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Scientists are leveraging powerful AI models to analyse huge neural datasets and to create predictive models that mirror the brain’s own processes.[1](/citation/2025-02-2-1-1/) This fundamental understanding is the bedrock for ambitious goals like building functional digital twins of specific brain regions,[2](/citation/2025-02-2-1-2/) which could dramatically accelerate the development of therapies. \n\nThis synergy is a two-way street: insights into the brain’s remarkable energy efficiency are inspiring the development of next-generation, brain-like \"neuromorphic\" computer chips, promising to make powerful AI accessible on small, everyday devices.[3](/citation/2025-02-2-1-3/)\n\nThe immediate focus is on developing sophisticated closed-loop brain-computer interfaces (BCIs) to treat conditions like depression by reading and actively regulating brain states in real time.[4](/citation/2025-02-2-1-4/) This requires overcoming significant hurdles in materials science to create biocompatible, flexible electronics that can integrate safely and effectively with neural tissue.\n\nEventually, these technologies will extend our minds — both in cognitive capabilities and in the physical location of data-logging and processing. Invasive implants, such as those restoring motor function, and non-invasive wearables are already offloading and augmenting cognitive tasks. We are likely to become ever more reliant on such tools, and research will make their integration into our lives ever more seamless. \n\nHowever impressive the advances, progress in this area also creates obvious ethical issues about permission to read from and write to the brain, ownership of neural data and inequities emerging in cognitive ability, to name just a few concerns. Navigating the landscape of data privacy, technological dependency and the integration of AI with human consciousness will be as critical as the scientific breakthroughs themselves.\n\n**KEY TAKEAWAYS**\n\nThe convergence of neuroscience, sensing technology and materials science is paving the way for unprecedented interventions in understanding, augmenting and repairing the functions of the human brain. Efforts to understand the **Fundamentals of cognition** are progressing through a range of technologies that can generate and analyse huge neural datasets. Researchers are also making progress in **Decrypting the brain**, using AI to understand how neural activity leads to thought, intention and behaviour. This is allowing the construction of useful **Neuromodulation systems**: precise, targeted interventions, often involving feedback loops, that can influence attention, learning, memory and affective states. As a result, we are moving into an era of **Exogenous cognition** that will integrate artificial cognitive systems with biological brains. Such developments are set to fundamentally alter human experience."},"intro":{"text":"The field of cognitive enhancement is rapidly accelerating. As research progresses, allowing greater understanding of how our brains process information to create experience, memory and understanding, it creates the potential for altering human cognitive capabilities. This could revolutionise medicine and reshape the definition of human intelligence and the experience of being human."},"anticipatoryImpact":{"text":"Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.\n\n* Anticipated impact on who we are as humans\n* Anticipated impact on how we will all live together\n* Anticipated impact on the well-being of humankind and sustainable future of our planet"},"indicatorValues":[{"id":"65c55cf49e947c438698aa6d","value":"0.516","numericValue":0.516,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68a684e30ac1330579fd7891","value":"0.6073","numericValue":0.6073,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"editions":[{"id":"66ab1bb636a8f2f336a557bf","name":"2024","slug":"2024","numericValue":2024},{"id":"684951c963371e51d83bdf31","name":"2025","slug":"2025","numericValue":2025}],"anticipatoryImpactImage":{"image":{"id":"image_gesda-platform/image-asset/psp-pl-2-25-2-1_image__PSP-PL2_25_2.1_zqjkfe","url":"https://res.cloudinary.com/shapeable/image/upload/v1760069987/gesda-platform/image-asset/psp-pl-2-25-2-1_image__PSP-PL2_25_2.1_zqjkfe.webp"}},"embeds":{"citations":[{"id":"691a787ec0043bba84a9de2b","slug":"2025-02-2-1-1","url":"https://doi.org/10.1016/j.neuron.2024.04.018","name":"A unifying framework for functional organization in early and higher ventral visual cortex","authors":[{"id":"691a787ec0043bba84a9de29","name":"E. Margalit et al.","slug":"e-margalit-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Neuron","accessDate":null,"startPage":2435,"volume":112,"footnoteNumber":1,"year":null},{"id":"691a787fc0043bba84a9de2f","slug":"2025-02-2-1-2","url":"https://doi.org/10.1038/s41586-025-08829-y","name":"Foundation model of neural activity predicts response to new stimulus types","authors":[{"id":"691a787fc0043bba84a9de2d","name":"E. Y. Wang et al.","slug":"e-y-wang-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature","accessDate":null,"startPage":470,"volume":640,"footnoteNumber":2,"year":null},{"id":"691a7880c0043bba84a9de33","slug":"2025-02-2-1-3","url":"https://doi.org/10.1038/s41467-025-57352-1","name":"The