Background and Objectives Alzheimer disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. To explore this, we used neuroanatomical normative modeling to index regional patterns of variability in cortical thickness. We aimed to characterize individual differences and outliers in cortical thickness in patients with AD, people with mild cognitive impairment (MCI), and controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, β-amyloid, phosphorylated-tau, and ApoE genotype. Finally, we examined whether cortical thickness heterogeneity was predictive of conversion from MCI to AD. Methods Cortical thickness measurements across 148 brain regions were obtained from T1-weighted MRI scans from 62 sites of the Alzheimer9s Disease Neuroimaging Initiative. AD was determined by clinical and neuropsychological examination with no comorbidities present. Participants with MCI had reported memory complaints, and controls were cognitively normal. A neuroanatomical normative model indexed cortical thickness distributions using a separate healthy reference data set (n = 33,072), which used hierarchical Bayesian regression to predict cortical thickness per region using age and sex, while adjusting for site noise. Z-scores per region were calculated, resulting in a Z-score brain map per participant. Regions with Z-scores <−1.96 were classified as outliers. Results Patients with AD (n = 206) had a median of 12 outlier regions (out of a possible 148), with the highest proportion of outliers (47%) in the parahippocampal gyrus. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with AD had more outlier regions than people with MCI (n = 662) or controls (n = 159) (F(2, 1,022) = 95.39, p = 2.0 × 10⁻¹⁶). They were also more dissimilar to each other than people with MCI or controls (F(2, 1,024) = 209.42, p = 2.2 × 10⁻¹⁶). A greater number of outlier regions were associated with worse cognitive function, CSF protein concentrations, and an increased risk of converting from MCI to AD within 3 years (hazard ratio 1.028, 95% CI 1.016–1.039, p = 1.8 × 10⁻¹⁶). Discussion Individualized normative maps of cortical thickness highlight the heterogeneous effect of AD on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual9s disease progression or treatment response in clinical trials.

Retinal thickness can be measured non-invasively with optical coherence tomography (OCT) and may offer compelling potential as a biomarker for Alzheimer’s disease (AD). Retinal thinning is hypothesized to be a result of retrograde atrophy and/or parallel neurodegenerative processes. Changes in the visual pathway are of particular interest in posterior cortical atrophy (PCA), the most common atypical AD phenotype predominantly affecting the parietal-occipital cortices. We therefore evaluated retinal thickness as non-invasive biomarker of neurodegeneration in well-characterized participants with posterior cortical atrophy (PCA) and typical Alzheimer’s disease (tAD). Retinal thickness measures were acquired from 48 patient participants (N = 25 PCA; N = 23 tAD) fulfilling consensus diagnostic criteria and 70 age-matched controls. Participants were recruited between 2014 and 2016. All participants underwent optical coherence tomography (OCT) imaging, including measurement of peripapillary retinal nerve fiber layer (pRNFL) thickness and total macular thickness (mRT). Participants did not show evidence of any significant ophthalmological conditions. Subgroup analyses were performed in participants with available MRI and CSF measures, providing evidence of neurodegeneration and underlying AD pathology respectively. There was no evidence of overall between-group differences in pRNFL thickness (mean PCA 98.7 ± 12.2; tAD 99.9 ± 8.7; controls 99.6 ± 10.0 μm, one-way analysis of variance (ANOVA) p = 0.92) or total mRT (mean PCA 266.9 ± 16.3; tAD 267.8 ± 13.6; controls 269.3 ± 13.6 μm, one-way ANOVA p = 0.75). Similarly, subgroup analysis with MRI biomarkers (PCA = 18, tAD = 17, controls = 31) showing neurodegeneration, and CSF biomarkers (PCA = 18, tAD = 14, controls = 13) supporting underlying AD pathology did not provide evidence of overall between-group differences in pRNFL or mRT measures (all p > 0.3). Retinal thickness did not discriminate tAD and PCA from controls or from one another despite unequivocal differences on standard clinical, neuro-imaging and CSF measures. Findings from this well-characterized sample, including cases with PCA, do not support the hypothesis that retinal neurodegeneration, measured using conventional OCT, is a useful biomarker for AD or PCA.

