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Sensors and Materials, Vol. 35, No. 9 (2023) 3227–3239
MYU Tokyo
S & M 3387

Miniaturized Neural Observation System
for in vivo Brain Imaging in Freely Moving Rats
Ronnakorn Siwadamrongpong,1† Yoshinori Sunaga,1† Kiyotaka Sasagawa,1*
Yasumi Ohta,1 Hironari Takehara,1 Makito Haruta,1,2
Hiroyuki Tashiro,1,3 and Jun Ohta1
1Division of Materials Science, Graduate School of Science and Technology,

Nara Institute of Science and Technology,
8916-5 Takayama, Ikoma, Nara 630-0192, Japan
2Department of Opto-Electronic System Engineering, Faculty of Science and Engineering,
Chitose Institute of Science and Technology,758-65 Bibi, Chitose, Hokkaido 066-8655, Japan
3Department of Health Sciences, Faculty of Medical Sciences, Kyushu University,
3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan(Received June 28, 2023; accepted September 11, 2023)

Keywords: CMOS image sensor, implantable device, brain surface imaging, in vivo imaging

In this study, we developed a miniaturized observation system for rat brain imaging using an
implantable imager. A small data-acquisition device was designed to be mounted on the back ofan adult rat, reducing the disturbance to the output of the imager caused by a long cable. With aweight of approximately 6.8 g, the movement of adult rats with an average weight of 200 g
was unrestricted. The readout characteristics were evaluated at the light level. The in vivo
experiments were performed using mice and freely moving rats. Using the developed data
analysis pipeline, blood f low in the vessels was observed.

1.

Introduction

The brain is a complex and important organ that requires further investigation. Brain studies
at the behavioral level have explored the relationship between actions and neuronal activities inanimal models resembling human cases. This information enhances the understanding andtreatment of neurological disorders. Among the observation techniques, intrinsic optical imaging
is employed to observe neural activities without genetic manipulation.(1,2)
Microscopy is a powerful tool for optical functional brain imaging that facilitates theobservation of blood vessels and cells.(3,4) However, microscopes are generally very large
and difficult to use under free-moving conditions. Technologies such as miniature
microscopes, fiber bundles, and multimode fiber endoscopy have been developed for invivo imaging under freely moving conditions.(5–9) Previously, as an alternative approach,
we developed an ultracompact imager using our original small image sensor.(10–27) This
technique reduces the invasiveness and limitation of behavior. With these advantages, we*Corresponding author: e-mail: sasagawa@ms.naist.jp†These authors contributed equally.

https://doi.org/10.18494/SAM4563

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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demonstrated deep brain imaging and simultaneously observed multiple regions of themouse brain.(17,22) Furthermore, fluorescence imaging can be performed using an emission
filter.(16,21) Multimodal measurement can be realized by combining an image sensor with anothermeasurement device or equipment, such as a neural amplifier or microdialysis system.(25,27)
To avoid heat generation and other issues, the brain-implantable imaging device has
minimal circuitry on the image sensor chip. Hence, the device output is an analog signaltransmitted to an analog conversion system through a long cable. This configuration
renders the system susceptible to external noise. In this study, we developed a prototype
miniaturized system wherein a data acquisition device could be mounted on the back of a
rat to reduce the disturbance caused by cables.

2.

Miniaturized Data Acquisition System for in vivo Imaging

For measuring brain activity, we fabricated an image sensor system that can be mounted on
the back of a rodent. This sensor is an implantable image sensor used for in vivo imaging and canbe applied for fluorescence observations in the deep brain and blood flow measurements on thebrain surface. A concept image of the system is shown in Fig. 1. In this study, we aim to achieve
excellent brain surface imaging performance with a high signal-to-noise ratio (SNR).
2.1

Implantable device with CMOS image sensor

The implantable device used for brain surface imaging is the same as that previously
reported. A small CMOS image sensor and six green μ-LEDs (ES-CEGHM12A, Epistar,
Taiwan) are mounted on a flexible polyimide printed circuit (FPC) board and arranged in series.
Figure 2(a) shows the device with μ-LEDs. The sensor is a custom-designed 120 × 268 pixel
CMOS image sensor fabricated with a 0.35 μm standard CMOS process. The image sensor andits specifications are presented in Fig. 2(b) and Table 1, respectively. The pixel size isFig. 1.

