The Role of Attention in Somatosensory Processing: A Multi-trait, Multi-method Analysis

Sensory processing abnormalities in autism have largely been described by parent report. This study used a multi-method (parent-report and measurement), multi-trait (tactile sensitivity and attention) design to evaluate somatosensory processing in ASD. Results showed multiple significant within-method (e.g., parent report of different traits)/cross-trait (e.g., attention and tactile sensitivity) correlations, suggesting that parent-reported tactile sensory dysfunction and performance-based tactile sensitivity describe different behavioral phenomena. Additionally, both parent-reported tactile functioning and performance-based tactile sensitivity measures were significantly associated with measures of attention. Findings suggest that sensory (tactile) processing abnormalities in ASD are multifaceted, and may partially reflect a more global deficit in behavioral regulation (including attention). Challenges of relying solely on parent-report to describe sensory difficulties faced by children/families with ASD are also highlighted

Regionally specific human GABA concentration correlates with tactile discrimination thresholds

The neural mechanisms underlying variability in human sensory perception remain incompletely understood. In particular, few studies have attempted to investigate the relationship between in vivo measurements of neurochemistry and individuals' behavioral performance. Our previous work found a relationship between GABA concentration in the visual cortex and orientation discrimination thresholds (Edden et al., 2009). In the present study, we used magnetic resonance spectroscopy of GABA and psychophysical testing of vibrotactile frequency thresholds to investigate whether individual differences in tactile frequency discrimination performance are correlated with GABA concentration in sensorimotor cortex. Behaviorally, individuals showed a wide range of discrimination thresholds ranging from 3 to 7.6 Hz around the 25 Hz standard. These frequency discrimination thresholds were significantly correlated with GABA concentration (r = −0.58; p < 0.05) in individuals' sensorimotor cortex, but not with GABA concentration in an occipital control region (r = −0.04). These results demonstrate a link between GABA concentration and frequency discrimination in vivo, and support the hypothesis that GABAergic mechanisms have an important role to play in sensory discrimination

GABA levels in left and right sensorimotor cortex correlate across individuals

Differences in -aminobutyric acid (GABA) levels measured with Magnetic Resonance Spectroscopy have been shown to correlate with behavioral performance over a number of tasks and cortical regions. These correlations appear to be regionally and functionally specific. In this study, we test the hypothesis that GABA levels will be correlated within individuals for functionally related regions—the left and right sensorimotor cortex. In addition, we investigate whether this is driven by bulk tissue composition. GABA measurements using edited MRS data were acquired from the left and right sensorimotor cortex in 24 participants. T1-weighted MR images were also acquired and segmented to determine the tissue composition of the voxel. GABA level is shown to correlate significantly between the left and right regions (r = 0.64, p < 0.03). Tissue composition is highly correlated between sides, but does not explain significant variance in the bilateral correlation. In conclusion, individual differences in GABA level, which have previously been described as functionally and regionally specific, are correlated between homologous sensorimotor regions. This correlation is not driven by bulk differences in voxel tissue composition

A Working Taxonomy for Describing the Sensory Differences of Autism

Background: Individuals on the autism spectrum have been long described to process sensory information differently than neurotypical individuals. While much effort has been leveraged towards characterizing and investigating the neurobiology underlying the sensory differences of autism, there has been a notable lack of consistency in the terms being used to describe the nature of those differences. Main body: We argue that inconsistent and interchangeable terminology-use when describing the sensory differences of autism has become problematic beyond mere pedantry and inconvenience. We begin by highlighting popular terms that are currently being used to describe the sensory differences of autism (e.g. sensitivity , reactivity and responsivity ) and discuss why poor nomenclature may hamper efforts towards understanding the aetiology of sensory differences in autism. We then provide a solution to poor terminology-use by proposing a hierarchical taxonomy for describing and referring to various sensory features. Conclusion: Inconsistent terminology-use when describing the sensory features of autism has stifled discussion and scientific understanding of the sensory differences of autism. The hierarchical taxonomy proposed was developed to help resolve lack of clarity when discussing the sensory differences of autism and to place future research targets at appropriate levels of analysis

