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Linking human brain functional connectivity to underlying neurotransmission Preprint on bioRxiv, 2026 bsky.app/profile/leon...

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This had lots of ups and downs over a long time. Super happy to finally have it out! Special thanks go to @juergendukart.bsky.social and all collaborators! 🎉 @goliashf.bsky.social @abhaykoushik.bsky.social @sdmuthu.bsky.social @misicbata.bsky.social @koeniglab.bsky.social @sbe.bsky.social 18/fin

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GitHub - LeonDLotter/mapconn: mapconn toolbox

Finally, some resources: Code: github.com/LeonDLotter/... Method as a Python tool: github.com/LeonDLotter/... (Full working example coming soon) Again, the preprint: doi.org/10.64898/202... In the future, we plan an integration into NiSpace: github.com/LeonDLotter/NiSpace 17/n

github.com

mapconn toolbox. Contribute to LeonDLotter/mapconn development by creating an account on GitHub.

Here, we apply our approach to resting-state fMRI and MEG data on one side, and PET maps on the other. A strength of the approach is its flexibility. I'm excited to see it extended to other biological priors, EEG (think about clinical applicability), and task fMRI (e.g., a reward paradigm)! 16/n

Limitations: Correlative design, dependence on PET map quality, intercorrelated neurotransmitter maps, and small pharmacological cohorts. Effects nonetheless replicate across 6 cohorts, two imaging modalities, multiple parcellations, and are present at the individual subject level! 15/n

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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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.

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.

... Such local patterns may then show as negative associations (fMRI AUC–). Only ultra-slow fluctuations, producing networks with high spatial coherence – such as those supported by long-range projecting neurons – may actually "survive" in fMRI (→ NET AUC+). 11/n

Interpretation? fMRI and MEG may reflect the same signal: Assume regions interact at high frequencies such as found in MEG gamma (AUC+). The same neural sources in fMRI are observed through heavy filtering, which may reduce interregional info and leave a local-appearing BOLD pattern. ... 10/n

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.

Before going into interpretation, we look at reproducibility and external validation. Do the AUC profiles across neurotransmitter systems replicate in independent samples? And do we find the same associations in a different imaging modality? 8/n

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

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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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

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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Resting-state (rs)fMRI connectivity has been measured for 30 years. But its neurobiological basis remains poorly understood - BOLD is indirect, unspecific, and shaped by neurovascular coupling. Which neurotransmitter systems drive the networks we observe remains largely an open question. 3/n

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TL;DR: We develop a framework linking fMRI & MEG connectivity to underlying neurotransmitter distributions. It is robust, reproducible, applicable at the individual level, sensitive to pharmacological manipulation & clinical alterations. A step toward biologically informed connectome analysis. 2/n

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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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The many-to-many problem of endophenotypes in psychiatry - a biological perspective Molecular Psychiatry, 2026 bsky.app/profile/leon...

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Joint work, led by @juergendukart.bsky.social, together with Casey Paquola, @sbe.bsky.social, and @leoschilbach.bsky.social. Sorry for tagging you 3 months after the article was published! 😬 6/fin

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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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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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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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Despite decades of research, no reliable (imaging) biomarkers exist for any major psychiatric diagnosis. Why? Current neuroimaging markers like fMRI connectivity or cortical thickness are too unspecific – E/I imbalance and accelerated brain age have been reported for nearly every disorder. 2/n

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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

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Regional patterns of human cortex development correlate with underlying neurobiology Nature Communications, 2024 bsky.app/profile/leon...

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Temporal dissociation between local and global functional adaptations of the maternal brain to childbirth: A longitudinal assessment Neuropsychopharmacology, 2024 doi.org/10.1038/s413... bsky.app/profile/leon...

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Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion Neuroscience & Biobehavioral Reviews, 2023 bsky.app/profile/leon...

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Recovery-Associated Resting-State Activity and Connectivity Alterations in Anorexia Nervosa Biological Psychiatry: CNNI, 2021 bsky.app/profile/leon...

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Concluding, resting-state alterations in Anorexia nervosa seem to be a temporary phenomenon independent of grey matter volume decreases. Limitations: Mainly the small sample size. 🤏 Replication in a larger sample is needed. 👀 Edit 2026: An ENIGMA meta-analysis is on the way! 5/fin

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While grey matter volume alterations demonstrated similar recovery dynamics, voxel-wise controlling did barely affect rsfMRI group differences, suggesting differing mechanisms in place (panel C). Correlations with clinical variables did not survive multiple comparison correction. 4/n

Correlations among resting-state measures and with clinical outcome; effect sizes of resting-state group comparisons with and without controlling for voxelwise gray matter volume.

Strong widespread resting-state activity and connectivity decreases in severely underweight patients (T1acu) that nearly resolved after inpatient treatment (T2acu) and were normalized in long-term recovered patients (T3rec). 3/n

Voxelwise functional connectivity and activity.

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We were interested in how resting-state fMRI alterations in Anorexia nervosa develop from starvation to weight-recovered state and how they relate to grey matter volume changes. Different rsfMRI metrics in acute, partially and long-term weight-recovered patients showed: 2/n

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*Rescuing Twitter Threads* series 2021: This was my first fist-author and MD thesis paper! *Recovery-Associated Resting-State Activity and Connectivity Alterations in Anorexia Nervosa* doi.org/10.1016/j.bp... PDF: leondlotter.de/doc/2021_ANR... 1/n #neuroskyence #neuroimaging #AcademicSky #SciSky

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And finally: Thanks go to my great coauthors and mentors, especially to Kerstin Konrad, who all helped in shaping and guiding this fun project. 🥳 14/fin

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GitHub - LeonDLotter/NiSpace: Neuroimaging Spatial Colocalization Environment (under development).

