Brain-on-a-chip technology for modeling psychiatric disorders: A new frontier in precision psychiatry

Taslim Uddin; Adel Gouri; Faria Sharmin; Khadidja Belhouala; Nusrat Jahan Nisha; Nazifa Anjum; Susmita Biswas; Sajid Asgar; Sarah Tahera Mahmud; Afia Asma; Nayeemul Islam; Bachir Benarba; Taslim Uddin; Adel Gouri; Faria Sharmin; Khadidja Belhouala; Nusrat Jahan Nisha; Nazifa Anjum; Susmita Biswas; Sajid Asgar; Sarah Tahera Mahmud; Afia Asma; Nayeemul Islam; Bachir Benarba

doi:10.3934/Neuroscience.2026028

AIMS Neuroscience

2026, Volume 13, Issue 4: 618-634. doi: 10.3934/Neuroscience.2026028

Previous Article Next Article

Perspective

Brain-on-a-chip technology for modeling psychiatric disorders: A new frontier in precision psychiatry

1.
Department of Biotechnology and Genetic Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
2.
The Quest, Chittagong, Bangladesh
3.
Laboratory of Clinical Biochemistry, Faculty of Medicine, University of Badji Mokhtar Annaba, Algeria
4.
Department of Public Health and Informatics, Bangladesh University of Professionals, Dhaka-1216, Bangladesh
5.
Department of Agri-Food, Faculty of Science and Technology, University of Ain Temouchent, 46000 Ain Temouchent, Algeria
6.
Department of Chemical, Materials and Biomedical Engineering, University of Georgia, Athens, Georgia, 30605, USA
7.
Department of Medicine, Sir Salimullah Medical College & Mitford Hospital, Dhaka-1100, Bangladesh
8.
Department of Neuromedicine, Jashore Medical College & Hospital, Jashore, Jashore sadar-7400, Bangladesh
9.
Department of Community medicine, Dhaka medical college and Hospital, Dhaka-1000, Bangladesh
10.
Department Health Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom
11.
Department of Neurology, Chittagong medical college, Chittagong-4203, Bangladesh
12.
Laboratory Research on Biological Systems and Geomatics, Faculty of Nature and Life Sciences, University of Mascara, Algeria

Received: 08 August 2026 Revised: 30 September 2026 Accepted: 10 October 2026 Published: 23 October 2026

Brain-on-a-chip (BoC) technology is an emerging human-relevant platform for investigating complex neuropsychiatric disorders, including major depressive disorder (MDD) and schizophrenia. By integrating microfluidic systems with human induced pluripotent stem cell (iPSC)-derived neural cells, three-dimensional cultures, multiregion architectures, microelectrode arrays, and biosensors, BoC platforms enable controlled investigation of neural connectivity, network activity, and cellular interactions. iPSC-based models provide disease-relevant phenotypes that may support the development of more complex psychiatric BoC systems; however, dedicated and comprehensively validated BoC models for MDD and schizophrenia remain limited. Important challenges include incomplete cellular maturation and complexity, limited vascular and immune integration, technical variability, long-term stability, and insufficient clinical validation. Future advances integrating patient-specific cells, artificial intelligence, multi-omics, and standardized multimodal measurements may improve disease modeling and therapeutic research. With rigorous disease-specific validation and stronger links to clinically characterized data, BoC technology may contribute to the future development of precision psychiatry.
- brain-on-a-chip,
- organ-on-a-chip,
- psychiatric disorders,
- depression,
- schizophrenia,
- microfluidics,
- neuroengineering,
- induced pluripotent stem cells (iPSC)
Citation: Taslim Uddin, Adel Gouri, Faria Sharmin, Khadidja Belhouala, Nusrat Jahan Nisha, Nazifa Anjum, Susmita Biswas, Sajid Asgar, Sarah Tahera Mahmud, Afia Asma, Nayeemul Islam, Bachir Benarba. Brain-on-a-chip technology for modeling psychiatric disorders: A new frontier in precision psychiatry[J]. AIMS Neuroscience, 2026, 13(4): 618-634. doi: 10.3934/Neuroscience.2026028

Related Papers:

