The social brain network in 22q11.2 deletion syndrome: a diffusion tensor imaging study

Olszewski, A.; Kikinis, Zora; Gonzalez, Christie S.; Coman, Ioana L.; Makris, Nikolaos; Xue, Gui; Rathi, Yogesh; Zhu, Angela; Antshel, Kevin M.; Fremont, Wanda; Kubicki, Marek; Bouix, Sylvain; Shenton, Martha E.; Kates, Wendy R.

doi:10.1186/s12993-017-0122-7

Cited by 31 publications

(31 citation statements)

References 88 publications

(87 reference statements)

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“…We found that more than half of the studies, particularly of the mirroring and mentalizing network, are based on clinical populations. They include major psychiatric and neurological disorders characterized by prominent social impairments, such as autism spectrum disorder (ASD) (Fishman, Datko, Cabrera, Carper, & Müller, 2015;Kana, Libero, Hu, Deshpande, & Colburn, 2014;Mueller et al, 2013), behavioral-variant frontotemporal dementia (Downey et al, 2015), and prosopagnosia (Gomez et al, 2015;Grossi et al, 2014;Song et al, 2015;Thomas et al, 2008Thomas et al, , 2009Valdés-Sosa et al, 2011), as well as those with secondary impairments in social cognition, such as schizophrenia (Fujino et al, 2014;Saito et al, 2017), amyotrophic lateral sclerosis (ALS) (Crespi et al, 2014(Crespi et al, , 2016, mild cognitive impairment (MCI) (Fujie et al, 2008), traumatic brain injury (Genova et al, 2015;Levin et al, 2011;Scheibel et al, 2011), Parkinson's disease (PD) (Baggio et al, 2012), brain lesion/stroke (Oishi et al, 2015;Philippi et al, 2009), velocardiofacial syndrome (Jalbrzikowski et al, 2014;Olszewski et al, 2017;Radoeva et al, 2012), multiple sclerosis (MS) (Mike et al, 2013) and diffuse low-grade glioma (Herbet et al, 2014;Herbet, Lafargue, Moritz-Gasser, Menjot de Champfleur, et al, 2015;Yordanova, Duffau, & Herbet, 2017). In terms of social cognitive measurements, the literature has employed several behavioral paradigms to probe each social function (see Table 1-3).…”

Section: Methodological Summarymentioning

confidence: 99%

“…Benton tests, Philadelphia battery) (Gomez et al, 2015;Grossi et al, 2014;Thomas et al, 2008;Unger et al, 2016), and face memory tasks (e.g. Cambridge tests) (Gomez et al, 2015;Song et al, 2015;Unger et al, 2016); empathy was assessed by the "empathy quotient" (Chou et al, 2011;Herbet, Lafargue, Moritz-Gasser, Menjot de Champfleur, et al, 2015;Nakagawa et al, 2015;Takeuchi et al, 2013), the "interpersonal reactivity index" (Fujino et al, 2014;Parkinson & Wheatley, 2014), and the "trait emotional intelligence questionnaire" (empathy subsets) (Olszewski et al, 2017); mentalizing abilities were evaluated by "false belief" stories (Grosse Wiesmann et al, 2017), cartoon animations Scheibel et al, 2011), comic strip vignettes (Herbet et al, 2014;Kana et al, 2014), and the "reading the mind in the eyes" task (Anderson et al, 2015;Cabinio et al, 2015;Herbet et al, 2014;Mike et al, 2013;Yordanova et al, 2017). Such a wide variety of seemingly disparate disorders as well as diverse behavioral paradigms provide excellent opportunities for exploring the relationship between WM tracts and social functions.…”

Section: Methodological Summarymentioning

confidence: 99%

“…Reduced FA in the left UF in MCI patients correlates with impaired emotion recognition and expression (Fujie et al, 2008). For patients with velocardiofacial syndrome, one study reported that empathy scores correlated with RD in the right ATR and negatively correlated with the number of streamlines in right UF (Olszewski et al, 2017); the patients' emotional recognition performance for fear expression was also positively correlated with AD in the left UF (Jalbrzikowski et al, 2014). In addition, for patients with multiple sclerosis, facial emotion recognition performance is negatively correlated with lesion volume in bilateral UF, as well as the left fornix (Mike et al, 2013).…”

