Study on the Recovery of Visual Perceptual Function Dysfunction in Patients With Diabetes
Phase: NA
Conditions: Diabetic Retinopathy (DR), Visual Perception
Sponsor: The First Affiliated Hospital of Anhui Medical University

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Essential Elements of the Protocol and Statistical Analysis Plan
Brief Title:
Study on the recovery of visual perceptual function dysfunction in patients with
diabetes
NCT number:
NCT ID not yet assigned
Unique Protocol Id:
PJ2022-09-48
Principal Investigator (PI) Information:
Lixia Feng
Department of Ophthalmology,
The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
E-Mail : lixiafeng@163.com;
Phone number :13075590585
Protocol Version Number and Date: Version1.0 ; August 10th, 2024

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Catalogue
1. Current Status of Clinical Trials and Research Objectives................................................................3
2. Type of Study Design.........................................................................................................................5
3.1 Inclusion Criteria:............................................................................................................................ 6
4. Expected Number of Cases Required................................................................................................ 6
5. Experimental Methods.......................................................................................................................6
6. Clinical Observation, Follow-up, and Measures to Ensure Subject Compliance..............................7
7. Criteria for Discontinuing Clinical Research and Regulations for Ending Clinical Research.......... 7
8. Data Management and Regulations for Data Traceability.................................................................8
9. Subject Coding, Random Number Table, and Procedures for Storing Case Report Forms.............. 8
10. Adverse Event Recording, Reporting, and Follow-up.....................................................................8
11. Statistical Analysis Plan...................................................................................................................8
12. Quality Control and Quality Assurance...........................................................................................9
13. Subject Recruitment and Informed Consent Process.......................................................................9
14. Expected Progress and Completion Date.........................................................................................9
15. Follow-up and Medical Measures After the Study.......................................................................... 9
16. Responsibilities of All Parties and Other Regulations.....................................................................9
17. References........................................................................................................................................9

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1. Current Status of Clinical Trials and Research Objectives
1.1 Research Status
1.1.1 Significance of Visual Perceptual Learning Training for Diabetic Retinopathy Patients
Diabetic retinopathy (DR) is one of the most common chronic complications of diabetes and a
leading cause of visual impairment in adults[1]. In China, the prevalence of DR among diabetic
patients is as high as 23%, with 3 ~ 4 million patients losing their vision annually due to DR[2-3],
severely impairing their visual function and quality. Traditional research has focused on diabetic
retinal vascular lesions, with vascular endothelial changes being the only clinically successful
therapeutic target for DR patients. Current clinical treatments primarily address retinal vascular
damage. However, we have observed that even after surgical treatment or retinal laser
photocoagulation, DR patients with stable conditions still experience significant visual dysfunction,
lacking further treatment options to improve visual function. This highlights the urgent need to
explore effective rehabilitation methods for visual dysfunction in DR patients after clinical treatment
stabilization.
1.1.2 Domestic and International Progress in Visual Perceptual Learning
Visual perceptual learning (VPL) training refers to a method of improving the visual system's
ability to perceive external visual information through repeated specific visual tasks. In 2014, Sabel
BA and Gudlin J[4-5] conducted a randomized clinical trial on "vision restoration training" for
glaucoma patients, demonstrating significant improvement in visual function in the training group
compared to the control group. Sabel BA proposed the "residual vision activation theory," suggesting
that in cases of retinal or visual cortex damage (e.g., glaucoma, stroke, amblyopia, age-related
macular degeneration), repeated stimulation can enhance electrical conduction and synchronization
among partially damaged neural synapses and downstream neural networks, leading to partial
recovery of visual function[6]. The visual system can actively adapt to and reflect changes in the
external environment. When synaptic strength and quantity change and persist over time, this process
is termed neuroplasticity. Its manifestations include induced dendritic growth or shortening, changes