Purpose of review: The study aims to provide a summary of recent developments for diagnosing and managing posterior cortical atrophy (PCA). We present current efforts to improve PCA characterisation and recommendations regarding use of clinical, neuropsychological and biomarker methods in PCA diagnosis and management and highlight current knowledge gaps. Recent findings: Recent multi-centre consensus recommendations provide PCA criteria with implications for different management strategies (e.g. targeting clinical features and/or disease). Studies emphasise the preponderance of primary or co-existing Alzheimer’s disease (AD) pathology underpinning PCA. Evidence of approaches to manage PCA symptoms is largely derived from small studies. Summary: PCA diagnosis is frequently delayed, and people are likely to receive misdiagnoses of ocular or psychological conditions. Current treatment of PCA is symptomatic — pharmacological and non-pharmacological — and the use of most treatment options is based on small studies or expert opinion. Recommendations for non-pharmacological approaches include interdisciplinary management tailored to the PCA clinical profile — visual-spatial — rather than memory-led, predominantly young onset — and psychosocial implications. Whilst emerging disease-modifying treatments have not been tested in PCA, an accurate and timely diagnosis of PCA and determining underlying pathology is of increasing importance in the advent of disease-modifying therapies for AD and other albeit rare causes of PCA.

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e.g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e.g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.

Crowding is a breakdown in the ability to identify objects in clutter, and is a major constraint on object recognition. Crowding particularly impairs object perception in peripheral, amblyopic and possibly developing vision. Here we argue that crowding is also a critical factor limiting object perception in central vision of individuals with neurodegeneration of the occipital cortices. In the current study, individuals with posterior cortical atrophy (n = 26), typical Alzheimer’s disease (n = 17) and healthy control subjects (n = 14) completed centrally-presented tests of letter identification under six different flanking conditions (unflanked, and with letter, shape, number, same polarity and reverse polarity flankers) with two different target-flanker spacings (condensed, spaced). Patients with posterior cortical atrophy were significantly less accurate and slower to identify targets in the condensed than spaced condition even when the target letters were surrounded by flankers of a different category. Importantly, this spacing effect was observed for same, but not reverse, polarity flankers. The difference in accuracy between spaced and condensed stimuli was significantly associated with lower grey matter volume in the right collateral sulcus, in a region lying between the fusiform and lingual gyri. Detailed error analysis also revealed that similarity between the error response and the averaged target and flanker stimuli (but not individual target or flanker stimuli) was a significant predictor of error rate, more consistent with averaging than substitution accounts of crowding. Our findings suggest that crowding in posterior cortical atrophy can be regarded as a pre-attentive process that uses averaging to regularize the pathologically noisy representation of letter feature position in central vision. These results also help to clarify the cortical localization of feature integration components of crowding. More broadly, we suggest that posterior cortical atrophy provides a neurodegenerative disease model for exploring the basis of crowding. These data have significant implications for patients with, or who will go on to develop, dementia-related visual impairment, in whom acquired excessive crowding likely contributes to deficits in word, object, face and scene perception.

Objective: We report (1) the quantitative investigation of text reading in posterior cortical atrophy (PCA), and (2) the effects of 2 novel software-based reading aids that result in dramatic improvements in the reading ability of patients with PCA. Methods: Reading performance, eye movements, and fixations were assessed in patients with PCA and typical Alzheimer disease and in healthy controls (experiment 1). Two reading aids (single- and double-word) were evaluated based on the notion that reducing the spatial and oculomotor demands of text reading might support reading in PCA (experiment 2). Results: Mean reading accuracy in patients with PCA was significantly worse (57%) compared with both patients with typical Alzheimer disease (98%) and healthy controls (99%); spatial aspects of passages were the primary determinants of text reading ability in PCA. Both aids led to considerable gains in reading accuracy (PCA mean reading accuracy: single-word reading aid = 96%; individual patient improvement range: 6%–270%) and self-rated measures of reading. Data suggest a greater efficiency of fixations and eye movements under the single-word reading aid in patients with PCA. Conclusions: These findings demonstrate how neurologic characterization of a neurodegenerative syndrome (PCA) and detailed cognitive analysis of an important everyday skill (reading) can combine to yield aids capable of supporting important everyday functional abilities. Classification of evidence: This study provides Class III evidence that for patients with PCA, 2 software-based reading aids (single-word and double-word) improve reading accuracy.