Multifunctional Neural Interfaces for Closed-Loop Control of Neural Activity

Microfabrication and nanotechnology have significantly expanded the technological capabilities for monitoring and modulating neural activity with the goal of studying the nervous system and managing neurological disorders. This feature article initially provides a tutorial‐like review of the prominent technologies for enabling this two‐way communication with the nervous system via electrical, chemical, and optical means. Following this overview, the article discusses emerging high‐throughput methods for identifying device attributes that enhance the functionality of interfaces. The discussion then extends into opportunities and challenges in integrating different device functions within a small footprint with the goal of closed‐loop control of neural activity with high spatiotemporal resolution and reduced adverse tissue response. The article concludes with an outline of future directions in the development and applications of multifunctional neural interfaces

Improving interaction and perception of brain structure using fiber clustering

Diffusion tensor imaging (DTI) is an emerging technique in magnetic resonance imaging. Recently, it has been the object of increased interest in neuroscience applications seeking to image brain fiber tracts. Examples are the identification of major white matter tracts in the human brain afflicted by a specific pathology or those particularly at risk for a given surgical approach.\u3cbr/\u3e\u3cbr/\u3eBased on DTI data, fiber tracking now enables the geometrical reconstruction of such tracts.1 However, when attempting to visualize individual fibers, cluttered images are often generated, which makes insights difficult to obtain. It is also necessary to identify different fiber structures with anatomical significance for quantification and comparison purposes.\u3cbr/\u3e\u3cbr/\u3

Improving perception of brain structure using fiber clustering

Diffusion tensor imaging (DTI) is an emerging technique in magnetic resonance imaging. Recently, it has been the object of increased interest in neuroscience applications seeking to image brain fiber tracts. Examples are the identification of major white matter tracts in the human brain afflicted by a specific pathology or those particularly at risk for a given surgical approach.\u3cbr/\u3e\u3cbr/\u3eBased on DTI data, fiber tracking now enables the geometrical reconstruction of such tracts.1 However, when attempting to visualize individual fibers, cluttered images are often generated, which makes insights difficult to obtain. It is also necessary to identify different fiber structures with anatomical significance for quantification and comparison purposes.\u3cbr/\u3e\u3cbr/\u3

A Minimally Invasive 64-Channel Wireless μeCoG Implant

Emerging applications in brain-machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that overcomes these difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex. The device is comprised of a highly flexible, high-density, polymer-based 64-channel electrode array and a flexible antenna, bonded to 2.4 mm × 2.4 mm CMOS integrated circuit (IC) that performs 64-channel acquisition, wireless power and data transmission. The IC digitizes the signal from each electrode at 1 kS/s with 1.2 μV input referred noise, and transmits the serialized data using a 1 Mb/s backscattering modulator. A dual-mode power-receiving rectifier reduces data-dependent supply ripple, enabling the integration of small decoupling capacitors on chip and eliminating the need for external components. Design techniques in the wireless and baseband circuits result in over 16× reduction in die area with a simultaneous 3× improvement in power efficiency over the state of the art. The IC consumes 225 μW and can be powered by an external reader transmitting 12 mW at 300 MHz, which is over 3× lower than IEEE and FCC regulations