Association Between Retinal Features From Multimodal Imaging and Schizophrenia

The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia

BACKGROUND AND HYPOTHESIS

Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verifying whether quantitative retinal morphological data enable age prediction and whether schizophrenia patients present with a positive retinal age gap (RAG).

STUDY DESIGN

Two samples of patients and controls were enrolled: one included 59 SZ patients and 60 controls, all of whom underwent optical coherence tomography (OCT) enabling the measurement of 72 variables. A second sample of 65 SZ patients and 70 controls was then combined with the first sample, to generate a database where each subject was represented by 28 morphological variables. Four different machine learning (ML) algorithms were used for age prediction based on z-standardized OCT data. The associations between RAG, demographic, and clinical data were also analyzed.

STUDY RESULTS

Patients from both samples had significantly higher retinal age and positive RAG ranging between 5.88 and 7.44 years depending on the specific sample. Predictions based on the larger group but with fewer OCT variables exhibited higher prediction relative error. All ML algorithms generated similar outcomes regarding retinal age. RAG correlated with the dose of antipsychotic medication and the severity of symptoms. Correlations with chronological age showed that RAG was the highest in younger patients, and from the age of about 45 years, it decreased.

CONCLUSIONS

ML-based results corroborated accelerated retinal aging in schizophrenia and showed its associations with pharmacological treatment and syndrome severity. The finding of a larger RAG in younger patients is novel and requires replication.

Oculomics approaches using retinal imaging to predict mental health disorders: a systematic review and meta-analysis

OBJECTIVE

This meta-analysis evaluated the diagnostic performance of oculomics approaches, including deep learning, machine learning, and logistic regression models, in detecting major mental disorders using retinal imaging.

METHODS

A systematic review identified 11 studies for inclusion. Study quality was assessed using the QUADAS-2 tool, revealing a high risk of bias, particularly in patient selection and index test design. Pooled sensitivity and specificity were estimated using random-effects models, and diagnostic performance was evaluated through a summary receiver operating characteristic curve.

RESULTS

The analysis included 13 diagnostic models across 11 studies, covering major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, and autism spectrum disorder using color fundus photography, and optical coherence tomography (OCT), and OCT angiography. The pooled sensitivity was 0.89 (95% CI: 0.78-0.94), and specificity was 0.87 (95% CI: 0.74-0.95). The pooled area under the curve was 0.904, indicating high diagnostic accuracy. However, all studies exhibited a high risk of bias, primarily due to case-control study designs, lack of external validation, and selection bias in 77% of studies. Some models showed signs of overfitting, likely due to small sample sizes, insufficient validation, or dataset limitations. Additionally, no distinct retinal patterns specific to mental disorders were identified.

CONCLUSION

While oculomics demonstrates potential for detecting mental disorders through retinal imaging, significant methodological limitations, including high bias, overfitting risks, and the absence of disease-specific retinal biomarkers, limit its current clinical applicability. Future research should focus on large-scale, externally validated studies with prospective designs to establish reliable retinal markers for psychiatric diagnosis.

Genetic Analysis of Retinal Cell Types in Neuropsychiatric Disorders

Importance

As an accessible part of the central nervous system, the retina provides a unique window to study pathophysiological mechanisms of brain disorders in humans. Imaging and electrophysiological studies have revealed retinal alterations across several neuropsychiatric and neurological disorders, but it remains largely unclear which specific cell types and biological mechanisms are involved.

Objective

To determine whether specific retinal cell types are affected by genomic risk for neuropsychiatric and neurological disorders and to explore the mechanisms through which genomic risk converges in these cell types.

Design, Setting, and Participants

This genetic association study combined findings from genome-wide association studies in schizophrenia, bipolar disorder, major depressive disorder, multiple sclerosis, Parkinson disease, Alzheimer disease, and stroke with retinal single-cell transcriptomic datasets from humans, macaques, and mice. To identify susceptible cell types, Multi-Marker Analysis of Genomic Annotation (MAGMA) cell-type enrichment analyses were applied and subsequent pathway analyses performed. The cellular top hits were translated to the structural level using retinal optical coherence tomography (acquired between 2009 and 2010) and genotyping data in the large population-based UK Biobank cohort study. Data analysis was conducted between 2022 and 2024.