(Color online) Concept of miniaturized neural observation system.

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(a)
Fig. 2.

(b)(Color online) Photograph of (a) implantable device and (b) custom-designed image sensor.

Table 1
Specifications of the image sensor.
CMOS technology
Operating voltage (V)Chip size (mm 2)
Pixel type
Pixel size (μm 2)
Pixel number
Photodiode
Fill factor (%)

0.35 μm 2-poly 4-metal standard CMOS
3.3
1.05 × 2.90
three-transistor active pixel sensor
7.5 × 7.5
120 × 268
N-well/P-substrate44

7.5 × 7.5 μm2, which corresponds to the imaging area of 900 × 2010 μm2. The wavelength of
μ-LEDs is 535 nm, which is highly absorbable by hemoglobin in red blood cells. The assembled
device is coated with parylene-C to ensure waterproofing and biocompatibility. The lightweightcable connects the FPC to the data acquisition device and an external current source for μ-LEDs.
2.2

Data acquisition device for image sensor

The data acquisition device is designed to operate a custom-designed image sensor and tocommunicate with a computer through a USB. The device is shown in Fig. 3(a). It is designed asa microcontroller-based device with a size of 3.0 × 4.5 cm2 and a weight of approximately 6.8 g.
A block diagram of the device is presented in Fig. 3(b). Because a typical Sprague-Dawley rat
weighs approximately 200 g, wearing this device is not expected to significantly interfere withits behavior. An ARM Cortex-M7-based microcontroller (μcontroller) (ATSAMS70N20,
Microchip, USA) is used, providing a USB high-speed, a timer counter, and flexible digital andanalog functions suitable for an expandable system. An external 12-bit pipeline analog-to-digital3230

Sensors and Materials, Vol. 35, No. 9 (2023)(a)Fig. 3.

(b)(Color online) (a) Image of the data acquisition device and (b) block diagram of the imaging system.

converter (ADC) (AD9235-40, Analog Device, USA) is used to achieve an average conversion
speed greater than 2 Msps, which is required for brain surface imaging at 60 fps using the target
image sensor.
The microcontroller firmware is designed to support image sensor operations and data
management for communication. A specific header is assigned to the converted signal from
ADC in unused bits and stored in the memory until data transfer. The amount of stored data issummarized and compared with the data transfer size after reading each row of pixels. This
operation allows a smaller microcontroller memory to support dynamic data size and overall
speed improvement with increasing variation in pixel exposure time. Finally, the pixel is read,and the device remains in the standby mode until the next frame. The interval between each row
or frame is controlled by the interrupt routine service. With the designed function, the device
achieves a peak conversion speed of 6.4 Msps and an average speed, including data transfer, of
3.4 Msps, corresponding to approximately 100 frames/s for the 120 × 268 pixel sensor.
2.3

Readout performance

For each light intensity, the readout performance of the data acquisition device with thecustom-designed image sensor is tested at a frame rate of 60 fps. To control the light intensity inthe experimental setup, a 530 nm light source (M530L4, Thorlabs, USA) and a current-controlled
LED driver (DC2200, Thorlabs, USA) are used. The light intensity at each controlled current ismeasured using a power meter (S130VC; Thorlabs, USA). Furthermore, the output signal andtemporal noise of the system are measured. Figure 4(a) shows the output signal as a function ofincident light intensity. The characteristics of the output signal size are approximately linear up
to a power of 6 × 10−6 W/cm2, at which the pixels of the image sensor saturate.
The temporal noise of an imaging system can be classified into light-independent and lightdependent components. The former is caused by the pixel reset and readout circuits, whereas thelatter is caused by photon shot noise. The total temporal noise σtot is given by
2
2
,
σ tot
=
σ 02 + σ PS

(1)

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(a)

(b)

Fig. 4. (a) Output signal and (b) temporal readout noise with light intensity at 60 fps. The wavelength of irradiatedlight is 535 nm.

where σ0 is light-independent noise and σPS is photon shot noise proportional to the square rootof the received light intensity I.

