Retinal optical coherence tomography intensity spatial correlation features as new biomarkers for confirmed Alzheimer’s disease

Abstract

Background

The nature and severity of Alzheimer’s disease (AD) pathologies in the retina and brain correspond. However, retinal biomarkers need to be validated in clinical cohorts with confirmed AD biomarkers and optical coherence tomography (OCT). The main objective of this study was to investigate whether retinal metrics measured by OCT aid in the early screening and brain pathology monitoring for confirmed AD.

Methods

This was a case–control study. All participants underwent retinal OCT imaging, and neurological examinations, including amyloid-β (Aβ) positron emission tomography. Participants were subdivided into cognitively normal (CN), mild cognitive impairment (MCI), and AD-derived dementia (ADD). Except retinal thickness, we developed the grey level co-occurrence matrix algorithm to extract retinal OCT intensity spatial correlation features (OCT-ISCF), including angular second matrix (ASM), correlation (COR), and homogeneity (HOM), one-way analysis of variance was used to compare the differences in retinal parameters among the groups, and to analyze the correlation with brain Aβ plaques and cognitive scores. The repeatability and robustness of OCT-ISCF were evaluated using experimental and simulation methods.

Results

This study enrolled 82 participants, subdivided into 20 CN, 22 MCI, and 40 ADD. Compared with the CN, the thickness of retinal nerve fiber layer and myoid and ellipsoid zone were significantly thinner (P < 0.05), and ASM, COR, and HOM in several retinal sublayers changed significantly in the ADD (P < 0.05). Notably, the MCI showed significant differences in ASM and COR in the outer segment of photoreceptor compared with the CN (P < 0.05). The changing pattern of OCT-ISCF with interclass correlation coefficients above 0.8 differed from that caused by speckle noise, and was affected by OCT image quality index. Moreover, the retinal OCT-ISCF were more strongly correlated with brain Aβ plaque burden and MoCA scores than retinal thickness. The accuracy using retinal OCT-ISCF (AUC = 0.935, 0.830) was better than that using retinal thickness (AUC = 0.795, 0.705) in detecting ADD and MCI.

Conclusions

The study demonstrates that retinal OCT-ISCF enhance the association and detection efficacy of AD pathology compared to retinal thickness, suggesting retinal OCT-ISCF have the potential to be new biomarkers for AD.

Introduction

Alzheimer’s disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia. Before the clinical symptoms, brain pathological changes in the AD, such as amyloid beta (Aβ) plaques and phosphorylated tau, have occurred. Currently, AD treatments can slow the progression of the disease instead of curing it, making early screening and intervention for AD important. At present, positron emission tomography (PET) and cerebrospinal fluid (CSF) examinations are the gold standard for diagnosing AD. However, the above examinations are not widely used for early screening and progression monitoring because they are invasive and non-portable [1,2,3]. As the extension of the central nervous system, the retina shares similar cellular structure, immune response, and microvascular circulation characteristics to the brain [4]. More importantly, the retina exhibits AD-specific pathological changes, including Aβ deposits, and common effects of AD on both retina and brain are reported during disease progression and in response to therapy (Fig. 1A). In addition, retinal imaging is a non-invasive, portable, quick, and cost-effective tool. Therefore, the retina has the potential to be a detection window for early screening and progression monitoring in AD.

Fig. 1

Schematic diagram of retinal pathological changes in AD based on retinal OCT imaging features. A Retinal pathological changing patterns from normal to AD-derived dementia (ADD). B The correspondence between retinal pathological changes and OCT imaging features in AD, such as optical scattering property and structural morphology. C Retinal OCT imaging features quantization metrics, including OCT intensity spatial correlation features (OCT-ISCF) and thickness in different retinal sublayers. GCIPL, ganglion cells and inner plexiform layer, INL, inner nuclear layer; MEZ, myoid and ellipsoid zone; ONL, outer nuclear layer; OPL, outer plexiform layer; OS, outer segment of photoreceptor; RNFL, retinal nerve fiber layer; RPE, retinal pigment epithelium. This figure contains modified images from Servier Medical Art (https://smart.serviercom) licensed by a Creative Commons Attribution 3.0 Unported License