Main Outcomes and Measures

Cell type-specific enrichment of genetic risk loading for neuropsychiatric and neurological disorder traits in the gene expression profiles of retinal cells.

Results

Expression profiles of amacrine cells (interneurons within the retina) were robustly enriched in schizophrenia genetic risk across mammalian species and in different developmental stages. This enrichment was primarily driven by genes involved in synapse biology. Moreover, expression profiles of retinal immune cell populations were enriched in multiple sclerosis genetic risk. No consistent cell-type associations were found for bipolar disorder, major depressive disorder, Parkinson disease, Alzheimer disease, or stroke. On the structural level, higher polygenic risk for schizophrenia was associated with thinning of the ganglion cell inner plexiform layer, which contains dendrites and synaptic connections of amacrine cells (B, -0.09; 95% CI, -0.16 to -0.03; P = .007; n = 36 349; mean [SD] age, 57.50 [8.00] years; 19 859 female [54.63%]). Higher polygenic risk for multiple sclerosis was associated with increased thickness of the retinal nerve fiber layer (B, 0.06; 95% CI, 0.02 to 0.10; P = .007; n = 36 371; mean [SD] age, 57.51 [8.00] years; 19 843 female [54.56%]).

Conclusions and Relevance

This study provides novel insights into the cellular underpinnings of retinal alterations in neuropsychiatric and neurological disorders and highlights the retina as a potential proxy to study synaptic pathology in schizophrenia.

Preclinical Retinal Disease Models: Applications in Drug Development and Translational Research

Retinal models play a pivotal role in translational drug development, bridging preclinical research and therapeutic applications for both ocular and systemic diseases. This review highlights the retina as an ideal organ for studying advanced therapies, thanks to its immune privilege, vascular and neuronal networks, accessibility, and advanced imaging capabilities. Preclinical retinal disease models offer unparalleled insights into inflammation, angiogenesis, fibrosis, and hypoxia, utilizing clinically translatable bioimaging tools like fundoscopy, optical coherence tomography, confocal scanning laser ophthalmoscopy, fluorescein angiography, optokinetic tracking, and electroretinography. These models have driven innovations in anti-inflammatory, anti-angiogenic, and neuroprotective strategies, with broader implications for systemic diseases such as rheumatoid arthritis, Alzheimer's, and fibrosis-related conditions. By emphasizing the integration of the 3Rs principles and novel imaging modalities, this review highlights how retinal research not only enhances therapeutic precision but also minimizes ethical concerns, paving the way for more predictive and human-relevant approaches in drug development.

Cardiovascular comorbidities in Chinese inpatients with schizophrenia spectrum disorders

Cardiovascular diseases (CVDs) are the leading cause of premature mortality in patients with schizophrenia spectrum disorders (SSDs). However, the detailed categorization of these conditions remains insufficiently explored. This study aims to identify CVDs comorbidity patterns among inpatients with SSDs and to investigate associated factors. Electronic medical records (EMRs) data from three neuropsychiatric hospitals (2015-2023) in China was conducted. Comorbidity patterns were revealed through latent class analysis (LCA), and multinomial logit analysis were utilized to evaluate the effect of factors on these patterns, calculating odds ratios (ORs) and 95% confidence intervals (CIs). Among the 2830 inpatients with SSD, four distinct comorbidity patterns were identified based on their dominant characteristics: low-risk CVDs (47.86%), primary hypertension (30.15%), heart failure (12.99%), and cardiac valve and vascular disorders (8.99%). Compared to the low-risk CVD group, male patients demonstrated a higher probability of primary hypertension (OR = 1.15) and heart failure (OR = 5.36). Significant associations were observed between comorbid CVDs and the use of typical antipsychotics, atypical antipsychotics, anxiolytics and sedatives, antidepressants, and mood stabilizers. Notably, perphenazine (OR = 22.06) and chlorpromazine hydrochloride (OR = 7.09) were strongly linked to comorbid heart failure. Among Chinese patients with SSDs, four distinct CVD comorbidity patterns were identified, with hypertension and heart failure displaying strong specificity. Variations in demographic characteristics and psychotropic medication use provide valuable insights for treatment and management.