road to commercial success for neuromorphic technologies","authors":[{"id":"691a787fc0043bba84a9de31","name":"D. R. Muir and S. Sheik","slug":"d-r-muir-and-s-sheik"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Communications","accessDate":null,"startPage":3586,"volume":16,"footnoteNumber":3,"year":null},{"id":"691a7880c0043bba84a9de37","slug":"2025-02-2-1-4","url":"https://doi.org/10.1038/s41591-021-01480-w","name":"Closed-loop neuromodulation in an individual with treatment-resistant depression","authors":[{"id":"691a7880c0043bba84a9de35","name":"K. W. Scangos et al.","slug":"k-w-scangos-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Medicine","accessDate":null,"startPage":1696,"volume":27,"footnoteNumber":4,"year":null}],"imageAssets":[]},"surveyObservations":{"text":"Major breakthroughs in understanding the **Fundamentals of cognition** appear to be a decade away still, but could significantly impact developments in other areas of science and technology — and in society more broadly. **Neuromodulation systems** are much more mature and are already being deployed, reducing the Anticipation Potential scores of this sub‑topic. In contrast, we are far from fully **Decrypting the brain**, and together with **Exogenous cognition**, this is an area where developments are more uncertain, with no major breakthroughs expected for 12 to 15 years."},"color":{"id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD","darkValue":"#045059","veryDarkValue":"#011b1e"},"banner":{"id":"68e8aad463d1c853e9788d12","name":"2.1.1 Fundamentals of cognition. 2026","description":{"text":""},"image":{"id":"image_gesda-platform/banner/2-1-1-fundamentals-of-cognition-2026_image__2.1.1_Fundamentals_of_cognition_zfc88g","url":"https://res.cloudinary.com/shapeable/image/upload/v1760078502/gesda-platform/banner/2-1-1-fundamentals-of-cognition-2026_image__2.1.1_Fundamentals_of_cognition_zfc88g.webp","thumbnails":{"mainBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1440/v1760078502/gesda-platform/banner/2-1-1-fundamentals-of-cognition-2026_image__2.1.1_Fundamentals_of_cognition_zfc88g.webp","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_2880/v1760078502/gesda-platform/banner/2-1-1-fundamentals-of-cognition-2026_image__2.1.1_Fundamentals_of_cognition_zfc88g.webp"}}}},"chartImage":{"id":"65c55cee9e947c438698a95e","slug":"chart-2-1-cognitive-enhancement","image":{"id":"image_gesda-22/image-asset/chart-2-1-cognitive-enhancement_image__TRR-2_1-TBC-01","url":"https://res.cloudinary.com/shapeable/image/upload/v1668986372/gesda-22/image-asset/chart-2-1-cognitive-enhancement_image__TRR-2_1-TBC-01.png","url2x":null}},"citations":[{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-02-2-1-1","url":"https://doi.org/10.1016/j.neuron.2024.04.018","name":"A unifying framework for functional organization in early and higher ventral visual cortex","authors":[{"id":"691a787ec0043bba84a9de29","name":"E. 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Wang et al.","slug":"e-y-wang-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature","accessDate":null,"startPage":470,"volume":640,"footnoteNumber":2,"year":null},{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-02-2-1-3","url":"https://doi.org/10.1038/s41467-025-57352-1","name":"The road to commercial success for neuromorphic technologies","authors":[{"id":"691a787fc0043bba84a9de31","name":"D. R. Muir and S. 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Scangos et al.","slug":"k-w-scangos-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Medicine","accessDate":null,"startPage":1696,"volume":27,"footnoteNumber":4,"year":null}],"subTopics":[{"id":"65c55d4f9e947c438698b676","name":"Fundamentals of cognition","path":"/sub-topics/fundamentals-of-cognition","outlineNumber":"2.1.1","slug":"fundamentals-of-cognition","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a325","name":"Teal","value":"#44AFCD"},"topic":{"id":"65c55d599e947c438698b7aa","slug":"cognitive-enhancement","path":"/topics/cognitive-enhancement"},"intro":{"text":"One of the fundamental goals of neuroscience is to understand how the brain’s physical processes give rise to the mind’s functions, such as perception, memory, decision-making and learning. Recent breakthroughs have been made in this area through recording neural activity on large scales and by using optogenetics, chemogenetics and electrical stimulation to perform targeted stimulation of specific brain circuits that explores their functions and processes.[5](/citation/2025-02-2-1-5/)"},"description":{"text":"It is now clear that cognitive processes are dynamic and distributed, making them extremely difficult not only to investigate but also to model.[6](/citation/2025-02-2-1-6/) However, researchers are beginning to unify insights across species and scales — from synaptic plasticity to network-level population dynamics — and are moving beyond investigating isolated brain regions toward an understanding of integrated, systems-level frameworks.