Bridging Brain Organoids and Intelligent Technologies

Brain organoids are self-assembled, three-dimensional miniature brain tissue models that mimic the cellular diversity, spatial organization, and some functions of human brain development. Meanwhile, artificial intelligence (AI) is transforming neuroscience by enabling the analysis of complex neural data, identification of disease biomarkers, and optimization of therapies. Combining these advances, Organoid Intelligence (OI) has emerged as a new interdisciplinary field that explores the interface between the biological properties of brain organoids and AI's computational power, aiming to build bio-computational systems and deepen our understanding of cognition.

Creative Biolabs, as an innovative pioneer in life sciences, is dedicated to promoting the deep integration of these two fields. With a strong foundation in stem cell technology, neurobiology, and computational biology, we have launched a series of advanced brain organoid culture platforms and high-throughput analysis technologies to help researchers more accurately replicate the intricate structure and function of the human brain.

Understanding Organoid Intelligence

In OI, brain organoids can serve as "biological computers" to accomplish information processing, respond to external stimuli, and have basic learning ability. For example, electrical stimulation patterns can be delivered through high-density multi-electrode arrays (MEAs), and coupled with computer algorithms, a closed-loop feedback system is established to let brain organoids process and respond to specific inputs.

Figure 1 Architecture of an OI system.^1,2

This technology which includes AI algorithms, MEAs, and cell engineering approaches can even enable brain organoids to learn and adapt, and exhibit short-term memory.

The combination of AI and organoids models demonstrates significant potential across multiple scientific disciplines.

• It is poised to increase the efficiency and accuracy of drug discovery and development, significantly reduce the time and cost of drug development, and reduce the use of animal models, thus, lessening ethical concerns.
• In the context of inflammatory bowel disease (IBD) research, multi-omics technologies combined with AI-based data mining and analysis were utilized to uncover cellular heterogeneity and metabolic states within intestinal organoids. AI is being utilized to directly analyze the morphological characteristics of organoids for automated quality control and phenotypic classification.

Brain Organoids as Computational Models

Brain organoids are a class of mini-brain structures that can be obtained by the self-assembly of human pluripotent stem cells (such as induced pluripotent stem cells, iPSCs) under the conditions of three-dimensional culture. They can recapitulate the development of the human brain to some extent and even some functions of the brain, such as the maturation and functional output of neurons, glial cells, neurotransmitters and other neural cells, as well as a certain spatial organizational structure.

In neuroscience research, brain organoids have been used to model a variety of neurological diseases and conditions, such as Alzheimer's disease (AD), Parkinson's disease (PD), autism, schizophrenia, neurodegenerative diseases, neurodevelopmental disorders and brain tumors.

• Modeling Neurodevelopmental Disorders
  Brain organoids recapitulate several neurodevelopmental disorders, including autism spectrum disorder (ASD), microcephaly, Rett syndrome, and Miller-Dieker syndrome. For example, organoids generated from ASD patients display abnormal proliferation of neural progenitor cells and elevated numbers of GABAergic neurons. One potential molecular mechanism responsible for these abnormal phenotypes is FOXG1 overexpression.
• Studying Neurodegenerative Diseases
  Brain organoids successfully recapitulate key disease pathologies observed in neurodegenerative disorders, such as AD and PD, including amyloid-beta (Aβ) plaque deposition, tau protein aggregation, and alpha-synuclein accumulation. In addition, they allow for studying disease progression at the cellular level and the testing of drug efficacy and toxicity.
• Brain Tumor Research
  Organoids have been used to successfully generate models of brain tumors such as glioblastoma multiforme (GBM), medulloblastoma (MB), and meningioma. For example, brain organoids overexpressing the oncogenes c-MYC and Otx2 were used to model medulloblastoma, leading to the discovery of novel targets and potential anti-tumor drugs.
• Brain Injury and Neural Regeneration
  Brain organoids have been used to model brain injury, and data suggest that inhibiting genes like KCNJ2 can ameliorate mechanical injury and improve neuroprotection and regeneration. Midbrain dopaminergic neuron (mDA) organoids show promise in cell therapy research on Parkinson's disease and other neurodegenerative diseases.
• Exploring Psychiatric Disorders
  Brain organoids can be used to uncover disease-associated cellular and neural network abnormalities in psychiatric disorders, including schizophrenia and identify the impact of disease-causing genetic mutations on neural development and tissue architecture.
• Drug Screening and Toxicity Testing
  Brain organoids have recently become ideal platforms for drug screening and safety toxicity testing due to their brain tissue-mimicking structure and function. This results in increased clinical translatability of novel therapeutics.

With the application of AI technology, brain organoids have been found to have short-term memory and learning capabilities.

Monitoring Neural Activity in Brain Organoids

To fully tap into the potential of brain organoids, it is crucial to effectively monitor their neural activity. To this end, various technical methods have been developed. Table 1 shows three commonly used monitoring techniques.

Table 1 Key monitoring methods.

The monitoring data obtained enables artificial intelligence models to learn more efficiently, thereby achieving more reliable predictions and insights.

How AI Helps Organoids Learn?

Decoding the complex neural data captured from organ tissues using AI technology has allowed scientists to see how they modify their cellular networks to adapt to stimuli over time. The AI algorithms can control the feedback signals as well, creating a biofeedback loop in which organoids are dynamically adapting to their environments. In these systems, organoids are also demonstrating evidence of short-term memory.

• Open Loops
  AI technology interfaces with organoids by providing input signals and dynamically tuning their responses in real-time. This enables classification, prediction, and identification of adaptations, demonstrating simple computational processing within a biological substrate.
• Closed Loops
  Organoids grown with high-density MEA systems can exchange electrical signals in a closed feedback loop. This allows organoids to process inputs and react, learning simple behavioral tasks through reinforcement learning, exhibiting goal-directed self-organization and short-term memory.
• AI-powered Feedback Loops
  Combining AI algorithms, high-density MEAs, and cellular engineering enables organoids to process information, respond to stimuli, and learn. AI continuously modulates stimuli based on organoid behavior, creating feedback loops that simulate learning. Real-time data processing advances understanding of neural development and neurodegenerative diseases.

AI-supported organoid intelligence is used for testing disease biomarkers in the desired diseases which helps in observing changes on cellular level without live brain models for more ethical and efficient neurological research.

At Creative Biolabs, our vast experience allows us to provide a range of services related to this subject. Please do not hesitate to contact us for more details.

Created July 2025

References

1. Smirnova, Lena, et al. "Organoid Intelligence (OI): The New Frontier in Biocomputing and Intelligence-in-a-Dish." Frontiers in Science, vol. 1, Feb. 2023, p. 1017235. DOI.org, https://doi.org/10.3389/fsci.2023.1017235.
2. Distributed under Open Access license CC BY 4.0, without modification.

For Research Use Only. Not For Clinical Use.