Multi-omic spatial effects on high-resolution AI-derived retinal thickness

Retinal thickness is a marker of retinal health and more broadly, is seen as a promising biomarker for many systemic diseases. Retinal thickness measurements are procured from optical coherence tomography (OCT) as part of routine clinical eyecare. We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. The macula is disproportionately affected by high disease burden retinal disorders such as age-related macular degeneration and diabetic retinopathy, which both involve metabolic dysregulation. Analysis of common genomic variants, metabolomic, blood and immune biomarkers, disease PheCodes and genetic scores across a fine-scale macular thickness grid, reveals multiple novel genetic loci including four on the X chromosome; retinal thinning associated with many systemic disorders including multiple sclerosis; and multiple associations to correlated metabolites that cluster spatially in the retina. We highlight parafoveal thickness to be particularly susceptible to systemic insults. These results demonstrate the gains in discovery power and resolution achievable with AI-leveraged analysis. Results are accessible using a bespoke web interface that gives full control to pursue findings.

Visual remediation of contrast processing impairments in schizophrenia: A preliminary clinical trial

Schizophrenia (SZ) is associated with visual processing impairments, which are related to higher-level functional impairments. This study investigated the impact of a novel visual remediation intervention (VisR) targeting low- and mid-level visual processing impairments in SZ. We hypothesized that VisR would lead to greater improvements in contrast processing when compared to an active control condition and explored potential treatment-related changes in symptom severity. SZ participants (N = 47) were randomized into one of four groups: an active control group (cognitive training; AC); Contrast Sensitivity Training + AC (CST + AC); Contour Integration Training + AC (CIT + AC); and CST + CIT. Participants completed 20-40 training sessions. Clinical symptom severity was assessed using the Positive and Negative Syndrome Scale and contrast processing was assessed using steady-state visual evoked potentials to increasing levels of contrast of isolated-check pattern stimuli. A significant Group × Timepoint × Contrast interaction indicated superiority of CST + CIT over AC for improving contrast processing. Furthermore, a large, significant Group × Timepoint interaction indicated that CST + CIT was associated with a greater reduction in positive symptoms compared to AC. In addition, lower severity of positive symptoms at baseline was associated with a greater improvement in contrast processing over the course of treatment. This initial evaluation of VisR demonstrated that it is well tolerated and may produce greater improvements in contrast processing and positive symptoms compared to an intervention targeting only high-level cognitive functions.

Signature of Altered Retinal Microstructures and Electrophysiology in Schizophrenia Spectrum Disorders Is Associated With Disease Severity and Polygenic Risk

BACKGROUND

Optical coherence tomography and electroretinography studies have revealed structural and functional retinal alterations in individuals with schizophrenia spectrum disorders (SSDs). However, it remains unclear which specific retinal layers are affected; how the retina, brain, and clinical symptomatology are connected; and how alterations of the visual system are related to genetic disease risk.

METHODS

Optical coherence tomography, electroretinography, and brain magnetic resonance imaging were applied to comprehensively investigate the visual system in a cohort of 103 patients with SSDs and 130 healthy control individuals. The sparse partial least squares algorithm was used to identify multivariate associations between clinical disease phenotype and biological alterations of the visual system. The association of the revealed patterns with individual polygenic disease risk for schizophrenia was explored in a post hoc analysis. In addition, covariate-adjusted case-control comparisons were performed for each individual optical coherence tomography and electroretinography parameter.

RESULTS

The sparse partial least squares analysis yielded a phenotype-eye-brain signature of SSDs in which greater disease severity, longer duration of illness, and impaired cognition were associated with electrophysiological alterations and microstructural thinning of most retinal layers. Higher individual loading onto this disease-relevant signature of the visual system was significantly associated with elevated polygenic risk for schizophrenia. In case-control comparisons, patients with SSDs had lower macular thickness, thinner retinal nerve fiber and inner plexiform layers, less negative a-wave amplitude, and lower b-wave amplitude.

CONCLUSIONS

This study demonstrates multimodal microstructural and electrophysiological retinal alterations in individuals with SSDs that are associated with disease severity and individual polygenic burden.