Abstract

Brain-on-a-chip (BoC) technology is an emerging human-relevant platform for investigating complex neuropsychiatric disorders, including major depressive disorder (MDD) and schizophrenia. By integrating microfluidic systems with human induced pluripotent stem cell (iPSC)-derived neural cells, three-dimensional cultures, multiregion architectures, microelectrode arrays, and biosensors, BoC platforms enable controlled investigation of neural connectivity, network activity, and cellular interactions. iPSC-based models provide disease-relevant phenotypes that may support the development of more complex psychiatric BoC systems; however, dedicated and comprehensively validated BoC models for MDD and schizophrenia remain limited. Important challenges include incomplete cellular maturation and complexity, limited vascular and immune integration, technical variability, long-term stability, and insufficient clinical validation. Future advances integrating patient-specific cells, artificial intelligence, multi-omics, and standardized multimodal measurements may improve disease modeling and therapeutic research. With rigorous disease-specific validation and stronger links to clinically characterized data, BoC technology may contribute to the future development of precision psychiatry.

Conflict of interest

The authors declare no conflict of interest.

Authors' contributions

Taslim Uddin: Conceptualization, Supervision, Writing Original–Draft; Adel Gouri: Supervision, Writing- Review & Editing; Faria Sharmin: Resources, Investigation; Khadidja Belhouala: Writing- Review & Editing; Nusrat Jahan Nisha: Formal analysis, Resources; Nazifa Anjum: Investigation, Formal analysis; Susmita Biswas: Project Administration, Validation; Sajid Asgar: Project Administration, Validation; Sarah Tahera Mahmud: Writing- Review & Editing, Supervision; Afia Asma: Literature review, Investigation; Nayeemul Islam: Investigation, Visualization; Bachir Benarba: Supervision, Project Administration, Writing- Review & Editing.