Optical coherence tomography (OCT) is a non-invasive, widespread technique with high axial resolution, which can obtain in vivo retinal imaging results consistent with histological staining to assess the degree of retinal neurodegeneration [5, 6]. Based on OCT, most studies have found retinal thickness alterations in AD [7], and it is significantly related to the neurodegeneration in the brain [8, 9]. However, many studies have reported that the retinal thickness increases due to glial hyperplasia in the early stage of AD [10, 11], while it decreases significantly in the advanced stage of AD caused by neurodegeneration [12]. To some extent, the high overlap between early AD and healthy individuals occurs due to the fluctuating altered characteristics of the retinal thickness. In other words, the retinal thickness lacks high sensitivity for early AD, and will be limited in early screening.

Recently, several studies have reported AD specific pathological biomarkers, such as Aβ, can affect the optical scattering properties of tissues near the deposition area, causing changes of reflection spectrum [13, 14]. Moreover, AD non-specific pathological changes, including retinal neurodegeneration, also cause changes in optical scattering properties [15]. Since Aβ deposition occurs earlier than neurodegeneration [16], the changes in retinal optical scattering properties are thought to precede the changes in retinal thickness (Fig. 1B). In general, the OCT intensity correlates with the tissular optical scattering properties [17,18,19]. Considering the localized diffuse nature of AD pathological alterations, OCT intensity spatial correlation features (OCT-ISCF) may detect subtle alteration in OCT intensity [20]. The OCT-ISCF are high-order structural features, characterizing the spatial correlation distribution of OCT intensity signal [21, 22]. To the best of our knowledge, there are no studies to confirm the feasibility of retinal OCT-ISCF for early screening and brain pathology monitoring in confirmed AD.

In the present work, we first developed a retinal sublayer feature extraction algorithm to obtain OCT-ISCF, and compared the detection performance of OCT-ISCF and retinal thickness in different stages of AD. The repeatability and robustness of OCT-ISCF were also evaluated using experimental and simulation methods. Further, we explored the association between retinal metrics and brain pathological changes in AD.

Methods

Participants

The study was approved by the Ethics Committee of the Third Affiliated Hospital of the PLA Army Medical University (Approval No. 2020–14) and the Affiliated Eye Hospital of Wenzhou Medical University (Approval No. 2020–012-K-10) in accordance with the principles of the Declaration of Helsinki, and written informed consent from all participants were obtained. This project has been registered in the Chinese Clinical Trials Registry (registration number: ChiCTR2000040786; registration date: 2020–12-10). From December 2020 to December 2023, 82 subjects were enrolled by a neurologist (Y.W.) in the Memory Disorders Clinic. All subjects underwent neurological examinations, routine ophthalmological examinations and retinal OCT imaging, which were described in eMethods in the Supplement. When collecting eye data for each patient, we tried to collect both eyes as much as possible, but considering the correlation of binocular data, only one eye data was analyzed in subsequent image analysis, and the right eye data was preferentially included. If the OCT image quality of the right eye is poor (image quality index less than 7 or image discontinuity), the left eye is selected. According to the National Institute of Aging and Alzheimer’s Association (NIA-AA) criteria, 62 patients with AD were diagnosed using brain Aβ-PET results. Based on Clinical Dementia Rating Scale (CDR), AD patients were subdivided into 40 patients with ADD and 22 patients with mild cognitive impairment (MCI) [23]. Twenty cognitively normal (CN) age- and sex-matched subjects with Aβ-PET negative were recruited.

Exclusion criteria: (1) History of retinal disease, ocular trauma, severe cataract, and high myopia/astigmatism; (2) Dementia not caused by AD, such as Parkinson’s dementia, and vascular dementia; (3) The retinal OCT image quality index is less than 7.

