Leon Lotter

@leondlotter.de

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👨‍💻 MD | Somewhere between PhD & postdoc 📊 Multimodal neuroimaging 🧠 ADHD | ASD 🛠️ NiSpace | https://nispace.readthedocs.io 🏫 INM-7, Research Centre Juelich 🎓 Max Planck School of Cognition 🐮 Vegan | 👤 He/him | 📷 By N. Brade 🌐 https://leondlotter.de

Early psychosis. AUC+ scores broadly reduced across neurotransmitter systems, strongest for serotonergic and dopaminergic indices; correlates with symptom severity and antipsychotic intake.
Pharmacological sensitivity. Risperidone, ketamine, and midazolam modulate AUC scores in line with their receptor targets; IV drug effects tracked dynamically via sliding-window analysis.
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.
Reproducibility and MEG extension. AUC profiles replicate across 6 fMRI cohorts and mirror rsfMRI in MEG — strongest cross-modal alignment in the beta band.
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
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
Positive control of the NEOFC approach: Resting-state network probability maps induce the expected AUC effects compared to spatial null models
Neurobiologically enriched organization of functional connectivity (NEOFC). Receptor density maps threshold individual FC matrices → percentile curves → single subject-level AUC scores.
Neurobiologically enriched organization of functional connectivity (NEOFC). Receptor density maps threshold individual FC matrices → percentile curves → single subject-level AUC scores.
The general, literature-derived vulnerability search grid can be refined based on an individual’s genetic profile and environmental exposures, generating a personalized vulnerability risk grid. This risk grid can then be interrogated using pathway-specific biomarkers to detect actual pathophysiological alterations along these pathways. Based on this biomarker evidence, personalized interventions can be selected to target the identified pathophysiological alterations.
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.
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.
The general, literature-derived vulnerability search grid can be refined based on an individual’s genetic profile and environmental exposures, generating a personalized vulnerability risk grid. This risk grid can then be interrogated using pathway-specific biomarkers to detect actual pathophysiological alterations along these pathways. Based on this biomarker evidence, personalized interventions can be selected to target the identified pathophysiological alterations.
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.
Network- and seed-based statistics.
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.
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.
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.
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.
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.
Summary 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.
Condensed visualization of the reported results (first line of each block, emphasized are neurobiological markers that showed consistent results) in context with related results of previous human studies investigating similar biological processes or cell populations (lines below). We do not claim this collection to be exhaustive. In the left upper panel, we show studies investigating general cellular remodeling processes; in the other panels, each header indicates one neurobiological marker with associated studies below. Each thin black line overlaid by a colored bar indicates results from one study. If a study reported multiple results pertaining to the same process (e.g., from two different brain regions), bars were laid over each other (Data S5 for individual listings). Thin black lines: overall time span investigated. Colored overlay: time period in which the respective study target was reported to show developmental changes (present study: nominal p < 0.05), independent of the sign of the association. Large dots: Timepoint of the maximum association. See also Fig. S28 and Data S5 for a more comprehensive overview including various topics. Abbreviations: ST = somatostatin, CR calretinin, sMRI structural MRI, CBF cerebral blood flow, PET positron emission tomography, ASL arterial spin labeling, ACh(E) acetylcholine (esterase), see Fig. 2 for abbreviations used in neurobiological marker names.
Explained spatial CT change variance in ABCD and IMAGEN data. The overall model performance is illustrated as scatter plots contrasting predicted CT change (y axis) with observed CT change (x axis). Scatters: single brain regions, color-coded by prediction error. Continuous line: linear regression fit through the observations. Dashed line: theoretical optimal fit. Brains: prediction errors corresponding to scatters. Rows: upper: cohort-average predicted by the reference (Braincharts) model, lower sample size due to subjects dropped during model adaptation (see Methods); middle: observed cohort-average (ComBat-harmonized); lower: observed single-subject values (ComBat-harmonized), one regression model was calculated for each subject, but the results were combined for illustration purposes. CT cortical thickness, adj. adjusted.
First row: Modeled CT change explained by individual neurobiological markers, exactly corresponding to univariate results in Figs. 3 and 4. X values are aligned to the first year of each tested modeled CT change time period (e.g., Δ(5,10) is aligned to 5 years on x-axis). Shades following each line visualize other possible alignments (Δ(5,10) is aligned to 6, 7, 8, 9, or 10 years). Vertical shaded boxes indicate time periods in which CT change was explained significantly (FDR). Following rows: Normalized log2-transformed gene expression trajectories for maximally 5 original atlases that loaded on factor-level neurobiological markers with λ > |0.3| (c.f., Fig. S15). Gene expression for each marker was derived from related single genes or from averaging across gene sets. Grey dots indicate the average neocortical expression of individual subjects. Black lines and shades show locally estimated scatterplot smoothing (LOESS) curves with 95% confidence intervals. Associations were tested for by averaging the LOESS data within and outside of each respective time period and comparing mean and ratio against null data randomly sampled from non-brain genes (positive-sided exact p values). ★: FDR-corrected across all tests; ✩: nominal p < 0.05. CT CT change, adj. adjusted, FDR false discovery rate, ns not significant, see Fig. 2 for abbreviations used in neurobiological marker names.