[7](/citation/2025-02-2-1-7/) \n\nAI is also beginning to play an important role in developing our understanding of the brain. Researchers are using large-scale AI models to analyse massive neural datasets and to identify functions of neural systems. Deep learning can be trained to predict neural responses and develop representations similar to those in the biological brain. This approach is helping to identify the underlying mechanisms of cognition.[8](/citation/2025-02-2-1-8/) Meanwhile, generative AI systems are able to reconstruct images and speech directly from brain activity, offering a potential window into internal thought processes and new avenues for restoring communication. \n\nA more ambitious goal is the creation of a functional digital twin of the brain.[9](/citation/2025-02-2-1-9/) This would be a high-fidelity computational model focused on the brain’s information processing rather than its precise physical structure. Achieving this requires the analysis and fusion of high-resolution neural data from many experiments and individuals to build a predictive model capable of running in silico experiments on brain behaviour. Achieving this could dramatically accelerate the research and development of neural therapies.\n\nA better understanding of human cognition, which is extremely data- and energy-efficient, could also lead to more efficient AI systems built by mimicking the brain’s architecture. Such “neuromorphic” computing systems are being developed for two key areas. The first is heterogeneous computing, where energy-efficient neuromorphic chips handle specific tasks within conventional devices. The second, and potentially more impactful, is “edge” computing, enabling powerful AI to run on small, portable devices without relying on the cloud.[10](/citation/2025-02-2-1-10/)"},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e89aff63d1c853e9788c3c","image":{"id":"image_gesda-platform/image-asset/2-1-1-sub-anti-2026_image__2.1.1_sub_anti_2026_lhklj0","url":"https://res.cloudinary.com/shapeable/image/upload/v1760074478/gesda-platform/image-asset/2-1-1-sub-anti-2026_image__2.1.1_sub_anti_2026_lhklj0.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a787","name":"2.1.1 - 25-year horizon","slug":"2-1-1-25-year-horizon","intro":{"text":"Digital modelling improves intervention outcomes"},"description":{"text":"Digital models of brains help predict outcomes of interventions and perform experiments in silico rather than in people and animals. Whole-brain recording remains a challenge due to the enormous data-processing challenges of sensing every neuron."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a786","name":"2.1.1 - 10-year horizon","slug":"2-1-1-10-year-horizon","intro":{"text":"Efforts to build a digital twin of the brain accelerate"},"description":{"text":"Functional digital twins emerge for specific, well-understood brain systems such as the visual cortex in primates."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a785","name":"2.1.1 - 5-year horizon","slug":"2-1-1-5-year-horizon","intro":{"text":"Practical neuromorphic applications emerge"},"description":{"text":"“Edge” computing applications that use neuromorphic principles make devices like sensors and wearables more efficient by running on field-programmable gate arrays (FPGAs) that emulate neuromorphic circuits."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aa66","value":"0.521","numericValue":0.521,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68edd93baf9e6d6d63270d6e","value":"0.550","numericValue":0.55,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-02-2-1-5","url":"https://doi.org/10.1016/j.cell.2016.03.047","name":"Targeting neural circuits","authors":[{"name":"P. 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Gandolfi et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Frontiers in Neuroscience","accessDate":null,"startPage":1565811,"volume":19,"footnoteNumber":10,"year":null}],"imageAssets":[]}},{"id":"65c55d4f9e947c438698b658","name":"Decrypting the brain","path":"/sub-topics/decrypting-the-brain","outlineNumber":"2.1.2","slug":"decrypting-the-brain","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a325","name":"Teal","value":"#44AFCD"},"topic":{"id":"65c55d599e947c438698b7aa","slug":"cognitive-enhancement","path":"/topics/cognitive-enhancement"},"intro":{"text":"Understanding how neural activity leads to thought, intention and behaviour requires the use of a number of neurotechnology tools that are in various stages of development. Large-scale electrophysiology, implantable recording devices, high-resolution imaging and adaptive decoding algorithms are all enabling researchers to capture and model the dynamics of cognition and internal brain states in real time.[11](/citation/2025-02-2-1-11/),[12](/citation/2025-02-2-1-12/),[13](/citation/2025-02-2-1-13/),[14](/citation/2025-02-2-1-14/) As datasets grow in complexity, the focus will shift to extracting generalisable rules from these resources. AI is becoming important here: AI-driven models can analyse vast datasets from multiple individuals to create more precise and generalisable representations of brain states.[15](/citation/2025-02-2-1-15/)"},"description":{"text":"Progress in decrypting neural states will provide opportunities to regulate brain states using brain-computer interfaces (BCIs). The use of AI in the development of BCIs has potential for revolutionising treatments for neuropsychiatric disorders and for the possibility of enhancing human cognition.