Daniel Kristanto
Postdoctoral Researcher · Computational Neuroimaging · University of Oldenburg
© Universität Oldenburg
/ Daniel Schmidt
I am a computational neuroimaging researcher currently completing my postdoctoral training in the Psychological Methods and Statistics Lab, Department of Psychology, at the Carl von Ossietzky Universität Oldenburg. My background is highly interdisciplinary. I earned a Ph.D. in Physics from Hong Kong Baptist University, following Master's and Bachelor's degrees in Engineering.
My research centers on methodological development in neuroimaging, grounded in a meta-scientific approach. I investigate precision functional mapping and brain-behavior relationships. To advance this work, I apply AI methods to neuroimaging data, specifically utilizing Graph Neural Networks, diffusion models, and transfer learning.
Beyond AI, I plan to directly bridge my engineering and physics background with cognitive neuroscience. I am exploring the future application of network control theory and stiff-sloppy analysis to complex neuroimaging data to better model dynamic brain states.
The Carl von Ossietzky Young Researchers' Fellowship currently supports my research. This funding enables me to establish an independent research profile and prepare for future third-party funding.
Details regarding my ongoing work are available in the Projects tab. I welcome collaborations across all disciplines to address challenging questions in neuroimaging.
Image generated using Figurelab AI (Nano Banana Pro model).
Data-Driven Mapping of Brain-Behavior Relationships (PhD Project)
Traditional neuroimaging often relies on rigid, localized models that fail to capture how large-scale, distributed neural networks give rise to complex human behavior and cognitive traits. This doctoral project utilized advanced data-driven modeling and machine learning algorithms to map the intricate boundaries of brain-behavior relationships. By evaluating both structural and functional brain connectivity patterns across large-scale datasets, the project successfully decoded the neural architectures underlying human reading performance and general cognitive abilities. This work demonstrated that human intelligence is linked to highly specific conjunctions of structural and functional neural subnetworks, providing a foundational algorithmic framework for individual-specific brain mapping.
Related Publications
Kristanto, D., Hildebrandt, A., Sommer, W., & Zhou, C. (2023). Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. NeuroImage, 279, 120304. https://doi.org/10.1016/j.neuroimage.2023.120304
Kristanto, D., Liu, X., Sommer, W., Hildebrandt, A., & Zhou, C. (2021). What do neuroanatomical networks reveal about the ontology of human cognitive abilities? iScience, 25(8). https://doi.org/10.1016/j.isci.2022.104706
Kristanto, D., Liu, M., Liu, X., Sommer, W., & Zhou, C. (2020). Predicting Reading Ability from Brain Anatomy and Function: From Areas to Connections. NeuroImage, 116966. https://doi.org/10.1016/j.neuroimage.2020.116966
Image generated using Figurelab AI (Nano Banana Pro model).
METEOR: Mastering the Multiverse of Analytical Decisions
Cognitive neuroscience faces a replication crisis compounded by a “real-world vs. lab” dilemma. One major hurdle is the oppressive number of defensible, a priori methodological choices researchers face when processing complex neural data, which can lead to highly variable results. Funded by the DFG Priority Programme “META-REP” (SPP 2317), the METEOR project addresses this gap. We build systematic, exhaustive knowledge frameworks and extend big-data statistical approaches to map these analytical “forking paths” in mobile EEG, fMRI, and behavioral data pipelines. By bringing together a multidisciplinary team, METEOR aims to identify which decisions drive analytical variance, predict heterogeneity in future findings, and establish robust standards for successful scientific replication.
