Optical Coherence Tomography Angiography (OCT-A): Emerging Landscapes in Neuro-Ophthalmology and Central Nervous System (CNS) Disorders

Abstract

Optical Coherence Tomography (OCT) is now being widely used in several branches of biomedical science ranging from ophthalmology to neurology. Emerging from it, optical coherence tomography angiography (OCT-A) is a noninvasive, depth-resolved imaging tool for the visualization of retinal vascular changes. In the field of neuro-ophthalmology, OCT-A proves to be superior than the conventional Fluorescein angiography (FA) or indocyanine green angiography (ICGA). This chapter discussed the role of OCT-A in different neuro-ophthalmological and central nervous system (CNS) disorders including multiple sclerosis, non-arteritic anterior ischemic optic neuropathy (NAION), papilledema, papillitis, glaucoma, Parkinson’s disease, Alzheimer’s disease, cerebral small vessel diseases, and stroke. Since neuro-ophthalmological and some neurologic conditions show consistent peripapillary and macular capillary changes, OCT-A can be a future useful tool in a physician’s armamentarium due to its capability for better delineation of the superficial and deeper retinal and choroidal vasculatures. Furthermore, its limitations, technical challenges, and future research directions are illustrated in this chapter.

1. Introduction

Recent advances in biomedical imaging have led several imaging modalities to improve our understanding of various ophthalmological and/or neurological diseases. First developed by Fujimoto’s group at the Massachusetts Institute of Technology (MIT) in 1991, Optical Coherence Tomography (OCT) works based on tissue backscattering properties [1]. OCT utilizes the short coherence length of broad-spectrum light sources to obtain cross-sectional images of biological tissue samples on a microscopic scale. Since its inception in the 1990s, it has rapidly become a crucial imaging technique in various biomedical fields, particularly in ophthalmology, where it can be used to visualize the anterior eye and retina through transparent media. As the brain and retina originate from the same embryologic origin, the retina provides a unique “window” into the central nervous system (CNS) because of having unmyelinated axons and a low concentration of glial cells. That is why retina is called “a relative vacuum” while studying neurons and axons and it can serve as a valuable surrogate marker of neurodegeneration, neuroprotection, and neurorestoration [2].

The major users of OCT technology over the last 20 years have been mostly ophthalmologists, but in these days, it is also being used widely by more specifically neuro-ophthalmologists and neurologists on patients with ocular and/or neurologic disorders. We previously summarized the updates on OCT’s applications in neuroscience and interested readers may refer to it [2]. Besides, one of the modern-day OCT successors, OCT Angiography (OCT-A) has drawn significant attention from biomedical communities for its unique capabilities in imaging microvasculature in different neuro-ophthalmological and neurological conditions. Some purely neuro-ophthalmological conditions (e.g., optic neuropathies, papilledema, glaucoma) and some neurologic conditions with ocular manifestations (e.g., multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, stroke) show changes in eye vasculature that can be detected, studied and monitored using OCT-A, are discussed together in this chapter due to their significant inter-relations [3, 4, 5, 6, 7, 8, 9].

In neuro-ophthalmology, OCT-A has been used in different types of optic nerve head (ONH) edema (e.g., ischemic, inflammatory, papilledema) and optic neuropathies (e.g., ischemic, inflammatory, hereditary) [10, 11]. There has been recent attention to implementing OCT-A in diabetic retinopathy as well [12, 13]. The key advantage OCT-A provides in addition to conventional structural OCT images, it can capture the microvascular architecture in unprecedented detail which can synergize the clinical diagnosis, monitoring disease progression and prognosis [14, 15]. Again, some central nervous system (CNS) diseases have retinal microvasculature involvement. For instance, neurodegenerative diseases like Alzheimer’s disease (AD) affects retina as well. The mechanism behind the reduced retinal vessel density in AD is not clear, but it has been proposed that decreased angiogenesis due to sequestration of vascular endothelial growth factor (VEGF) by Aβ plaques and competition between Aβ and VEGF receptor 2 is a factor. Studies have found Aβ plaques in the retinas of post-mortem AD patients and in mice with AD. These findings suggest that Optical Coherence Tomography Angiography (OCT-A) could be used to detect microvascular abnormalities in even pre-clinical AD pathology [8]. Recent applications of OCT-A have shed light on acute stroke diagnosis even from retina though it requires further research [7]. In this chapter, we will explore the potential applications of OCT-A in these fields, their challenges, and future directions.

