Biomarker Workshop 2025: ADHD – Autism – Anxiety – Hypersensitivity.
Thursday, 11 December 2025
Either full day or afternoon
Disclaimer: This text was translated from German to English using AI. While efforts were made to ensure accuracy and clarity, some nuances may differ from the original.
Background and Relevance
The diagnosis and treatment of neurodivergent conditions is undergoing a paradigm shift. While clinical practice still relies heavily on behavior-based observations and subjective assessments, neurobiological biomarkers are opening new possibilities for more precise, objective, and personalized approaches. This workshop brings together leading experts to discuss the current state of biomarker research and to develop concrete steps for clinical implementation.
The challenges are multifaceted: Autism spectrum disorders exhibit enormous heterogeneity, ADHD is associated with high comorbidity rates, and anxiety disorders occur with significantly increased prevalence among neurodivergent individuals. Traditional “one-size-fits-all” therapeutic approaches are insufficient to address this complexity. Biomarker research holds promise for identifying objective, measurable indicators that enable individualized diagnosis and treatment planning.
Multimodal approaches that integrate different layers of data are especially promising—ranging from genetic markers and neuroimaging-based signatures to electrophysiological, physiological, and metabolic indicators. The combination of these markers with advanced machine learning algorithms opens up new possibilities for prediction, subtyping, and personalized intervention planning.
Workshop Objectives
The workshop pursues several strategic objectives:
First, it aims to provide a comprehensive overview of the current state of international biomarker research in neurodiversity. The focus is on translationally relevant findings with potential for near-term clinical application.
Second, fostering interdisciplinary collaboration is central. Neurogeneticists, neuroimaging specialists, electrophysiologists, clinicians, bioinformaticians, and industry representatives will work together to develop innovative solutions. It is particularly important to include the perspectives of neurodivergent individuals themselves to ensure that the research addresses their actual needs.
Third, concrete roadmaps for clinical translation will be developed. This includes both the technical aspects of biomarker validation and the regulatory, ethical, and practical challenges of implementation.
Key Topics
EEG and Event-Related Potentials (ERPs)
Electrophysiology via EEG and ERPs represents one of the most promising and practical biomarker technologies for neurodivergent conditions. Unlike imaging techniques, EEG systems are cost-effective, non-invasive, and offer excellent temporal resolution of neural activity.
EEG Spectral Analysis: Resting-state EEG in autistic individuals shows characteristic changes in various frequency bands. Increased gamma activity (>30 Hz) is consistently observed in ASD and correlates with sensory hypersensitivity. Alpha rhythms (8–12 Hz) are often reduced or atypically organized, which is linked to attention and regulation problems. In ADHD, increased theta activity (4–8 Hz) and reduced beta activity (13–30 Hz) are typically observed, forming the basis for EEG-based neurofeedback therapies.
Event-Related Potentials (ERPs): ERPs provide unique insights into specific cognitive processes. The P300 component, which reflects attention and working memory processes, shows characteristic delays and amplitude reductions in ADHD. The N170 component, associated with face recognition, is frequently altered in ASD and correlates with social communication difficulties.
Mismatch Negativity (MMN): This automatic response to unexpected auditory stimuli is often reduced in ASD and may serve as a marker for sensory processing disorders. Particularly interesting is the correlation between MMN amplitude and the severity of autistic symptoms.
Steady-State Responses: Vigilance, Arousal, Central-Sensory Index: Auditory and visual steady-state responses show characteristic patterns in neurodivergent individuals. The 40-Hz gamma synchronization is often altered in ASD and may be related to issues in sensory integration.
Clinical implementation of EEG/ERP biomarkers is especially attractive since the technology is already widely available and standardized protocols exist. Portable EEG systems also allow for measurements in naturalistic environments, which is important for the ecological validity of biomarkers.
Neuroimaging Biomarkers
Neuroimaging research has made significant progress in recent years. Functional MRI studies have identified consistent differences in brain connectivity between autistic and neurotypical individuals. The Default Mode Network, in particular, shows characteristic alterations that could serve as biomarkers.