Molecular physiology unlocks the mystery that relates cognitive impairment with the retina in schizophrenia and autism spectrum disorders: a perspective review

Schizophrenia and Autism spectrum disorders (SSD and ASD) are neurodevelopmental disorders involving cognitive impairment. Timely diagnosis is important for early intervention; currently, no tools are available to help with early diagnosis. Molecular biomarkers of cognitive impairment have been extensively studied, but clinical correlation is crucial in screening for cognitive impairment in SSD and ASD. There has been growing interest in examining the retina to scan for neurological disorders since the retina is the only part of the central nervous system that can be directly imaged non-invasively and in a timely manner. This review discusses biomarkers of cognitive impairment and their correlation to the retina in SSD and ASD. It also discusses the possible involvement of the retina and molecular biomarkers, specifically Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) and ciliary neurotrophic factor (CNTF) in the pathophysiology of SSD and ASD. A protocol for early diagnosing cognitive impairment and its severity in SSD and ASD is also suggested. This review also mentions insights into the potential use of molecular biomarkers of cognitive impairment to enhance cognitive performance in ASD and SSD and areas where more research is needed to solve the mystery of the relationship between the retina and cognitive impairment in neurodevelopmental psychiatric disorders.

Retinal morphology across the menstrual cycle: insights from the UK Biobank

Oestradiol and progesterone levels are higher in menstruating women than men of the same age, and their receptors are present in their neurosensory retina and retinal pigment epithelium. However, the impact of this hormonal environment on retinal physiology in women remains unclear. Using self-reported menstrual cycle phases as a surrogate for fluctuating hormonal levels, we investigated associations with retinovascular indices on colour fundus photograph and retinal thickness in optical coherence tomography across regularly menstruating women in the UK Biobank. We found no differences in retinal thickness across the cycle; however, vessel density, arteriolar and venular, and fractal dimension were higher in the luteal phase than follicular. The calibre of the central retinal vessels did not differ. This study suggests that the menstrual cycle phase might be associated with retinal microvasculature density in non-invasive imaging. It raises awareness for this understudied area, providing insights into neuroscience fields and epidemiological studies.

Evaluating the Impact of Retinal Vessel Segmentation Metrics on Retest Reliability in a Clinical Setting: A Comparative Analysis Using AutoMorph

Purpose

Current research on artificial intelligence-based fundus photography biomarkers has demonstrated inconsistent results. Consequently, we aimed to evaluate and predict the test-retest reliability of retinal parameters extracted from fundus photography.

Methods

Two groups of patients were recruited for the study: an intervisit group (n = 28) to assess retest reliability over a period of 1 to 14 days and an intravisit group (n = 44) to evaluate retest reliability within a single session. Using AutoMorph, we generated test and retest vessel segmentation maps; measured segmentation map agreement via accuracy, sensitivity, F1 score and Jaccard index; and calculated 76 metrics from each fundus image. The retest reliability of each metric was analyzed in terms of the Spearman correlation coefficient, intraclass correlation coefficient (ICC), and relative percentage change. A linear model with the input variables contrast-to-noise-ratio and fractal dimension, chosen by a P-value-based backward selection process, was developed to predict the median percentage difference on retest per image based on image-quality metrics. This model was trained on the intravisit dataset and validated using the intervisit dataset.

Results

In the intervisit group, retest reliability varied between Spearman correlation coefficients of 0.34 and 0.99, ICC values of 0.31 to 0.99, and mean absolute percentage differences of 0.96% to 223.67%. Similarly, in the intravisit group, the retest reliability ranged from Spearman correlation coefficients of 0.55 and 0.96, ICC values of 0.40 to 0.97, and mean percentage differences of 0.49% to 371.23%. Segmentation map accuracy between test and retest never dropped below 97%; the mean F1 scores were 0.85 for the intravisit dataset and 0.82 for the intervisit dataset. The best retest was achieved with disc-width regarding the Spearman correlation coefficient in both datasets. In terms of the Spearman correlation coefficient, the worst retests of the intervisit and intravisit groups were tortuosity density and artery tortuosity density, respectively. The intravisit group exhibited better retest reliability than the intervisit group (P < 0.001). Our linear model, with the two independent variables contrast-to-noise ratio and fractal dimension predicted the median retest reliability per image on its validation dataset, the intervisit group, with an R2 of 0.53 (P < 0.001).