Analysis of retinal thickness parameters

All retinal OCT images were analyzed by a custom-built deep learning segmentation algorithm [24]. In Fig. 1C, the retinal image is divided into 8 layers, which are retinal nerve fiber layer (RNFL), ganglion cells and inner plexiform layer (GCIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), myoid and ellipsoid zone (MEZ), outer segment of photoreceptor (OS) and retinal pigment epithelium (RPE), respectively [25]. The average thickness of 8 retinal sublayers in the Early Treatment Diabetic Retinopathy Study (ETDRS) grid was measured.

Analysis of retinal OCT-ISCF and performance test

The gray level co-occurrence matrix (GLCM) algorithm was developed to extract retinal OCT-ISCF [26, 27]. The OCT-ISCF contained angular second order moment (ASM), contrast (CON), correlation (COR), homogeneity (HOM) and dissimilarity (DIS). Detailed calculations of the OCT-ISCF were described in the supplementary material (eMethods). We performed a small-scale study of retinal OCT-ISCF repeatability on 21 young healthy volunteers. Each subject had two repeated retinal OCT imaging acquisitions by the same operator using the same OCT scan protocol. The same performer analyzed the retinal OCT images to obtain the OCT-ISCF of full-layer retina. In addition, we took pre- and postoperative OCT images of 10 patients who underwent cataract surgery, to investigate the influence of OCT image quality index on the OCT-ISCF. Speckle noise usually occurs in OCT images and is represented as a scattered highlighted signal [28, 29]. To test the robustness of retinal OCT-ISCF for early detection of AD, we randomly added several highlighted signals to retinal OCT images of 5 CN subjects to simulate speckle noise. Subsequently, we analyzed the changes in OCT-ISCF before and after the addition of simulation noise.

Statistical analysis

All statistical analyses were performed by the SPSS 25. The measurement data were presented in the form of mean (standard deviation). The interclass correlation coefficient (ICC) was calculated to assess the repeatability of retinal OCT-ISCF. Shapiro–Wilk was used to test the normal distribution of data. One-way analysis of variance and Bonferroni post hoc test were used to test the measured values of normal distribution. If none of the three groups of data conforms to the normal distribution, the Kruskal–Wallis non-parametric test. Categorical variables were analyzed by chi-square test. Paired t-tests were used to analyze the variability of OCT-ISCF before and after cataract surgery, and speckle noise addition. Correlations of retinal metrics with brain Aβ plaque burden and Montreal Cognitive Assessment (MoCA) scores were analyzed by age-adjusted partial correlation analysis. The stepwise multivariate binary logistic regression analysis and receiver operating characteristics (ROC) curve were used to identify MCI and ADD by retinal thickness and OCT-ISCF, respectively. P < 0.05 was considered statistically significant.

Results

In this study, 82 subjects were subdivided into 40 ADD, 22 MCI and 20 CN. The basic demographics and ocular parameters of three groups are summarized in Table 1. There were no significant differences in above parameters, except for MoCA and CDR scores. The MoCA and CDR scores of ADD were lower than those of MCI, which were lower than those of CN.

Table 1 Clinical characteristics of subjects among CN, MCI, and ADD

Differences in retinal thickness among groups

eTable 1 shows the thickness of each retinal sublayers in three groups according to ETDRS grid. Compared with CN, the RNFL and MEZ in the outer inferior region and the MEZ in the inner temporal region reduced significantly only in the ADD (P < 0.05). Compared with the MCI, the ADD showed significant thinning of RNFL in the inferior region and OPL in the outer superior and temporal regions (P < 0.05).

