Radial cytoarchitecture and patterns of cortical connectivity in autism

Motion and pattern cortical potentials in adults with high-functioning autism spectrum disorder

PURPOSE

Autism spectrum disorder (ASD) is a condition in which visual perception to both static and moving stimuli is altered. The aim of this study was to investigate the early cortical responses of subjects with ASD to simple patterns and moving radial rings using visual evoked potentials (VEPs).

METHODS

Male ASD participants (n = 9) and typically developing (TD) individuals (n = 7) were matched for full, performance and verbal IQ (p > 0.263). VEPs were recorded to the pattern reversing checks of 50' side length presented with Michelson contrasts of 98 and 10 % and to the onset of motion-either expansion or contraction of low-contrast concentric rings (33.3 % duty cycle at 10 % contrast).

RESULTS

There were no significant differences between groups in the VEPs elicited by pattern reversal checkerboards of high (98 %) or low (10 %) contrast. The ASD group had a significantly larger N160 peak (1.85 x) amplitude to motion onset VEPs elicited by the expansion of radial rings (p = 0.001). No differences were evident in contraction VEP peak amplitudes nor in the latencies of the motion onset N160 peaks. There was no evidence of a response that could be associated with adaptation to the motion stimulus in the interstimulus interval following an expansion or contraction phase of the rings.

CONCLUSION

These data support a difference in processing of motion onset stimuli in this adult high-functioning ASD group compared to the TD group.

Functional alterations in neural substrates of geometric reasoning in adults with high-functioning autism

Individuals with autism spectrum condition (ASC) are known to excel in some perceptual cognitive tasks, but such developed functions have been often regarded as "islets of abilities" that do not significantly contribute to broader intellectual capacities. However, recent behavioral studies have reported that individuals with ASC have advantages for performing Raven's (Standard) Progressive Matrices (RPM/RSPM), a standard neuropsychological test for general fluid intelligence, raising the possibility that ASC's cognitive strength can be utilized for more general purposes like novel problem solving. Here, the brain activity of 25 adults with high-functioning ASC and 26 matched normal controls (NC) was measured using functional magnetic resonance imaging (fMRI) to examine neural substrates of geometric reasoning during the engagement of a modified version of the RSPM test. Among the frontal and parietal brain regions involved in fluid intelligence, ASC showed larger activation in the left lateral occipitotemporal cortex (LOTC) during an analytic condition with moderate difficulty than NC. Activation in the left LOTC and ventrolateral prefrontal cortex (VLPFC) increased with task difficulty in NC, whereas such modulation of activity was absent in ASC. Furthermore, functional connectivity analysis revealed a significant reduction of activation coupling between the left inferior parietal cortex and the right anterior prefrontal cortex during both figural and analytic conditions in ASC. These results indicate altered pattern of functional specialization and integration in the neural system for geometric reasoning in ASC, which may explain its atypical cognitive pattern, including performance on the Raven's Matrices test.

Computational model of excitatory/inhibitory ratio imbalance role in attention deficit disorders

Impairments in attentional behaviors, including over-selectivity, under-selectivity, distractibility and difficulty in shift of attention, are widely reported in several developmental disorders, including autism. Uncharacteristic inhibitory to excitatory neuronal number ratio (IER) and abnormal synaptic strength levels in the brain are two broadly accepted neurobiological disorders observed in autistic individuals. These neurobiological findings are contrasting and their relation to the atypical attentional behaviors is not clear yet. In this paper, we take a computational approach to investigate the relation of imbalanced IER and abnormal synaptic strength to some well-documented spectrum of attentional impairments. The computational model is based on a modified version of a biologically plausible neural model of two competing minicolumns in IT cortex augmented with a simple model of top-down attention. Top-down attention is assumed to amplify (attenuates) attended (unattended) stimulus. The inhibitory synaptic strength parameter in the model is set such that typical attentional behavior is emerged. Then, according to related findings, the parameter is changed and the model's attentional behavior is considered. The simulation results show that, without any change in top-down attention, the abnormal inhibitory synaptic strength values--and IER imbalance- result in over-selectivity, under-selectivity, distractibility and difficulty in shift of attention in the model. It suggests that the modeled neurobiological abnormalities can be accounted for the attentional deficits. In addition, the atypical attentional behaviors do not necessarily point to impairments in top-down attention. Our simulations suggest that limited changes in the inhibitory synaptic strength and variations in top-down attention signal affect the model's attentional behaviors in the same way. So, limited deficits in the inhibitory strength may be alleviated by appropriate change in top-down attention biasing. Nevertheless, our model proposes that this compensation is not possible for very high and very low values of the inhibitory strength.

Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity

The underconnectivity theory of autism attributes the disorder to lower anatomical and functional systems connectivity between frontal and more posterior cortical processing. Here we review evidence for the theory and present a computational model of an executive functioning task (Tower of London) implementing the assumptions of underconnectivity. We make two modifications to a previous computational account of performance and brain activity in typical individuals in the Tower of London task (Newman et al., 2003): (1) the communication bandwidth between frontal and parietal areas was decreased and (2) the posterior centers were endowed with more executive capability (i.e., more autonomy, an adaptation is proposed to arise in response to the lowered frontal-posterior bandwidth). The autism model succeeds in matching the lower frontal-posterior functional connectivity (lower synchronization of activation) seen in fMRI data, as well as providing insight into behavioral response time results. The theory provides a unified account of how a neural dysfunction can produce a neural systems disorder and a psychological disorder with the widespread and diverse symptoms of autism.

