Individuals with high-functioning autism sometimes exhibit intact or superior performance on visuospatial tasks, in contrast to impaired functioning in other domains such as language comprehension, executive tasks, and social functions. The goal of the current study was to investigate the neural bases of preserved visuospatial processing in high-functioning autism from the perspective of the cortical underconnectivity theory. We used a combination of behavioral, functional magnetic resonance imaging (fMRI), functional connectivity, and corpus callosum morphometric methodological tools. Thirteen participants with high-functioning autism and thirteen controls (age-, IQ-, and gender-matched) were scanned while performing an Embedded Figures Task (EFT). Despite the ability of the autism group to attain behavioral performance comparable to the control group, the brain imaging results revealed several group differences consistent with the cortical underconnectivity account of autism. First, relative to controls, the autism group showed less activation in left dorsolateral prefrontal and inferior parietal areas and more activation in visuospatial (bilateral superior parietal extending to inferior parietal and right occipital) areas. Second, the autism group demonstrated lower functional connectivity between higher-order working memory/executive areas and visuospatial regions (between frontal and parietal-occipital). Third, the size of the corpus callosum (an index of anatomical connectivity) was positively correlated with frontal-posterior (parietal and occipital) functional connectivity in the autism group. Thus, even in the visuospatial domain, where preserved performance among people with autism is observed, the neuroimaging signatures of cortical underconnectivity persist. KeywordsEmbedded figures task; Functional connectivity; Corpus callosum; functional MRI One of the enigmatic aspects of autism is that people with the disorder exhibit preserved or even enhanced performance on visuospatial tasks (Baron-Cohen & Hammer, 1997;Brian & Bryson, 1996;De Jonge et al., 2006;Jolliffe & Baron-Cohen, 1997;Lee et al., 2007;Manjaly et al., 2007;Mottron et al., 1999;Ring et al., 1999;Shah & Frith, Shah and Frith (1983) demonstrated that children with high-functioning autism exhibited superior performance relative to IQ-and age-matched controls on the Embedded Figures Task (EFT), in which individuals are asked to locate a simple figure that is embedded in a more complex configuration.A recently proposed theoretical account of autism, the cortical underconnectivity theory (Just et al., 2004;, provides a neurobiological explanation of the psychological processes underlying the disorder. This theory posits that autism is a neural systems disorder marked by inefficient interregional brain connectivity between frontal and posterior areas resulting in a deficit in integration of information at both psychological and neural levels. The theory predicts that if a task does not require tight integration between frontal and more po...

Human neuroimaging studies have increasingly converged on the possibility that the neural representation of specific numbers may be decodable from brain activity, particularly in parietal cortex. Multivariate machine learning techniques have recently demonstrated that the neural representation of individual concrete nouns can be decoded from fMRI patterns, and that some patterns are general over people. Here we use these techniques to investigate whether the neural codes for quantities of objects can be accurately decoded. The pictorial mode (nonsymbolic) depicted a set of objects pictorially (e.g., a picture of three tomatoes), whereas the digit-object mode depicted quantities as combination of a digit (e.g., 3) with a picture of a single object. The study demonstrated that quantities of objects were decodable from neural activation patterns, in parietal regions. These brain activation patterns corresponding to a given quantity were common across objects and across participants in the pictorial mode. Other important findings included better identification of individual numbers in the pictorial mode, partial commonality of neural patterns across the two modes, and hemispheric asymmetry with pictorially-depicted numbers represented bilaterally and numbers in the digit-object mode represented primarily in the left parietal regions. The findings demonstrate the ability to identify individual quantities of objects based on neural patterns, indicating the presence of stable neural representations of numbers. Additionally, they indicate a predominance of neural representation of pictorially depicted numbers over the digit-object mode.

Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa. The representations that were common across modalities were mainly right-lateralized in frontal and parietal regions. A second finding was that the neural patterns in parietal cortex that represent quantities were common across participants. These findings demonstrate a common neuronal foundation for the representation of quantities across sensory modalities and participants and provide insight into the role of parietal cortex in the representation of quantity information.