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Leon Lotter @leondlotter.de · 07/05/2026

Another one: In patients with early psychosis, AUC+ scores are reduced across neurotransmitter systems, but most prominently for serotonergic and dopaminergic indices! As far as we could test, reductions are independent of antipsychotic medication and may be associated with symptom severity. 14/n

Early psychosis. AUC+ scores broadly reduced across neurotransmitter systems, strongest for serotonergic and dopaminergic indices; correlates with symptom severity and antipsychotic intake.

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Leon Lotter @leondlotter.de · 07/05/2026

Application: Pharmaco-fMRI data for 3/4 substances show AUC score modulation largely in line with expected receptor targets (risperidone, ketamine, midazolam). For ketamine and midazolam administered intravenously during scans, NEOFC scores change dynamically during infusion (lower right). 13/n

Pharmacological sensitivity. Risperidone, ketamine, and midazolam modulate AUC scores in line with their receptor targets; IV drug effects tracked dynamically via sliding-window analysis.

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Leon Lotter @leondlotter.de · 07/05/2026

Back to a core finding: The NET effect is driven by a sensorimotor-insular network. In two independent samples, NET AUC+ is inversely associated with autonomic arousal – heart rate variability and pupil unrest – indicating a physiologically relevant signal. Correlation: 0.6 for pupil unrest! 12/n

Regional drivers and autonomic relevance. NET AUC+ is anchored in sensorimotor-insular cortex and inversely correlates with autonomic arousal (HRV and pupil unrest) across two datasets.

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Leon Lotter @leondlotter.de · 07/05/2026

Indeed, they do! AUC profiles correlate 0.8 – 0.9 between rsfMRI datasets, ICC approaches 0.9. MEG mirrors MRI results, but with a twist: We find high correspondence in the beta band (scatter plots). But especially gamma bands show pronounced AUC+ effects not present in MRI. 9/n

Reproducibility and MEG extension. AUC profiles replicate across 6 fMRI cohorts and mirror rsfMRI in MEG — strongest cross-modal alignment in the beta band.

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Leon Lotter @leondlotter.de · 07/05/2026

During our exploration, we realized: Our approach is intrinsically sensitive to positive associations (high connectivity in regions with high PET density: "AUC+"), but to test for negative associations, we can invert the PET maps. A range of transmitter systems showed strong "AUC–" effects! 7/n

fMRI connectivity organizes along neurobiology. NET shows the strongest and most consistent AUC+ effect across all subjects and cohorts; AUC− effects span most neurotransmitter systems.

Highlighted are AUC– effects

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Leon Lotter @leondlotter.de · 07/05/2026

Next, we find that, across 6 independent cohorts, rsfMRI connectivity aligns with neurobiology. Importantly, the strongest effect by far emerges for the noradrenaline transporter (NET), significant in every individual subject! AUC scores showed good-to-excellent 1-day-test-retest reliability! 6/n

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Leon Lotter @leondlotter.de · 07/05/2026

With this approach, we evaluate 25 PET atlases for their associations with individual connectome architecture. We test for statistical significance with spatial nulls of the PET maps. But first, we run a positive control confirming that we can trace underlying canonical resting-state networks. 5/n

Positive control of the NEOFC approach: Resting-state network probability maps induce the expected AUC effects compared to spatial null models

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Leon Lotter @leondlotter.de · 07/05/2026

We introduce the NEOFC framework: if a receptor contributes to regional signals, regions with higher density should show more synchronization. We quantify this by masking an individual's connectome by receptor/transporter density percentiles, producing a curve and a subject-level "AUC" score. 4/n

Neurobiologically enriched organization of functional connectivity (NEOFC). Receptor density maps threshold individual FC matrices → percentile curves → single subject-level AUC scores.

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Leon Lotter @leondlotter.de · 07/05/2026

My most recent and biggest work so far is finally out as a preprint! 🧠🧵 *Linking human brain functional connectivity to underlying neurotransmission* doi.org/10.64898/202... 1/n TL;DR below ↓ #neuroskyence #neuroimaging #MedSky #PsySciSky

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Leon Lotter @leondlotter.de · 07/05/2026

Pathway-specific imaging biomarkers – multimodal colocalization, multi-tracer PET, advanced MRS – interrogate that grid, linking genes X environment X brain to the individual. A step toward precision psychiatry with heterogeneity built in as a feature, rather than dealing with it post-hoc. 5/n

Schematic representation of the spatial co-localization approach for deriving biological pathway-specific information from multimodal neuroimaging.

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Leon Lotter @leondlotter.de · 07/05/2026

We propose a solution: a "vulnerability search grid", intersecting pathway-specific genetic risk (single-ontology PRS) with individual environmental exposures. Not a diagnosis, but a biologically informed prior that narrows the search space for what's actually going on in a given patient. 4/n

A A schematic representation of the general symptom-specific vulnerability search grid as defined by genetic and environmental risk factors. B A schematic representation of the individual vulnerability search grid as derived from individual genetic and environmental exposure.

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Leon Lotter @leondlotter.de · 07/05/2026

If altered neurotransmission underlies psychiatric disorders, we identify a problem: neurotransmitter systems are strongly colocalized – GABAa and 5-HT2a share ~80% of spatial variance. The same imaging signal can reflect entirely different pathophysiology in two patients. But you'd never know. 3/n

A Spatial correlation matrix of different neurotransmitter properties as derived from the positron emission tomography included in the JuSpace toolbox. B Exemplary visualization of the strong colocalization observed between GABAa and serotonergic 5-HT2a receptors. Regions with high expression of each receptor and their overlaps are displayed.

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Leon Lotter @leondlotter.de · 07/05/2026

Catching up on my paper threads 🧵 Here is a perspective from this year, lead-authored by my supervisor, @juergendukart.bsky.social The many-to-many problem of endophenotypes in psychiatry - a biological perspective doi.org/10.1038/s413... #neuroskyence #neuroimaging #MedSky #PsychSciSky 1/n