Structural imaging using T1-weighted MRI and Diffusion Tensor Imaging (DTI) reveals differences in brain anatomy and white matter. Machine learning algorithms can leverage this multimodal imaging data to diagnose ASD with over 85% accuracy.
PET and SPECT imaging enable the study of neurotransmitter systems and neuroinflammation. Altered serotonin and GABA signaling show characteristic patterns in both ASD and ADHD.
Physiological and Metabolic Markers
Autonomic nervous system dysfunctions are common and measurable in neurodivergent individuals. Heart rate variability, skin conductance, and pupillometry provide non-invasive means of assessing physiological regulation.
Metabolomic approaches identify characteristic metabolic profiles. The tryptophan-serotonin pathway, in particular, shows consistent alterations in ASD. Markers of oxidative stress and mitochondrial function are also promising.
The microbiome is increasingly in focus, as the gut-brain axis plays a key role in neurodivergent conditions. Specific bacterial signatures correlate with autistic behaviors and could serve as therapeutic targets.
Digital Health and Wearable Technology
Digitization opens new possibilities for continuous monitoring and data-driven interventions. Smartphone apps can capture behavioral patterns characteristic of various neurodivergent conditions. Speech and movement analyses show promising results as digital biomarkers.
Wearable devices allow continuous monitoring of physiological parameters in daily life. These real-world data offer insights into the everyday challenges and coping strategies of neurodivergent individuals. Modern wearables can also record EEG data in everyday settings, bridging the gap between lab and real-world findings.
Virtual and augmented reality technologies can create standardized, controlled environments for assessment and therapy. VR-based sensory profiles could enable individualized environmental adaptations.
Challenges and Solutions
Translating biomarker research into clinical practice poses significant challenges. The heterogeneity of neurodivergent conditions makes it difficult to identify universally valid markers. Instead, subtype-specific signatures are likely required.
Methodological standardization is essential for reproducibility. International consortia are working on harmonized protocols for data collection and analysis. Especially for EEG/ERP measurements, established standards already exist (e.g., from the International Federation of Clinical Neurophysiology), which can serve as a foundation for biomarker protocols.
The European Medicines Agency (EMA) and the FDA are developing specific guidelines for neurodiversity biomarkers, with EEG-based markers receiving special attention due to their established clinical use.
Ethical considerations are central. Biomarker testing must not lead to discrimination or unwanted selection. The neurodivergent community must be involved in all phases of research and implementation.
Cost-benefit considerations are critical for broad implementation. EEG/ERP systems have a significant advantage here, as they are much more affordable than MRI or PET and are already available in many clinical settings.
Outlook and Future Perspectives
The coming years will be crucial for establishing precision medicine in neurodiversity. Large international cohort studies with tens of thousands of participants will provide the statistical power needed for robust biomarker identification. EEG/ERP measurements will play a central role, as they are practical for large samples.
Artificial intelligence will be key in integrating multimodal data. Deep learning algorithms can detect complex patterns in EEG signals that are inaccessible to traditional analysis methods. Combining EEG features with genetic, imaging, and clinical data promises especially robust biomarker profiles.
Personalizing therapies based on individual biomarker profiles could significantly improve treatment outcomes. EEG-based neurofeedback therapies for ADHD already demonstrate how electrophysiological biomarkers can be used therapeutically. Pharmacogenomic testing could optimize medication selection, while EEG monitoring could track treatment effects in real time.
The integration of real-time EEG in wearable technologies opens new possibilities for continuous monitoring and adaptive interventions. Such systems could predict upcoming sensory overloads and initiate preventive measures.
Ultimately, biomarker research should contribute to improving the lives of neurodivergent individuals—not by enforcing normalization, but by better understanding their individual needs and developing tailored support. EEG/ERP biomarkers offer the special advantage of being accessible and understandable to affected individuals themselves, which can lead to more active participation in their own healthcare.