Prof. Dr. Christine Ecker

This is a distributed project, with representatives at all main TRR379 sites.

High-field magnetic resonance imaging to explore brain structure and function remains limited to high-resource settings. Novel, low-field (<0.1 T) imaging offers a more cost-effective/accessible alternative. However, the validity of low-field data at spatial resolutions relevant to research and clinic (vertex-level) remains unclear. Hence, we examine paired high-field (reference) and low-field (single/multi-orientation scans processed through established/novel pipelines) data (12 children [10-12 yrs] in a low- and middle-income country [LMIC]). We assess high-field/low-field correspondence between vertex-level measures of cortical volume, surface area, and cortical thickness; and compare analytic strategies. High/low-field images show weak-to-moderate global correspondence (cortical volume, surface area: Pearson’s r ≤ 0.6, cortical thickness r ≤ 0.3), and weak-to-very strong local correspondence (r ≤ 0.99). Greatest correspondence is achieved with multi-orientation images and a pipeline adjusted for low-resolution images (recon-all-clinical); or image enhancement (SynthSR) plus standard processing (FastSurfer); but agreement varies across brain based on input, analytic strategy, and neuroanatomical feature. We provide an application to interactively explore our results. Thus, low-field imaging can provide reliable, high-resolution estimates of cortical volume and surface area, but not cortical thickness; and analytic approaches should be selected based on multiple considerations. Once validated, this research may help deploy low-field imaging to aid research/evidence-based clinical work in high- and low-resource settings, including LMIC.

Neurodevelopmental conditions, such as autism, are highly heterogeneous at both the mechanistic and phenotypic levels. Therefore, parsing heterogeneity is vital for uncovering underlying processes that could inform the development of targeted, personalized support. We aimed to parse heterogeneity in autism by identifying subgroups that converge at both the phenotypic and molecular levels. An imaging transcriptomics approach was used to link neuroanatomical imaging-derived phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human Brain Atlas. Neuroimaging and clinical data of 359 autistic participants ages 6 to 30 years were provided by EU-AIMS (European Autism Interventions) LEAP (Longitudinal European Autism Project). Individuals were stratified using data-driven clustering techniques based on the correlation between brain phenotypes and transcriptomic profiles. The resulting subgroups were characterized on the clinical, neuroanatomical, and molecular levels. We identified 3 subgroups of autistic individuals based on the correlation between imaging-derived phenotypes and transcriptomic profiles that showed different clinical phenotypes. The individuals with the strongest transcriptomic associations with imaging-derived phenotypes showed the lowest level of symptom severity. The gene sets most characteristic for each subgroup were significantly enriched for genes previously implicated in autism etiology, including processes such as synaptic transmission and neuronal communication, and mapped onto different gene ontology categories. Autistic individuals can be subgrouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, which reveal subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.

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