Edit 2026: The toolboxes we developed and used for the analyses (JuSpyce and ABAnnotate) are now integrated into *NiSpace* (Neuroimaging Spatial Colocalization Environment): github.com/LeonDLotter/... 13/n

github.com

Neuroimaging Spatial Colocalization Environment (under development). - LeonDLotter/NiSpace

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All our data, code, methods, and the paper itself are openly available, and we hope that they will be of use for others! Some links to our resources: The paper: doi.org/10.1016/j.ne... Interactive figure (2B): leondlotter.github.io/MAsync/citen... Code & data: github.com/LeonDLotter/... 12/n

doi.org

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(i) consider our results of relevance for future INS research that can build on our hypotheses, and (ii) emphasize the value of multimodal association analyses not only for psychiatric research but also to advance models of cognition in typically developing subjects! 11/n

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While clearly noting the limitations of our approach, especially in terms of the relatively low number of available studies and the indirect nature of phenotypic-molecular spatial associations analyses, we: 10/n

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Although requiring experimental tests, this model would connect within-brain and between-brain synchronization; it would integrate with potential physiology of predictive processing; and it would draw a connection to neurodevelopmental disorders, esp of the autism spectrum. 9/n

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Integrating these data, we hypothesized that human INS is tightly linked to social attentional processing, with the right TPJ as a sensory-integration hub at the brain system level, and potentially facilitated by GABA-mediated E/I balance at the neurophysiological level. 8/n

ummary of results and hypotheses. Abbreviations: INS = interpersonal neural synchronization, DAN/VAN = dorsal/ventral attention network, DMN = default mode network, ToM = theory of mind network, TPJ = temporoparietal junction, IFG = inferior frontal gyrus, dm/vmPFC = dorsomedial/ventromedial prefrontal cortex, AI = anterior insula, PMC = premotor cortex, IPL = inferior parietal lobule, OFC = orbitofrontal cortex, VS = ventral striatum, OXT = oxytocin, E/I = excitation/inhibition balance.

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Interestingly, the meta-analytic whole-brain INS distribution was correlated to GABAergic and glutamatergic receptor systems (A). In line with that, gene enrichment analyses showed assocations with layer IV/V GABAergic interneurons, and neurodevelopmental disorders (B & D)! 7/n

Spatial associations of INS to neurotransmitter receptor and synaptic density distributions, as well as genetic markers of neuronal cell types, brain development, and psychiatric disorders.

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We found convergence in the right temporoparietal junction (TPJ) and the left inferior prefrontal cortex (A & B)! The TPJ coactivated with mainly cortical regions and thalami (C & D) and the INS data was associated with attention and mentalizing processes & networks (E & F). 6/n

Brain-functional INS correlates resulting from fMRI and fNIRS meta-analyses with their neuronal connectivity and neurobehavioral association patterns. A: Results of the main fMRI INS meta-analysis. Upper: Unthresholded Z-map derived from ALE p values. Middle/lower: Significant INS clusters after thresholding. B: Upper: Parcel-wise fNIRS INS results. C: Meta-analytic coactivation network using the rTPJ INS cluster as a seed (black contour). D: Functional resting-state connectivity between MACM clusters. E: Relationships of the rTPJ cluster and MACM network to major resting-state networks. Relative: Proportion of “INS-voxels” within a given network vs. all “INS-voxels”. Absolute: Proportion of “INS-voxels” within a given network vs. all voxels within the network. F: Functional decoding of INS-related activation using Neurosynth topics.

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We started with meta-analyses of fMRI and fNIRS hyperscanning studies that assessed temporal synchronization of brain activities between interacting subjects. Overall, we included 22 fMRI studies and 69 fNIRS studies with 740 and 3,721 subjects in separate meta-analyses. 5/n

Structured literature search. A: Flow chart depicting the literature search process in line with the PRISMA 2020 statement. SetYouFree was used for the automatic literature search, duplicate detection, and the cross-reference search. The resulting records, together with results from other sources, were submitted to Cadima to manually identify eligible studies. Note that the exclusion criteria listed in Reports excluded were not mutually exclusive. Records are entries in the publication lists resulting from the main search and screened on the abstract level, reports are publications screened in full, studies are included publications, and experiments are sets of data derived from independent study samples and can cover data from multiple studies. B: Citation network generated from OpenCitations data, including overview figures of reported INS foci and fNIRS probe setups. An interactive version with metadata for each individual study is available at https://leondlotter.github.io/MAsync/citenet. Note that the OpenCitations database only contains citations and references made openly accessible by the publishers and thus does likely not include all existing links among publications. Abbreviations: fMRI = functional magnetic resonance imaging, fNIRS = functional near-infrared spectroscopy, y = years, INS = interpersonal neural synchronization.

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While many theoretical accounts of neurophysiological mechanisms have been proposed, empirical evidence is still lacking. We aimed to provide a comprehensive meta-analysis of the field, generating initial evidence for involved physiological processes derived from human data. 4/n

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When we interact with one another, both our behavior and our physiology synchronize. Recent evidence from neuroimaging studies suggests that this synchronization may not be limited to our body physiology, but extents to our brain activity. 3/n

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We performed a meta-analysis of neuroimaging studies on interpersonal neural synchronization (INS) and contextualized the results using diverse public databases to develop new hypotheses on involved physiological processes. 2/n

The multimodal data fusion approach to explore the neurobiology of human INS. The figure outlines the multimodal data fusion workflow applied in the present study. Depicted are data sources and major analysis steps applied to generate multilevel knowledge and new hypotheses about the neurobiological basis of INS. Abbreviations: INS = interpersonal neural synchrony, fMRI = functional magnetic resonance imaging, fNIRS = functional near-infrared spectroscopy, GCEA = gene-category enrichment analysis.