Abnormal white matter integrity in young children with autism

This study investigated white matter integrity in young children with autism using diffusion tensor imaging (DTI). Twenty-two children with autism, mean age 3:2 years, and 32 controls, mean age 3:4 years, participated in the study. Tract-based spatial statistics (TBSS) revealed white matter abnormalities in several distinct clusters within the genu and body of the corpus callosum (CC), left superior longitudinal fasciculus (SLF) and right and left cingulum (Cg). TBSS-VOIs analysis was performed in the clusters where differences in fractional anisotropy (FA) were detected to investigate the relationship between changes in FA and diffusivity indices. In all VOIs, increase in FA was caused by a decrease in radial diffusivity (Dr), while no changes in axial diffusivity (Da) or mean diffusivity (MD) were observed. Tractography analysis was applied to further study the CC, SLF, and Cg. Witelson parcellation scheme was used for the CC. Significant increase in FA was seen in children with autism in the mid-body of the CC as well as in the left Cg. It is suggested that such abnormal white matter integrity in young children with autism may adversely affect connectivity between different brain regions and may be linked to some of the behavioral impairments apparent in autism.

Early and adult hippocampal TGF-β1 overexpression have opposite effects on behavior

TGF-β1 is an anti-inflammatory cytokine that is augmented in the brain of autistic patients and that can affect brain development. In this work, we studied the effects of overexpressing TGF-β1 in the dentate gyrus of adult or young mice on behavior. TGF-β1 overexpression during postnatal development led to a long-term decrease in social interaction and to long-term increases in self-grooming and depression-related behaviors. Our analysis shows that these behavioral changes correlate with the long-term downregulation of TGF-β1 and IL-6 expression in the dentate gyrus, as well as to decreases in the mRNA levels of the synaptic protein neuroligin 3 and in the number of Reelin-positive neurons in the dentate gyrus. In contrast, chronic expression of TGF-β1 during adulthood led to transient opposite effects on these behaviors. These results show a central role of hippocampal TGF-β1 in the programming and modulation of social interaction, repetitive behavior and depression-related behavior. Finally, our data suggest a role of hippocampal TGF-β1 and early-life neuroinflammation in the development of the behavioral alterations observed in autism spectrum disorders.

Functional connectivity magnetic resonance imaging classification of autism

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

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

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.

Imaging derived cortical thickness reduction in high-functioning autism: key regions and temporal slope

Cortical thickness (CT) changes possibly contribute to the complex symptomatology of autism. The aberrant developmental trajectories underlying such differences in certain brain regions and their continuation in adulthood are a matter of intense debate. We studied 28 adults with high-functioning autism (HFA) and 28 control subjects matched for age, gender, IQ and handedness. A surface-based whole brain analysis utilizing FreeSurfer was employed to detect CT differences between the two diagnostic groups and to investigate the time course of age-related changes. Direct comparison with control subjects revealed thinner cortex in HFA in the posterior superior temporal sulcus (pSTS) of the left hemisphere. Considering the time course of CT development we found clusters around the pSTS and cuneus in the left and the paracentral lobule in the right hemisphere to be thinner in HFA with comparable age-related slopes in patients and controls. Conversely, we found clusters around the supramarginal gyrus and inferior parietal lobule (IPL) in the left and the precentral and postcentral gyrus in the right hemisphere to be thinner in HFA, but with different age-related slopes in patients and controls. In the latter regions CT showed a steady decrease in controls but no analogous thinning in HFA. CT analyses contribute in characterizing neuroanatomical correlates of HFA. Reduced CT is present in brain regions involved in social cognition. Furthermore, our results demonstrate that aberrant brain development leading to such differences is proceeding throughout adulthood. Discrepancies in prior morphometric studies may be induced by the complex time course of cortical changes.

Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders

Growing consensus suggests that autism spectrum disorders (ASD) are associated with atypical brain networks, thus shifting the focus to the study of connectivity. Many functional connectivity studies have reported underconnectivity in ASD, but results in others have been divergent. We conducted a survey of 32 functional connectivity magnetic resonance imaging studies of ASD for numerous methodological variables to distinguish studies supporting general underconnectivity (GU) from those not consistent with this hypothesis (NGU). Distinguishing patterns were apparent for several data analysis choices. The study types differed significantly with respect to low-pass filtering, task regression, and whole-brain field of view. GU studies were more likely to examine task-driven time series in regions of interest, without the use of low-pass filtering. Conversely, NGU studies mostly applied task regression (for removal of activation effects) and low-pass filtering, testing for correlations across the whole brain. Results thus suggest that underconnectivity findings may be contingent on specific methodological choices. Whereas underconnectivity reflects reduced efficiency of within-network communication in ASD, diffusely increased functional connectivity can be attributed to impaired experience-driven mechanisms (e.g., synaptic pruning). Both GU and NGU findings reflect important aspects of network dysfunction associated with sociocommunicative, cognitive, and sensorimotor impairments in ASD.