Related Publications
Kristanto, D., Burkhardt, M., Thiel, C. M., Debener, S., Gießing, C., & Hildebrandt, A. (2024). The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neuroscience & Biobehavioral Reviews, 105846. https://doi.org/10.1016/j.neubiorev.2024.105846
Short, C. A., Hildebrandt, A., Bosse, R., Debener, S., Özyağcılar, M., Paul, K., Wacker, J., & Kristanto, D. (2025). Lost in a large EEG multiverse? Comparing sampling approaches for representative pipeline selection. Journal of Neuroscience Methods, 424, 110564. https://doi.org/10.1016/j.jneumeth.2025.110564
Jacobsen, N. S. J., Kristanto, D., Welp, S., Inceler, Y. C., & Debener, S. (2025). Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration. Psychophysiology, 62, e14743. https://doi.org/10.1111/psyp.14743
Leung, A. Y.*, Kristanto, D.*, Gießing, C., Ioannidis, J.P.A., Hildebrandt, A., & Schmalz, X. (2025). Multiverse of developmental dyslexia subtyping methods: A Shiny app for analytical decision-making. https://doi.org/10.1101/2025.07.23.25332032 (* shared first authorship)
Image generated using Figurelab AI (Nano Banana Pro model).
The Individual Brain Project
Group-level analyses obscure critical individual variations in brain network topology. The Individual Brain Project tackles the technical barriers restricting Precision Functional Mapping (PFM)—specifically data sparsity in clinical scans and inconsistent structural-functional alignment. By combining a meta-scientific Knowledge Space with generative diffusion models, we recover individualized neural endophenotypes from short-duration MRI data. We then apply an advanced correspondence framework to align these functional networks across subjects, deploying the entire pipeline through the standardized Individual Brain Toolbox. This systematically dismantles infrastructural bottlenecks, pushing individualized brain parcellation from isolated algorithmic novelty to scalable clinical application.
Related Publications
Kristanto, D. (2026). Beyond Open Repositories: The Need for an Empirically Guided Metascientific Framework in Precision Functional Mapping (D9r4k_v1). PsyArXiv. https://osf.io/preprints/psyarxiv/d9r4k_v1/
Kristanto, D., Craig, A., & Taswell, C. (2025). Structure-Behavior-Action Framework for Coherent Scientific Enterprise. Brainiacs Journal of Brain Imaging And Computing Sciences, 6(3). https://doi.org/10.48085/Q273E0FA6
Image generated using Figurelab AI (Nano Banana Pro model).
ASPIRE Project
Medical research in data-rich fields often operates in silos, leaving literature fragmented and AI models acting as uninterpretable "black boxes". The ASPIRE project introduces an AI-powered, interactive decision-support system to harmonize Parkinson's Disease (PD) research. By integrating human-curated Knowledge Graphs with pre-trained Foundation Models, we construct a reliable baseline of domain-specific evidence. We deploy a collaborative team of specialized AI agents—an Analyst, Theorist, and Coder—that interact directly with researchers to design, execute, and interpret complex functional connectivity analyses. This framework bridges the credibility gap in medical AI, transforming isolated clinical datasets into a cumulative, transparent engine for PD biomarker discovery.
Related Publications
Kristanto, D., Castro, D. R. D., & Abdolalizadeh, A. (2025). The Research Landscape of Dynamic Functional Connectivity in Parkinson's Disease: Systematic Review and Interactive Tool. bioRxiv. https://doi.org/10.64898/2025.12.08.692999
Image generated using Figurelab AI (Nano Banana Pro model).
PRECISE: Precision Network Control
Why do individual brains age differently, and why do clinical interventions such as brain stimulation succeed in some patients but fail entirely in others? Project PRECISE tackles this by translating abstract psychological concepts—brain reserve, maintenance, and compensation—into measurable physical mechanisms. By unifying Precision Functional Mapping (PFM) with longitudinal data and advanced Network Control Theory, we map the exact structural highways and individualized topological gatekeepers of the human brain. Using causal machine learning, we compute a "jamming parameter": the exact biological threshold where a network's structural reserve exhausts, its anatomical maintenance degrades, and the energy required to compensate mathematically fails. This unified framework provides the mechanistic blueprint required to predict individualized cognitive decline, map divergent Parkinson's disease trajectories, and target clinical interventions with improved precision.
