Visual Nervous System Research Articles

CdSe quantum dots photoelectric memristors for simulating biological visual system behavior

The visual system is the most important in the biological neurosensory system, so simulation of the biological visual system is the key for developing artificial nervous system. This work incorporates a CdSe quantum dot layer into silk fibroin based memristor device, endowing the devices with light-responsive capability. Furthermore, interesting phenomena were observed when the device operated as artificial synapses: excessive voltage stimulation led to a reduction in synaptic weight compared to the responses observed under normal electrical stimulation. This behavior mirrors sensations associated with pain, neuroprotection, and potential injuries to the neural system. At the end, we designed a visual perception system, simulating processes of the biological light intensity perception and the visual degradation response under intense light stimulation. Our research demonstrates the feasibility of constructing an artificial visual nervous system using a hardware system based on memristors.

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation.

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. Neural correlates of elicited perceptions could be used for fast automatic optimization, with electroencephalography as a natural choice as it can be acquired non-invasively. Nonetheless, its low signal-to-noise ratio may hinder discrimination of similar visual patterns, preventing its use in the optimization of electrical stimulation. Our work investigates for the first time the discriminability of the electroencephalographic responses to visual stimuli compatible with electrical stimulation, employing a newly acquired dataset whose stimuli encompass the concurrent variation of several features, while neuroscience research tends to study the neural correlates of single visual features. We then performed above-chance single-trial decoding of multiple features of our newly crafted visual stimuli using relatively simple machine learning algorithms. A decoding scheme employing the information from multiple stimulus presentations was implemented, substantially improving our decoding performance, suggesting that such methods should be used systematically in future applications. The significance of the present work relies in the determination of which visual features can be decoded from electroencephalographic responses to electrical stimulation-compatible stimuli and at which granularity they can be discriminated. Our methods pave the way to using electroencephalographic correlates to optimize electrical stimulation parameters, thus increasing the effectiveness of current visual neuroprostheses.

Computer Vision and Deep Learning Transforming Image Recognition and Beyond

Computer vision is a cutting-edge information processing technology that seeks to mimic the human visual nervous system. Its primary aim is to emulate the psychological processes of human vision to interpret and depict objective scenery. This revolutionary field encompasses a wide range of applications, including life sciences, medical diagnosis, military operations, scientific research, and many others. At the heart of computer vision lies the theoretical core, which includes deep learning, image recognition, target detection, and target tracking These elements combine to enable computers to process, analyze, and understand images, allowing for the classification of objects based on various patterns One of the standout advantages of deep learning techniques, when compared to traditional methods, is their ability to automatically learn and adapt to the specific features required for a given problem. This adaptive nature of deep learning networks has opened up new possibilities and paved the way for remarkable breakthroughs in the field of computer vision. This paper examines the practical application of computer vision processing technology and convolutional neural networks (CNNs) and elucidates the advancements in artificial intelligence within the field of computer vision image recognition. It does so by showcasing the tangible benefits and functionalities of these technologies.

An Artificial Visual System for Three Dimensional Motion Direction Detection

For mammals, enormous amounts of visual information are processed by neurons of the visual nervous system. The research of the direction selectivity is of great significance and local direction-selective ganglion neurons have been discovered. However, research is still at the one dimensional level and concentrated on a single cell. It remains challenging to explain the function and mechanism of the overall motion direction detection. In our previous papers, we have proposed a motion direction detection mechanism on the two dimensional level to solve these problems. The previous studies did not take into account that the information in the left and right retina is different and cannot be used to detect the three dimensional motion direction. Further effort is required to develop a more realistic system in three dimensions. In this paper, we propose a new three-dimensional artificial visual system to extend motion direction detection mechanism into three dimensions. We assumed that a neuron could detect the local motion of a single voxel object within three dimensional space. We also took into consideration that the information of the left and right retinas is different. Based on this binocular disparity, a realistic motion direction mechanism for three dimensions was established: the neurons received signals from the primary visual cortex of each eye and responded to motion in specific directions. There are a series of local direction-selective ganglion neurons arrayed on the retina by a logical AND operation. The response of each local direction detection neuron will be further integrated by the next neural layer to obtain the global motion direction. We carry out several computer simulations to demonstrate the validity of the mechanism. It shows that the proposed mechanism is capable of detecting the motion of complex three dimensional objects, which is consistent with most known physiological experimental results.