2. Basic concepts of OCT-A

OCT-A is an imaging tool for visualizing the retinal vasculature in the eye based on the traditional OCT but allows for high-speed imaging acquisition of the retinal blood vessels. OCT-A uses algorithms such as Ultrahigh-sensitive optical microangiography, split-spectrum amplitude-decorrelation angiography, full spectrum amplitude decorrelation algorithm, etc. These algorithms produce three-dimensional images of the retinal vasculature in less than 3 seconds. The images are created by taking repeated B-scans of the same cross-section of the eye and detecting changes in the phase and intensity of the OCT signal caused by the movement of red blood cells. OCT-A systems can be used to acquire images with a field of view larger than 6×6 mm, with varying levels of lateral resolution [5]. Images with a wider field of view and improved lateral resolution can be obtained by using a high-speed Swept Source (SS) OCT-A system or wide-field montages (Figures 1 and 2).

Figure 1.

Wide field montage of healthy retinal microvasculature. The retinal nerve fiber layer is shown in red, the ganglion cell and inner plexiform layers are shown in green, and the inner nuclear and outer plexiform layers are shown in blue. (b), the fovea (c), and (d), the temporal region are all indicated by white boxes. (Reprinted from ref. [14]).

Figure 2.

General scanning protocol for optical coherence tomography angiography (OCT-A). (a) To find the relative flow signal, several B-scans are performed on the “x” fast axis at each of the “y” slow scan axis points. (b) Top view of the same basic scan pattern as in (a), with multiple B-scans performed along each “y” location of the slow axis on the fast axis. AngiovueTM OCT-A was used to obtain sample scans. (a, b). Reprinted from ref. [5].

In addition to traditional OCT functions, OCT-A technology allows for the analysis of multiple retinal layers and corresponding vasculatures, including the inner retina, middle retina, outer retina, choriocapillaris, and choroid. This enables the examination of various vascular features, such as the presence of neovascularization, increased tortuosity, and areas of capillary loss. Quantitative analysis, such as measuring the area of the foveal avascular zone (FAZ) and the relative density, can also be performed using methods like fractal analysis or pixel counting. The flow rate, which is the average decorrelation value of sequential B-scans, can also be used as a surrogate for blood flow rate. The Optovue™ system includes an analysis package that automatically analyzes the FAZ and vessel density in different retinal sub-regions [5]. Table 1 shows some common OCT-A parameters.

Parameter	Definition
Vessel length density*	Length-based measurement of vessel density: total length of the perfused vasculature per unit area in the region of measurement (Zeiss definition)
Vessel density*	Area-based measurement of vessel density: expresses how much area is taken up by vessels (Optovue definition)
Perfusion density	Area-based measurement of vessel density: total area of the perfused vasculature per unit area in the region of measurement (Zeiss definition)
Fractal dimension	Describes shape or texture and determines complexity of an image
Lacunarity	Expresses patchiness or inhomogeneity of an image
Vessel perimeter index	Expresses vessel perimeter in relation to total image area
Foveal avascular zone (FAZ)	Avascular area in the center of the macula within the fovea
FAZ area	Area of the FAZ
FAZ diameter/perimeter	Diameter/Perimeter of the FAZ
FAZ circularity index	Index describing how circular the area of the FAZ is; values closer to 1 indicate higher circularity
Vessel tortuosity	Abnormal curvature of the vessels
Narrowed/dilated vessels	Morphologically obvious thinning or dilation of vessels
Branching complexity	Altered complexity of vessel branching

Table 1.

Commonly used OCT-A expressions and parameters.

*

Vessel density can be measured by area- or length-based measurements. Length-based measurements are more sensitive to changes in small capillaries; in area-based measurements, larger vessels have greater influence. Synonyms for vessel density: vascular/microvascular/capillary/flow density. Reprinted with permission from ref. [15].