Kristanto, D. (2026). Beyond Open Repositories: The Need for an Empirically Guided Metascientific Framework in Precision Functional Mapping (D9r4k_v1). PsyArXiv. https://osf.io/preprints/psyarxiv/d9r4k_v1/
Altinisik, Y., Jann, M., Kristanto, D., Gießing, C., Cankaya, E., & Spiess, M. (2026). The multiverse evaluation of theory-based hypotheses against their
complements using specification curves: A study on overdispersion in count data analysis.
Eidswick, J., Kristanto, D., Vasileva, M., Yulia, G., & Roheger, M. (2026). Adverse Childhood Experiences, Physical Activity, and Dysregulation in Adolescence:
A Multilevel Longitudinal Mediation Analysis in the ABCD Study.
Kristanto, D., Craig, A., & Taswell, C. (2025). Structure-Behavior-Action Framework for Coherent Scientific Enterprise. Brainiacs Journal of Brain Imaging And Computing Sciences, 6(3). https://doi.org/10.48085/Q273E0FA6
Al-Naji, A., Abdolalizadeh, A., & Kristanto, D. (2025). Morphometric Latent Factors in Autism and Their Association with Receptor Profiles and Behavior. bioRxiv. https://doi.org/10.64898/2025.12.08.692851
Kristanto, D., Castro, D. R. D., & Abdolalizadeh, A. (2025). The Research Landscape of Dynamic Functional Connectivity in Parkinson's Disease: Systematic Review and Interactive Tool. bioRxiv. https://doi.org/10.64898/2025.12.08.692999
Wang, R., Chang, Z., Liu, X., Kristanto, D., Gartner, E. G. G., Liu, X., Liu, M., Wu, Y., Lui, M., & Zhou, C. (2025). Weak but influential: Nonlinear contributions of structural connectivity to human cognitive abilities and brain functions. arXiv. https://doi.org/10.48550/arXiv.2505.24125
Leung, A. Y.*, Kristanto, D.*, Gießing, C., Ioannidis, J.P.A., Hildebrandt, A., & Schmalz, X. (2025). Multiverse of developmental dyslexia subtyping methods: A Shiny app for analytical decision-making. https://doi.org/10.1101/2025.07.23.25332032 (* shared first authorship)
Short, C. A., Hildebrandt, A., Bosse, R., Debener, S., Özyağcılar, M., Paul, K., Wacker, J., & Kristanto, D. (2025). Lost in a large EEG multiverse? Comparing sampling approaches for representative pipeline selection. Journal of Neuroscience Methods, 424, 110564. https://doi.org/10.1016/j.jneumeth.2025.110564
Short, C., Breznau, N., Bruntsch, M., Burkhardt, M., Busch, N., Cesnaite, E., Frank, M., Gießing, C., Krähmer, D., Kristanto, D., Lonsdorf, T. B., Neuendorf, C., Nguyen, H. H. V., Rausch, M., Schmalz, X., Schneck, A., Tabakci, C., & Hildebrandt, A. (2025). Multi-curious: A Multi-Disciplinary Guide to Multiverse Analysis. MetaArXiv. https://doi.org/10.31222/osf.io/4yzeh_v1
Leung, A. Y., Kristanto, D., & Schmalz, X. (2025). Re-SearchTerms: A Shiny app for exploring terminology variations in psychology and metascience. OSF. https://doi.org/10.31219/osf.io/qsp7x_v2
Jacobsen, N. S. J., Kristanto, D., Welp, S., Inceler, Y. C., & Debener, S. (2025). Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration. Psychophysiology, 62, e14743. https://doi.org/10.1111/psyp.14743
Burkhardt, M., Hildebrandt, A., Gießing, C., & Kristanto, D. (2024). Quantifying Similarity between Graph-Theoretic Resting-State fMRI Data Processing Pipelines for Efficient Multiverse Analysis. Brainiacs Journal of Brain Imaging And Computing Sciences, 5, Issue 2 Edoc. https://doi.org/10.48085/XEE8F298E
Kristanto, D., Burkhardt, M., Thiel, C. M., Debener, S., Gießing, C., & Hildebrandt, A. (2024). The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neuroscience & Biobehavioral Reviews, 105846. https://doi.org/10.1016/j.neubiorev.2024.105846
Kristanto, D., Gießing, C., Marek, M., Zhou, C., Debener, S., Thiel, C., & Hildebrandt, A. (2023). An Extended Active Learning Approach to Multiverse Analysis: Predictions of Latent Variables from Graph Theory Measures of the Human Connectome and Their Direct Replication. In 2023 Guardians Workshop (Guardians) (pp. 1–13). IEEE. https://doi.org/10.48085/J962E0F53