References

[1]	Arias D, Saxena S, Verguet S (2022) Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine 54: 101675. https://doi.org/10.1016/j.eclinm.2022.101675
[2]	Jalali H, Rahimian S, Shahsavarian N, et al. (2024) The organoid modeling approach to understanding the mechanisms underlying neurodegeneration: A comprehensive review. Life Sci 358: 123198. https://doi.org/10.1016/j.lfs.2024.123198
[3]	Rodrigues RO, Shin SR, Bañobre-López M (2024) Brain-on-a-chip: an emerging platform for studying the nanotechnology-biology interface for neurodegenerative disorders. J Nanobiotechnology 22: 573. https://doi.org/10.1186/s12951-024-02720-0
[4]	Dixon TA, Muotri AR (2023) Advancing preclinical models of psychiatric disorders with human brain organoid cultures. Mol Psychiatry 28: 83-95. https://doi.org/10.1038/s41380-022-01708-2
[5]	Marom A, Mahto SK, Shor E, et al. (2015) Microfluidic Chip for Site-Specific Neuropharmacological Treatment and Activity Probing of 3D Neuronal “Optonet” Cultures. Adv Healthc Mater 4: 1478-1483,1422. https://doi.org/10.1002/adhm.201400643
[6]	Bang S, Jeong S, Choi N, et al. (2019) Brain-on-a-chip: A history of development and future perspective. Biomicrofluidics 13: 051301. https://doi.org/10.1063/1.5120555
[7]	Zhang H, Huang N, Bian S, et al. (2025) Brain organoids-on-chip for neural diseases modeling: History, challenges and trends. J Pharm Anal 15: 101323. https://doi.org/10.1016/j.jpha.2025.101323
[8]	Shanks N, Greek R, Greek J (2009) Are animal models predictive for humans?. Philos Ethics Humanit Med 4: 2. https://doi.org/10.1186/1747-5341-4-2
[9]	Zhang YS, Aleman J, Shin SR, et al. (2017) Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. Proc Natl Acad Sci U S A 114: E2293-E2302. https://doi.org/10.1073/pnas.1612906114
[10]	Servais B, Mahmoudi N, Gautam V, et al. (2024) Engineering brain-on-a-chip platforms. Nat Rev Bioeng 2: 691-709. https://doi.org/10.1038/s44222-024-00184-3
[11]	Hong Y, Yang Q, Song H, et al. (2023) Opportunities and limitations for studying neuropsychiatric disorders using patient-derived induced pluripotent stem cells. Mol Psychiatry 28: 1430-1439. https://doi.org/10.1038/s41380-023-01990-8
[12]	Fahlman M, Fabiano S, Gueskine V, et al. (2019) Interfaces in organic electronics. Nat Rev Mater 4: 627-650. https://doi.org/10.1038/s41578-019-0127-y
[13]	Wan J, Zhou S, Mea HJ, et al. (2022) Emerging Roles of Microfluidics in Brain Research: From Cerebral Fluids Manipulation to Brain-on-a-Chip and Neuroelectronic Devices Engineering. Chem Rev 122: 7142-7181. https://doi.org/10.1021/acs.chemrev.1c00480
[14]	Obien ME, Deligkaris K, Bullmann T, et al. (2015) Revealing neuronal function through microelectrode array recordings. Front Neurosci 8: 423. https://doi.org/10.3389/fnins.2014.00423
[15]	Frega M, Tedesco M, Massobrio P, et al. (2014) Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology. Sci Rep 4: 5489. https://doi.org/10.1038/srep05489
[16]	Miccoli B, Braeken D, Li YE (2018) Brain-on-a-chip Devices for Drug Screening and Disease Modeling Applications. Curr Pharm Des 24: 5419-5436. https://doi.org/10.2174/1381612825666190220161254
[17]	Alam El Din DM, Moenkemoeller L, Loeffler A, et al. (2025) Human neural organoid microphysiological systems show the building blocks necessary for basic learning and memory. Commun Biol 8: 1237. https://doi.org/10.1038/s42003-025-08632-5
[18]	Li S, Liu Y, Hua S, et al. (2025) Advanced Brain-on-a-Chip for Wetware Computing: A Review. Adv Sci (Weinh) 12: e08120. https://doi.org/10.1002/advs.202508120
[19]	Low LA, Mummery C, Berridge BR, et al. (2021) Organs-on-chips: into the next decade. Nat Rev Drug Discov 20: 345-361. https://doi.org/10.1038/s41573-020-0079-3
[20]	Cerutti L, Brofiga M (2024) Unraveling brain diseases: The promise of brain-on-a-chip models. J Neurosci Methods 405: 110105. https://doi.org/10.1016/j.jneumeth.2024.110105
[21]	Ingber DE (2022) Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat Rev Genet 23: 467-491. https://doi.org/10.1038/s41576-022-00466-9
[22]	Cerina M, Piastra MC, Frega M (2023) The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research. Prog Biomed Eng 5: 032002. https://doi.org/10.1088/2516-1091/acce12