3. Neuro-ophthalmological conditions

3.1 Papilledema and pseudopapilledema

Papilledema is swelling of the optic nerve head (ONH) caused by increased intracranial pressure (ICP) eventually causing stasis in axonal transport. Distinguishing it from pseudopapilledema, caused by a congenital optic disc elevation (crowded optic disc) or optic disc drusen, can be difficult based on ophthalmoscopic features alone [16]. Studies have suggested that the shape and thickness of the retinal nerve fiber layer (RNFL) around the ONH as seen on spectral-domain OCT (SD-OCT) can distinguish between ONH drusen and edema. However, SD-OCT can only show the presence of ONH drusen and cannot determine if there is also swelling present. In such cases, fluorescein dye tests can be used to determine if there is true edema present [3]. Fard et al. demonstrated that whole image and nasal peripapillary sector capillary densities using OCT-A had diagnostic accuracy for differentiating true and pseudo-disc swelling [16]. They showed there was a significantly lower whole image and nasal sector peripapillary capillary density of the inner retina in pseudopapilledema eyes than papilledema eyes. The peripapillary vasculature values were found to be lower in both papilledema and pseudopapilledema eyes compared to healthy eyes when analyzed using commercial machine software. However, when using their customized software, the peripapillary capillary density in papilledema eyes was not significantly different from healthy eyes (Figure 3). On the other hand, the peripapillary capillary density in pseudopapilledema eyes was found to be significantly lower compared to healthy eyes.

Figure 3.

Disc photograph (a) of an eye with NAION and OCT-A (b) showing superior focal loss of microvasculature (yellow arrows) and a corresponding inferior visual field defect (c). Reprinted with permission from ref. [17].

Again, the main differentiating characteristic of papilledema from other pseudo edemas (e.g., non-arteritic anterior ischemic optic neuropathy (NAION)) on ONH OCT-A is the vascular dropout observed in NAION [3]. Rougier et al. [10] examined the changes in the blood vessels in eyes with disc edema and found that there were changes in the capillary network around the ONH in cases of non-arteritic NAION and papillitis, while in cases of papilledema, there were dilated and tortuous superficial blood vessels without any changes in the capillary network. The study suggests that the decreased visibility of the capillary network in cases of papilledema is likely due to the swelling of the optic disc, rather than an actual lack of blood flow, and that this may be related to changes in blood flow regulation. The cases are highlighted in Table 2 for the enhancement of clinical knowledge of the readership and the author’s observation of the retrospective analysis of all such related cases are highlighted too. The authors concluded that the morphological analysis of OCT-A appeared to be more beneficial than the quantification analysis in the acute phase, enabling the differentiation between the three kinds of ONH edema: ischemic, inflammatory, and papilledema [10].

Table 2.

OCT-A of different kinds of optic nerve head (ONH) edema and corresponding example clinical cases.

Figures are reprinted with permission from ref. [10].

Another significant application of OCT-A is in glaucoma. Studies have shown that changes in the blood vessels in the eyes, seen through OCT angiography (OCT-A), can be useful in diagnosing primary open-angle glaucoma. This is because these changes are consistent with the pathophysiology of the disease and can provide complementary information to traditional diagnostic methods. Vessel density loss associated with glaucoma can be detected by OCT-A. Peripapillary, macular, and choroidal vessel density parameters may complement visual field and structural OCT measurements in the diagnosis of glaucoma. Interested authors may refer to a separate chapter on this topic in this book [18]. OCT-A may be particularly useful in evaluating patients who are suspected of having glaucoma and in monitoring advanced cases [19, 20].

3.2 Optic neuropathies

Optic neuropathies refer to a spectrum of disorders with abnormalities and dysfunction of the optic nerve [11]. Table 3 summarizes different types of optic neuropathies and their pathology and epidemiology.