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in
dendritic
density,
functional
adjustments,
and
subsequent
cortical
reorganization
and
neurovascular regeneration[7]. Studies have also shown that amblyopic patients can improve visual
function through occlusion therapy or training, and even adult amblyopia may benefit from repeated
perceptual learning[8]. Yan and Zhou J[9] conducted a randomized clinical trial on 23 myopic subjects,
training the non-dominant eye at the cutoff frequency. Post-training, subjects showed improved
contrast sensitivity and visual acuity at the trained spatial frequency, with effects lasting at least four
months. These results suggest that VPL may offer a non-invasive training method to compensate for
optical defects in mild to moderate myopia. Aimola L[10] demonstrated that perceptual learning could
partially improve visual perception in stroke patients, mitigating visual field defects caused by stroke.
Age-related visual decline is common, and VPL can effectively improve near reading ability in
individuals with early presbyopia symptoms[11]. These studies indicate that the visual system retains
plasticity across age groups, and VPL is the most widely used method to modify visual system
plasticity and promote visual function recovery.
1.1.3 Mechanisms of Perceptual Learning Training
Visual perception learning training refers to the method of improving the visual system's ability
to perceive external visual information by continuously performing specific visual training tasks [12].
The neural mechanism of visual perception learning has always been a research focus and difficulty
in this field, although a lot of exploration has been conducted from the aspects of neurophysics and
neurobiology, it is still very unclear. There are mainly four theories: (1) the plasticity theory of the
primary cortex [13] : perceptual learning can make the neural encoding of stimuli in the primary cortex
more accurate; (2) Reweighting Theory [14]: By readjusting the weights of stimulus information in
visual processing, the theory aims to enhance the efficiency of utilizing such information. (3)
Reverse Hierarchy Theory: Visual signals are expressed in a top-down manner by feedback from the
higher cortex to the primary cortex; (4) Dual plasticity theory [15]: Perceptual learning may trigger the
plasticity of the visual cortex towards stimulus attributes and task types. However, in fact, more
research has not integrated different models into a unified perspective, and several issues highly

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related to the core mechanism of VPL have become increasingly controversial. Scholars have trained
macaques on direction recognition tasks [16]. Compared with untrained macaques, the neurons in the
V4 region of the trained macaques showed stronger responses and improved direction recognition
ability after training. There are also studies using functional magnetic resonance imaging (fMRI) to
investigate brain area activation during contrast discrimination task training [17]. In the early stages of
training, there is strong activation in the higher visual cortex, while in the later stages, activity in the
lower visual cortex increases, indicating that visual perception learning training can activate
corresponding brain areas to improve visual function. However, the relationship between the lower
and higher visual cortices is complex and requires further research to prove.
The current clinical treatment methods are often difficult to make the visual function of patients
with diabetes retinopathy (DR) fully recover to the normal level, and lack of follow-up treatment
methods to further improve the visual function. How to find an effective way to restore the visual
function of DR patients after damage is an urgent problem to be solved. This study conducted online
remote visual perception learning training based on Internet cloud computing for DR patients who
were in stable condition after surgery, and comprehensively evaluated the changes of visual
perception function of DR patients before and after training by using perceptual function detection
combined with OCT and psychological scale. The purpose was to evaluate the improvement effect of
visual perception training on DR patients' visual function and influencing factors, and provide
guidance strategies for the evaluation, follow-up and rehabilitation treatment of visual function after
DR clinical treatment.
1.2 Research Objectives
1.2.1 To explore the improvement of visual perceptual function in DR patients through VPL training.
1.2.2 To provide guidance for visual function recovery in DR patients with visual impairment.
2. Type of Study Design
Prospective, randomized controlled clinical study.

