White Matter Microstructure in Persistent Psychotic-Like Experiences

A Voxelwise dMRI Study using ABCD 6.0 and kwneuro

Incomplete. This tutorial has some incomplete sections that may be filled out in the future.

How to read this tutorial. This document describes a real clinical study investigating white matter differences in adolescents with psychotic-like experiences. It includes illustrative Python code using the kwneuro library to demonstrate how the pipeline was implemented; the reader does not need to run the code to follow the study.

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Abstract

Using diffusion MRI data from the Adolescent Brain Cognitive Development (ABCD) Study Release 6.0, we compared white matter microstructure between adolescents following a persistent-distressing psychotic-like experience (PLE) trajectory (n = 253) and a normative low-symptom trajectory (n = 253). Five microstructural metrics (FA, MD, NDI, ODI, and FWF) were estimated voxelwise, registered to a study-specific template, harmonised across 21 scanner sites with ComBat, and compared with a voxelwise GLM controlling for age, sex, and household income. The full pipeline was implemented in kwneuro, a Python library for dMRI analysis.

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1. Introduction

Persistent, distressing psychotic-like experiences (PLEs) in adolescence are associated with elevated risk for later psychotic disorder, depression, and functional impairment. Karcher et al. (2023) applied latent class growth analysis to ABCD Study data and identified five PLE trajectory classes. This study contrasts Group 1 (persistent-distressing) and Group 5 (low-distressing / normative), to detect neurobiological correlates of early psychotic risk.

Five dMRI metrics are derived from two biophysical models.

Metric	Model	Interpretation
FA - Fractional Anisotropy	DTI	Directional coherence; non-specific
MD - Mean Diffusivity	DTI	Overall displacement; non-specific
NDI - Neurite Density Index	NODDI	Intra-neurite volume fraction
ODI - Orientation Dispersion Index	NODDI	Fibre fanning and crossing
FWF - Free Water Fraction	NODDI	Extracellular free-water compartment

Analysing all five jointly disambiguates the biological source of any group difference that a single FA map cannot resolve.

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2. Study Cohort

Participants were drawn from the ABCD 6.0 baseline assessment (age 9–11 years).

	Group 1 — Persistent Distressing	Group 5 — Low Distressing
N	253	253
Age	9.80 ± 0.62 years	9.98 ± 0.65 years
Female	47%	51%

Statistical models include age, sex, and household income (3-level ordinal) as covariates. Scanner model is treated as a batch variable and addressed via ComBat harmonisation before statistical testing.

Cohort Demographics

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3. Analysis Pipeline

The pipeline proceeds in seven stages: (1) DWI denoising, (2) brain extraction, (3) microstructure estimation, (4) study-specific template construction, (5) subject-to-template registration, (6) ComBat harmonisation, and (7) voxelwise GLM. All stages are implemented in kwneuro, which provides transparent disk-based caching so that any step can be rerun in isolation after a parameter change.

3.1 Preprocessing and Microstructure Estimation

Denoising (Patch2Self), brain extraction (HD-BET), and microstructure estimation (DTI + NODDI) are run per subject. The Cache context manager handles checkpointing so the batch loop is safely restartable.

The figure below shows the five metric maps for four representative subjects — two from each group — all at the same axial slice.

3.2 Study-Specific Template Construction

A balanced subset of 25 subjects per group (50 total) was selected with a fixed random seed to ensure neither group dominates the template geometry. The template is built jointly from FA and mean b=0 using iterative groupwise registration (ANTs), capturing both white matter structure and cortical boundaries.

Study-specific template: FA (top row) and mean b=0 (bottom row) across six axial slices.

3.3 Registration to Template Space

All five metric maps for each subject are warped to template space. The deformation is estimated jointly from subject FA and mean b=0 (multi-metric SyN; mutual-information cost), then applied to the remaining metrics. A per-subject white matter mask from Atropos tissue segmentation on the FA map constrains the registration optimiser to white matter.

Registration quality: template FA, warped subject FA, and overlay for two example subjects.

3.4 Group White Matter Mask

Each subject’s Atropos WM mask is warped to template space using the saved transforms. Averaging across all 506 subjects produces a voxelwise coverage fraction; voxels covered by ≥50% of subjects form the final group analysis mask used as the search volume for ComBat and the voxelwise GLM.

3.5 Scanner Harmonisation and Voxelwise Statistics

ComBat (Johnson et al., 2007; Fortin et al., 2017) is applied independently per metric, removing site-specific additive and multiplicative effects while preserving variance attributable to age, sex, income, and group. The voxelwise GLM then tests:

$$\text{metric} \sim \beta_0 + \beta_1 \cdot \text{group} + \beta_2 \cdot \text{age} + \beta_3 \cdot \text{sex} + \beta_4 \cdot \text{income}$$

at each WM voxel, with FDR correction (q < 0.05). Positive t-values on β₁ indicate higher metric in Group 1 (persistent-distressing); negative values indicate lower metric.

Each dot below is one subject; horizontal bars are site medians. Sites are sorted left-to-right by their pre-harmonisation grand mean FA, so any inter-site offset is immediately visible on the left panel and should collapse on the right.

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4. Results

To be completed

Based on prior literature on early psychosis, the primary hypothesis is that Group 1 will show reduced NDI and elevated FWF in frontal and callosal white matter, reflecting lower axonal density and extracellular free-water accumulation. A joint reduction in FA and NDI with stable ODI would localise the effect to axonal loss rather than altered fibre geometry.

Voxelwise t-statistic maps for each metric (thresholded at |t| > 3.0; FDR q < 0.05):

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5. Conclusions

Challenge	kwneuro solution
Cohort-scale preprocessing (N = 506)	Transparent per-subject caching; restartable batch loop
GPU-efficient brain extraction	brain_extract_dwi_batch — single model-load pass
Microstructure specificity	DTI + NODDI jointly; five complementary maps
Paediatric registration target	Study-specific template, balanced 25+25 subset
Cross-subject normalisation	Multi-metric ANTs SyN (FA + mean b=0) with Atropos WM mask
21-site scanner variability	ComBat harmonisation
Voxelwise group comparison	GLM + FDR, whole white matter search volume

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References

• Avants, B.B. et al. (2008). Symmetric diffeomorphic image registration with cross-correlation. Medical Image Analysis, 12(1), 26–41.

• Daducci, A. et al. (2015). AMICO: Accelerated microstructure imaging via convex optimization. NeuroImage, 105, 32–44.

• Fadnavis, S. et al. (2020). Patch2Self: Denoising diffusion MRI with self-supervised learning. NeurIPS, 33.

• Fortin, J.P. et al. (2017). Harmonization of multi-site diffusion tensor imaging data. NeuroImage, 161, 149–170.

• Isensee, F. et al. (2019). Automated brain extraction of multi-sequence MRI using artificial neural networks. Human Brain Mapping, 40(17), 4952–4964.

• Johnson, W.E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118–127.

• Karcher, N.R. et al. (2023). Trajectories of psychotic-like experiences and associations with outcomes in the ABCD Study. JAMA Psychiatry.

• Zhang, H. et al. (2012). NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.