Motion detection and direction recognition in a photonic spiking neural network consisting of VCSELs-SA.

Motion detection and direction recognition are two important fundamental visual functions among the many cognitive functions performed by the human visual system. The retina and visual cortex are indispensable for composing the visual nervous system. The retina is responsible for transmitting electrical signals converted from light signals to the visual cortex of the brain. We propose a photonic spiking neural network (SNN) based on vertical-cavity surface-emitting lasers with an embedding saturable absorber (VCSELs-SA) with temporal integration effects, and demonstrate that the motion detection and direction recognition tasks can be solved by mimicking the visual nervous system. Simulation results reveal that the proposed photonic SNN with a modified supervised algorithm combining the tempotron and the STDP rule can correctly detect the motion and recognize the direction angles, and is robust to time jitter and the current difference between VCSEL-SAs. The proposed approach adopts a low-power photonic neuromorphic system for real-time information processing, which provides theoretical support for the large-scale application of hardware photonic SNN in the future.

A Novel Image Edge Detection Method Based on the Asymmetric STDP Mechanism of the Visual Path

The detection of image edges plays an important role for image processing. In view of the fact that these existing methods cannot effectively detect the edge of the image when facing the image with rich details. This paper proposes a novel method of asymmetric spike-timing-dependent plasticity (STDP) image edge detection based on the visual physiological mechanism. In the proposed method, the original image is preprocessed by the Gabor filter to simulate the visual physiological orientation characteristics to obtain the image information in different directions, and the orientation feature fusion is used to reconstruct the primary edge feature information of the image. Then, based on the mechanism of the visual nervous system, a neuron network composed of dynamic synapses based on the asymmetric STDP mechanism is constructed to further process it to obtain impulse response images. In order to eliminate disturbance of the neuron’s system noise on the impulse response image, the impulse response image is filtered by a Gaussian filter. Then, the lateral inhibition between neurons is applied to refine the filtered image edges. Finally, the result is normalized, and the final edge of the experimental image is obtained. Experimental results based on the colony image data set collected in the laboratory indicate that the proposed method achieved better performance than these state-of-the-art methods; meanwhile, the AUC value remains above 0.6.

A novel motion direction detection mechanism based on dendritic computation of direction-selective ganglion cells

The visual system plays a vital role when the brain receives and processes information. Approximately ninety percent of the information received by the brain comes from the visual system, and motion detection is a crucial part of processing visual information. To further understand the generation of direction selectivity, we propose a novel apparent motion detection mechanism using direction-selective ganglion cells (DSGCs). Considering the simplicity of neural computation, each neuron is responsible for detection in a specific direction. For example, eight neurons are employed to detect movements in eight directions, and local information is collected by scanning. The global motion direction is obtained according to the degree of activation of the neurons. We report that this method not only has striking biological similarities with hypercomplex retinal cells, but can also make accurate discriminations. The pioneering mechanism may lead to a new technique for understanding more complex principles of the visual nervous system.