σ PS ∝ I(2)

Thus, the total noise is written as a function of I,
2
σ tot
=
σ 02 + k PS I ,(3)where kPS is the coefficient of light intensity dependence.
Figure 4(b) shows a plot of the temporal readout noise with light intensity at 60 fps and afitting line based on Eq. (3). From the fitting trend, the light-independent noise component iscalculated as 3.8 LSB. At a pixel saturation of 6 × 10−6 W/cm2, the total temporal noise isestimated to be 6.2 LSB. Compared with our previous system, these values were limited by theperformance of the proposed system and can be reduced by signal processing, as discussed in
Sects. 3.1 and 3.2. This system is applicable for biological imaging experiments with sufficient
light levels during observation.
SNR is calculated using the measurement results, as shown in Fig. 5. According to the theory,the slope of the curve should be 20 and 10 dB/decade in the light-independent and high-lightintensity regions, respectively, and the photon shot noise is dominant in the latter.(28) For the
proposed system, the photon-shot noise is dominant at sufficiently high light intensities.
Consequently, the peak SNR and full-well capacity of the sensor used herein are estimated to beapproximately 45 dB and 90 ke−, respectively.

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Fig. 5.

3.

SNR as a function of light intensity at 60 fps. The wavelength of irradiated light is 535 nm.

in vivo Imaging Demonstration

The prototype system is mounted on the brain surfaces of mice and rats for in vivo imaging
experiments. All animal experiments were performed in accordance with the protocols approvedby the Nara Institute of Science and Technology (NAIST).
3.1

Brain surface imaging in mouse

Compared with conventional systems, the experiments are initially conducted on adult mice
(FVB/NJcl; CLEA Japan, Inc., Japan). Owing to the mouse brain surface activity, the implanted
device used herein can measure blood flow velocity in blood vessels and the variation in blood
volume. In this experiment, the mice were held in a stereotaxic device to confirm imaging
performance. The skull is opened under urethane anesthesia and craniotomy is performed. The
imaging device was inserted without removing the dura mater. During the observation period,
LEDs were illuminated with a current of 0.9 mA and the frame rate was set to 92 fps. According
to the previous study, a blue μ-LED powered with a current of 0.5 mA increased the surrounding
brain temperature by around 0.5 °C over 1 h.(22) Operating the implantable device with theabove configuration is not expected to cause any significant effect on the observed brain region.
Figure 6(a) and 6(b) show the brain surface and image captured by the sensor. The brain
surface area imaged by the sensor is marked with a red rectangle. Furthermore, blood vessels on
the brain surface are clearly observed in the captured images. In brain surface measurements,
although the overall luminance remains constant, the blood flow information is included in theluminance variation along the blood vessel area. Therefore, the luminance difference from thereference image was obtained through image processing. Herein, the reference image is a frameaveraged image obtained throughout the measurement period. Figure 6(c) shows an example of adifferent image. Light and dark areas appear along the blood vessels owing to the variation inabsorbance caused by the movement of red blood cells in the blood vessels.
Compared with conventional systems, the data collected from brain surface imaging have ahigher noise level. Therefore, noise reduction is achieved through image processing. In Fig. 6(c),Sensors and Materials, Vol. 35, No. 9 (2023)

(a)

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(b)

(c)

Fig. 6. (Color online) (a) Photograph of the mouse brain surface with imaging area indicated by the red rectangle,(b) image captured using the image sensor, and (c) difference image from the reference image.

stripe-like noise can be observed in the horizontal direction. This horizontal offset is a result of
the blank time between lines varying slightly at each row owing to the microcontroller operation.
While measuring the brain function, because the luminance from each frame remained fairly
constant, the average pixel value for each row was calculated from 10 nearby frames and used tocorrect the pixel value of each row in the target frame. The resulting image is presented inFig. 7(a).
Time-domain fluctuations from the sensor and slight movements during the observation
appear in the movie data. The spectrum of each pixel is obtained using a fast Fourier transform(FFT), and the unwanted frequency components are removed.(14,23) From the collected data,high-frequency components at frequencies above 17.98 Hz are removed. Additionally, the lowfrequency components between 2.89 and 2.95 Hz are removed to reduce the heartbeat noise. The
images obtained after applying an inverse FFT are shown in Fig. 7(b). Furthermore, the bloodflow becomes discernible after image processing. The consecutive processed images are shownin Fig. 8.
3.2