[16](/citation/2025-02-2-1-16/) Disentangling the neural signals relevant to a specific condition, like depression, from the myriad of other brain activities remains a challenge. The vision is to develop sophisticated closed-loop systems that would continuously record brain activity while AI algorithms decode internal states such as mood and then guide personalised therapies in real time. These interventions could range from deep brain stimulation to pharmacology.\n\nA more futuristic and complex vision involves a true fusion of the brain and computer, creating a single, enhanced mind. However, a fundamental obstacle is the unique variability of individual brains, which can be modelled only on a neuron-by-neuron scale. The scale of this challenge suggests that this kind of model of the brain will remain out of reach for the foreseeable future."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8a32863d1c853e9788ce4","image":{"id":"image_gesda-platform/image-asset/2-1-2-sub-anti-2026_image__2.1.2_sub_anti_2026_a3vmpu","url":"https://res.cloudinary.com/shapeable/image/upload/v1760076560/gesda-platform/image-asset/2-1-2-sub-anti-2026_image__2.1.2_sub_anti_2026_a3vmpu.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a78a","name":"2.1.2 - 25-year horizon","slug":"2-1-2-25-year-horizon","intro":{"text":"Brain-control systems revolutionise therapies for some disorders"},"description":{"text":"Built-in, closed-loop control systems seamlessly decode and regulate a wide variety of cognitive and internal states, revolutionising therapies for many brain disorders."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a789","name":"2.1.2 - 10-year horizon","slug":"2-1-2-10-year-horizon","intro":{"text":"The ability to regulate certain brain states becomes possible"},"description":{"text":"The focus in research shifts from reading brain states to actively regulating some of them, like depressed mood, allowing a move towards therapeutic intervention."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a788","name":"2.1.2 - 5-year horizon","slug":"2-1-2-5-year-horizon","intro":{"text":"Foundation models of the brain are driven by AI"},"description":{"text":"AI-driven models become capable of aggregating information from across different individuals to more accurately read out and understand complex emotional and cognitive states."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aaba","value":"0.495","numericValue":0.495,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68edd961af9e6d6d63270d7d","value":"0.660","numericValue":0.66,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-02-2-1-11","url":"https://doi.org/10.1038/nature24636","name":"Fully integrated silicon probes for high-density recording of neural activity","authors":[{"name":"J. 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Precise, targeted modulation of neural circuits is emerging as a powerful approach to influence attention, learning, memory and affective states."},"description":{"text":"Their operation relies on understanding — gained through techniques ranging from deep-brain stimulation and transcranial electrical and magnetic stimulation to optogenetics and chemogenetics — of how specific patterns of stimulation can shape behaviour and cognition. Yet the future of neuromodulation lies not only in stimulation techniques, but in adaptive, feedback-driven systems that can respond to neural and behavioural states in real time.[17](/citation/2025-02-2-1-17/)\n\nMaterials science remains a significant roadblock. Current implantable systems are built from a narrow range of materials, usually dry, rigid electronics, that are in stark contrast to biological tissue, which is soft and dynamic. This disparity hinders the development of higher-resolution brain-computer interfaces: future progress depends on materials that are flexible enough to cope with the movement of neural tissue, biocompatible and capable of being miniaturised and packaged in a way that protects the electronics within the body.[18](/citation/2025-02-2-1-18/) While current research is promising, introducing new materials and manufacturing techniques is a slow process because of the extensive testing and approval required.[19](/citation/2025-02-2-1-19/) \n\nBeyond electronics, alternative forms of modulation include optogenetics[20](/citation/2025-02-2-1-20/) and sonogenetics,[21](/citation/2025-02-2-1-21/) which offer higher selectivity but require substantial material and manufacturing innovations. Significant clinical testing will also be necessary.\n\nAnother key area of development is the detailed mapping of the brain’s connectome, cell function and spatial arrangement on the scale of individual neurons.[22](/citation/2025-02-2-1-22/) These maps are already becoming available for animal models and will enable more precise, targeted neuromodulation and closed-loop systems that can record and stimulate neural activity with high precision."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8928f63d1c853e9788bc1","image":{"id":"image_gesda-platform/image-asset/2-1-3-sub-anti-2026_image__2.1.3_sub_anti_2026_kwzu0i","url":"https://res.cloudinary.com/shapeable/image/upload/v1760072322/gesda-platform/image-asset/2-1-3-sub-anti-2026_image__2.1.3_sub_anti_2026_kwzu0i.