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Visual depth perception is the basic function of the visual nervous system. To a stimulus in the stereo space, the visual nervous system could generate a perception about depth of its position. Experimental observations have demonstrated that interocular blur differences could lead to illusory perceptions about depths of moving stimuli. However, to stimuli with interocular blur differences, influences of different factors on illusory depth perceptions are still unclear. To explore these influences, this paper constructs a plastic two-layer k-winner-take-all (k-WTA) spiking network, simulating primary visual cortical responses. With incompatible stimuli presented into two eyes in experiments, binocular rivalry could occur in the primary visual cortex and interact with depth perception. To simulate binocular rivalry, the network consists of two parallel visual channels driven by left-eye and right-eye stimuli and competing with each other through mutual inhibition. In simulations, the horizontally moving stimulus is filtered with different Gaussian filters to generate paired monocular stimuli with interocular blur differences. The blurry strength, the moving direction and the moving speed perform as varying factors of moving stimuli. The network updates its dynamics through probabilistic inference, reflecting impacts of each factor on both neural responses and binocular rivalry. The modified responses could simulate illusory depth perceptions of stimuli as observed in experiments. To stimuli with interocular blur differences, varying factors could modify binocular rivalry in the network, inducing distinguishing illusory depth perceptions. Based on probabilistic inference, our model could provide possible explanations to illusory depth perceptions with interocular blur differences.

Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array (Adv. Mater. 51/2021)

Optoelectronic Device Arrays In article number 2105485, Tae-Yeon Seong and co-workers experimentally demonstrate dynamic photoadaptation behavior of an integrated optoelectronic device array that mimics the functionality of the biological visual nervous system. The device array, which is designed to adapt to repeated optical stimuli that change according to external conditions, exhibits excellent performance. This autonomic response to stimuli is essential to the improvement of next-generation bionic electronics.

A visual-degradation-inspired model with HSV color-encoding for contour detection

BackgroundGiven energy metabolism, visual information degradation plays an essential role in the retina- lateral geniculate nucleus (LGN)-primary visual cortex (V1)-secondary visual cortex (V2) pathway, and is a pivotal issue for visual information processing. Degradation helps the visual nervous system conserve brain energy and efficiently perceive the real world even though a small fraction of visual information reaches the early visual areas. The coding of contour features (edge and corner) is achieved in the retina-LGN-V1-V2 pathway. Based on the above, we proposed a contour detection model based on degradation (CDMD). New methodInspired by pupillary light reflex regulation, we took into consideration the novel approach of the hue-saturation-value (HSV) module for color encoding to meet the subtle chromaticity change rather than using the traditional red-green-blue (RGB) module, following the mechanisms of dark (DA) and light (LA) adaptation processes in photoreceptors. Meanwhile, the degradation mechanism was introduced as a novel strategy focusing only on the essential information to detect contour features, mimicking contour detection by visual perception under the restriction of axons in each optic nerve biologically. Ultimately, we employed the feedback mechanism achieving the optimal HSV value for each pixel of the experimental datasets. ResultsWe used the publicly available Berkeley Segmentation Data Set 500 (BSDS500) to assess the effectiveness of our CDMD model, introduced the F-measure to evaluate the results. The F-measure score was 0.65, achieved by our model. Moreover, CDMD with HSV has a better sensitivity for subtle chromaticity changes than CDMD with RGB. Comparison with existing methodsExperimental results demonstrated that our CDMD model, which functions close to the real visual system, achieved a more competitive performance with low computational cost than some state-of-the-art non-deep-learning and biologically inspired models. Compared with deep-learning-based algorithms, our model contains fewer parameters and computation time, does not require additional visual features, as well as an extra training process. ConclusionsOur proposed CDMD model is a novel approach for contour detection, which mimics the cognitive function of contour detection in early visual areas, and realizes a competitive performance in image processing. It contributes to bridging the gap between the biological visual system and computer vision.

Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array.

Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real-time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.

Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors.

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

A new discovery on visual information dynamic changes from V1 to V2: corner encoding

The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, and only a small fraction of visual information reaches the primary visual cortex (V1). This, nevertheless, does not affect our visual perception. Furthermore, how neurons in the secondary visual cortex (V2) encode such a small amount of visual information has yet to be addressed. To this end, the current paper established a visual network model for retina-lateral geniculate nucleus (LGN)-V1–V2 and quantitatively accounted for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrated that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode “corner” information, due to the effects of synaptic plasticity, but the similar function does not exist in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the “contour” feature (edge and corner) is achieved in the pathway of retina-LGN-V1–V2.