Conclusions

Our findings highlight a considerable volatility in the reliability of some retinal biomarkers. Improving retest could allow disease progression modeling in smaller datasets or an individualized treatment approach. Image quality is moderately predictive of retest reliability, and further work is warranted to understand the reasons behind our observations better and thus ensure consistent retest results.

Psychiatry and sensation: the epigenetic links

A complex interaction among sensory, social and epigenetic determinants in psychiatric conditions was described across all age strata. The high prevalence of mental disorders in individuals with sensory deficits might be attributed to the interaction among social isolation, cognitive functioning and sensory processing. The epigenetic implications of such interactions were examined: environmental and social factors can affect gene expression and impact the pathogenesis of psychiatric disorders also through sensory processing. This article discussed the role of social determinants, in other words, social isolation, loneliness and chronic stress, in promoting psychiatric disorders and, in a vicious circle, sensory deficits (vision, hearing, olfaction and somatosensation). We emphasized the importance of integrating social, sensory and epigenetic factors to target different treatments for psychiatric conditions.

Deep Spectral Library of Mice Retina for Myopia Research: Proteomics Dataset generated by SWATH and DIA-NN

The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that the use of a predicted retinal-specific spectral library combined with the robust quantification achieved within this dataset will contribute to a better understanding of the proteome complexity. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.

Association between retinal nerve fiber layer thickness and psychiatric disorders: a mendelian randomization study

BACKGROUND

Retinal nerve fiber layer thickness, as a new visual indicator that may help diagnose mental disorders, is gaining attention from researchers. However, the causal relationship between retinal nerve fiber layer thickness and mental disorders is still to be effectively proved.

METHODS

A bidirectional Two-sample Mendelian randomization analysis was utilized to analyse aggregated data from large-scale genome-wide association studies, we selected genetic loci for retinal nerve fiber layer thickness in independent retinal abnormalities and three prevalent psychiatric disorders (schizophrenia, depression, bipolar disorder) as instrumental variables. The Two-sample Mendelian randomization analysis was mainly performed by inverse variance weighting and weighted median method. The Cochran Q test and leave-one-out sensitivity were used to ensure the robustness of the results. The Mendelian random polymorphism residuals and outliers were used to detect single nucleotide polymorphism outliers, and MR-Egger intercept test was used to test single nucleotide polymorphism horizontal pleiotropy.

RESULTS

IVW showed that retinal nerve fiber layer thickness was positively associated with schizophrenia (OR = 1.057, 95%CI: 1.000-1.117, P < 0.05), in the study of bipolar disorder, MR analysis also suggested a positive causal relationship between retinal nerve fiber layer thickness and bipolar disorder (OR = 1.025, 95%CI: 1.005-1.046, P < 0.05), which indicated possible causal relationships between retinal nerve fiber layer thickness and these two diseases. Depression (OR = 1.000143, 95%CI: 0.9992631-1.001024, P = 0.74) indicated no significant causal association. No reverse causal effects of psychiatric disorders on retinal nerve fiber layer thickness were found.

CONCLUSIONS

A statistically significant causal relationship between retinal nerve fiber layer thickness and schizophrenia and bipolar disorder has been supported by genetic means, indicating RNFL has potential to aid in the diagnosis of schizophrenia and bipolar disorder.

Oculomic stratification of COVID-19 patients' intensive therapy unit admission status and mortality by retinal morphological findings

To investigate if retinal thickness has predictive utility in COVID-19 outcomes by evaluating the statistical association between retinal thickness using OCT and of COVID-19-related mortality. Secondary outcomes included associations between retinal thickness and length of stay (LoS) in hospital. In this retrospective cohort study, OCT scans from 230 COVID-19 patients admitted to the Intensive Care Unit (ITU) were compared with age and gender-matched patients with pneumonia from before March 2020. Total retinal, GCL + IPL, and RNFL thicknesses were recorded, and analysed with systemic measures collected at the time of admission and mortality outcomes, using linear regression models, Pearson's R correlation, and Principal Component Analysis. Retinal thickness was significantly associated with all-time mortality on follow up in the COVID-19 group (p = 0.015), but not 28-day mortality (p = 0.151). Retinal and GCL + IPL layer thicknesses were both significantly associated with LoS in hospital for COVID-19 patients (p = 0.006 for both), but not for patients with pneumonia (p = 0.706 and 0.989 respectively). RNFL thickness was not associated with LoS in either group (COVID-19 p = 0.097, pneumonia p = 0.692). Retinal thickness associated with LoS in hospital and long-term mortality in COVID-19 patients, suggesting that retinal structure could be a surrogate marker for frailty and predictor of disease severity in this group of patients, but not in patients with pneumonia from other causes.

