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Laura Nawijn

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5 papers

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YNIMG Journal 2025 Journal Article

Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

Nick Steele
Ashley A. Huggins
Rajendra A. Morey
Ahmed Hussain
Courtney Russell
Benjamin Suarez-Jimenez
Elena Pozzi
Hadis Jameei

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

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YNIMG Journal 2023 Journal Article

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Xi Zhu
Yoojean Kim
Orren Ravid
Xiaofu He
Benjamin Suarez-Jimenez
Sigal Zilcha-Mano
Amit Lazarov
Seonjoo Lee

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

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YNIMG Journal 2022 Journal Article

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Delin Sun
Gopalkumar Rakesh
Courtney C. Haswell
Mark Logue
C. Lexi Baird
Erin N. O'Leary
Andrew S. Cotton
Hong Xie

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1, 340 cases with posttraumatic stress disorder (PTSD) (6. 2–81. 8 years old) and 2, 057 trauma-exposed controls without PTSD (6. 3–85. 2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63. 704, p < 0. 001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12. 082, p = 0. 007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9. 114, p = 0. 028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0. 001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0. 001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0. 001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0. 001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0. 001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

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YNICL Journal 2022 Journal Article

Associations of depression and regional brain structure across the adult lifespan: Pooled analyses of six population-based and two clinical cohort studies in the European Lifebrain consortium

Julia Binnewies
Laura Nawijn
Andreas M. Brandmaier
William F.C. Baaré
David Bartrés-Faz
Christian A. Drevon
Sandra Düzel
Anders M. Fjell

OBJECTIVE: Major depressive disorder has been associated with lower prefrontal thickness and hippocampal volume, but it is unknown whether this association also holds for depressive symptoms in the general population. We investigated associations of depressive symptoms and depression status with brain structures across population-based and patient-control cohorts, and explored whether these associations are similar over the lifespan and across sexes. METHODS: We included 3,447 participants aged 18-89 years from six population-based and two clinical patient-control cohorts of the European Lifebrain consortium. Cross-sectional meta-analyses using individual person data were performed for associations of depressive symptoms and depression status with FreeSurfer-derived thickness of bilateral rostral anterior cingulate cortex (rACC) and medial orbitofrontal cortex (mOFC), and hippocampal and total grey matter volume (GMV), separately for population-based and clinical cohorts. RESULTS: = -0.25). Effect sizes were slightly larger for presence of moderate-to-severe depression. Associations were similar across age groups and sex. Across population-based cohorts, no associations between depression and brain structures were observed. CONCLUSIONS: Fitting with previous meta-analyses, depressive symptoms and depression status were associated with lower mOFC, rACC thickness, and hippocampal and total grey matter volume in clinical patient-control cohorts, although effect sizes were small. The absence of consistent associations in population-based cohorts with mostly mild depressive symptoms, suggests that significantly lower thickness and volume of the studied brain structures are only detectable in clinical populations with more severe depressive symptoms.

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YNIMG Journal 2021 Journal Article

Associations between depression, lifestyle and brain structure: A longitudinal MRI study

Julia Binnewies
Laura Nawijn
Marie-José van Tol
Nic J.A. van der Wee
Dick J. Veltman
Brenda W.J.H. Penninx

BACKGROUND: Depression has been associated with decreased regional grey matter volume, which might partly be explained by an unhealthier lifestyle in depressed individuals which has been ignored by most earlier studies. Also, the longitudinal nature of depression, lifestyle and brain structure associations is largely unknown. This study investigates the relationship of depression and lifestyle with brain structure cross-sectionally and longitudinally over up to 9 years. METHODS: = 609). Cortical thickness of medial orbitofrontal cortex (mOFC), rostral anterior cingulate cortex (rACC) and hippocampal volume were derived using FreeSurfer. Using Generalized Estimating Equations, we investigated associations of depression and lifestyle (Body mass index (BMI), smoking, alcohol consumption, physical activity and sleep duration) with brain structure and change in brain structure over 2 (n = 179) and 9 years (n = 82). RESULTS: Depression status (B = -.053, p = .002) and severity (B = -.002, p = .002) were negatively associated with rACC thickness. mOFC thickness was negatively associated with BMI (B = -.004, p < .001) and positively with moderate alcohol consumption (B = .030, p = .009). All associations were independent of each other. No associations were observed between (change in) depression, disease burden or lifestyle factors with brain change over time. CONCLUSIONS: Depressive symptoms and diagnosis were independently associated with thinner rACC, BMI with thinner mOFC, and moderate alcohol consumption with thicker mOFC. No longitudinal associations were observed, suggesting that regional grey matter alterations are a long-term consequence or vulnerability indicator for depression but not dynamically or progressively related to depression course trajectory.

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