accuracy [19]. With the voxel-based morphometry approach, the accuracy was 90% [20]. One study performed autism membership classification based on resting state connectivity data, producing an accuracy of 79% (and for the sub-group under 20 yrs, 91%) [21], whereas another study obtained an accuracy of 96% [22]. There is apparently something distinctive about the brain structure and brain activation in autism. However, neither of these approaches relates a brain property to a specific type of concept or thought that is altered in autism. The current study examines whether such classification is possible based on the neural representations of interpersonal social interactions, which might be expected to be altered in autism. In effect, the study seeks specific neurocognitive disruptions directly related to thought alterations and not simply biological markers of the thought disorder. We asked whether it is possible to distinguish autism from control participants based on their neural activation patterns during their consideration of various social interactions, examining whether the self components of social representations are altered in autism.

In addition to relating altered neural activation patterns to social concepts, the study attempted to determine what anatomical alterations in autism might be associated with the psychological alterations in the conception of self. One theory of autism relates the disorder’s behavioral and brain activation symptoms to altered frontal-posterior anatomical connectivity in the cortex, compromising the communication bandwidth between frontal and posterior areas [23]. The white matter tract that provides such connectivity between some of the main frontal and posterior midline regions involved in the representation of self is the cingulum bundle, whose structural properties can be measured noninvasively using magnetic resonance-based imaging of the diffusion of water molecules. An alteration in the representation of self could be due to the quality of this white matter tract. An a priori hypothesis was that the degree of alteration in the representation of self in individuals with autism would be related to the quality of their cingulum bundle. To examine this relation, diffusion images of this tract were obtained, in addition to the fMRI activation evoked by thoughts of various social interactions.

Another hypothesis was that the degree of alteration in the representation of self in individuals with autism would be related to behavioral measures of various social abilities, such as face processing and Theory of Mind (c.f. [12]). To test this hypothesis, appropriate neuropsychological measures were acquired for participants with autism.

Autism is rightly considered to be a heterogeneous disorder, with suggestions made that it be referred to as “the autisms” [24]. There are anecdotal comments that every person with autism is autistic in their own way. Although autism is undoubtedly heterogeneous, a striking finding in brain reading studies of neurotypical people is the high degree of commonality (homogeneity) of neural representations of concepts across individuals. A classifier trained to identify the thoughts associated with physical objects like a banana from the neural activation patterns of a group of participants can then identify, with reasonable accuracy, the thoughts of a new participant whose data were not included in the training [2]. This activation commonality probably arises because of the commonalities in the structure, function, and experience of human brains as they process information related to physical objects. But how would a psychiatric or neurological disorder affect the commonality among the members of the affected population, particularly in a domain of thought that is altered in the disorder? Given the apparent heterogeneity of autism, should there thus be less commonality among people with autism than among people without autism when they are thinking about social concepts? That is, if autism entails altered conceptions of social interactions, are the alterations heterogeneous across people with autism or is there a commonality? New machine learning methods allow a comparison of the commonality within the autism and the control groups.

The central issue remains whether it is possible to identify a participant as autistic, not just on the basis of a fortuitous statistical relation, but on the basis of some fundamental alteration of the brain activity that underpins particular types of thought that are among the defining characteristic of the disorder.

Below we first apply factor analysis to reduce the dimensionality of the brain activation evoked by the various social interactions. Then we perform classification of the multivoxel patterns that correspond to particular social interactions in order to identify the interaction and to distinguish the neural patterns of the two groups. The advantages of the approach are that it 1. focuses on the representations of social interactions, which are likely to be altered in autism and which like other concepts, are neurally represented by multiple voxels in multiple regions, and 2. is capable of detecting group differences in the activation patterns of multiple voxels in multiple regions.