scholarly journals Investigating the influence of autism spectrum traits on face processing mechanisms in developmental prosopagnosia

2021 ◽  
Author(s):  
Regan Fry ◽  
Xian Li ◽  
Travis Clark Evans ◽  
Michael Esterman ◽  
Jim Tanaka ◽  
...  

Autism traits are commonly used as exclusionary criteria in studies of developmental prosopagnosia (DP). We investigated whether autism traits result in qualitatively different face processing in 43 DPs with high vs. low autism quotient (AQ) scores and 27 controls. Compared to controls, behavioral face recognition deficits were similar between the high and low AQ DP groups aside from worse emotion recognition in the high AQ DPs. Both DP groups showed reduced face selectivity in task-based fMRI, although higher AQ DPs showed decreased face selectivity in the posterior superior temporal sulcus. Resting-state fMRI showed similar face network connectivity between DP groups. This suggests that face processing is similar between the DP groups, with additional emotion processing deficits in higher AQ DPs.

2019 ◽  
Author(s):  
Daniel A Handwerker ◽  
Geena Ianni ◽  
Benjamin Gutierrez ◽  
Vinai Roopchansingh ◽  
Javier Gonzalez-Castillo ◽  
...  

AbstractHumans process faces using a network of face-selective regions distributed across the brain. Neuropsychological patient studies demonstrate that focal damage to nodes in this network can impair face recognition, but such patients are rare. We approximated the effects of damage to the face network in neurologically normal human participants using thetaburst transcranial magnetic stimulation (TBS). Multi-echo functional magnetic resonance imaging (fMRI) resting-state data were collected pre- and post-TBS delivery over the face-selective right superior temporal sulcus (rpSTS), or a control site in the right motor cortex. Results showed that TBS delivered over the rpSTS reduced resting-state connectivity across the extended face-processing network. This connectivity reduction was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas across the ventral, medial and lateral brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). TBS delivered over the motor cortex did not produce significant changes in resting-state connectivity across the face-processing network. These results demonstrate that, even without task-induced fMRI signal changes, disrupting a single node in a brain network can decrease the functional connectivity between nodes in that network that have not been directly stimulated.Author SummaryHuman behavior is dependent on brain networks that perform different cognitive functions. We combined thetaburst transcranial magnetic stimulation (TBS) with resting-state fMRI to study the face processing network. Disruption of the face-selective right posterior superior temporal sulcus (rpSTS) reduced fMRI connectivity across the face network. This impairment in connectivity was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas on the ventral and medial brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). Thus, combined TBS/fMRI can be used to approximate and measure the effects of focal brain damage on brain networks, and suggests such an approach may be useful for mapping intrinsic network organization.Technical TermsTBS vs TMSTranscranial magnetic stimulation (TMS) is a method that induces current in neural tissue by using a rapidly changing magnetic field. The pattern of magnetic field changes can vary. Thetaburst TMS (TBS) is a type of TMS where the same stimulation pattern fluctuates at around a 5Hz cycle.Multi-echo fMRIDuring typical fMRI, protons are excited and there is a delay, the echo time, before data are collected. That delay is typically designed to result in a high contrast for blood oxygenation differences. In multi-echo fMRI, data are collected at several echo times each time protons are excited. This results in data that have different levels of contrast for blood oxygenation differences. This added information can be used to empirically decrease noise.Face networkA group of brain regions that show significant activity changes in response to visual face stimuli. While these regions have been defined using univariate analyses with task-based fMRI, they often significantly correlate with each other at rest. In this manuscript, the following regions were a priori defined as part of the face network: posterior superior temporal sulcus (pSTS), amygdala, fusiform face area (FFA), and occipital face area (OFA).Matrix based analysis (MBA)A recent approach that uses a Bayesian multilevel modeling framework to identify pairs of ROIs where a decrease in correlation magnitude was larger than expected along with a measure of statistical evidence. With this approach, correlations between all pairs of ROIs are assessed as part of a single model rather than many independent statistical tests.


2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.


2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.


eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Charlotte J Stagg ◽  
Velicia Bachtiar ◽  
Ugwechi Amadi ◽  
Christel A Gudberg ◽  
Andrei S Ilie ◽  
...  

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.


2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.


PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143126 ◽  
Author(s):  
Minyoung Jung ◽  
Maria Mody ◽  
Daisuke N. Saito ◽  
Akemi Tomoda ◽  
Hidehiko Okazawa ◽  
...  

Author(s):  
S. Vidhusha ◽  
A. Kavitha

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.


2019 ◽  
Vol 215 (3) ◽  
pp. 545-551 ◽  
Author(s):  
Gin S. Malhi ◽  
Pritha Das ◽  
Tim Outhred ◽  
Richard A. Bryant ◽  
Vince Calhoun

BackgroundSubsyndromal emotional symptoms in adolescence may represent precursors for full-blown emotional disorders in early adulthood. Understanding the neurobiological mechanisms that drive this development is essential for prevention.AimsSelf-referential processing and emotion regulation are remodelled substantively during adolescence, therefore this study examined integration of key neural networks involved in these processes.MethodAt baseline, clinical and resting-state functional magnetic resonance imaging data were collected for 88 adolescent girls (mean age 15 years), and 71 of these girls underwent repeat clinical assessment after 2 years. These 71 girls were then partitioned into two groups depending on the presence (ES+) or absence (ES−) of emotional symptoms, and differences in dynamic functional network connectivity were determined and correlated with clinical variables.ResultsThe two groups displayed a differential pattern of functional connectivity involving the left lateral prefrontal network (LPFN). Specifically, in the ES+ group this network displayed positive coupling with the right LPFN but negative coupling with the default mode network, and the inverse of this pattern was found in the ES− group. Furthermore, the coupling strengths between left and right LPFN at the irst time point predicted follow-up depression and state anxiety scores.ConclusionsOur findings suggest that in adolescent girls, emotional symptoms may emerge as a result of impaired integration between networks involved in self-referential information processing and approach-avoidance behaviours. These impairments can compromise the pursuit of important goals and have an impact on emotion processing and finally may lead to the development of emotional disorders, such as anxiety and depression in adulthood.Declaration of interestNone.


2019 ◽  
Vol 50 (14) ◽  
pp. 2324-2334 ◽  
Author(s):  
Jonathan P. Stange ◽  
Lisanne M. Jenkins ◽  
Stephanie Pocius ◽  
Kayla Kreutzer ◽  
Katie L. Bessette ◽  
...  

AbstractBackgroundLittle is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.MethodsParticipants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.ResultsSeveral fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1–4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79–88%).ConclusionsThese results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.


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