Experiments with rats under freely moving conditions

The results obtained in the aforementioned experiments with mice are in agreement withthose obtained using conventional imaging devices; however, the data acquisition device in thissystem is not sufficiently small to facilitate the observation of free-running behavior in mice.
Figure 9(a) shows the device mounted on a rat subjected to craniotomy; the imaging device isplaced on the rat’s head and fixed with dental cement without removing the dura mater. The
brain surface is shown in Fig. 9(b).
Figure 9(c) shows an image obtained using the CMOS imaging system. A bright-field image
with features similar to those in the red rectangle in Fig. 9(b) is observed. No significant noise

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(a)

(b)

Fig. 7.
image.

(Color online) Images of each processing step: (a) horizontal-line-corrected image and (b) FFT-filteredFig. 8.
flow.

(Color online) Consecutive images from data processing. The red arrow indicates the direction of blood

due to disturbances caused by the rat’s movement is observed. The same image processing
procedure is performed on the obtained data. The horizontal and temporal noise reductions are
similar to those described previously. The signal is weaker in rats because of the thicker duramater. Therefore, a two-dimensional Fourier transform is applied to each frame to reduce thespatial high-frequency components.
The results of image processing are shown in Fig. 10. Although the signal is weak, blood flow
is barely observed by image processing. The thickness of the dura mater differed between miceand rats. In our previous study, we obtained clearer images by removing the dura mater andperforming measurements;(13) however, this complicates achieving stable measurements without
injuring the brain surface. Alternatively, it may be possible to use a transparent artificial duramater or apply techniques that increase the permeability of the dura mater.(29) Although theexperimental duration is not demonstrated in this work, our past studies showed that observation

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(a)

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(b)

(c)

Fig. 9. (Color online) Photographs of (a) rat with data-acquisition device for the freely moving condition and (b) rat
brain surface. The imaging area of the CMOS device is indicated by the red rectangle. (c) Image captured using theimage sensor.

Fig. 10. (Color online) Consecutive images from data processing. The arrow indicates the direction of blood flow.

can be performed even 10 h after device implantation; this can be extended to more than one
month by using a fiber-optic plate window.(13,20,24) It is expected that this system can achieve
this operational duration.

4.

Conclusions

In this study, we developed a neural observation system using an implantable imaging device
and a small microcontroller-based data acquisition device weighing approximately 6.8 g, which
is mountable on the back of an adult rat. During system evaluation, its readout characteristics
were investigated with light intensity at 60 fps. The noise level was slightly greater than thatof the conventional system owing to the large horizontal noise affecting it. Hence, under3236

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the conditions of functional brain imaging, the effect of the noise was reduced by image
processing. Furthermore, analog conversion was performed closer to the sensor.
Additionally, mounting the circuitry for signal processing on the observable object may
pave the way to further advancements in wireless communications in the future.

Acknowledgments
This work was supported by JSPS KAKENHI (Grant Numbers JP21H03809 and
JP23H05450) and the VLSI Design and Education Center (VDEC), University of Tokyo, in
collaboration with the Cadence Corporation and Mentor Graphics Corporation.

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About the Authors
Ronnakorn Siwadamrongpong received his B.E. degree in electricalengineering from Chulalongkorn University, Thailand, in 2018 and his M.E.
degree from the Nara Institute of Technology (NAIST), Japan. He is currently
pursuing his Ph.D. degree at Photonic Device Science Laboratory, NAIST. His
research interests include embedded systems, chemical sensors, andimplantable devices. (ronnakorn.siwadamrongpong.rh9@ms.naist.jp)

Yoshinori Sunaga received his B.E. degree from Chiba University, Japan, in2012 and M.E. and Ph.D. degrees from the Graduate School of Materials
Science, Nara Institute of Science and Technology (NAIST), Japan, in 2014
and 2017, respectively. In 2017, he joined NAIST as a postdoctoral fellow andbegan collaborative research between NAIST and the University of Houston.
In 2019, he joined the University of Houston as a postdoctoral fellow. In 2022,
he joined NAIST as a postdoctoral fellow. He is interested in developing novel
devices for observing and assessing brain activity in animals to reveal new
brain mechanisms. (sunaga.yoshinori@ms.naist.jp)
Kiyotaka Sasagawa received his B.S. degree from Kyoto University in 1999
and his M.E. and Ph.D. degrees in materials science from NAIST, Japan, in
2001 and 2004, respectively. Subsequently, he became a researcher at the
National Institute of Information and Communications Technology in Tokyo,
Japan. In 2008, he joined NAIST as an assistant professor and became an
associate professor in 2019. His research interests include bioimaging,biosensing, and electromagnetic field imaging. (sasagawa@ms.naist.jp)