Kristanto, D., Hildebrandt, A., Sommer, W., & Zhou, C. (2023). Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. NeuroImage, 279, 120304. https://doi.org/10.1016/j.neuroimage.2023.120304
Kristanto, D., Liu, X., Sommer, W., Hildebrandt, A., & Zhou, C. (2021). What do neuroanatomical networks reveal about the ontology of human cognitive abilities? iScience, 25(8). https://doi.org/10.1016/j.isci.2022.104706
Kristanto, D., Liu, M., Liu, X., Sommer, W., & Zhou, C. (2020). Predicting Reading Ability from Brain Anatomy and Function: From Areas to Connections. NeuroImage, 116966. https://doi.org/10.1016/j.neuroimage.2023.120304
Kristanto, D., & Leephakpreeda, T. (2018). Effective dynamic prediction of air conditions within car cabin via bilateral analyses of theoretical models and artificial neural networks. Journal of Thermal Science and Technology, 13(2), JTST0020–JTST0020. https://doi.org/10.1299/jtst.2018jtst0020
Kristanto, D., & Leephakpreeda, T. (2017). Sensitivity analysis of energy conversion for effective energy consumption, thermal comfort, and air quality within car cabin. Energy Procedia, 138, 552–557. https://doi.org/10.1016/j.egypro.2017.10.158
Kristanto, D., & Leephakpreeda, T. (2017). Energy Conversion for Thermal Comfort and Air Quality Within Car Cabin. In IOP Conference Series: Materials Science and Engineering (Vol. 187, No. 1, p. 012037). IOP Publishing. https://doi.org/10.1088/1757-899X/187/1/012037
Kristanto, D., Wardhana, A., & Rosita, W. (2016). Comparison of Valve Static Friction Detection Method Based on Graphical Fitting. Journal of Automation, Control, and Instrumentation, 8(2). https://doi.org/10.5614/joki.2016.8.2.4
METEOR: Multiverse Exploration Tool for fMRI
METEOR is a decision-support application designed to map the vast landscape of analytical choices in graph-based fMRI data preprocessing and analysis. Drawing from a systematic literature review that identified 61 distinct steps and 102 options, this tool allows researchers to visually explore the "multiverse" of defensible analysis pipelines. By making contentious options—such as scrubbing, global signal regression, and spatial smoothing—transparent, METEOR serves as an educational reference for designing well-informed robustness and multiverse analyses in cognitive network neuroscience.
MAP-DyS: Mapping Dyslexia Subtypes
MAP-DyS is an open-access Shiny app that systematically visualizes the methodological variability in developmental dyslexia subtyping research. It aggregates analytical decision points across existing literature—ranging from theoretical models and data preprocessing to statistical clustering methods—enabling users to interactively navigate the multiverse of subtyping pathways. The tool helps researchers critically evaluate existing practices, understand how methodological choices influence diagnostic subgroups, and improve transparency and reproducibility in subtyping research.
DynaPD: Dynamic Functional Connectivity in Parkinson's Disease
DynaPD is an open-source interactive tool built to synthesize the conceptual and methodological diversity of dynamic functional connectivity (dFC) research in Parkinson's Disease. The application allows researchers to dynamically explore study designs, analytical pipelines (such as sliding-window techniques and k-means clustering), and reported findings on neural flexibility. It functions as a living evidence synthesis and decision-support resource to consolidate a fragmented literature landscape and guide the design of future fMRI studies in PD.