[23]	Castiglione H, Vigneron PA, Baquerre C, et al. (2022) Human Brain Organoids-on-Chip: Advances, Challenges, and Perspectives for Preclinical Applications. Pharmaceutics 14: 2301. https://doi.org/10.3390/pharmaceutics14112301
[24]	Smirnova L, Hartung T (2024) The Promise and Potential of Brain Organoids. Adv Healthc Mater 13: e2302745. https://doi.org/10.1002/adhm.202302745
[25]	Tan SY, Feng X, Cheng LKW, et al. (2023) Vascularized human brain organoid on-chip. Lab Chip 23: 2693-2709. https://doi.org/10.1039/D2LC01109C
[26]	Allwardt V, Ainscough AJ, Viswanathan P, et al. (2020) Translational Roadmap for the Organs-on-a-Chip Industry toward Broad Adoption. Bioengineering (Basel) 7: 112. https://doi.org/10.3390/bioengineering7030112
[27]	Avior Y, Ron S, Kroitorou D, et al. (2021) Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response. Transl Psychiatry 11: 201. https://doi.org/10.1038/s41398-021-01319-5
[28]	Vadodaria KC, Ji Y, Skime M, et al. (2019) Altered serotonergic circuitry in SSRI-resistant major depressive disorder patient-derived neurons. Mol Psychiatry 24: 808-818. https://doi.org/10.1038/s41380-019-0377-5
[29]	Vadodaria KC, Ji Y, Skime M, et al. (2019) Serotonin-induced hyperactivity in SSRI-resistant major depressive disorder patient-derived neurons. Mol Psychiatry 24: 795-807. https://doi.org/10.1038/s41380-019-0363-y
[30]	Lu K, Hong Y, Tao M, et al. (2023) Depressive patient-derived GABA interneurons reveal abnormal neural activity associated with HTR2C. EMBO Mol Med 15: e16364. https://doi.org/10.15252/emmm.202216364
[31]	Miller AH, Raison CL (2016) The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 16: 22-34. https://doi.org/10.1038/nri.2015.5
[32]	Troubat R, Barone P, Leman S, et al. (2021) Neuroinflammation and depression: A review. Eur J Neurosci 53: 151-171. https://doi.org/10.1111/ejn.14720
[33]	Shahin MH, Liu Q (2025) Artificial Intelligence in Clinical and Translational Science: From Bench Insights to Bedside Impact. Clin Transl Sci 18: e70383. https://doi.org/10.1111/cts.70383
[34]	Réda C, Kaufmann E, Delahaye-Duriez A (2019) Machine learning applications in drug development. Comput Struct Biotechnol J 18: 241-252. https://doi.org/10.1016/j.csbj.2019.12.006
[35]	Rahman T, Lauriello J (2016) Schizophrenia: an overview. Focus 14: 300-307. https://doi.org/10.1176/appi.focus.20160006
[36]	Brennand KJ, Simone A, Jou J, et al. (2011) Modelling schizophrenia using human induced pluripotent stem cells. Nature 473: 221-225. https://doi.org/10.1038/nature09915
[37]	Page SC, Sripathy SR, Farinelli F, et al. (2022) Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance. Proc Natl Acad Sci U S A 119: e2109395119. https://doi.org/10.1073/pnas.2109395119
[38]	Räsänen N, Tiihonen J, Koskuvi M, et al. (2022) The iPSC perspective on schizophrenia. Trends Neurosci 45: 8-26. https://doi.org/10.1016/j.tins.2021.11.002
[39]	Kim NS, Wen Z, Liu J, et al. (2021) Pharmacological rescue in patient iPSC and mouse models with a rare DISC1 mutation. Nat Commun 12: 1398. https://doi.org/10.1038/s41467-021-21713-3
[40]	Orhan F, Malwade S, Khanlarkhani N, et al. (2023) Kynurenic acid promotes activity-dependent synaptic pruning in schizophrenia. bioRxiv . https://doi.org/10.1101/2023.10.19.563090
[41]	Bianco F, Tonna N, Lovchik RD, et al. (2012) Overflow microfluidic networks: application to the biochemical analysis of brain cell interactions in complex neuroinflammatory scenarios. Anal Chem 84: 9833-9840. https://doi.org/10.1021/ac302094z
[42]	Brennand K, Savas JN, Kim Y, et al. (2015) Phenotypic differences in hiPSC NPCs derived from patients with schizophrenia. Mol Psychiatry 20: 361-368. https://doi.org/10.1038/mp.2014.22
[43]	Nath SC, Menendez L, Friedrich Ben-Nun I (2023) Overcoming the Variability of iPSCs in the Manufacturing of Cell-Based Therapies. Int J Mol Sci 24: 16929. https://doi.org/10.3390/ijms242316929
[44]	West AR, Floresco SB, Charara A, et al. (2003) Electrophysiological interactions between striatal glutamatergic and dopaminergic systems. Ann N Y Acad Sci 1003: 53-74. https://doi.org/10.1196/annals.1300.004
[45]	Lassus B, Naudé J, Faure P, et al. (2018) Glutamatergic and dopaminergic modulation of cortico-striatal circuits probed by dynamic calcium imaging of networks reconstructed in microfluidic chips. Sci Rep 8: 17461. https://doi.org/10.1038/s41598-018-35802-9
