A Materials Roadmap to Functional Neural Interface Design.

Adv Funct Mater

Department of Bioengineering, Center for the Basis of Neural Cognition, McGowan Institute of Regenerative Medicine, NeuroTech Center, University of Pittsburgh Brain Institute, Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, 208 Center for Biotechnology, 300 Technol

Published: March 2018


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Advancement in neurotechnologies for electrophysiology, neurochemical sensing, neuromodulation, and optogenetics are revolutionizing scientific understanding of the brain while enabling treatments, cures, and preventative measures for a variety of neurological disorders. The grand challenge in neural interface engineering is to seamlessly integrate the interface between neurobiology and engineered technology, to record from and modulate neurons over chronic timescales. However, the biological inflammatory response to implants, neural degeneration, and long-term material stability diminish the quality of interface overtime. Recent advances in functional materials have been aimed at engineering solutions for chronic neural interfaces. Yet, the development and deployment of neural interfaces designed from novel materials have introduced new challenges that have largely avoided being addressed. Many engineering efforts that solely focus on optimizing individual probe design parameters, such as softness or flexibility, downplay critical multi-dimensional interactions between different physical properties of the device that contribute to overall performance and biocompatibility. Moreover, the use of these new materials present substantial new difficulties that must be addressed before regulatory approval for use in human patients will be achievable. In this review, the interdependence of different electrode components are highlighted to demonstrate the current materials-based challenges facing the field of neural interface engineering.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963731PMC
http://dx.doi.org/10.1002/adfm.201701269DOI Listing

Publication Analysis

Top Keywords

neural interface
12
interface engineering
8
neural interfaces
8
neural
6
interface
5
materials
4
materials roadmap
4
roadmap functional
4
functional neural
4
interface design
4

Similar Publications

Single camera estimation of microswimmer depth with a convolutional network.

J R Soc Interface

September 2025

Institute of Intelligent Systems and Robotics, Sorbonne Université, Paris, Île-de-France, France.

A number of techniques have been developed to measure the three-dimensional trajectories of protists, which require special experimental set-ups, such as a pair of orthogonal cameras. On the other hand, machine learning techniques have been used to estimate the vertical position of spherical particles from the defocus pattern, but they require the acquisition of a labelled dataset with finely spaced vertical positions. Here, we describe a simple way to make a dataset of images labelled with vertical position from a single 5 min movie, based on a tilted slide set-up.

View Article and Find Full Text PDF

Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.

Biomed Phys Eng Express

September 2025

electrical engineering department, Indian Institute of Technology Roorkee, Research wing, electrical department, Roorkee, uttrakhand, 247664, INDIA.

Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments.

View Article and Find Full Text PDF

Magnetic Implantable Devices and Materials for the Brain.

Small Methods

September 2025

Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, 100871, China.

Understanding the brain's complexity and developing treatments for its disorders necessitates advanced neural technologies. Magnetic fields can deeply penetrate biological tissues-including bone and air-without significant attenuation, offering a compelling approach for wireless, bidirectional neural interfacing. This review explores the rapidly advancing field of magnetic implantable devices and materials designed for modulation and sensing of the brain.

View Article and Find Full Text PDF

Deep learning-augmented inductively coupled plasma atomic emission spectrometry for multivariate authentication of green tea origin and grades.

Food Res Int

November 2025

Institute of Eco-Environmental Forensics, School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China; Key Laboratory of Colloid and Interface Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China.

The origin and grade of green tea significantly influence its market value, yet their concurrent authentication remains challenging. Here, we developed a robust method combining multiple metallic elements and back propagation neural network (BPNN) to identify the origin and grade of tea simultaneously. This strategy utilizes inductively coupled plasma atomic emission spectrometry (ICP-AES) to analyze the content of multiple elements in green tea (e.

View Article and Find Full Text PDF

Hexaazaisowurtzitane (CL-20) is a high-energy-density compound with poor thermal stability, which hinders its application in composite energetic systems. A bi-interface structure of polydopamine-coated graphene oxide (GO@PDA) is shown to markedly improve thermal stability compared with pristine CL-20 and single-layer coatings. Reactive molecular dynamics simulations enhanced by a neural network potential (NNP) reveal that the delayed onset of decomposition arises from suppressed NO release and altered spatial density distribution, while interfacial -OH and -COOH groups consume intermediates, redirect decomposition pathways, and inhibit autocatalytic chain reactions.

View Article and Find Full Text PDF