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3. Inclusion and Exclusion Criteria for Subjects
3.1 Inclusion Criteria:
（1）Pre-treatment diagnosis of moderate-to-severe non-proliferative or proliferative DR, with the
affected eye having undergone pan-retinal photocoagulation at least 4 weeks prior or vitreoretinal
surgery at least 8 weeks prior, with stable conditions and no macular edema or recurrent retinal
hemorrhage.（2）Best-corrected ETDRS visual acuity (BCVA) ≥0.1.（3）Clear refractive media.
（4）Age ≤65 years, able to understand and cooperate with examinations and training.
3.2 Exclusion Criteria:
（1）Significant refractive media opacity or other ocular diseases affecting visual function (e.g.,
high myopia, glaucoma, strabismus, amblyopia, macular degeneration).（2）History of psychiatric
disorders affecting cognitive function, alcoholism, or traumatic brain injury. （3 ）Postoperative
ocular complications within 8 weeks prior to testing.
3.3 Elimination Criteria:
（1）Temporary refusal of examination or withdrawal from the study.（2）Incomplete examination
records.
4. Expected Number of Cases Required
Sample Size: 24 cases (estimated using G*Power analysis, increased to 36 to account for potential
dropout rates).
5. Experimental Methods
5.1 Grouping
Subjects are randomly assigned to the Visual Perceptual Learning Training (VPLT) group or the
Control Group (CG) using random number generation.
5.2 Study Design
Only the surgical eye is included (for bilateral surgery, the eye with better vision meeting inclusion

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criteria is selected). The VPLT group undergoes 40 minutes of daily monocular VPL training for 3
months, with follow-ups at 1 and 3 months. The CG receives no training and is followed up after 3
months.
5.3 Assessments:
Non-contact intraocular pressure measurement.
Ultra-widefield laser scanning fundus examination to exclude glaucoma and new hemorrhages.
Refractive correction for BCVA.
Optical coherence tomography (OCT) and OCT angiography (OCTA) for retinal blood flow and
structural parameters.
Visual perceptual function assessment using a Matlab R2016a-based program, including:
Grating acuity (GA) at 0.8 contrast.
First-order motion stimulus perception sensitivity (1stM) at 2.0 c/d spatial frequency.
Second-order contrast stimulus perception sensitivity (2ndS) at 1.0 c/d spatial frequency.
Second-order motion stimulus perception sensitivity (2ndM) at 2.0 c/d spatial frequency.
Hospital Anxiety and Depression Scale (HADs) and Low Vision Quality of Life Questionnaire
(LVQOL), administered by the same researcher.
6. Clinical Observation, Follow-up, and Measures to Ensure Subject Compliance
Clinical Observation: Indicators as described in the experimental methods.
Follow-up: Conducted via telephone.
Compliance Measures: Detailed contact information is collected during hospitalization, with timely
follow-up via phone or WeChat post-surgery.
7. Criteria for Discontinuing Clinical Research and Regulations for Ending Clinical Research
Discontinuation Criteria: Subjects may withdraw at any time. The study must be terminated
immediately if safety risks arise.

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Ending Criteria: The study concludes when the last subject completes the final follow-up.
8. Data Management and Regulations for Data Traceability
Subject-related documents are archived per Chinese laws, retained for at least 5 years post-study.
All subject data, including identifiable information and medical records, are confidential. Participants
are informed of data confidentiality procedures before signing consent forms. Research team
members must maintain confidentiality unless authorized.
9. Subject Coding, Random Number Table, and Procedures for Storing Case Report Forms
Same as Section 8.
10. Adverse Event Recording, Reporting, and Follow-up
Any serious adverse events are reported immediately to the clinical trial leader, with detailed
documentation of timing, handling measures, and outcomes.
11. Statistical Analysis Plan
Statistical analysis was performed based on the per-protocol (PP) principle. Data are presented
as mean ± SD for normally distributed continuous variables, median (P25, P75) for non–normally
continuous variables, and frequency (percentage) for categorical variables. The Shapiro-Wilk test
was employed to assess the normality of the continuous variables. For normally distributed variables,
between-group differences were assessed using unpaired t-test. Repeated measures analysis of
variance (ANOVA) and simple effects analysis were conducted to examine changes in visual
perceptual function and psychological status before and after VPL training. For each participant, the
magnitude of improvement for each visual perceptual function (ΔGA, Δ1stM, Δ2ndS, Δ2ndM) was
calculated using the formula: ΔI = 20 log10 (Post-training Measure / Pre-training Measure) dB.
Spearman correlation analysis was performed to evaluate the relationships among general
demographic characteristics, visual perceptual function, retinal structure and blood flow parameters,