Modeled lifespan CT change explained by neurobiological markers, selected from the univariate analyses (Figs. 3 and 4; 9 FDR-corrected significant markers). See Fig. 3 for descriptions of global plot elements. Top: overall explained modeled CT change variance, the two colored lines highlight contributions of molecular and cellular markers. Middle: Marker-wise contributions to the overall explained spatial variance. Note that, as the used total dominance statistic describes the average R2 associated with each predictor relative to the “full model” R2, the sum of the predictor-wise values at each timepoint in the middle plot equals the R2 values expressed in the upper panel. Bottom: Spearman correlations between modeled CT change and markers to visualize the sign of the association patterns. CT cortical thickness, see Fig. 2 for abbreviations used in neurobiological marker names.
Associations between modeled lifespan CT change and neurobiological markers derived from imaging modalities. Developmental periods covered by this study as defined by Kang et al.27 are shown on top. Time periods were aligned to the center of each modeled CT change step (e.g., Δ(5,10) = 7.5). Colored lines show the amount of spatial modeled CT change variance explained (y axis) by the combined markers (upper) or each marker individually (lower) throughout the lifespan (x axis). Each y value represents the results of one multiple (upper) or single (lower) linear regression model predicting CT change across regions from neurobiological marker densities across regions. Stars indicate positive-sided significance of each regression model based on null regression models estimated on permuted marker maps; ★: FDR-corrected across all tests shown in each panel of the plot; ✩: nominal p < 0.05. To provide an estimate of the actual observed effect size, gray areas show the distributions of modeled CT change explained by permuted marker maps (n = 10,000). For the lower panel, null results were combined across marker maps. See Fig. S6C for all CT change maps, and Fig. S4 for all predictor maps. CT cortical thickness, PET positron emission tomography, MRI magnetic resonance imaging, FDR false discovery rate, see Fig. 2 for abbreviations used in neurobiological marker names.
Lifespan trajectories of colocalization between neurobiological markers and modeled cross-sectional CT. For each marker, the upper panel shows a surface projection of the parcellated data; yellow-violet: nuclear imaging markers, yellow-green: gene-expression, yellow-gray: microstructural; yellow = higher density. The center panel shows the marker’s colocalization trajectory: Z-transformed Spearman correlation coefficients are shown on the y axis, age on the x axis; blue-to-orange lines indicate percentiles of modeled CT data (see legend, note that these do not show actual percentiles of colocalization strengths); the green line (LOESS = locally estimated scatterplot smoothing) was smoothed through the percentile data to highlight trajectories (shades: 95% confidence intervals). The lower panel shows year-to-year changes (y axis) derived from the LOESS line in the upper plot. See Fig. S7 for trajectories including ABCD and IMAGEN subjects and Fig. S8 for trajectories split by sex. Coloc. colocalization, SV2A synaptic vesicle glycoprotein 2A, M1 muscarinic receptor 1, mGluR5 metabotropic glutamate receptor 5, 5HT1a/1b/2a/4/6 serotonin receptor 1a/2a/4/6, CB cannabinoid receptor 1, GABAa γ-aminobutyric acid receptor A, HDAC histone deacetylase, 5HTT serotonin transporter, FDOPA fluorodopa, DAT dopamine transporter, D1/2 dopamine receptor 1/2, NMDA = N-methyl-D-aspartate glutamate receptor, GI glycolytic index, MU mu opioid receptor, A4B2 = α4β2 nicotinic receptor, VAChT vesicular acetylcholine transporter, NET noradrenaline transporter, CBF cerebral blood flow, CMRglu cerebral metabolic rate of glucose, COX1 cyclooxygenase 1, H3 histamine receptor 3, TSPO translocator protein, Microstr cortical microstructure, Ex excitatory neurons, In inhibitory neurons, Oligo oligodendrocytes, Endo endothelial cells, Micro microglia, OPC oligodendrocyte progenitor cells, Astro astrocytes.
Dimensionality reducted multilevel brain atlases.
Modeled cortical thickness development across the lifespan for each brain region from the Destrieux atlas, shown separately for left and right hemisphere, as well as females and males.