Increased GABA concentrations in type 2 diabetes mellitus are related to lower cognitive functioning

Type 2 diabetes mellitus is associated with accelerated cognitive decline. The underlying pathophysiological mechanisms still remain to be elucidated although it is known that insulin signaling modulates neurotransmitter activity, including inhibitory γ-aminobutyric acid (GABA) and excitatory glutamate (Glu) receptors. Therefore, we examined whether levels of GABA and Glu are related to diabetes status and cognitive performance. Forty-one participants with type 2 diabetes and 39 participants without type 2 diabetes underwent detailed cognitive assessments and 3-Tesla proton MR spectroscopy. The associations of neurotransmitters with type 2 diabetes and cognitive performance were examined using multivariate regression analyses controlling for age, sex, education, BMI, and percentage gray/white matter ratio in spectroscopic voxel. Analysis revealed higher GABA+ levels in participants with type 2 diabetes, in participants with higher fasting blood glucose levels and in participants with higher HbA(1c) levels, and higher GABA+ levels in participants with both high HbA(1c) levels and less cognitive performance. To conclude, participants with type 2 diabetes have alterations in the GABAergic neurotransmitter system, which are related to lower cognitive functioning, and hint at the involvement of an underlying metabolic mechanism

Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-Site Integrative Data Analysis by the Autism Sensory Research Consortium

BACKGROUND: Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these supra-modal traits in the autistic population. METHODS: Leveraging a combined sample of 3868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a general response pattern factor for each supra-modal construct and determine the added value of modality-specific response pattern scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO, and SEEK (sub)constructs. RESULTS: All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses supported the validity of a supra-modal HYPER construct (ω LIMITATIONS: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to caregiver report of observable behaviors and excluded multisensory items that reflect many real-world sensory experiences. CONCLUSION: Of the three sensory response patterns, only HYPER demonstrated sufficient evidence for valid interpretation at the supra-modal level, whereas supra-modal HYPO/SEEK constructs demonstrated substantial psychometric limitations. For clinicians and researchers seeking to characterize sensory reactivity in autism, modality-specific response pattern scores may represent viable alternatives that overcome many of these limitations

Frequency drift in MR spectroscopy at 3T

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B-0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.</p

Examining Our Sensory World, How Much Do We Rely on Prior Information?

Sensory differences are characteristic of autism and may result from atypical predictive coding, a perceptual framework based upon combining top-down and bottom-up sensory information. Few studies have systematically manipulated these factors to better understand how predictive coding occurs and how it relates to autism. Our first study aim was to develop a psychophysical task addressing this gap. Our second aim was to examine how any predictive coding differences relate to the intolerance of uncertainty (IoU) often seen in autism, as research supports a relationship between this construct and atypical predictive coding. We recruited 20 participants to perform a 2-Alternative Forced-Choice (2AFC) frequency discrimination task, manipulating the quantity of bottom-up and top-down sensory information they received. Participants also completed three questionnaires to provide measures of autistic traits, associated sensory atypicalities, and IoU

Neurometabolite differences in Autism as assessed with Magnetic Resonance Spectroscopy:A systematic review and meta-analysis

1H-Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique that can be used to quantify the concentrations of metabolites in the brain in vivo. MRS findings in the context of autism are inconsistent and conflicting. We performed a systematic review and meta-analysis of MRS studies measuring glutamate and gamma-aminobutyric acid (GABA), as well as brain metabolites involved in energy metabolism (glutamine, creatine), neural and glial integrity (e.g. n-acetyl aspartate (NAA), choline, myo-inositol) and oxidative stress (glutathione) in autism cohorts. Data were extracted and grouped by metabolite, brain region and several other factors before calculation of standardised effect sizes. Overall, we find significantly lower concentrations of GABA and NAA in autism, indicative of disruptions to the balance between excitation/inhibition within brain circuits, as well as neural integrity. Further analysis found these alterations are most pronounced in autistic children and in limbic brain regions relevant to autism phenotypes. Additionally, we show how study outcome varies due to demographic and methodological factors , emphasising the importance of conforming with standardised consensus study designs and transparent reporting. </p