Classification	Diseases	Pathology	Epidemiology
Inflammatory	Demyelinating optic neuritis (ON)	Early sign of neuromyelitis optica spectrum disorder (NMOSD), multiple sclerosis (MS), or other autoimmune disorders on the spinal cord, brain, and optic nerve Anti-aquaporin-4 (AQP4) and anti-myelin oligodendrocyte glycoprotein (MOG) auto-antibodies can be discovered Exist subtype of no specific antibody identified	Affecting young adults ranging from 18 to 45 years of age, with a mean age of 30–35 years Strong female predominance Approximate annual incidence of 5/100,000, with an estimated prevalence of 115/100,0008
Ischemic	Non-arteritic anterior ischemic optic neuropathy (NAION)	Non-inflammatory disease of short posterior ciliary artery Coincidence of cardiovascular risk factors in a patient with “crowded” optic discs	2.3 and 10.3 per 100,000 population per year
	Arteritic anterior ischemic optic neuropathy (AION)	Temporal arteritis (also called giant-cell arteritis)	About 8000 individuals per year in the United States
	Posterior ischemic optic neuropathy (PION)	Inadequate blood flow (ischemia) to retrobulbar portion of the optic nerve	PION most commonly affects the elderly
Hereditary	Leber hereditary optic neuropathy (LHON)	Mitochondrial inherited (transmitted from mother to offspring) degeneration of RGCs and their axons	In Northern European populations, about one in 9000 people carry one of the three primary LHON mutations

Table 3.

Different types of optic neuropathies.

Reprinted with permission from ref. [11].

Anterior ischemic optic neuropathies (AAION) can be divided into two types: arteritic (AAION) and nonarteritic (NAION). AAION is commonly linked to giant cell arteritis, a severe form of vasculitis that affects vision. NAION, on the other hand, mainly affects individuals with cardiovascular risk factors and those with crowded optic discs [3]. NAION is the most common optic neuropathy (other than glaucoma) beyond the age of 50 years. The pathogenesis of NAION is related to vascular dysfunction and is thought to be a result of the occlusion of the short posterior ciliary arteries [17].

Studies using quantitative analysis in OCT-A (Optical Coherence Tomography Angiography) have shown that AAION has more abnormal blood vessels compared to NAION. This may be justified by the fact that AAION is characterized by more swelling of the optic disk [3]. Though there is no definite quantitative cut-off value for differentiating AAION from NAION, clinical applications of OCT-A in NAION are reported in several studies investigating the retinal vessels, choroidal vasculature, and optic disc perfusion. Karrabi et al. summarized different studies comparing NAION, fellow eyes, and normal eyes and interested readers may refer to it [3].

OCT-A studies have consistently shown changes in the blood vessels of patients with NAION. The most common changes include tortuous capillaries, irregularity, and loss of the peripapillary vessels, particularly in the temporal and superior sectors. Disc edema or hemorrhage can affect the signal and cause a decrease in blood flow density, which may not necessarily indicate an ischemic process but rather be a result of compressive edema or imaging artifacts. Two patterns of vasculature loss in NAION have been observed, a diffuse loss of the microvasculature network and additional sectoral choroidal vascular dropout. Decreased vessel density, which is the proportion of the measured area occupied by vessels, has been found in patients with NAION compared to healthy individuals. This reduction can be reversed and may also have prognostic values. However, it should be kept in mind that part of this vascular density reduction and its reversibility can be a result of the artifact caused by optic disc edema during the acute phase of the disease [3].

Though macular OCT-A findings in NAION are debated and controversial, there is a good correlation between perfusion and visual acuity, visual field defect, and structural changes in the ONH. The decrease in peripapillary vessel density and the location of visual field defects as well as peripapillary retinal nerve fiber layer (RNFL) thinning has been reported. The whole and temporal peripapillary vessel density was strongly correlated with visual acuity, and the dropout of the temporal peripapillary superficial retinal microvasculature was associated with visual acuity loss. OCT-A revealed hypoperfusion in the retinal pigment epithelium (RPE) and peripapillary capillaries (PPC) following NAION, especially at the level of the choroid, corresponding to both functional and structural impairments. Irreversible vascular damage can lead to a decrease in perfusion, which can negatively affect visual outcomes in selective quadrants. The temporal and superior quadrants had the most reduction in vessel density, which is consistent with the commonly identified inferonasal field defect (Figure 3) [3, 17].

In other types of optic neuropathy, toxic and traumatic optic neuropathy, OCT-A might not be useful and structural assessment is still superior to vascular parameters in differentiating toxic and traumatic optic neuropathies [3]. Apart from it, Montorio et al. [21] found that OCT-A can provide a detailed and quantitative analysis of early retinal vascular perfusion alterations over time after traumatic retinopathy, demonstrating that the impairment of the retinal microvasculature and its progressive changes over time occurred even in the absence of compromised visual acuity. Hence, OCT-A may be useful for monitoring the course of vascular changes in traumatic optic neuropathy [21].

