Lachlan T. Strike

Background: Friedreich ataxia (FRDA) is a rare neurodegenerative disorder with heterogenous clinical progression, complicating prognosis and trial design. Neuroimaging offers objective biomarkers of disease progression, yet variability in progression patterns remains poorly understood. Objective: The objective of this study is to identify distinct neurodegenerative progression patterns in FRDA using longitudinal multimodal magnetic resonance imaging (MRI) and to evaluate associations with clinical, demographic, and genetic factors. Methods: Longitudinal structural and diffusion MRI data from 54 patients with FRDA and 57 controls were analyzed. Annualized progression rates of macrostructural (volumetric) and microstructural (diffusion) features across cerebellum, brainstem, and spinal cord regions were clustered using Gaussian Mixture Models. Following model selection, clusters were evaluated for biological plausibility of longitudinal imaging trajectories, bootstrap and subsampling reproducibility, and consistency of case‐control composition. Associations with demographic, genetic, and clinical variables were examined, and Random Forest modeling assessed predictors of cluster membership. Results: Four statistical clusters were identified, three of which represented robust, biologically interpretable progression patterns characterized by predominant microstructural degeneration, predominant macrostructural atrophy, and minimal measurable progression. The microstructure‐ and macrostructure‐dominant patterns were enriched for FRDA participants, whereas the minimal‐progression pattern contained more controls. GAA1 repeat length was the only variable consistently associated with cluster membership, with larger expansions observed in the microstructure‐dominant pattern. Disease duration and clinical progression rates did not significantly differentiate progression patterns. Conclusions: Longitudinal multimodal MRI shows distinct neurodegenerative progression patterns in FRDA that are not fully captured by conventional clinical measures. This data‐driven framework provides a basis for investigating imaging‐derived disease heterogeneity and its potential relevance to participant stratification in clinical trials. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Research on genetic and environmental influences on brain function generally focuses on connections between brain areas. A different yet unexplored approach is to examine activity within local brain regions. We investigated the influence of genes and environmental effects on two specific measures of local brain function: regional homogeneity and fractional amplitude of low-frequency fluctuations. Participants were drawn from a sample of adolescent twins on two occasions (mean ages 11.5 and 13.2 yr, n = 278 and 248). Results showed that genetic and environmental factors influenced brain function in almost all 210 cortical regions examined. Moreover, genetic and common environmental factors influencing regional homogeneity and fractional amplitude of low-frequency fluctuation values at wave 1 (9 to 14 yr) also influenced values at wave 2 (10 to 16 yr) for many regions. However, the influence of genetic and common environmental factors varied across the cortex, exhibiting different patterns in different regions. Furthermore, we found new (ie independent) genetic and environmental influences on brain activity at wave 2, again with regional patterns. Exploratory analyses found weak associations between anxiety and depressive symptoms and local brain function in several regions of the temporal lobe. These findings are consistent with similar studies of other resting-state functional MRI metrics (ie functional connectivity).

Research on genetic and environmental influences on brain function generally focuses on connections between brain areas. A different yet unexplored approach is to examine activity within local brain regions. We investigated the influence of genes and environmental effects on two specific measures of local brain function: Regional Homogeneity (ReHo) and fractional Amplitude of Low-Frequency Fluctuations (fALFF). Participants were drawn from a sample of adolescent twins on two occasions (mean ages 11.5 and 13.2 years, N = 278 and 248). Results showed that genetic and environmental factors influenced brain function in almost all 210 cortical regions examined. Moreover, genetic and common environmental factors influencing ReHo and fALFF values at wave 1 (9-14 years) also influenced values at wave 2 (10-16 years) for many regions. However, the influence of genetic and common environmental factors varied across the cortex, exhibiting different patterns in different regions. Furthermore, we found new (i.e., independent) genetic and environmental influences on brain activity at wave 2, again with regional patterns. Exploratory analyses found weak associations between anxiety and depressive symptoms and local brain function in several regions of the temporal lobe. These findings are consistent with similar studies of other resting-state functional MRI metrics (i.e., functional connectivity).

Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine‐grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum ( 47%–91%). Additionally, the good to excellent test–retest reliability for a subsample of QTIM participants suggests that non‐genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome‐wide association studies.

Recent advances in technology have made possible to quantify fine-grained individual differences at many levels, such as genetic, genomics, organ level, behavior, and clinical. The wealth of data becoming available raises great promises for research on brain disorders as well as normal brain function, to name a few, systematic and agnostic study of disease risk factors (e.g., genetic variants, brain regions), the use of natural experiments (e.g., evaluate the effect of a genetic variant in a human population), and unveiling disease mechanisms across several biological levels (e.g., genetics, cellular gene expression, organ structure and function). However, this data revolution raises many challenges such as data sharing and management, the need for novel analysis methods and software, storage, and computing. Here, we sought to provide an overview of some of the main existing human datasets, all accessible to researchers. Our list is far from being exhaustive, and our objective is to publicize data sharing initiatives and help researchers find new data sources.

The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35–40% versus 20–25% in males) and an RS-CS connection more common in males (approximately 40–45% versus 25–30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28–0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection.

Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n = 8), sleep disturbances (n = 20) and cognitive function (n = 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADS gene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.

We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9–14 years; session 2: N = 304, 10–16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological samples for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.

We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9-14 years; session 2: N = 304, 10-16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological samples for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.