The workflow of the present study, from data sources (left side) to data processing and analysis method (middle) to the research questions and results (right side). A A collection of postmortem “cellular” and in vivo “molecular” brain atlases was parcellated and dimensionality reduced. B “Modeled” predicted CT data was extracted from a normative model. C We calculated the colocalization between neurobiological markers and CT at each point throughout the lifespan (see Fig. 2). D We evaluated how combined and individual neurobiological markers could explain lifespan CT change (see Figs. 3 and 4). E The strongest associated markers were examined in detail, accounting for shared spatial patterns (see Fig. 5). F A developmental gene expression dataset was used to generate trajectories of gene expression associated with each neurobiological marker. G Periods in which CT change was significantly explained were validated in developmental gene expression data (see Fig. 7). H Single-subject longitudinal data was extracted from two developmental cohorts. I Findings based on the normative model were validated in single-subject data (see Fig. 8). Abbreviations: CT = cortical thickness, ABA = Allen Brain Atlas, MRI = magnetic resonance imaging. Here, data plots are employed for demonstration purposes; for definitions of plot elements, please refer to the individual figures as refererred to above, similarly, source data are provided in Source Data files of each following figure. ABCD Study®, Teen Brains. Today’s Science. Brighter Future.® and the ABCD Study Logos are registered marks of the U.S. Department of Health & Human Services (HHS).
Main results overview animation. Top: Molecular and cellular neurobiological markers with the strongest spatial association to cortical thickness development. Brains with regional importance estimates next to the spatial association patterns displayed as scatter plots. Bottom: Cortical thickness change explained (y axis) vs. age windows (x axis) for the 9 molecular and cellular markers with the strongest effects.
Screenshot of the exact same post I am responding to. BlueSky showed the link to the article with it’s heading cut off at an unfortunate spot: „Autistic individuals have increased risk of chronic physical health“.
Subject-level spatial colocalization between postpartum rsfMRI alterations and hormonal/neurotransmitter receptor densities. Left side: Parcellated and Z-standardized whole-brain hormone and neurotransmitter receptor distributions. Right side: Spatial colocalization analyses. Upper panel: Baseline analyses testing for colocalization between receptor distributions and rsfMRI data of each postpartum subject at baseline relative to the control group. X-axis: Z-transformed Spearman correlation coefficients, y-axis: rsfMRI metrics, colors: receptor maps. Lower panels: Longitudinal analyses following up on each baseline results if associated group permutation p values survived false discovery rate-correction (filled stars). The plot design equals Figure 2 with the y axis showing Z-transformed Spearman correlation coefficients.
Subject-level spatial colocalization between postpartum rsfMRI alterations and hormonal/neurotransmitter receptor densities. Left side: Parcellated and Z-standardized whole-brain hormone and neurotransmitter receptor distributions. Right side: Spatial colocalization analyses. Upper panel: Baseline analyses testing for colocalization between receptor distributions and rsfMRI data of each postpartum subject at baseline relative to the control group. X-axis: Z-transformed Spearman correlation coefficients, y-axis: rsfMRI metrics, colors: receptor maps. Lower panels: Longitudinal analyses following up on each baseline results if associated group permutation p values survived false discovery rate-correction (filled stars). The plot design equals Figure 2 with the y axis showing Z-transformed Spearman correlation coefficients.
Longitudinal development of rsfMRI clusters. Development of baseline cluster-wise averaged global connectivity metrics across 6 postpartum months. Boxplots: x-axes show time in weeks postpartum (dimensional scale), y-axes show the Z-standardized rsfMRI metric. Each dot represents one subject at one individual time point, lines connect longitudinal scans. Boxplots show the distribution across subjects at each time point (black dot = mean, middle line = median, boxes = quartile 1 and 3, whiskers = range if below 1.5 * interquartile range from quartile 1 or 3). Heatmaps show effect sizes (Hedge’s g) of within- and between-group comparisons, overlaid labels mark significances (filled star = false discovery-corrected, empty star = nominal p < .05, ns = p > .05. Significance of linear mixed models is printed in the lower left corner.
Longitudinal development of rsfMRI clusters. Development of baseline cluster-wise averaged local connectivity and activity metrics across 6 postpartum months. Boxplots: x-axes show time in weeks postpartum (dimensional scale), y-axes show the Z-standardized rsfMRI metric. Each dot represents one subject at one individual time point, lines connect longitudinal scans. Boxplots show the distribution across subjects at each time point (black dot = mean, middle line = median, boxes = quartile 1 and 3, whiskers = range if below 1.5 * interquartile range from quartile 1 or 3). Heatmaps show effect sizes (Hedge’s g) of within- and between-group comparisons, overlaid labels mark significances (filled star = false discovery-corrected, empty star = nominal p < .05, ns = p > .05. Significance of linear mixed models is printed in the lower left corner.
Clusters of differing local activity (fALFF), local connectivity (LCOR), and global connectivity (GCOR) in the nulliparous (NP) and postpartum (PP) groups at baseline. Cluster-level and whole-brain results from baseline (T0) rsfMRI analyses. Brain maps for each rsfMRI metric (rows) and each contrast (columns) show voxel-level negative log10-transformed p values overlaid by cluster-level results in darker shades (non-parametric cluster mass permutation).