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a78d","name":"2.1.3- 25-year horizon","slug":"2-1-3-25-year-horizon","intro":{"text":"Advanced biocompatible materials allow brain enhancement"},"description":{"text":"A wide range of flexible, biocompatible materials that can be safely implanted into the brain become widely available for applications ranging from mood therapy to brain-functional enhancement. The mesoscopic connectome of the human brain is completed for normal and various disease states."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a78c","name":"2.1.3 - 10-year horizon","slug":"2-1-3-10-year-horizon","intro":{"text":"Genetic brain therapies controlled by light and sound"},"description":{"text":"Optogenetic and sonogenetic therapies are approved for use in humans, offering more selective ways of regulating brain activity. Ethical concerns remain."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a78b","name":"2.1.3 - 5-year horizon","slug":"2-1-3-5-year-horizon","intro":{"text":"Full human brain-cell atlas published"},"description":{"text":"Researchers map the complete connectome of non-human primates and release a full human brain-cell atlas that details the spatial arrangement of all cell types in the brain."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aaa8","value":"0.516","numericValue":0.516,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68edd988af9e6d6d63270d8c","value":"0.590","numericValue":0.59,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-02-2-1-17","url":"https://doi.org/10.1186/s42234-024-00163-4","name":"Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation","authors":[{"name":"I. 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Advances in AI —  particularly in foundation models, adaptive agents and generative tools — offer the potential to extend memory, decision-making and learning even further beyond the biological substrate."},"description":{"text":"As brain-computer interfaces mature, and as models become more context-aware and biologically informed, new hybrid cognitive architectures may emerge. Non-invasive BCIs, such as glasses[23](/citation/2025-02-2-1-23/) and earbuds with integrated physiological sensors, are already collecting vast amounts of data,[24](/citation/2025-02-2-1-24/) which, when combined with AI, could lead to cognitive offloading and augmentation.[25](/citation/2025-02-2-1-25/),[26](/citation/2025-02-2-1-26/)\n\nInvasive BCIs — implants inserted into the brain via a small craniotomy[27](/citation/2025-02-2-1-27/) — have even deeper potential. Neuralink, a privately funded company, has developed a hermetically sealed implant that translates motor intentions into digital commands (and vice versa) with immediate applications in restoring function for individuals with paralysis. Patients are already using Neuralink interfaces to control a virtual hand and play games, and the ambition is significantly greater: the company envisions an ability to treat blindness and aphasia, the full-body control of exoskeletons, and perhaps pain and mood management. \n\nSuch advanced BCIs raise significant concerns about data privacy,[28](/citation/2025-02-2-1-28/) the potential for misuse of personal brain data and the risk of creating a dependency on technology that could diminish natural cognitive abilities. Given the eventual (beyond 2050) possibilities of direct cerebral advertisements, identity hacking and the potential for identity destruction, the ethical landscape that will emerge over the next few decades will be complex. A careful, nuanced and informed approach will be required as AI integrates with human consciousness."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8924563d1c853e9788bbb","image":{"id":"image_gesda-platform/image-asset/2-1-4-sub-anti-2026_image__2.1.4_sub_anti_2026_ywlsrl","url":"https://res.cloudinary.com/shapeable/image/upload/v1760072248/gesda-platform/image-asset/2-1-4-sub-anti-2026_image__2.1.4_sub_anti_2026_ywlsrl.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a790","name":"2.1.4 - 25-year horizon","slug":"2-1-4-25-year-horizon","intro":{"text":"Whole-brain interfaces become possible with BCI technology"},"description":{"text":"Whole-brain interfaces using BCIs with millions of electrodes allow cognitive enhancement and the treatment of complex conditions like memory loss and psychosis. 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Ethical concerns over BCI hacking and artificial consciousness are widely debated."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a325","name":"Teal","slug":"teal","value":"#44AFCD"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aab4","value":"0.534","numericValue":0.534,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68edd9adaf9e6d6d63270d9b","value":"0.630","numericValue":0.63,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-02-2-1-23","url":"https://doi.org/10.1145/3342197.3344516","name":"AttentivU: designing EEG and EOG compatible glasses for physiological sensing and feedback in the car","authors":[{"name":"N. 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