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      Atypical co‐development of the thalamus and cortex in autism: Evidence from age‐related white–gray contrast change

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          Abstract

          Recent studies have shown that white–gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age‐related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co‐development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age‐related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) ( N = 434). We used structural MRI data from the largest high‐quality cross‐sectional sample (NYU) as well as two other large high‐quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co‐development features in ASD and TD exhibit contrasting patterns; specifically, some higher‐order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.

          Abstract

          We measure how the trajectories of white–gray matter contrast co‐change in the cortex and thalamus in young autistic individuals and their typically developing peers. We find two largely opposing patterns, constituting lower and higher thalamocortical relay domains, and this pattern is well‐correlated with autistic traits.

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          The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism

          Autism spectrum disorders (ASD) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, life-long nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. While the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI and phenotypic information from 539 individuals with ASD and 573 age-matched typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 males with ASD and 403 male age-matched TC. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo and hyperconnectivity in the ASD literature; both were detected, though hypoconnectivity dominated, particularly for cortico-cortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and highlighted less commonly explored regions such as thalamus. The survey of the ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international datasets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.
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            Automated anatomical labelling atlas 3

            Following a first version AAL of the automated anatomical labeling atlas (Tzourio-Mazoyer et al., 2002), a second version (AAL2) (Rolls et al., 2015) was developed that provided an alternative parcellation of the orbitofrontal cortex following the description provided by Chiavaras, Petrides, and colleagues. We now provide a third version, AAL3, which adds a number of brain areas not previously defined, but of interest in many neuroimaging investigations. The 26 new areas in the third version are subdivision of the anterior cingulate cortex into subgenual, pregenual and supracallosal parts; subdivision of the thalamus into 15 parts; the nucleus accumbens, substantia nigra, ventral tegmental area, red nucleus, locus coeruleus, and raphe nuclei. The new atlas is available as a toolbox for SPM, and can be used with MRIcron.
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              Partial least squares analysis of neuroimaging data: applications and advances.

              Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroimaging.
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                Author and article information

                Contributors
                bezgingl@gmail.com , gleb.bezgin@mcgill.ca
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                27 March 2024
                April 2024
                : 45
                : 5 ( doiID: 10.1002/hbm.v45.5 )
                : e26584
                Affiliations
                [ 1 ] Montreal Neurological Institute McGill University Montreal Quebec Canada
                [ 2 ] The Hospital for Sick Children University of Toronto Toronto Ontario Canada
                Author notes
                [*] [* ] Correspondence

                Gleb Bezgin, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

                Email: bezgingl@ 123456gmail.com ; gleb.bezgin@ 123456mcgill.ca

                Author information
                https://orcid.org/0000-0002-1069-9201
                Article
                HBM26584
                10.1002/hbm.26584
                10966578
                38533724
                f52a1a20-59bb-4a66-898e-c9bf14617113
                © 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 05 December 2023
                : 14 March 2023
                : 17 December 2023
                Page count
                Figures: 4, Tables: 1, Pages: 10, Words: 8130
                Funding
                Funded by: Azrieli Foundation , doi 10.13039/501100005155;
                Award ID: ANRP‐MIRI13‐3388
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                April 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:27.03.2024

                Neurology
                autism spectrum disorder,cortex,mri,neurodevelopment,thalamus
                Neurology
                autism spectrum disorder, cortex, mri, neurodevelopment, thalamus

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