This chapter examines the convergent trajectories of artificial intelligence and neuroscience over the coming decades. The analysis explores how their intersection is reshaping approaches to brain health and neurological treatment. This chapter begins by projecting key technological developments. These include computational models of neural circuits achieving biological fidelity, AI-accelerated therapeutic discovery pathways that compress drug development timelines, and brain-computer interface evolution enabling seamless brain-machine communication. Recent clinical trials demonstrate this technology, showing paralyzed patients controlling computers using thought alone. The analysis identifies critical challenges including data integration across measurement modalities, translational gaps between sophisticated neural models and clinical applications, and regulatory systems struggling to adapt to accelerating neurotechnology development. Central to the discussion is the emergence of personalized brain health approaches, particularly “neural digital twins.” These are individualized computational models calibrated to specific neural characteristics that enable virtual testing of treatments before implementation. This chapter also examines environmental neuromodulation techniques and neuroplasticity optimization strategies. This chapter examines long-term implications for major neurological disorders, documenting shifts from immune-focused multiple sclerosis treatments toward comprehensive circuit adaptation approaches, Alzheimer’s research moving from management to prevention paradigms, and the emergence of integrative models for Long COVID neurological sequelae that explain diverse symptoms through common underlying mechanisms. Throughout this technological evolution, this chapter emphasizes maintaining ethical foundations through anticipatory ethics frameworks, addressing distributional justice concerns as advanced neurotechnologies emerge primarily in wealthy nations, and preserving cognitive autonomy in an age of increasing brain-machine integration. The analysis concludes that while extraordinary capabilities are emerging—from brain-computer interfaces creating seamless neural-computational connections to AI systems revolutionizing drug discovery—the most profound questions concern not what becomes technically possible but how these possibilities align with human values and flourishing, requiring wisdom proportional to the powerful capabilities being developed.

The Long View for Brain Health Research

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发表日期：

2025年

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Thorsten Rudroff

摘要

This chapter examines the convergent trajectories of artificial intelligence and neuroscience over the coming decades. The analysis explores how their intersection is reshaping approaches to brain health and neurological treatment. This chapter begins by projecting key technological developments. These include computational models of neural circuits achieving biological fidelity, AI-accelerated therapeutic discovery pathways that compress drug development timelines, and brain-computer interface evolution enabling seamless brain-machine communication. Recent clinical trials demonstrate this technology, showing paralyzed patients controlling computers using thought alone. The analysis identifies critical challenges including data integration across measurement modalities, translational gaps between sophisticated neural models and clinical applications, and regulatory systems struggling to adapt to accelerating neurotechnology development. Central to the discussion is the emergence of personalized brain health approaches, particularly “neural digital twins.” These are individualized computational models calibrated to specific neural characteristics that enable virtual testing of treatments before implementation. This chapter also examines environmental neuromodulation techniques and neuroplasticity optimization strategies. This chapter examines long-term implications for major neurological disorders, documenting shifts from immune-focused multiple sclerosis treatments toward comprehensive circuit adaptation approaches, Alzheimer’s research moving from management to prevention paradigms, and the emergence of integrative models for Long COVID neurological sequelae that explain diverse symptoms through common underlying mechanisms. Throughout this technological evolution, this chapter emphasizes maintaining ethical foundations through anticipatory ethics frameworks, addressing distributional justice concerns as advanced neurotechnologies emerge primarily in wealthy nations, and preserving cognitive autonomy in an age of increasing brain-machine integration. The analysis concludes that while extraordinary capabilities are emerging—from brain-computer interfaces creating seamless neural-computational connections to AI systems revolutionizing drug discovery—the most profound questions concern not what becomes technically possible but how these possibilities align with human values and flourishing, requiring wisdom proportional to the powerful capabilities being developed.