MIGRATION OF MELAMINE AND FORMALDEHYDE FROM KIDS' BAMBOO DINNER SETS

In recent years, products resembling the composition and / or appearance of plastic materials have been available on the market, which are actually made of a material containing various fillers and additives, such as melamine-formaldehyde resin, bamboo flour and other substances. They are advertised as natural bamboo, environmentally friendly and safe. The migration of melamine and formaldehyde from melamine-formaldehyde resin to food is possible. Melamine and formaldehyde are authorized substances in the EU for use in food contact materials under Regulation (EU) 10/2011. In 2019, melamine was classified as a possible human carcinogen by the IARC. It is toxic and increases the risk of kidney cancer. In 2012, formaldehyde was classified as carcinogenic to humans by the IARC. It could damage the central nervous system and especially the upper colic of the visual nervous system. The purpose of this study is to determine whether there is a migration of melamine and formaldehyde from kids' bamboo dinner sets offered on the Bulgarian market and thus to prove the presence of melamine-formaldehyde resin in their composition. A total of 24 samples - 6 bamboo sets, consisting of 4 items - bowls, cups, plates and utensils (fork and spoon) were analyzed. Standardized methods HPLC-UV DD CEN/TS 13130-27:2005 for melamine and spectrophotometric DD CEN/TS 13130-23:2005 for formaldehyde were used. Migration of melamine and formaldehyde from all tested products was found. Melamine migration exceeds the specific migration limit (SML) in 62% of products, and the highest value reaches 2.6 times SML. All results of formaldehyde are within the EU legislation permissible values. The material from which the bamboo products are made must be classified as plastic, as it contains melamine-formaldehyde resin. The results also show significant inhomogeneity of the composite material both within a batch and between batches.

Participation of L-Lactate and Its Receptor HCAR1/GPR81 in Neurovisual Development.

During the development of the retina and the nervous system, high levels of energy are required by the axons of retinal ganglion cells (RGCs) to grow towards their brain targets. This energy demand leads to an increase of glycolysis and L-lactate concentrations in the retina. L-lactate is known to be the endogenous ligand of the GPR81 receptor. However, the role of L-lactate and its receptor in the development of the nervous system has not been studied in depth. In the present study, we used immunohistochemistry to show that GPR81 is localized in different retinal layers during development, but is predominantly expressed in the RGC of the adult rodent. Treatment of retinal explants with L-lactate or the exogenous GPR81 agonist 3,5-DHBA altered RGC growth cone (GC) morphology (increasing in size and number of filopodia) and promoted RGC axon growth. These GPR81-mediated modifications of GC morphology and axon growth were mediated by protein kinases A and C, but were absent in explants from gpr81−/− transgenic mice. Living gpr81−/− mice showed a decrease in ipsilateral projections of RGCs to the dorsal lateral geniculate nucleus (dLGN). In conclusion, present results suggest that L-lactate and its receptor GPR81 play an important role in the development of the visual nervous system.

A model of feedforward, global, and lateral inhibition in the locust visual system predicts responses to looming stimuli.

Detection of looming obstacles is a vital task for both natural and artificial systems. Locusts possess a visual nervous system with an extensively studied obstacle detection pathway, culminating in the lobula giant movement detector (LGMD) neuron. While numerous models of this system exist, none to date have incorporated recent data on the anatomy and function of feedforward and global inhibitory systems in the input network of the LGMD. Moreover, the possibility that global and lateral inhibition shape the feedforward inhibitory signals to the LGMD has not been investigated. To address these points, a novel model of feedforward inhibitory neurons in the locust optic lobe was developed based on the recent literature. This model also incorporated global and lateral inhibition into the afferent network of these neurons, based on their observed behaviour in existing data and the posited role of these mechanisms in the inputs to the LGMD. Tests with the model showed that it accurately replicates the behaviour of feedforward inhibitory neurons in locusts; the model accurately coded for stimulus angular size in an overall linear fashion, with decreasing response saturation and increasing linearity as stimulus size increased or approach velocity decreased. The model also exhibited only phasic responses to the appearance of a grating, along with sustained movement by it at constant speed. By observing the effects of altering inhibition schemes on these responses, it was determined that global inhibition serves primarily to normalize growing excitation as collision approaches, and keeps coding for subtense angle linear. Lateral inhibition was determined to suppress tonic responses to wide-field stimuli translating at constant speed. Based on these features being shared with characterizations of the LGMD input network, it was hypothesized that the feedforward inhibitory neurons and the LGMD share the same excitatory afferents; this necessitates further investigation.

