Exploring whether current approaches are sufficient to understand and treat complex brain disorders
The human brain, a mere three-pound organ consuming 20% of our body's energy, is perhaps the most complex biological system we've ever attempted to understand 8 . When things go wrong within its intricate circuitry—when neurons misfire, connections weaken, or chemical balances shift—the consequences can be devastating: Alzheimer's erasing cherished memories, Parkinson's disrupting fluid movement, autism altering social connections, or depression casting a shadow over daily life.
For decades, computational neuroscience has sought to decode these mysteries by creating mathematical models that simulate how brains function, both in health and disease. Yet, as we stand in 2025, with unprecedented amounts of brain data and artificial intelligence capabilities, a crucial question emerges: Are our current modeling approaches sufficient, or do we need entirely new paradigms to truly understand and treat brain disorders?
This isn't just an academic debate—the answer could determine how quickly we develop effective treatments for the millions worldwide suffering from neurological and psychiatric conditions.
At its core, computational neuroscience uses mathematical models, computer simulations, and theoretical frameworks to understand the principles governing brain development, structure, physiology, and cognitive abilities 6 . It straddles the line between biological realism and theoretical abstraction, seeking to explain how billions of interconnected neurons give rise to thoughts, emotions, perceptions, and actions. The field has evolved from early models of single neurons to today's ambitious attempts to simulate entire brain circuits.
Most computational approaches to studying brain diseases fall into three broad categories, each with distinct strengths and limitations for modeling pathological conditions 8 :
| Model Type | Core Question | Application to Disease | Key Limitations |
|---|---|---|---|
| Descriptive Models | How can we quantitatively characterize neural data? | Statistical analysis of EEG patterns in epilepsy; characterizing neural spike patterns in Parkinson's | Often phenomenological, lacking mechanistic insight |
| Normative Models | What computational principles optimize brain function? | Understanding reward processing deficits in addiction; decision-making impairments in depression | May oversimplify biological implementation |
| Mechanistic Models | How do specific biological components generate neural activity? | Simulating channelopathies in epilepsy; synaptic loss in Alzheimer's | Computationally expensive, potentially too complex |
The descriptive modeling approach has shown particular value in identifying biomarkers for disease diagnosis and progression. For instance, researchers can analyze EEG recordings from epileptic patients to detect characteristic patterns that precede seizures, potentially enabling early warning systems.
Normative frameworks, particularly Bayesian and reinforcement learning models, have revolutionized our understanding of psychiatric conditions. These approaches frame mental disorders as disturbances in the brain's fundamental computational algorithms.
Mechanistic models represent the most biologically-grounded approach, attempting to replicate the actual components and connections that malfunction in disease states. The ambitious Blue Brain Project exemplifies this approach's potential—and its enormous computational demands 6 .
For example, research has revealed that depression may involve distorted reward prediction errors—the mismatch between expected and actual outcomes—while anxiety disorders often feature malfunctioning threat estimation systems. The emerging field of computational psychiatry specifically uses these frameworks to develop more quantitative, mechanism-based approaches to diagnosis and treatment 8 .
Despite these advances, current modeling approaches face significant challenges in adequately capturing the complexity of brain disorders.
Many models excel at either micro-scale molecular processes or macro-scale brain networks, but few can effectively bridge these levels to show how molecular disruptions manifest as clinical symptoms.
Many neurodegenerative diseases unfold over decades, while most models are trained on and validated against much shorter timescales. This makes it difficult to distinguish between compensatory mechanisms and true disease-modifying processes.
Brain disorders manifest differently across individuals, yet many models still focus on finding "average" dysfunction patterns. As one neuroscientist notes, "Ensuring that AI and neurotechnologies are representative, inclusive, and free from bias are vital to preventing inequity" 4 .
The recent Algonauts 2025 Challenge provides a compelling case study of how new modeling paradigms might overcome current limitations. This biennial computational neuroscience competition tasked research teams with solving a particularly difficult problem: predicting human brain activity in response to long, multimodal movies—a significant step closer to real-world experience than the simple images or short clips used in previous challenges 5 .