Retinal OCT-ISCF performance test and differences among groups

In eTable 2, ICCs of all retinal OCT-ISCF are greater than 0.8. eFigure 1 shows all retinal OCT-ISCF are significantly altered before and after surgery. eTable 3 summarizes retinal OCT-ISCF in various retinal sublayers among three groups. Figure 2 shows that compared with CN, ASM in GCIPL, INL, OPL, OS and RPE sublayers, COR in GCIPL, OPL, OS and RPE sublayers, and HOM in OPL and OS sublayers in ADD varies significantly (P < 0.05). It is worth noting that there were also significant differences in ASM and COR in OS sublayer between CN and MCI (P < 0.05). In eFigure 2, the results show that the CON, COR, and HOM increase significantly after speckle noise addition, while the ASM decreases significantly. In contrast, the change pattern in OCT-ISCF between AD and CN showed increased ASM and decreased COR, which was opposite to those of adding speckle noise.

Fig. 2

OCT-ISCF parameters with statistical differences among groups. (A-C) stands for ASM, COR, and HOM parameters respectively. (ASM, angular second moment; COR, correlation; GCIPL, ganglion cells and inner plexiform layer, HOM, homogeneity; INL, inner nuclear layer; OPL, outer plexiform layer; OS, outer segment of photoreceptor; RPE, retinal pigment epithelium; *, P < 0.05; **, P < 0.01)

Diagnostic performance of retinal metrics for different stages of AD

Figure 3 provides the diagnostic performance of retinal metrics for different stages of AD using ROC analysis. To discriminate ADD from CN, the thickness parameters of RNFL, OPL, MEZ, and RPE were included in a multi-variate model with area under the curve (AUC) of 0.795, while the OCT-ISCF parameters COR of the RNFL, GCIPL, INL, and RPE sublayers, the ASM of the RNFL, GCIPL, and OPL sublayers, and HOM of OS sublayers were included in another multi-variate model with AUC of 0.935 (Fig. 3A). To discriminate MCI from CN, the thickness parameters of the MEZ and RPE were included in a multi-variate model with AUC of 0.705, while the COR of the retina full-layer, GCIPL, INL, OPL, MEZ sublayers, and ASM of the OPL and MEZ were included in the analysis with AUC of 0.830 (Fig. 3B).

Fig. 3

The differential efficacy of retinal thickness and OCT-ISCF parameters between groups. A The ROC curves of Retinal thickness and OCT-ISCF parameters identify ADD and CN; B The ROC curves of Retinal thickness and OCT-ISCF parameters identify MCI and CN

Significant correlation of retinal metrics with brain Aβ plague burden and cognitive function

Figure 4 presents correlation coefficients of brain Aβ plaque burden and MoCA scores with retinal sublayers thickness in ETDRS grid and OCT-ISCF. Specifically, there were significant correlations between retinal sublayers thickness with standardised uptake value ratio (SUVr) in different brain regions. The above correlation coefficients ranged from −0.318 to 0.362 (Fig. 4A). Moreover, there also were significant correlations between SUVr in different brain regions and retinal OCT-ISCF in several sublayers. Of note, the correlation coefficients between retinal OCT-ISCF and brain SUVr ranged from −0.494 to 0.40 (Fig. 4B).

Fig. 4

Correlation of retinal sublayers thickness and OCT-ISCF parameters with brain Aβ-SUVr values and MoCA scores. A The retinal sublayers thickness in ETDRS grid were correlated with the SUVr in different brain regions; B The retinal sublayers OCT-ISCF were correlated with the SUVr in different brain regions; C The correlation between retinal thickness parameters and MoCA scores; D The correlation between retinal OCT-ISCF and MoCA scores. (COR, correlation; GCIPL, ganglion cells and inner plexiform layer; I-I, inner-inferior; OPL, outer plexiform layer; O-S, outer-superior; RNFL, retinal nerve fiber layer; RPE, retinal pigment epithelium; SUVr, standardised uptake value ratio

There were also significant correlations of MoCA scores with retinal sublayer thickness and OCT-ISCF. In terms of retinal thickness, MoCA scores were significantly correlated with RNFL, GCIPL, INL, OPL and MEZ sublayers. Specifically, the highest correlation between retinal thickness and MoCA scores in the inner retina was the RNFL sublayer (r = 0.330, P = 0.003), and that in the outer retina was the OPL sublayer (r = 0.314, P = 0.004) (Fig. 4C). Moreover, there were significant correlations between MoCA scores and retinal OCT-ISCF in the RNFL, GCIPL, INL, OPL, OS, and RPE sublayers, with the highest correlation between the GCIPL sublayer and MoCA scores in the inner retinal layer (r = 0.40, P < 0.001) and that between the OS sublayer and MoCA scores in the outer layer (r = 0.448, P < 0.001) (Fig. 4D).