OCT-A has been used in studying hereditary optic neuropathies (e.g., Leber’s hereditary optic neuropathy or LHON) as well. First described in 1871 by Leber, LHON is a maternally inherited mitochondrial disease caused by mutations in the mitochondrial DNA that affects complex I of the oxidative phosphorylation chain [3, 22]. Yu et al. demonstrated that the retinal structure and the perfusion of the macular and peripapillary areas are reduced in subacute LHON, and the retinal structure and the perfusion of the peripapillary area are further reduced in chronic LHON [22].

Recently there has been growing interest in implementing OCT-A in diabetic retinopathy (DR). OCT-A has a potential role as an objective tool for evaluating diabetic retinopathy (DR) and its impact on the retina. It has been shown to visualize features associated with DR, including microaneurysms and neovascularization, and quantify changes in retinal capillaries and choriocapillaris (Figures 4 and 5). Additionally, OCT-A can potentially detect DR earlier than what is visible on traditional fundus examination. It is a promising technology for accurately classifying DR and for identifying eyes that have experienced vision loss due to diabetic macular ischemia [12, 13].

Figure 4.

A healthy control subject’s optical coherence tomography angiography (OCT-A; 3 mm × 3  mm region) is shown in the top panel (A, B) and reveals a dense network of capillaries in the superficial vascular plexus, which surrounds the foveal avascular zone. A diabetic patient’s OCT-A pictures (bottom panel; C, D) display vascular abnormalities in both the superficial and deep plexus layers, including microaneurysms (red arrows), capillary nonperfusion, and other vascular anomalies. (green arrows). A larger foveal avascular zone is shown. (FAZ). After the projection artifacts were eliminated, (B,D) were produced. Reprinted from ref. [12].

Figure 5.

Widefield optical coherence tomography angiography (OCT-A; 15 × 9 mm area; A,B) and color-coded maps showing low- or non-perfusion regions of a superficial vascular plexus in a diabetic (Right panel; C,D) and a healthy control individual (Left panel; A-C) (Right panel; B-D). In a diabetic eye, there are more areas of retinal nonperfusion, especially in the temporal regions. (D; labelled as yellow). It’s imperative to keep in mind that the typical person has occasional yellow spots in the periphery. (C; labelled as yellow). Reprinted from ref. [12].

Furthermore, implementing Deep Learning (DL) in OCT-A image analysis for DR has highlighted several benefits such as early detection and progression assessment [23]. The capability of OCT-A to detect the clinical onset of DR and prediction for its progression may become valuable for the personalized management of diabetic eye disease and arguably open a new horizon of research owing to the silent epidemic of diabetes around the globe.

4. Neurological conditions with ocular manifestations

4.1 Multiple sclerosis (MS)

Multiple sclerosis (MS) is a neurodegenerative disease characterized by demyelination in CNS due to autoimmune-mediated inflammatory processes. Recently, vascular and metabolic factors are increasingly being recognized to play important roles in the neuroinflammatory mechanism of MS [4]. The optic nerve is commonly injured in MS with or without optic neuritis (ON). Optic neuritis (ON) is unilateral or bilateral inflammation of the optic nerve due to many causes, including multiple sclerosis (MS), optic neuritis associated with neuromyelitis optica (NMO), infectious, or isolated. In patients with MS, ON is the presenting symptom in 25% and occurs in 75% of cases during the disease course [3].

According to post-mortem studies, demyelinating plaques can be seen in the optic nerves of up to 99% of MS patients, making optic nerve involvement a common aspect of the disease process [5]. The parafoveal and ONH (Figure 6) flow index, a representation of relative blood flow velocity in the vasculature, was determined to be significantly lower in MS eyes with a history of ON (MSON) in comparison to eyes without a history of ON (MSNON), compared to healthy controls [5, 24, 25].

Figure 6.