Association between polygenic risk for schizophrenia and retinal morphology: A cross-sectional analysis of the United Kingdom Biobank

We examined the relationship between genetic risk for schizophrenia (SZ), using polygenic risk scores (PRSs), and retinal morphological alterations. Retinal structural and vascular indices derived from optical coherence tomography (OCT) and color fundus photography (CFP) and PRSs for SZ were analyzed in N = 35,024 individuals from the prospective cohort study, United Kingdom Biobank (UKB). Results indicated that macular ganglion cell-inner plexiform layer (mGC-IPL) thickness was significantly inversely related to PRS for SZ, and this relationship was strongest within higher PRS quintiles and independent of potential confounders and age. PRS, however, was unrelated to retinal vascular characteristics, with the exception of venular tortuosity, and other retinal structural indices (macular retinal nerve fiber layer [mRNFL], inner nuclear layer [INL], cup-to-disc ratio [CDR]). Additionally, the association between greater PRS and reduced mGC-IPL thickness was only significant for participants in the 40-49 and 50-59 age groups, not those in the 60-69 age group. These findings suggest that mGC-IPL thinning is associated with a genetic predisposition to SZ and may reflect neurodevelopmental and/or neurodegenerative processes inherent to SZ. Retinal microvasculature alterations, however, may be secondary consequences of SZ and do not appear to be associated with a genetic predisposition to SZ.

Specific association between retinal neural layer thinning and neurological soft signs in schizophrenia

There is growing evidence of disproportionate retinal thinning in schizophrenia, but doubts are still raised regarding its significance in the context of the neurobiology of the disease. Therefore, we examined whether these abnormalities are significantly associated with neurological soft signs (NSS) which are closely related to the risk of schizophrenia. This cross-sectional study analyzing linear correlations between variables involved 56 schizophrenia inpatients and 60 controls. The results confirmed such relationships, and only in the patient sample. In addition, retinal abnormalities and NSS were significantly correlated with duration of illness. These findings provide further evidence for linked neurodevelopmental and neurodegenerative aspects of schizophrenia.

Structural correlations between brain magnetic resonance image-derived phenotypes and retinal neuroanatomy

BACKGROUND AND PURPOSE

The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank.

METHODS

Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders.

RESULTS

Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10-5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers.

CONCLUSIONS

Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections.

Oculomics: A Crusade Against the Four Horsemen of Chronic Disease

Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.

Intravitreal MPTP drives retinal ganglion cell loss with oral nicotinamide treatment providing robust neuroprotection

Neurodegenerative diseases have common underlying pathological mechanisms including progressive neuronal dysfunction, axonal and dendritic retraction, and mitochondrial dysfunction resulting in neuronal death. The retina is often affected in common neurodegenerative diseases such as Parkinson's and Alzheimer's disease. Studies have demonstrated that the retina in patients with Parkinson's disease undergoes changes that parallel the dysfunction in the brain. These changes classically include decreased levels of dopamine, accumulation of alpha-synuclein in the brain and retina, and death of dopaminergic nigral neurons and retinal amacrine cells leading to gross neuronal loss. Exploring this disease's retinal phenotype and vision-related symptoms is an important window for elucidating its pathophysiology and progression, and identifying novel ways to diagnose and treat Parkinson's disease. 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is commonly used to model Parkinson's disease in animal models. MPTP is a neurotoxin converted to its toxic form by astrocytes, transported to neurons through the dopamine transporter, where it causes mitochondrial Complex I inhibition and neuron degeneration. Systemic administration of MPTP induces retinal changes in different animal models. In this study, we assessed the effects of MPTP on the retina directly via intravitreal injection in mice (5 mg/mL and 50 mg/mL to 7, 14 and 21 days post-injection). MPTP treatment induced the reduction of retinal ganglion cells-a sensitive neuron in the retina-at all time points investigated. This occurred without a concomitant loss of dopaminergic amacrine cells or neuroinflammation at any of the time points or concentrations tested. The observed neurodegeneration which initially affected retinal ganglion cells indicated that this method of MPTP administration could yield a fast and straightforward model of retinal ganglion cell neurodegeneration. To assess whether this model could be amenable to neuroprotection, mice were treated orally with nicotinamide (a nicotinamide adenine dinucleotide precursor) which has been demonstrated to be neuroprotective in several retinal ganglion cell injury models. Nicotinamide was strongly protective following intravitreal MPTP administration, further supporting intravitreal MPTP use as a model of retinal ganglion cell injury. As such, this model could be utilized for testing neuroprotective treatments in the context of Parkinson's disease and retinal ganglion cell injury.

Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES

BACKGROUND

Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or the atherogenic index of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is a lack of research on non-invasive and rapid prediction of cardiovascular risk. We aimed to develop and validate a machine-learning model for predicting cardiovascular risk based on variables encompassing clinical questionnaires and oculomics.

METHODS

We collected data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training dataset (80% from the year 2008 to 2011 KNHANES) was used for machine learning model development, with internal validation using the remaining 20%. An external validation dataset from the year 2012 assessed the model's predictive capacity for TyG-index or AIP in new cases. We included 32122 participants in the final dataset. Machine learning models used 25 algorithms were trained on oculomics measurements and clinical questionnaires to predict the range of TyG-index and AIP. The area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score were used to evaluate the performance of our machine learning models.

RESULTS

Based on large-scale cohort studies, we determined TyG-index cut-off points at 8.0, 8.75 (upper one-third values), 8.93 (upper one-fourth values), and AIP cut-offs at 0.318, 0.34. Values surpassing these thresholds indicated elevated cardiovascular risk. The best-performing algorithm revealed TyG-index cut-offs at 8.0, 8.75, and 8.93 with internal validation AUCs of 0.812, 0.873, and 0.911, respectively. External validation AUCs were 0.809, 0.863, and 0.901. For AIP at 0.34, internal and external validation achieved similar AUCs of 0.849 and 0.842. Slightly lower performance was seen for the 0.318 cut-off, with AUCs of 0.844 and 0.836. Significant gender-based variations were noted for TyG-index at 8 (male AUC=0.832, female AUC=0.790) and 8.75 (male AUC=0.874, female AUC=0.862) and AIP at 0.318 (male AUC=0.853, female AUC=0.825) and 0.34 (male AUC=0.858, female AUC=0.831). Gender similarity in AUC (male AUC=0.907 versus female AUC=0.906) was observed only when the TyG-index cut-off point equals 8.93.

CONCLUSION

We have established a simple and effective non-invasive machine learning model that has good clinical value for predicting cardiovascular risk in the general population.

Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: Findings from the UK-Biobank

The human retina is part of the central nervous system and can be easily and non-invasively imaged with optical coherence tomography. While imaging the retina may provide insights on central nervous system-related disorders such as schizophrenia, a typical challenge are confounders often present in schizophrenia which may negatively impact retinal health. Here, we therefore aimed to investigate retinal changes in the context of common genetic variations conveying a risk of schizophrenia as measured by polygenic risk scores. We used population data from the UK Biobank, including White British and Irish individuals without diagnosed schizophrenia, and estimated a polygenic risk score for schizophrenia based on the newest genome-wide association study (PGC release 2022). We hypothesized that greater genetic susceptibility to schizophrenia is associated with retinal thinning, especially within the macula. To gain additional mechanistic insights, we conducted pathway-specific polygenic risk score associations analyses, focusing on gene pathways that are related to schizophrenia. Of 65484 individuals recruited, 48208 participants with available matching imaging-genetic data were included in the analysis of whom 22427 (53.48%) were female and 25781 (46.52%) were male. Our robust principal component regression results showed that polygenic risk scores for schizophrenia were associated with retinal thinning while controlling for confounding factors (b = -0.03, p = 0.007, pFWER = 0.01). Similarly, we found that polygenic risk for schizophrenia specific to neuroinflammation gene sets revealed significant associations with retinal thinning (b = -0.03, self-contained p = 0.041 (reflecting the level of association), competitive p = 0.05 (reflecting the level of enrichment)). These results go beyond previous studies suggesting a relationship between manifested schizophrenia and retinal phenotypes. They indicate that the retina is a mirror reflecting the genetic complexities of schizophrenia and that alterations observed in the retina of individuals with schizophrenia may be connected to an inherent genetic predisposition to neurodegenerative aspects of the condition. These associations also suggest the potential involvement of the neuroinflammatory pathway, with indications of genetic overlap with specific retinal phenotypes. The findings further indicate that this gene pathway in individuals with a high polygenic risk for schizophrenia could contribute through acute-phase proteins to structural changes in the retina.