Section: Mirroring Networkmentioning

confidence: 99%

“…These sophisticated tools are increasing popularity in other fields of neuroscience such as vision research (e.g. Rokem et al, 2017), but thus far, were only employed by three social neuroscience studies (Anderson et al, 2015;Olszewski et al, 2017;Pyles et al, 2013). We recommend that future research use these advanced methodologies whenever possible in diffusion data analysis, because they can significantly increase the sensitivity and fidelity of tractography (Jones et al, 2013), and most importantly, improve reliability and reproducibility.…”

Section: Data Acquisition and Diffusion Modelingmentioning

confidence: 99%

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White Matter and Social Cognition

Wang

Metoki

Alm

et al. 2017

Preprint

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There is a growing consensus that social cognition and behavior emerge from interactions across distributed regions of the "social brain". Social neuroscience has traditionally focused its attention on functional response properties of these gray matter networks and neglected the vital role of white matter (WM) connections in establishing such networks and their functions. In this article, we conduct a comprehensive review of prior research on structural connectivity in social neuroscience and highlight the importance of this literature in clarifying brain mechanisms of social cognition. We pay particular attention to the research on three key social processes: face processing, embodied cognition, and theory of mind, and their respective underlying neural networks. To fully identify and characterize the anatomical architecture of these networks, we further implement probabilistic tractography on a large sample of diffusion-weighted imaging data. The combination of an in-depth literature review and the empirical investigation gives us an unprecedented, well-defined landscape of WM pathways underlying major social brain networks. Finally, we discuss current problems in the field, outline suggestions for best practice in diffusion imaging data collection and analysis, and offer new directions for future research.

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Section: Methodological Summarymentioning

confidence: 99%

Section: Methodological Summarymentioning

confidence: 99%

Section: Mirroring Networkmentioning

confidence: 99%

Section: Data Acquisition and Diffusion Modelingmentioning

confidence: 99%

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White Matter and Social Cognition

Wang

Metoki

Alm

et al. 2017

Preprint

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“…This is also evident in the high correlation between penetrance scores. This suggests that the neurodevelopmental indicators observed are not attributable to a particular psychopathology, but rather that these features reflect penetrance for neuropsychiatric illness more generally, a notion which has also been proposed for the clinical phenotype 21 . However, it should be stressed, due to the comparatively weak effects seen in the binary model, this effect is not simply due to the presence of a CNV.…”

Section: Discussionmentioning

confidence: 79%

Genetic risk for schizophrenia and developmental delay is associated with shape and microstructure of midline white matter structures

Drakesmith

Parker

Smith

et al. 2018

Preprint

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Genomic copy number variants (CNVs) are amongst the most highly penetrant genetic risk factors for neuropsychiatric disorders. The scarcity of carriers of individual CNVs and their phenotypical heterogeneity limits investigations of the associated neural mechanisms and endophenotypes. We applied a novel design based on CNV penetrance for schizophrenia and developmental delay that allows us to identify structural sequelae that are most relevant to neuropsychiatric disorders. Our focus on brain structural abnormalities was based on the hypothesis that convergent mechanisms contributing to neurodevelopmental disorders would likely manifest in the macro-and microstructure of white matter and cortical and subcortical grey matter. 21 adult participants carrying neuropsychiatric risk CNVs (including those located at 22q11.2, 15q11.2, 1q21.1, 16p11.2, and 17q12) and 15 ageand gender matched controls underwent T1-weighted structural, diffusion and quantitative T1 relaxometry MRI.The macro-and microstructural properties of the cingulum bundles were associated with penetrance for both developmental delay and schizophrenia, in particular curvature along the anterior-posterior axis (Sz: p corr =0.026; DD: p corr =0.035) and intracellular volume fraction (Sz: p corr =0.019; DD: p corr =0.064) Further principal component analysis showed alterations in the interrelationships between the volumes of several mid-line white matter structures (Sz: p corr =0.055; DD : p corr =0.027). In particular, the ratio of volumes in the splenium and body of the corpus callosum was significantly associated with both penetrance scores (Sz: p=0.037; DD; p=0.006). Our results are consistent with the notion that a significant alteration in developmental trajectories of mid-line white-matter structures constitutes a common neurodevelopmental aberration contributing to risk for schizophrenia and intellectual disability.

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Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

Zöller

Sandini

Schaer

et al. 2021

Human Brain Mapping

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How the brain's white‐matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting‐state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting‐state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.

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The social brain network in 22q11.2 deletion syndrome: a diffusion tensor imaging study

Cited by 31 publications

References 88 publications

White Matter and Social Cognition

White Matter and Social Cognition

Genetic risk for schizophrenia and developmental delay is associated with shape and microstructure of midline white matter structures

Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

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