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		Yasumi Ohta received her B.S. degree in physics from Nara Women’s
University in 1984. She enrolled in the Faculty of Science at Kyoto University
in 2004 and switched to a Master’s program at the Graduate School of Science,
Kyoto University, in 2006. She received her M.S. and Ph.D. degrees inbiophysics from Kyoto University, Kyoto, Japan, in 2008 and 2011,
respectively. From 1984 to 1986, she worked at Mitsubishi Electric Corp. in
Hyogo, Japan. In 2011, she joined the Nara Institute of Science and Technology(NAIST) in Nara, Japan, as a postdoctoral fellow. Her research interests
include implantable bioimaging CMOS sensors and neuroscience.
(ohtay@ms.naist.jp)

		Hironari Takehara received his ME degree in applied chemistry from Kansai
University, Osaka, Japan, in 1986, and his Ph.D. degree in materials sciencefrom the Nara Institute of Science and Technology (NAIST), Nara, Japan, in
2015. From 1986 to 2012, he was a semiconductor process engineer at
Panasonic Corporation, Kyoto, Japan. In 2015 and 2019, he joined NAIST as a
postdoctoral fellow and assistant professor, respectively. His current researchinterests involve CMOS image sensors and bioimaging.
(t-hironari@ms.naist.jp)

		Makito Haruta received his B.E. degree in bioscience and biotechnology
from Okayama University, Okayama, Japan, in 2009, and his M.S. degree inbiological science and Dr. Eng. degree in material science from Nara Instituteof Science and Technology (NAIST), Nara, Japan, in 2011 and 2014,
respectively. He was a postdoctoral fellow with NAIST from 2014 to 2016. He
joined the Institute for Research Initiatives, NAIST, in 2016, as a project
assistant professor. In 2019, he joined the Graduate School of Science andTechnology, NAIST, as an assistant professor. In 2023, he became an associate
professor at the Faculty of Science and Engineering, Chitose Institute of
Science and Technology, and since 2023, he has been a guest associate
professor at NAIST. His research interests include CMOS imaging and flexible
implantable devices for understanding biological functions.
(m-haruta@photon.chitose.ac.jp)

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3239

Hiroyuki Tashiro received his B.E. and M.E. degrees in electrical andelectronic engineering from Toyohashi University of Technology (TUT),
Aichi, Japan, in 1994 and 1996, respectively. He received his Ph.D. inengineering from Nara Institute of Science and Technology (NAIST), Nara,
Japan, in 2017. In 1998, he joined Nidek Co., Ltd., Aichi, Japan, where heworked on the research and development of ophthalmic surgical systems andretinal prostheses. In 2004, he became an assistant professor at the Faculty of
Medical Sciences, Kyushu University, Fukuoka, Japan and has been an
associate professor at NAIST since 2019. His research interests include
artificial vision and neural interfaces.
(tashiro.hiroyuki.289@m.kyushu-u.ac.jp)
Jun Ohta received his B.E., M.E., and Dr. Eng. degrees in applied physics
from The University of Tokyo, Japan, in 1981, 1983, and 1992, respectively. In
1983, he joined Mitsubishi Electric Corporation, Hyogo, Japan. From 1992 to
1993, he was a visiting scientist at the Optoelectronics Computing Systems
Center at the University of Colorado, Boulder. In 1998, he joined the Graduate
School of Materials Science, Nara Institute of Science and Technology(NAIST), Nara, Japan, as an associate professor, and was appointed a professor
in 2004. His current research interests include smart CMOS image sensors forbiomedical applications and retinal prosthetic devices. He is a Fellow of IEEE,
the Japan Society of Applied Physics, and the Institute of Image, Information,
and Television Engineers. (ohta@ms.naist.jp)