Optical coherence tomography angiography (OCT-A) of the optic nerve head (ONH) in a representative Multiple Sclerosis (MS) patient. Split-spectrum amplitude decorrelation angiography (SSADA) results (N = nasal, T = temporal) show that, in comparison to (c) a healthy control example, (a) MS eyes with a history of ON (MSON) and (b) MS eyes without a history of ON (MSNON) both exhibit an apparent qualitative reduction of the ONH microvascular density in the peripapillary area (between circles), mostly in the temporal region. (c). Bar = 0.5 mm (Reprinted from ref. [5]).

On the other hand, in the largest study of OCT-A on MS so far, Murphy et al. [26] showed retinal SVP densities measured by OCT-A are reduced in MS eyes in both MSON and MSNON [26]. They also found that reduced SVP densities correlate with reduced visual function, longer disease duration, and higher levels of global disability in expanded disability status scale (EDSS) and multiple sclerosis functional composite (MSFC) assessments. The group suggested that OCT-A may have additive value as a biomarker in MS, in addition to routine OCT evaluation. The suggestion is coherent with a previous study by Spain et al. [25] where they investigated 68 eyes from MS patients and 55 healthy eyes with OCT and OCT-A to assess the structural and vascular change in the peripapillary area. The results showed that MS patients, regardless of whether they had a history of optic neuritis (ON), had a lower ONH flow index and reduced thickness of the retinal nerve fiber layer compared to healthy control eyes. The differences were even more pronounced in MS patients with a history of ON. In comparison to their previous study with a smaller sample size, this study showed that MS patients without a history of ON had a 5.5% decrease in the ONH flow index compared to healthy controls, while MS patients with ON showed a 14.7% decrease compared to healthy controls [25].

Kleerekooper et al. summarized the current state of development of OCT-A in MS and some other neuroinflammatory disorders (e.g., neuromyelitis optica spectrum disorder (NMOSD)) and interested readers may refer to it [4]. Although, OCT-A gives new insight into various neuroinflammatory disorders using qualitative features of the retinal microvasculature. Prior to OCT-A being fully implemented into clinical practice, however, concerns with image quality and the creation of standardization must be resolved.

4.2 Alzheimer’s disease (AD)

The neuropathology of Alzheimer’s disease (AD) involves the buildup of beta-amyloid plaques and neurofibrillary tangles, which cause inflammation and neurodegeneration [8]. It is now recognized that changes in vascular remodeling also play a role in AD, dementia, and mild cognitive impairment (MCI). Due to their similar anatomic, embryonic, and physiologic characteristics, there has been evidence that the retinal vascular network is a surrogate marker of small cerebral microvascular changes [6, 8, 15, 27]. As the retinal vascular network can be observed directly in AD by OCT-A, there has been evidence of decreased retinal vascular density in AD [8, 15]. In a recently conducted systematic review by Katsimpris et al. summarized the OCT-A metrics in AD [6]. They found whole and parafoveal superficial venous plexus (SVP) vessel density were inversely associated with AD. This conclusion was coherent with the systematic review of Rifai et al. where the authors reported a significant increase in the foveal avascular zone (FAZ) area and a significant decrease in parafoveal SVP and whole SVP density in AD [27]. However, a possible limitation of OCT-A in AD is less suitable for advanced AD patients. Advanced AD patients are easily fatigued by imaging and are more prone to fixation errors [8]. OCT-A may not be useful in subjects with advanced dementia and may be most useful for patients with a new-onset or milder form of the disease. Nevertheless, though recent advances in biomedical imaging modalities like positron emission tomography (PET) have revolutionized the visualization of amyloid-β plagues presence in vivo in cognitively healthy individuals, it is not yet feasible as a large-scale screening procedure due to its expensive nature [6]. Compared to this, quantitative OCT-A measurements might provide cost-effective useful biomarkers for assessing the course of AD-related neurodegeneration. From a clinical perspective, it would be really beneficial if a cost-effective retinal OCT-A screening can make an early diagnosis of AD and its disease progression even before severe brain degeneration. Though it requires further study, OCT-A shows promise to develop early retinal biomarkers for pre-clinical AD pathology and its progression from dementia.

