Summary: No two brains have the exact same shape, yet most neural implants follow a one-size-fits-all approach. Researchers at Penn State have developed a new method to 3D print soft, stretchable bioelectrodes that are customized to match the individual ridges (gyri) and grooves (sulci) of a person’s brain.
These hydrogel-based sensors provide near-perfect contact with the cortical surface and deliver improved signal quality while avoiding damage to delicate brain tissue or disruption of fluid transport.
Key Findings
- Superior signal quality: Because the electrodes conform precisely to each brain’s surface geometry, they maintain almost complete connectivity to cortical signals, improving the fidelity of neural monitoring.
- Biocompatibility: In 28-day tests in rat models, the hydrogel electrodes produced no detectable immune response and retained stable performance, supporting their safety for longer-term implantation.
- Large, folded surface managed: When flattened, an adult human brain covers roughly 2,000 square centimeters—about the size of two large pizzas. The new 3D-printed mesh design is the first to adapt comfortably to this complex, highly folded terrain.
- Commercial scalability: The integrated workflow—MRI-based mapping, finite element analysis, and direct ink 3D printing—offers a clear path to scale manufacturing of patient-specific bioelectrodes for monitoring and potentially treating neurodegenerative disorders.
Source: Penn State
Soft electrodes matched to an individual’s brain surface could advance neural interfaces for monitoring and treating neurodegenerative diseases, a new Penn State-led study finds.
Neural interfaces rely on tiny sensors—bioelectrodes—to record and sometimes modulate electrical activity in the brain. Most conventional electrodes are rigid and produced for standardized use, which makes it difficult for them to conform to the brain’s intricate, person-specific folding patterns.
To solve this, the research team developed a workflow that turns patient MRI scans into custom electrode designs and then fabricates those designs with 3D printing. The electrodes are soft, stretchable, and primarily made from hydrogel, a water-rich material that better matches the mechanical properties of brain tissue.
Using MRI data from 21 participants, the team generated finite element simulations to capture each brain’s unique cortical topography. Those simulations guided the computer-aided design of electrodes that precisely matched individual gyral and sulcal patterns. The resulting models and electrodes were 3D printed using a direct ink writing process, enabling rapid, low-cost customization compared with conventional clean-room fabrication.
Published in Advanced Materials, the study reports that these honeycomb-inspired, tissue-like electrodes conform to the brain more closely than traditional designs. The honeycomb architecture reduces stiffness while maintaining mechanical strength, lowering material use and fabrication time without compromising durability.
Gyrification—the formation of gyri and sulci—creates a highly folded cortical surface that supports fast neural communication and fits a large organ into the skull. While the major folds are broadly consistent across people, the specific layout of ridges and grooves varies with individual factors such as age, sex, and body size. Traditional electrodes do not account for those variations, which can cause poor contact, signal loss, or tissue irritation.
“Each person’s brain structure is different,” said Tao Zhou, Wormley Family Early Career Professor and corresponding author. “Yet we fit neural interfaces as if all brains were identical. That gap motivated our effort to tailor electrodes to each patient’s anatomy.”
The hydrogel electrodes are engineered to match brain tissue stiffness (on the order of 0.1–10 kPa) and include a honeycomb pattern that balances flexibility and robustness. This design allows the gel to slip into sulci and sit across gyri without causing mechanical damage or significantly interfering with cerebrospinal fluid movement—both critical for safe, reliable long-term interfacing.
In physical tests, the team printed 21 brain models, applied the custom electrodes, and measured how well the devices conformed to each surface. The hydrogel electrodes achieved much closer contact than rigid counterparts, producing higher-quality and more consistent electrical readings. In vivo testing in rats over 28 days showed no immune response and stable sensing performance.
Because direct ink printing does not require specialized clean-room facilities, the method can reduce production costs and turnaround times for patient-specific devices. The researchers envision this platform supporting both advanced monitoring and eventual therapeutic applications, including optimized electrodes for detecting or treating conditions such as Parkinson’s disease and epilepsy.
“Personalized electrodes that conform to a patient’s cortical geometry improve reliability and signal fidelity,” Zhou said. “We aim to refine this technology to target disease-specific biomarkers and, ultimately, to collaborate with clinical teams to test these devices with patients.”
Additional co-authors from Penn State include Nanyin Zhang, Sulin Zhang, Marzia Momin, Luyi Feng, Salahuddin Ahmed, Jiashu Ren, Xiaoai Chen, Hyunjin Lee, Samuel R. Cramer, Xinyi Wang, Basma AlMahood, and Li-Pang Huang, among others. Funding for the project came from the U.S. National Science Foundation and the National Institutes of Health.
Key Questions Answered:
A: They are made mainly of hydrogel, a mostly water-based material. That softness lets them stretch and move with the brain, making the implant feel more integrated with the tissue rather than an inflexible foreign object.
A: The current focus is medical—monitoring and treating neurological diseases such as Parkinson’s or epilepsy—but the ability to 3D print affordable, custom-fit sensors could eventually broaden applications to more comfortable consumer brain-computer interfaces.
A: The honeycomb architecture provides high structural efficiency—good strength with minimal material—so the electrode remains robust enough to handle while staying flexible enough to conform into deep sulci without breaking.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full.
- Additional context was provided by staff editors.
About this neurotech research news
Author: Ty Tkacik
Source: Penn State
Contact: Ty Tkacik – Penn State
Image: Photo credit to Tao Zhou
Original Research: Open access. “3D-Printable, Honeycomb-Inspired Tissue-Like Bioelectrodes for Patient-Specific Neural Interface” by Marzia Momin, Luyi Feng, Xiaoai Chen, Salahuddin Ahmed, Basma AlMahmood, Li-Pang Huang, Jiashu Ren, Xinyi Wang, Hyunjin Lee, Samuel R. Cramer, Nanyin Zhang, Sulin Zhang, Tao Zhou. Advanced Materials. DOI: 10.1002/adma.202516291
Abstract
3D-Printable, Honeycomb-Inspired Tissue-Like Bioelectrodes for Patient-Specific Neural Interface
The brain’s individual gyral patterns call for patient-specific neural interfaces to ensure accurate neuromodulation, minimize adverse tissue responses, and maximize therapeutic safety and effectiveness. Conventional rigid electrocorticography electrodes, made through lithographic mass production, lack the conformability needed for heterogeneous cortical topographies, producing poor contact, signal loss, and foreign-body reactions.
To overcome these limitations, the authors present an integrated platform that combines MRI-based anatomical mapping, finite element analysis (FEA) for optimized mechanical design, and direct ink writing (DIW) 3D printing to fabricate electrodes customized to each patient’s gyral patterns. The honeycomb-inspired printable gel electrode (HiPGE) uses ultra-soft hydrogels and a bioinspired architecture engineered to match brain bending stiffness while remaining cost-efficient and durable. This mechanical match enables exceptional cortical conformability and adaptive interfacing, creating a scalable framework for improved neural interface engineering in neuromodulation and neuroprosthetic applications.