Correlation between the visual evoked potential and subjective perception at different illumination levels based on entropy analysis

The indoor light environment significantly affects the subjective feeling and work experience of personnel. The subjective feeling and pattern reversal visual evoked potential (PRVEP) of indoor personnel under different illuminance levels were tested in the field, and the correlation between the subjective feeling and objective physiological parameters was explored using fuzzy entropy analysis. The fuzzy entropy of the PRVEP and subjective voting values (stimulation and eye fatigue) under different illuminance levels first decreased and then increased with an increase in illumination. The fuzzy entropy of the PRVEP and subjective voting values were lowest at 700 lx, and the human visual nervous system was the least stimulated, with good visual comfort. There was a strong correlation between the fuzzy entropy of PRVEP and subjective voting. The communication between the environment and human body, subjective feeling, and objective response was determined by solving the mathematical relationship between the fuzzy entropy of PRVEP and subjective voting. A method of evaluating the subjective psychological changes in the human body using objective physiological parameters was obtained, and combined with subjective and objective indicators, a recommended illumination comfort zone was proposed.

A real-time binocular disparity detection system inspired by the spatio-temporal properties of the visual nervous system

A real-time binocular disparity detection system inspired by the spatio-temporal properties of the visual nervous system

Neural coupling mechanism in fMRI hemodynamics

Neural activity alters with the changes in cerebral blood flow (CBF) and blood oxygen saturation. Despite that these changes can be detected with functional magnetic resonance imaging (fMRI), the underlying physiological mechanism remains obscure. Upon activation of the specific brain region, CBF increases substantially, albeit with 6–8 s delay. Neuroscience has no scientific explanation for this experimental discovery yet. This study proposed a physiological mechanism for generating hemodynamic phenomena from the perspective of energy metabolism. The ratio of reduction (NADH) and oxidation states (NAD+) of nicotinamide adenine dinucleotide in cell was considered as the variable for CBF regulation. After the specific brain region was activated, brain glycogen was rapidly consumed as reserve energy, resulting in no significant change in the ratio of NADH and NAD+ concentrations. However, when the stored energy in the cell is exhausted, the dynamic equilibrium state of the transition between NADH and NAD + is changed, and the ratio of NADH and NAD+ concentrations is significantly increased, which regulates the blood flow to be greatly increased. Based on this physiological mechanism, this paper builds a large-scale visual nervous system network based on the Wang–Zhang neuron model, and quantitatively reproduced the hemodynamics observed in fMRI by computer numerical simulation. The results demonstrated that the negative energy mechanism, which was previously reported by our group using Wang–Zhang neuronal model, played a vital role in governing brain hemodynamics. Also, it precisely predicted the neural coupling mechanism between the energy metabolism and blood flow changes in the brain under stimulation. In nature, this mechanism is determined by imbalance and mismatch between the positive and negative energy during the spike of neuronal action potentials. A quantitative analysis was adopted to elucidate the physiological mechanism underlying this phenomenon, which would provide an insight into the principle of brain operation and the neural model of the overall brain function.