Apart from these, there has been growing interest in OCT-A-based screening for cerebral small vessel diseases (CSVD). Lee et al. [28] conducted a prospective cross-sectional study in the eyes of 69 (138 eyes) cognitively impaired patients to evaluate radial peripapillary capillary (RPC) network density through OCT-A and retinal nerve fiber layer (RNFL) thickness and determine their association with brain imaging markers. Among the 29 patients with amyloid-positive Alzheimer’s disease-related cognitive impairment (ADCI), 25 patients with subcortical vascular cognitive impairment (SVCI), and 15 amyloid-negative cognitively normal (CN) subjects were enrolled in the study. The authors found the microvasculature of the RPC network was related to the CSVD burden. However, the RNFL thickness did not reflect cerebral neurodegeneration (Figure 7 and Table 4).

Figure 7.

Representative images according to diagnostic groups. Representative patient images of OCT-A and brain magnetic resonance imaging (MRI). Images demonstrate the subcortical vascular cognitive impairment (SVCI), the Alzheimer’s disease-related cognitive impairment (ADCI), and the superficial radial peripapillary capillary network (upper row) and axial T2 fluid-attenuated inversion recovery (bottom row) of patients. The patient with SVCI has severe subcortical white matter hyperintensity and decreased peripapillary capillary network density in the temporal quadrant (arrows). (arrowheads). (Reprinted from ref. [28]).

	ADCI, n = 28	SVCI, n = 18	CN, n = 14	p values*
				ADCI vs. CN	SVCI vs. CN	ADCI vs. SVCI
Capillary density (CD) in the RPC network (%)
Superior	64.15 (6.39)	60.14 (6.42)	63.16 (6.18)	1	0.121	0.033
Inferior	67.19 (7.34)	64.06 (6.07)	63.43 (7.8)	0.238	1	0.171
Temporal	45.76 (7.13)	42.34 (6.29)	48.45 (7.08)	0.471	0.001	0.048
Nasal	49.69 (5.52)	50.25 (6.29)	50.51 (5.59)	1	1	1
RNFL thickness (μm)
Superior	129.80 (19.2)	124.19 (21.73)	126.5 (16.44)	1	1	1
Inferior	138.25 (22.21)	128.51 (19.5)	138.1 (18.51)	1	1	1
Temporal	80.48 (12.13)	76.84 (15.36)	77.79 (10.83)	1	0.906	1
Nasal	76.33 (15.64)	78.73 (11.84)	81.57 (10.99)	0.404	1	0.74

Table 4.

Comparisons of capillary density in the radial peripapillary capillary (RPC) network and retinal nerve fiber layer (RNFL) thickness among the three groups.

(Reprinted from ref. [28].)

*p values: after Bonferroni correction for multiple group comparison.

4.3 Stroke

Stroke may be defined as a neurological deficit attributed to an acute focal injury of the central nervous system (CNS) by a vascular cause, including cerebral infarction, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH), and is a major cause of disability and death worldwide [29]. Of the two types of strokes: ischemic and hemorrhagic, ischemic stroke accounts for 80% of stroke cases and is characterized as an episode of neurological dysfunctions caused by focal cerebral, spinal, or retinal infarction [29, 30]. Lacunar infarctions, which are tiny infarctions (3–15 mm in diameter) in the deep perforating artery region, account for around 25% of ischemic strokes [31]. The causes of lacunar infarction and whether it differs from cortical stroke are still up for debate despite the fact that it has been acknowledged as a recognized subtype of stroke for more than 50 years. OCT-A allows the understanding of the pathophysiological processes underlying lacunar infarction in other vascular beds, such as the retina, where small vessels can be visualized easily. Very recently, Duan et al. conducted the first OCT-A study and compared its metrics in lacunar and non-lacunar strokes [31]. They found retinal microvascular changes using OCT-A several years after the diagnosis of ischemic stroke. Though OCT-A is not yet established for the diagnosis of acute stroke, the modality can increase our understanding of different stroke sub-types and cerebrovascular diseases. They detected that increased FAZ axis ratio (FAR) of the deep capillary plexus (DCP) and decreased FAZ circularity (FC) of the DCP were associated with ischemic stroke. Also, decreased vascular orientation distribution (VOD) of the superficial capillary plexus (SCP) is associated with lacunar infarction compared with non-lacunar infarction.