A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism

BACKGROUND

Genome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism.

METHODS

GWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology.

RESULTS

Computer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets.

CONCLUSIONS

As statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.

Decreased interhemispheric functional connectivity in autism

The cortical underconnectivity theory asserts that reduced long-range functional connectivity might contribute to a neural mechanism for autism. We examined resting-state blood oxygen level-dependent interhemispheric correlation in 53 males with high-functioning autism and 39 typically developing males from late childhood through early adulthood. By constructing spatial maps of correlation between homologous voxels in each hemisphere, we found significantly reduced interhemispheric correlation specific to regions with functional relevance to autism: sensorimotor cortex, anterior insula, fusiform gyrus, superior temporal gyrus, and superior parietal lobule. Observed interhemispheric connectivity differences were better explained by diagnosis of autism than by potentially confounding neuropsychological metrics of language, IQ, or handedness. Although both corpus callosal volume and gray matter interhemispheric connectivity were significantly reduced in autism, no direct relationship was observed between them, suggesting that structural and functional metrics measure different aspects of interhemispheric connectivity. In the control but not the autism sample, there was decreasing interhemispheric correlation with subject age. Greater differences in interhemispheric correlation were seen for more lateral regions in the brain. These findings suggest that long-range connectivity abnormalities in autism are spatially heterogeneous and that transcallosal connectivity is decreased most in regions with functions associated with behavioral abnormalities in autism. Autism subjects continue to show developmental differences in interhemispheric connectivity into early adulthood.

Regional homogeneity of fMRI time series in autism spectrum disorders

Functional magnetic resonance imaging (fMRI) and functional connectivity MRI (fcMRI) studies of autism spectrum disorders (ASD) have suggested atypical patterns of activation and long-distance connectivity for diverse tasks and networks in ASD. We explored the regional homogeneity (ReHo) approach in ASD, which is analogous to conventional fcMRI, but focuses on local connectivity. FMRI data of 26 children with ASD and 29 typically developing (TD) children were acquired during continuous task performance (visual search). Effects of motion and task were removed and Kendall's coefficient of concordance (KCC) was computed, based on the correlation of the blood oxygen level dependent (BOLD) time series for each voxel and its six nearest neighbors. ReHo was lower in the ASD than the TD group in superior parietal and anterior prefrontal regions. Inverse effects of greater ReHo in the ASD group were detected in lateral and medial temporal regions, predominantly in the right hemisphere. Our findings suggest that ReHo is a sensitive measure for detecting cortical abnormalities in autism. However, impact of methodological factors (such as spatial resolution) on ReHo require further investigation.

A comparative perspective on minicolumns and inhibitory GABAergic interneurons in the neocortex

Neocortical columns are functional and morphological units whose architecture may have been under selective evolutionary pressure in different mammalian lineages in response to encephalization and specializations of cognitive abilities. Inhibitory interneurons make a substantial contribution to the morphology and distribution of minicolumns within the cortex. In this context, we review differences in minicolumns and GABAergic interneurons among species and discuss possible implications for signaling among and within minicolumns. Furthermore, we discuss how abnormalities of both minicolumn disposition and inhibitory interneurons might be associated with neuropathological processes, such as Alzheimer's disease, autism, and schizophrenia. Specifically, we explore the possibility that phylogenetic variability in calcium-binding protein-expressing interneuron subtypes is directly related to differences in minicolumn morphology among species and might contribute to neuropathological susceptibility in humans.

MR connectomics: Principles and challenges

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

How does visual thinking work in the mind of a person with autism? A personal account

My mind is similar to an Internet search engine that searches for photographs. I use language to narrate the photo-realistic pictures that pop up in my imagination. When I design equipment for the cattle industry, I can test run it in my imagination similar to a virtual reality computer program. All my thinking is associative and not linear. To form concepts, I sort pictures into categories similar to computer files. To form the concept of orange, I see many different orange objects, such as oranges, pumpkins, orange juice and marmalade. I have observed that there are three different specialized autistic/Asperger cognitive types. They are: (i) visual thinkers such as I who are often poor at algebra, (ii) pattern thinkers such as Daniel Tammet who excel in math and music but may have problems with reading or writing composition, and (iii) verbal specialists who are good at talking and writing but they lack visual skills.

Autism and dyslexia: a spectrum of cognitive styles as defined by minicolumnar morphometry

There is a continuum of cognitive styles amongst humans, defined by differences in minicolumnar numbers/width and arcuate/commissural white matter connectivities. Specifically, it is the connectivity within and between modular cortical circuits that defines conditions such as autism and developmental dyslexia. In autism, a model of local hyperconnectivity and long-range hypoconnectivity explains many of the behavioral and cognitive traits present in the condition, while the inverse arrangement of local hypoconnectivity and long-range hyperconnectivity in dyslexia sheds light on that condition as well. We propose that the cognitive styles present in autism and developmental dyslexia typify the extremes of a minicolumnar spectrum in humans.