Discussion

The purpose of this study is to investigate whether retinal metrics measured by OCT aid in the early screening and brain pathology monitoring for confirmed AD. To our knowledge, the present work is the first to systematically compare retinal OCT-ISCF and thickness in different stages of AD. The primary finding of our study is that the outer retinal OCT-ISCF can significantly improve the discrimination performance between AD and CN compared to traditional retinal thickness parameters. Furthermore, the retinal OCT-ISCF present excellent repeatability and robust for early detection of AD using experimental and simulation methods. Finally, the retinal OCT-ISCF are more strongly correlated with AD pathological changes, including brain Aβ plaque burden and MoCA scores, than retinal thickness.

Firstly, retinal OCT-ISCF in the OS layer have changed in MCI, prior to the retinal thickness in our study. Several histopathological works confirmed that AD biomarkers are deposited in the outer retina, including OS layer, which lead to retinal neurodegeneration [7, 30]. Recently, a study demonstrated that rod degeneration is the earliest AD retinal manifestations compared with cone and bipolar cells in AD mice [31]. The retinal MEZ sublayer thickness in the peripheral macular area in the ADD was significantly thinner than that in the CN. In addition, we also reported thickness thinning in the RNFL sublayer in ADD, consistent with previous studies [12, 32]. Interestingly, the trend of thickening in retinal RNFL sublayers existed in MCI, also reported by recent studies [10, 33]. Researchers attributed the retinal thickening at the MCI stage to glial cell proliferation prior to the retinal degeneration [34]. Considering the opposite effect of glial proliferation and neurodegeneration in retinal thickness, the high overlap between early AD and healthy individuals occurs, requiring more sensitivity retinal biomarkers for early AD detection. In nature, the retinal OCT-ISCF characterize the spatial correlation distribution of retinal OCT intensity signal, which is affected by tissular optical scattering property [35, 36]. Using the co-registered angle-resolved low-coherence interferometry OCT, Song et al. found that different retinal sublayers in AD mouse have alterations in tissue optical scattering signals, prior to retinal thickness changes. [37] However, the above technique adopted a complex structure design, making it difficult for clinical application. In contrast, we developed a radiomic analysis algorithm based on the OCT commercial device, and found that outer retinal OCT-ISCF alterations advanced to its thickness abnormality in clinical data. Using ROC analysis, the retinal OCT-ISCF showed higher accuracy than thickness parameters to distinguish CN with MCI and ADD. The results suggest that retinal OCT-ISCF may be a potential biomarker for the early screening of AD.

Secondly, the retinal OCT-ISCF are new indicators with excellent repeatability and robustness. The OCT-ISCF may be affected by light angles, refractive media, retinal disease and OCT speckle noise, etc. As we known, Henle’s fiber layer presents a different appearance in OCT images under different light angles [38]. Thus, we instructed all subjects to maintain in primary position, avoiding the effect of light angles on OCT intensity. In the retinal OCT repeat experiments, the ICCs of all retinal OCT-ISCF were greater than 0.8, suggesting OCT-ISCF have good repeatability. Moreover, retinal OCT-ISCF significantly altered after cataract surgery. Therefore, our study matched retinal OCT image quality index to minimize the effects of refractive media opacity. In addition, we also excluded retinal diseases, thus reducing the influence of retinal diseases on the retinal OCT-ISCF. As for OCT speckle noise, we simulated speckle noise by randomly adding highlighted signals to the OS layer on OCT images. As a result, OCT-ISCF changed significantly after the addition of speckle noise. However, the pattern of OCT-ISCF alterations by speckle noise was opposite to those caused by AD observed in this study. Overall, the retinal OCT-ISCF alterations seem to be associated with AD pathological changes, but not other relevant factors.

