Motion and morphometry in clinical and nonclinical populations

Pardoe, Heath R.; Hiess, Rebecca Kucharsky; Kuzniecky, Ruben

doi:10.1016/j.neuroimage.2016.05.005

Cited by 181 publications

(247 citation statements)

References 21 publications

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“…More recently, the hypothesis that such conflicting findings could reflect greater inter-subject variability in ASD patients than in neurotypical controls (i.e., idiosyncratic connectivity) has been proposed (Hahamy et al, 2015). A putative confounding contribution of ASD-related motion and its effect on functional connectivity readouts is also the subject of an open controversy in the imaging community (Deen and Pelphrey, 2012; Power et al, 2012, 2015; Pardoe et al, 2016). …”

Section: The Connectivity Theory Of Autism: Open Questions and Contromentioning

confidence: 99%

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Liska

Gozzi

2016

Front. Neurosci.

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Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted impaired or aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological underpinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to aberrant connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. Despite some obvious limitations in reliably modeling the full phenotypic spectrum of a complex developmental disorder like ASD, mouse models have played a central role in advancing our basic mechanistic and molecular understanding of this syndrome. Recent progress in mouse brain connectivity mapping via resting-state fMRI (rsfMRI) offers the opportunity to generate and test mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism. Here we discuss recent progress toward this goal, and illustrate initial examples of how the approach can be employed to establish causal links between ASD-related mutations, developmental processes, and brain connectional architecture. As the spectrum of genetic and pathophysiological components of ASD modeled in the mouse is rapidly expanding, the use of rsfMRI can advance our mechanistic understanding of the origin and significance of the connectional alterations associated with autism, and their heterogeneous expression across patient cohorts.

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Section: The Connectivity Theory Of Autism: Open Questions and Contromentioning

confidence: 99%

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Liska

Gozzi

2016

Front. Neurosci.

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“…For diffusion MRI-derived microstructure estimates, motion can also induce spurious differences between groups, even in cases of comparing groups with control subjects only, when no differences are expected 81 . These phenomena pose special challenges for the collection, analysis and interpretation of developmental neuroimaging data given that (i) motion is nonrandomly distributed with respect to age (children>adults), sex (male>female), and clinical status 82 , and (ii) motion-induced biases are prominent in brain regions (e.g. prefrontal cortices) that are notable for displaying anatomical differences as a function of age, sex and disease status 80 .…”

Section: Subject Motionmentioning

confidence: 99%

Studying neuroanatomy using MRI

et al. 2017

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The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro-and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

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“…Motion artifacts are especially a problem in developmental studies (Brown et al, 2010; Van Dijk et al, 2012) with younger age groups related to increased motion artifacts (Blumenthal et al, 2002). Moreover, images of children and adolescents with psychiatric disorders, such as attention-deficit/hyperactivity disorder (ADHD), tic disorders (Buse et al, 2016), autism spectrum disorder, schizophrenia (Pardoe et al, 2016), and conduct disorder (CD, Huebner et al, 2008) might be particularly prone to motion artifacts. For example, ADHD impulsivity and hyperactivity symptoms have been shown to relate to more severe motion artifacts (Rauch, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…These rating systems include categories ranging from “good” data, which is proposed to be included in further processing, to “moderate” data, and finally “bad” data, which should be excluded from further processing (Blumenthal et al, 2002; Wilke et al, 2002; Shaw et al, 2007; Pardoe et al, 2016; Reuter et al, 2015; Tisdall et al, 2016). However, the definition and range of additional categories in the “moderate” category, between the good and bad data categories, varies in previous work (Blumenthal et al, 2002; Shaw et al, 2007; Reuter et al, 2015; Tisdall et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, the definition and range of additional categories in the “moderate” category, between the good and bad data categories, varies in previous work (Blumenthal et al, 2002; Shaw et al, 2007; Reuter et al, 2015; Tisdall et al, 2016). For instance, some authors used a 4-point scale (from none to severe, Blumenthal et al, 2002; Reuter et al, 2015) while others used a 5-point scale (from no detectable motion to lowest quality/severe motion, Pardoe et al, 2016). Moreover, most authors to date that have reported using QC ratings have not specified which artifact type(s) they focused on (e.g., ringing, blurring, gray, and white matter differentiation etc.)…”

Section: Introductionmentioning

confidence: 99%

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Quality Control of Structural MRI Images Applied Using FreeSurfer—A Hands-On Workflow to Rate Motion Artifacts

et al. 2016

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In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g., FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e., determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here, we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies.

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Motion and morphometry in clinical and nonclinical populations

Cited by 181 publications

References 21 publications

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Studying neuroanatomy using MRI

Quality Control of Structural MRI Images Applied Using FreeSurfer—A Hands-On Workflow to Rate Motion Artifacts

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