Moreover, in a very recent study, Pachade et al. demonstrated that microvasculature density features from OCT-A images have the potential to be used to diagnose acute cerebral stroke from the retina. They found decreased microvasculature density, signifying a sparser vessel network, was associated with acute stroke in their study group. Using a self-supervised learning of OCT-A and fundus imaging, their diagnostic system may have a future role in relatively lower cost acute stroke diagnosis and warrants further research [7].

Apart from it, Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a rare autosomal dominant disease, is the leading cause of hereditary ischemic strokes. The vessel wall of the brain’s vasculature thickens as a result of a mutation in the Notch-3 gene, leading to lumen stenosis [15]. OCT-A changes in CADASIL were found to be significantly reduced vessel density in the DCP in the macular region. When compared to the control group, there were no differences in any other macular or optic nerve head OCT-A parameters.

Finally, in addition to improving our understanding of CSVD as stated in the previous section, OCT-A vascular parameters may shed light on the progression of ongoing microvascular vascular events (e.g., in hypertension, stroke, endothelial dysfunction, and inflammation) in patients.

4.4 Parkinson’s disease (PD)

Previous research on the retina in Parkinson’s disease (PD) patients relied solely on structural OCT scans [9]. Although OCT allows for the analysis of morphological changes in various neurological conditions, the addition of OCT-A will significantly enhance the diagnostic capabilities by enabling a closer examination of the functional and vascular components. Now, it has been established that retinal microvasculature density is associated with PD progression (Figure 8) [9, 32]. It was first demonstrated by Kwapong et al. [33] that OCT-A revealed decreased retinal microvascular density in early PD patients, which was later confirmed by Robbins et al. [34] too [33, 34]. In an advanced approach by Zou et al. showed combination of OCT and OCT-A in PD had better diagnostic ability than either alone, and may provide an additional biomarker for disease progression [8].

Figure 8.

Representative Optical Coherence Tomography Angiography (OCT-A) Images in PD vs control. OCT-A image and measurement of the density analysis of the superficial retinal capillary plexus (SCP). (A,B) Map of macular and peripapillary area. The inner and outer rings were divided into four quadrants: superior (SO & SI), nasal (NO & NI), inferior (IO & II), and temporal (TO & TI). (C,D) Foveal avascular zone (FAZ) area was significantly decreased in patients with Parkinson’s disease (PD) (D) compared to healthy controls (HC) (C). (Reprinted from ref. [32]).

However, the limitation of OCT-A in PD is that motion artifacts increase in advanced PD patients. The possible cause is PD’s cardinal motor symptoms such as tremors, bradykinesia, and rigidity, as well as neuropsychiatric symptoms like reduced attention control and bradyphrenia. The motor symptoms worsen as the disease progresses and that is why motion artifacts also increase with the duration of PD, making it difficult to obtain artifact-free OCT-A recordings, especially in the advanced stages of the disease [9]. An interesting study by Lauermann et al. found that motion artifacts in OCT-A images are equally common in both medicated PD patients and healthy controls [9]. However, more advanced stages of PD, indicated by longer disease duration and more severe motor symptoms, were associated with higher levels of motion artifacts. Thus, caution and expertise are necessary when evaluating the quality and interpreting the results of OCT-A images, particularly in the advanced stages of PD. More importantly, the authors suggest that as like as MRI scans are evaluated by specialized radiologists or neuroradiologists to ensure accurate results for clinical interpretation by other departments. Specialized eye clinics should create and assess OCT-A recordings and then provide the revised results to non-specialist colleagues for clinical use to mitigate such caveats. Nevertheless, OCT-A has opened a new avenue of research in early diagnosis, and disease progression from micro-perfusion changes in PD patients.

5. Conclusion

Optical Coherence Tomography Angiography (OCT-A) has emerged as a powerful tool in imaging microvasculature in different neuro-ophthalmological and central nervous system disorders. With its unique capability to provide detailed microvascular architecture, it has contributed to the diagnosis, monitoring progression, and prognosis of various conditions such as optic neuropathies, papilledema, glaucoma, multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, different cerebral small vessel diseases, and stroke. Despite the current limitations, the future of OCT-A in neuro-ophthalmology and neurology is promising, as ongoing research continues to explore its potential applications, overcome its challenges, and further advance its capabilities.