More importantly, the correlations between retinal OCT-ISCF and AD pathological changes are stronger than those in retinal thickness. The retinal OCT-ISCF alteration in AD may attribute to several reasons: Firstly, retinal Aβ plaques may contribute to the retinal OCT-ISCF alteration. Several studies conducted the retinal immunohistochemical analysis, and identified the presence of Aβ plaques in the retina [39, 40]. Using spectral imaging techniques, different groups have confirmed Aβ plaques cause changes in tissular optical scattering property, affecting the retinal OCT-ISCF [35, 41]. Considering the parallels in retina and brain pathophysiology of AD, we use brain Aβ plaque burden to indirectly represent retinal Aβ plaque burden. Previous studies reported that retinal thickness is linearly related to cerebral cortex SUVr value after controlling for any main effects of age, of which correlation coefficient is similar with our results [42, 43]. Of note, it’s first time to report the retinal OCT-ISCF have middle correlations with brain Aβ plaque burden, which are stronger than thickness. From the Aβ deposition perspective, retinal OCT-ISCF better reflect pathological changes in AD when compared to the thickness. Secondly, neurodegeneration may also cause changes in OCT-ISCF. The cognitive impairment often represents brain neurodegeneration, therefore we analyze the correlation between retinal metrics and cognitive performance [44, 45]. Previously, several studies demonstrated that retinal thickness has a linear relationship with MoCA scores in AD, consistent with our finding. One interesting finding is retinal OCT-ISCF are more strongly correlated with MoCA scores than retinal thickness. Specifically, the Pearson’s correlation coefficient of COR in GCIPL and RPE sublayers are 0.40, and 0.448, respectively. The above sublayers are the most common sites of retinal neurodegeneration [7, 46]. It is found that the optical density of RNFL in glaucoma patients is lower than that of normal subjects, and it gradually decreases with glaucoma progression, even before the thickness change [15]. In other words, retinal neurodegeneration is likely to cause the OCT-ISCF changes. Taken together, retinal OCT-ISCF have the potential to be used as an objective assessment of brain Aβ plaques burden and cognitive performance.

There are several limitations in our study. Firstly, the sample size is relatively small. Since there was no previous study to investigate OCT-ISCF parameters, we estimated sample sizes for the OCT-ISCF indicators with significant difference among groups in this study. For example, for the ASM and COR parameters in the retinal OS layer, we calculated effect size of 0.714 and 0.976, respectively, based on the Cohen’s formula [47]. Assuming alpha error and power of 0.05 and 0.8, respectively, the total sample sizes for the three groups could be calculated to be 24 and 15 using the G*power software, which are less than the actual included sample size of 82 in our study. As such, the current sample size in our study is sufficient to support the significant changes in retinal OCT-ISCF. Secondly, this study is cross-sectional. Although retinal metrics are associated with pathological alterations in AD, longitudinal data are still required to validate. Thirdly, we evaluate the refraction rather than ocular axial length. To some extent, spherical equivalent has a relationship with the ocular axial length, avoiding the effect of lateral magnification differences on the retinal metrics due to differences in ocular axial length. Finally, this study focuses on the research about AD and retinal metrics, which may be affected by ocular diseases and other neurodegenerative diseases. In the future, the retinal OCT-ISCF are necessary to be further validated in a general population.

Conclusions

The present study demonstrates that retinal OCT-ISCF significantly improve the association of pathological changes, including brain Aβ plaque burden and MoCA scores, and detection efficacy in AD compared to retinal thickness. In addition, a rigorous experimental design is used to clarify the excellent repeatability and robustness of retinal OCT-ISCF for AD screening and monitoring in clinical. These findings provide a theoretical basis for early screening and brain pathology monitoring in AD by using a combination of OCT and radiomic analysis methods, suggesting that retinal OCT-ISCF have the potential to be used as new biomarkers for AD.