Features of the frequency of angio-, retino- and neuropathy, general clinical and anthropometric parameters in patients with type 1 diabetes with different levels of albumin in the urine depending on the level of cystatin C

Cystatin C improves the risk stratification among people with diabetic nephropathy, the risk of mortality, cardiovascular disease, disorders of the visual organ and nervous system in the preclinical and early stages. The aim of the study was to study the frequency of angio-, retino- and neuropathy, as well as differences in general clinical and anthropometric parameters in patients with type 1 diabetes (T1D) with different levels of albumin in the urine depending on cystatin C. The sample was 78 men and 62 women aged 22-26 years, patients with T1D, who were hospitalized in the therapeutic department №1 and №2 of the Vinnytsia Regional Highly Specialized Endocrinology Center. The control group consisted of 8 healthy men and 13 healthy women of the same age. The level of microalbuminuria and cystatin C was determined for all patients by enzyme-linked immunosorbent assay. The frequency of angio-, retino- and neuropathy, general clinical (systolic, diastolic, pulse) and anthropometric (height, weight, body surface area, waist circumference, body mass index) was assessed indicators. Statistical processing of the obtained results was performed in the license package “Statistica 5.5”, using non-parametric evaluation methods. It was found that in the group of men with cystatin C<0.9, and in women with cystatin C>0.9 with increasing albuminuria, the percentage of patients with more severe microvascular complications of T1D. In patients with diabetes mellitus 1 compared with the control group systolic blood pressure is significantly higher and increases with increasing albumin levels in the urine (with cystatin C<0.9 – in men with normo-, microalbuminuria and proteinuria by 7.14%, 8.1% and 10.8%; in women with normo-, microalbuminuria by 7.5% and 10.0%, with cystatin C>0.9 – in men with normo-, microalbuminuria by 4.9% and 7.2%, in women with proteinuria by 19.5%). Similar changes were found for diastolic blood pressure (with cystatin C<0.9 – in men with proteinuria by 13.0%; in women with normo-, microalbuminuria by 11.4% and 13.4%; with cystatin C>0.9 – in men with microalbuminuria by 9.0%; in women with normo- and proteinuria by 9.5% and 21.5%) and heart rate (with cystatin C<0.9 – in men with microalbuminuria and proteinuria by 18.4% and 12.6%, in women with microalbuminuria by 9.13%; with cystatin C>0.9 – in men with microalbuminuria by 12.0%, in women with normo-, microalbuminuria and proteinuria by 10.1%, 16.3% and 25.3%). In patients with T1D compared to the control group, the length of the body is significantly smaller and decreases with increasing levels of albumin in the urine (with cystatin C<0.9 – in men with normo-, microalbuminuria and proteinuria by 3.6%, 6.7% and 9.0%; women with microalbuminuria by 1.9%, with cystatin C>0.9 – in men with normo-, microalbuminuria by 5.2% and 7.3%, in women with normoalbuminuria and proteinuria by 2.6% and 4.3%). Similar changes were found in men for body weight (with cystatin C<0.9 – with microalbuminuria and proteinuria by 13.6% and 30.1%; with cystatin C>0.9 – with normo- and microalbuminuria by 10.2% and 25.4%) and body surface area (with cystatin C<0.9 – with normo-, microalbuminuria and proteinuria by 5.8%, 10.8% and 18.9%; with cystatin C>0.9 – with normo- and microalbuminuria by 8.2% and 16.2%). The size of the waist circumference in patients with T1D with normoalbuminuria is significantly higher, and in patients of other groups significantly less than in healthy subjects (with cystatin C<0.9 – with normo-, microalbuminuria and proteinuria by 3.8%, 1.2% and 5.2%; cystatin C>0.9 – with microalbuminuria by 5.1%). Compared to healthy women, the waist circumference was significantly higher in sick women (with cystatin C<0.9 – with normo- and microalbuminuria by 11.2% and 10.7%; with cystatin C>0.9 – with normo- and proteinuria by 9.7% and 6.0%). In patients with T1D men with proteinuria compared with the control group, the value of the body mass index was significantly lower by 9.7% (cystatin C<0.9). The value of the body mass index was significantly higher in patients with normoalbuminuria and microalbuminuria – by 11.8% and 17.7% (cystatin C<0.9), respectively, and in patients with proteinuria by 7.2% (cystatin C>0.9) compared with healthy women. Thus, between healthy and patients with T1D with varying degrees of albuminuria, differences in general clinical and anthropometric parameters were found, and they are greater the higher the level of cystatin C.