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and psychological scale scores. Multivariable linear regression models were then constructed with
ΔGA, Δ1stM, Δ2ndS, and Δ2ndM as dependent variables to identify factors associated with training
efficacy.
12. Quality Control and Quality Assurance
The principal investigator conducts regular monitoring, including pre-study and interim on-site
audits.
13. Subject Recruitment and Informed Consent Process
Before each patient is enrolled in this study, the research physician has the responsibility to
provide a complete and comprehensive introduction to the purpose, research process, and potential
risks of this study to the patient or their designated representative. Patients should be informed of
their rights, risks, and benefits. Prior to enrollment, patients should sign an informed consent form
and keep it in electronic medical records.
14. Expected Progress and Completion Date
Clinical trial completion: August 2024.
15. Follow-up and Medical Measures After the Study
None.
16. Responsibilities of All Parties and Other Regulations
None.
17. References
[1]Shan Y, Xu Y, Lin X. Burden of vision loss due to diabetic retinopathy in China from 1990 to

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2017: findings from the global burden of disease study[J], Acta Ophthalmol, 2021, 99(2):267-273．
[2]Sun H, Saeedi p, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level
diabetes prevalence estimates for 2021 and projections for 2045[J]. Diabetes Res Clin Pract, 2022,
183:109119.
[3]Flaxel C J, Adelman R A, Bailey S T, et al. Diabetic Retinopathy preferred practice pattern[J].
Ophthalmology, 2020,127(1):66-145.
[4]Sabel BA, Gudlin J. Vision restoration training for glaucoma: a randomized clinical trial[J].
JAMA Ophthalmol, 2014, 132(4):381-389.
[5]Gudlin J, Mueller I, Thanos S, Sabel BA. Computer based vision restoration therapy in glaucoma
patients: a small open pilot study[J]. Restor Neurol Neurosci, 2008, 26(4-5):403-12.
[6]Sabel BA, Henrich-Noack p, Fedorov A, Gall C. Vision restoration after brain and retina damage:
the "residual vision activation theory"[J]. Prog Brain Res, 2011, 192:199-262.
[7]He Juan, Zhang Li Research progress on perceptual learning for visual function reconstruction in
patients with amblyopia [J]. International Journal of Ophthalmology, 2015, 15 (11): 1913-1916
[8]Polat U, Ma-Naim T, Belkin M, Sagi D. Improving vision in adult amblyopia by perceptual
learning[J]. Proc Natl Acad Sci USA, 2004, 101(17):6692-6697.
[9]Yan F F, Zhou J, Zhao W, et al. perceptual learning improves neural processing in myopic
vision[J]. Journal of Vision, 2015, 15(10): 12-12.
[10]Aimola L, Lane A R, Smith D T, et al. Efficacy and feasibility of home-based training for
individuals with homonymous visual field defects[J]. Neurorehabilitation and neural repair, 2014,
28(3): 207-218.
[11]Shi Qing, Liu Zhenxing, Zhou Qi, etc The improvement effect of visual perception learning on
early presbyopia symptoms [J]. Chinese Journal of Optometry and Visual Science, 2015, 17 (9): 528-
532
[12]SAGI D. perceptual learning in vision research[J]. Vision Res, 2011, 51(13): 1552-1566.
[13]Yan Y, Rasch M J, Chen M, et al. perceptual training continuously refines neuronal population

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codes in primary visual cortex[J]. Nat Neurosci, 2014, 17(10):1380-1387.
[14]Li R W, Levi D, Klein S A. perceptual learning improves efficiency by re-tuning the decision
template for position discrimination[J]. Nat Neurosci, 2004, 7(2):178-183.
[15]Watanabe T, Sasaki Y. perceptual learning: toward a comprehensive theory[J]. Annu Rev
psychol, 2015, 66:197-221.
[16]Yang T, Maunsell J H. The effect of perceptual learning on neuronal responses in monkey visual
area V4[J]. J Neurosci, 2004, 24(7):1617-1626.
[17]Sigman M, pan H, Yang Y, et al. Top-down reorganization of activity in the visual pathway after
learning a shape identification task[J]. Neuron, 2005, 46(5):823-835.