Reliability of multimodal MRI brain measures in youth at risk for mental illness

Drobinin, Vladislav; Gestel, Holly Van; Helmick, Carl; Schmidt, Matthias; Bowen, Chris V.; Uher, Rudolf

doi:10.1002/brb3.1609

Cited by 9 publications

(8 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 3 , the parcel‐wise or edge‐wise ICCs were shown for all brain features in the BNU1 dataset. In consistent with previous studies (for instance, Buimer et al, 2020 ; Chen et al, 2015 ; Drobinin et al, 2020 ; Noble et al, 2019 ; Zuo & Xing, 2014 ), both T1 and DWI brain features showed moderate or good overall reliability, while the resting‐state BOLD brain features showed poor or moderate reliability. For T1 data, the CA showed the highest and almost perfect reliability, while the CT showed the lowest reliability.…”

Section: Resultssupporting

confidence: 90%

PhiPipe: A multi‐modal MRI data processing pipeline with test–retest reliability and predicative validity assessments

Qiao

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non‐invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi‐modal MRI data are still rare, in contrast to single‐modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi‐modal MRI processing pipeline. PhiPipe could process T1‐weighted, resting‐state BOLD, and diffusion‐weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test–retest reliability of PhiPipe's brain features by computing intra‐class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single‐modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple‐to‐use solution to multi‐modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

show abstract

Section: Resultssupporting

confidence: 90%

PhiPipe: A multi‐modal MRI data processing pipeline with test–retest reliability and predicative validity assessments

Qiao

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…In Figure 3, the parcel-wise or edge-wise ICCs were shown for all brain features in the BNU1 dataset. In consistent with previous studies (for instance, Zuo and Xing, 2014;Noble et al, 2019;Buimer et al, 2020;Drobinin et al, 2020), both T1 and DWI brain features showed moderate or good overall reliability, while the resting-state BOLD brain features showed poor or moderate reliability. For T1 data, the CA showed the highest and almost perfect reliability, while the CT showed the lowest reliability.…”

Section: The Reliability and Validity Of Brain Features Using The Def...supporting

confidence: 91%

PhiPipe: a multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

Qiao

et al. 2022

Preprint

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still few, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines.Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging.We evaluated the test-retest reliability of PhiPipe’s brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA.The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image preprocessing pipelines.

show abstract

“…In contrast, a thinner neocortex, but not surface area, was pervasively associated with psychopathology in the older Dunedin cohort (Romer et al., 2021). Discrepancies across studies may be due to the relatively lower reliability of cortical thickness when compared with measures of cortical volume and surface area (Drobinin et al., 2020). It is also possible that cortical thickness may become more closely associated with psychopathology across adolescent (and adult) development.…”

Section: Discussionmentioning

confidence: 99%

The relationship between brain structure and general psychopathology in preadolescents

Mewton

Lees

Squeglia

et al. 2021

Child Psychology Psychiatry

View full text Add to dashboard Cite

Background An emerging body of literature has indicated that broad, transdiagnostic dimensions of psychopathology are associated with alterations in brain structure across the life span. The current study aimed to investigate the relationship between brain structure and broad dimensions of psychopathology in the critical preadolescent period when psychopathology is emerging. Methods This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study® (n = 11,875; age range = 9–10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher‐order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area. Results Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, parental education, income, and maternal psychopathology. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There were no statistically significant relationships between psychopathology and cortical thickness in this sample of preadolescents. Conclusions The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. Future research may focus on whether the widespread and pervasive relationships between general psychopathology and brain structure reflect cognitive dysfunction that is a feature across a range of mental illnesses.

show abstract

Reliability of multimodal MRI brain measures in youth at risk for mental illness

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 9 publications

References 63 publications

PhiPipe: A multi‐modal MRI data processing pipeline with test–retest reliability and predicative validity assessments

PhiPipe: A multi‐modal MRI data processing pipeline with test–retest reliability and predicative validity assessments

PhiPipe: a multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

The relationship between brain structure and general psychopathology in preadolescents

Contact Info

Product

Resources

About