Retina as a potential biomarker in schizophrenia spectrum disorders: a systematic review and meta-analysis of optical coherence tomography and electroretinography

INTRODUCTION

Abnormal findings on optical coherence tomography (OCT) and electroretinography (ERG) have been reported in participants with schizophrenia spectrum disorders (SSDs). This study aims to reveal the pooled standard mean difference (SMD) in retinal parameters on OCT and ERG among participants with SSDs and healthy controls and their association with demographic characteristics, clinical symptoms, smoking, diabetes mellitus, and hypertension.

METHODS

Using PubMed, Scopus, Web of Science, and PSYNDEX, we searched the literature from inception to March 31, 2023, using specific search terms. This study was registered with PROSPERO (CRD4202235795) and conducted according to PRISMA 2020.

RESULTS

We included 65 studies in the systematic review and 44 in the meta-analysis. Participants with SSDs showed thinning of the peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell layer- inner plexiform cell layer, and retinal thickness in all other segments of the macula. A meta-analysis of studies that excluded SSD participants with diabetes and hypertension showed no change in results, except for pRNFL inferior and nasal thickness. Furthermore, a significant difference was found in the pooled SMD of pRNFL temporal thickness between the left and right eyes. Meta-regression analysis revealed an association between retinal thinning and duration of illness, positive and negative symptoms. In OCT angiography, no differences were found in the foveal avascular zone and superficial layer foveal vessel density between SSD participants and controls. In flash ERG, the meta-analysis showed reduced amplitude of both a- and b-waves under photopic and scotopic conditions in SSD participants. Furthermore, the latency of photopic a-wave was significantly shorter in SSD participants in comparison with HCs.

DISCUSSION

Considering the prior report of retinal thinning in unaffected first-degree relatives and the results of the meta-analysis, the findings suggest that retinal changes in SSDs have both trait and state aspects. Future longitudinal multimodal retinal imaging studies are needed to clarify the pathophysiological mechanisms of these changes and to clarify their utility in individual patient monitoring efforts.

Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces

Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complete a series of standardized cognitive tasks in order to challenge specific and global cognitive abilities, and then quantify performance for the desired end purpose. The latter may include a diagnostic confirmation of a disease, description of a state or ability, or matching cognitive characteristics to a particular occupational role requirement. Metrics derived from cognitive assessments are putatively informative about important features of the brain and its function. For this reason, the research sector also makes use of cognitive assessments, most frequently as a stimulus for cognitive activity from which to extract functional neuroimaging data. Such "task-related activations" form the core of the most widely used neuroimaging technologies, such as fMRI. Much of what we know about the brain has been drawn from the interleaving of cognitive assessments of various types with functional brain imaging technologies. Despite innovation in neuroimaging (i.e., quantifying the neural response), relatively little innovation has occurred on task presentation and volitional response measurement; yet these together comprise the core of cognitive performance. Moreover, even when cognitive assessment is interleaved with functional neuroimaging, this is most often undertaken in the research domain, rather than the primary applications of cognitive assessment in diagnosis and monitoring, education and personnel selection. There are new ways in which brain imaging-and even more importantly, brain modulation-technologies can be combined with automation and artificial intelligence to deliver next-generation cognitive assessment methods. In this review paper, we describe some prototypes for how this can be done and identify important areas for progress (technological and otherwise) to enable it to happen. We will argue that the future of cognitive assessment will include semi- and fully-automated assessments involving neuroimaging, standardized perturbations via neuromodulation technologies, and artificial intelligence. Furthermore, the fact that cognitive assessments take place in a social/interpersonal context-normally between the patient and clinician-makes the human-machine interface consequential, and this will also be discussed.