[46]	Iannielli A, Ugolini GS, Cordiglieri C, et al. (2019) Reconstitution of the Human Nigro-striatal Pathway on-a-Chip Reveals OPA1-Dependent Mitochondrial Defects and Loss of Dopaminergic Synapses. Cell Rep 29: 4646-4656.e4. https://doi.org/10.1016/j.celrep.2019.11.111
[47]	Yu Y, de Campos RPS, Hong S, et al. (2019) A microfluidic platform for continuous monitoring of dopamine homeostasis in dopaminergic cells. Microsyst Nanoeng 5: 10. https://doi.org/10.1038/s41378-019-0049-2
[48]	Kim S, Kang S, Choe J, et al. (2023) A Microfluidic System for Investigating Anticipatory Medication Effects on Dopamine Homeostasis in Dopaminergic Cells. Anal Chem 95: 3153-3159. https://doi.org/10.1021/acs.analchem.2c04923
[49]	Abashkin DA, Kurishev AO, Karpov DS, et al. (2021) Cellular Models in Schizophrenia Research. Int J Mol Sci 22: 8518. https://doi.org/10.3390/ijms22168518
[50]	Tong Z, Kwak E, Aguiar A, et al. (2021) Compartmentalized microfluidic chambers enable long-term maintenance and communication between human pluripotent stem cell-derived forebrain and midbrain neurons. Lab Chip 21: 4016-4030. https://doi.org/10.1039/D1LC00505G
[51]	Ndyabawe K, Cipriano M, Zhao W, et al. (2021) Brain-on-a-Chip Device for Modeling Multiregional Networks. ACS Biomater Sci Eng 7: 350-359. https://doi.org/10.1021/acsbiomaterials.0c00895
[52]	Rouleau N, Murugan NJ, Kaplan DL (2023) Functional bioengineered models of the central nervous system. Nat Rev Bioeng 1: 252-270. https://doi.org/10.1038/s44222-023-00027-7
[53]	Nebuloni F, Do QB, Cook PR, et al. (2024) A fluid-walled microfluidic platform for human neuron microcircuits and directed axotomy. Lab Chip 24: 3252-3264. https://doi.org/10.1039/D4LC00107A
[54]	Poinsot M, Dos Santos M, Marthy B, et al. (2026) Biologically grounded on-chip model identifies selective topographic reorganization within hyperexcitable corticostriatal networks. Proc Natl Acad Sci U S A 123: e2513459123. https://doi.org/10.1073/pnas.2513459123
[55]	Drevets WC, Price JL, Furey ML (2008) Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct 213: 93-118. https://doi.org/10.1007/s00429-008-0189-x
[56]	Chen P, Ye E, Jin X, et al. (2019) Association between Thalamocortical Functional Connectivity Abnormalities and Cognitive Deficits in Schizophrenia. Sci Rep 9: 2952. https://doi.org/10.1038/s41598-019-39367-z
[57]	Frimat JP, Luttge R (2019) The Need for Physiological Micro-Nanofluidic Systems of the Brain. Front Bioeng Biotechnol 7: 100. https://doi.org/10.3389/fbioe.2019.00100
[58]	Kim J, Yoon T, Kim P, et al. (2023) Manufactured tissue-to-tissue barrier chip for modeling the human blood-brain barrier and regulation of cellular trafficking. Lab Chip 23: 2990-3001. https://doi.org/10.1039/D3LC00124E
[59]	Jorfi M, D'Avanzo C, Kim DY, et al. (2018) Three-Dimensional Models of the Human Brain Development and Diseases. Adv Healthc Mater 7: 10.1002/adhm.201700723. https://doi.org/10.1002/adhm.201700723
[60]	Zhang B, Korolj A, Lai BFL, et al. (2018) Advances in organ-on-a-chip engineering. Nat Rev Mater 3: 257-278. https://doi.org/10.1038/s41578-018-0034-7
[61]	Tandon N, Bhumiratana S (2022) The Translational Mindset Is Essential for Taking Tissue-Engineered Therapeutics from Lab to Clinic. ACS Biomater Sci Eng 8: 4629-4633. https://doi.org/10.1021/acsbiomaterials.1c01396
[62]	Roy N, Cucullo L (2026) Organs-on-Chips in Drug Development: Engineering Foundations, Artificial Intelligence, and Clinical Translation. Biosensors (Basel) 16: 155. https://doi.org/10.3390/bios16030155
[63]	Lavazza A, Massimini M (2018) Cerebral organoids: ethical issues and consciousness assessment. J Med Ethics 44: 606-610. https://doi.org/10.1136/medethics-2017-104555
[64]	Bredenoord AL, Clevers H, Knoblich JA (2017) Human tissues in a dish: The research and ethical implications of organoid technology. Science 355: eaaf9414. https://doi.org/10.1126/science.aaf9414
[65]	Tarricone G, Carmagnola I, Chiono V (2022) Tissue-Engineered Models of the Human Brain: State-of-the-Art Analysis and Challenges. J Funct Biomater 13: 146. https://doi.org/10.3390/jfb13030146
[66]	Rouleau N, Murugan NJ, Kaplan DL (2021) Toward Studying Cognition in a Dish. Trends Cogn Sci 25: 294-304. https://doi.org/10.1016/j.tics.2021.01.005

Reader Comments

Your name:*

Email:*
© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)