The challenge followed a rigorous experimental design:
Participants worked with nearly 80 hours of naturalistic movie stimuli from the CNeuroMod project
Four participants underwent fMRI scanning while watching these movies
Competing teams developed algorithms to predict brain responses from stimulus features
Models were judged on their ability to predict brain activity in response to completely novel movies
The top-performing models demonstrated several breakthroughs with significant implications for disease modeling:
| Team (Rank) | Model Architecture | Key Features | Mean Correlation Score |
|---|---|---|---|
| TRIBE (1st) | Transformer-based | Trimodal integration, modality dropout | 0.2125 |
| VIBE (2nd) | Dual transformers | Separate feature fusion & prediction | 0.2125 |
| SDA (3rd) | Hierarchical RNN | Bidirectional LSTMs, brain-inspired curriculum | 0.2094 |
| MedARC (4th) | Linear encoder | Architectural simplicity, ensemble methods | 0.2117 |
The most significant finding was that all top teams relied on multimodal feature extraction, combining visual, auditory, and language information from pre-trained models.
Perhaps surprisingly, architectural complexity didn't determine success. This suggests that for brain activity prediction, how models integrate information may matter more than their architectural complexity 5 .
The models also consistently outperformed unimodal approaches in higher-order associative cortices—precisely the brain regions most often implicated in complex psychiatric disorders. This strongly suggests that understanding higher brain functions requires modeling multisensory integration, a capability missing from many current disease models 5 .
The advances demonstrated in projects like the Algonauts Challenge rely on an evolving toolkit of computational and experimental resources:
| Tool Category | Specific Examples | Function in Research |
|---|---|---|
| Feature Extractors | V-JEPA2 (vision), Whisper (speech), Llama 3.2 (language) | Convert complex stimuli into mathematical representations |
| Model Architectures | Transformers, RNNs/LSTMs, Linear encoders | Map features to neural activity patterns |
| Ensembling Methods | Parcel-specific weighting, Model averaging | Improve prediction robustness and accuracy |
| Data Resources | CNeuroMod dataset, Neuropixel recordings | Provide large-scale training and validation data |
| Analysis Frameworks | Dynamic causal modeling, Population decoding | Interpret model results and relate to brain function |
As computational neuroscience advances, it raises profound ethical questions that demand careful consideration.
The development of increasingly sophisticated brain models and digital twins creates privacy concerns, as individuals—particularly those with rare diseases—might become identifiable through their unique neural signatures 4 .
Technologies that approach "mind-reading" capabilities through brain decoding could access our most private thoughts, emotions, and memories, potentially before we're consciously aware of them ourselves 4 .
The field of neuroenhancement—using brain-computer interfaces to improve cognitive function—promises help for those with neurological deficits but raises questions about fairness and accessibility when used for enhancement 4 .
As with other AI technologies, ensuring that computational neuroscience tools are representative, inclusive, and free from bias is vital for equitable application across diverse populations 4 .
The evidence from cutting-edge research suggests that yes, we do need new paradigms in computational neuroscience—but not necessarily the ones we might expect. The future lies not in increasingly complex models for their own sake, but in frameworks that better capture the multimodal, individualized, and multi-scale nature of brain function and dysfunction.
Multimodal integration as a standard modeling approach, recognizing that higher-order brain functions—and their pathologies—emerge from continuous cross-talk between sensory streams 5 .
Focus on individual differences rather than population averages, potentially through digital twin approaches that create personalized brain models updated with real-world data 4 .
Tighter theory-experiment cycles where models actively guide experimental design and experimental results immediately refine models, creating a virtuous cycle of discovery 7 .
Cross-disciplinary collaboration between experimental neuroscientists, computational modelers, AI researchers, and clinicians to ensure models remain biologically grounded and clinically relevant 8 .
As one theoretical neuroscientist aptly noted, "The brain systems are too complex to comprehend by experiments and intuition alone" 8 . Computational models provide the essential bridge between biological mechanism and clinical manifestation.
By embracing new paradigms that prioritize multimodal integration, individual variability, and cross-disciplinary collaboration, we move closer to the goal of the BRAIN Initiative 2.0: "Producing conceptual foundations for understanding the biological basis of mental processes" 7 and ultimately transforming how we diagnose, treat, and potentially prevent brain disorders.
The path forward requires both humility about what we don't yet understand and boldness to develop the new modeling paradigms that will illuminate the brain's deepest mysteries—transforming our approach to neurological and psychiatric diseases in the process.