Posterior cortical atrophy phenotypic heterogeneity revealed by decoding 18F-FDG-PET

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures. Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons. We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score. Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Graded Multidimensional Clinical and Radiologic Variation in Patients With Alzheimer Disease and Posterior Cortical Atrophy

BACKGROUND AND OBJECTIVES

Alzheimer disease (AD) spans heterogeneous typical and atypical phenotypes. Posterior cortical atrophy (PCA) is a striking example, characterized by prominent impairment in visual and other posterior functions in contrast to typical, amnestic AD. The primary study objective was to establish how the similarities and differences of cognition and brain volumes within AD and PCA (and by extension other AD variants) can be conceptualized as systematic variations across a transdiagnostic, graded multidimensional space.

METHODS

This was a cross-sectional, single-center, observational, cohort study performed at the National Hospital for Neurology & Neurosurgery, London, United Kingdom. Data were collected from a cohort of patients with PCA and AD, matched for age, disease duration, and Mini-Mental State Examination (MMSE) scores. There were 2 sets of outcome measures: (1) scores on a neuropsychological battery containing 22 tests spanning visuoperceptual and visuospatial processing, episodic memory, language, executive functions, calculation, and visuospatial processing and (2) measures extracted from high-resolution T1-weighted volumetric MRI scans. Principal component analysis was used to extract the transdiagnostic dimensions of phenotypical variation from the detailed neuropsychological data. Voxel-based morphometry was used to examine associations between the PCA-derived clinical phenotypes and the structural measures.

RESULTS

We enrolled 93 participants with PCA (mean: age = 59.9 years, MMSE = 21.2; 59/93 female) and 58 AD participants (mean: age = 57.1 years, MMSE = 19.7; 22/58 female). The principal component analysis for PCA (sample adequacy confirmed: Kaiser-Meyer-Olkin = 0.865) extracted 3 dimensions accounting for 61.0% of variance in patients' performance, reflecting general cognitive impairment, visuoperceptual deficits, and visuospatial impairments. Plotting AD cases into the PCA-derived multidimensional space, and vice versa, revealed graded, overlapping variations between cases along these dimensions, with no evidence for categorical-like patient clustering. Similarly, the relationship between brain volumes and scores on the extracted dimensions was overlapping for PCA and AD cases.

DISCUSSION

These results provide evidence supporting a reconceptualization of clinical and radiologic variation in these heterogenous AD phenotypes as being along shared phenotypic continua spanning PCA and AD, arising from systematic graded variations within a transdiagnostic, multidimensional neurocognitive geometry.

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients

OBJECTIVE

This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE.

METHODS

We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making.

RESULTS

We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r =  - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965.

CONCLUSIONS

Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Prosopagnosia: face blindness and its association with neurological disorders

Loss of facial recognition or prosopagnosia has been well-recognized for over a century. It has been categorized as developmental or acquired depending on whether the onset is in early childhood or beyond, and acquired cases can have degenerative or non-degenerative aetiologies. Prosopagnosia has been linked to involvement of the fusiform gyri, mainly in the right hemisphere. The literature on prosopagnosia comprises case reports and small case series. We aim to assess demographic, clinical and imaging characteristics and neurological and neuropathological disorders associated with a diagnosis of prosopagnosia in a large cohort. Patients were categorized as developmental versus acquired; those with acquired prosopagnosia were further subdivided into degenerative versus non-degenerative, based on neurological aetiology. We assessed regional involvement on [18F] fluorodeoxyglucose-PET and MRI of the right and left frontal, temporal, parietal and occipital lobes. The Intake and Referral Center at the Mayo Clinic identified 487 patients with possible prosopagnosia, of which 336 met study criteria for probable or definite prosopagnosia. Ten patients, 80.0% male, had developmental prosopagnosia including one with Niemann-Pick type C and another with a forkhead box G1 gene mutation. Of the 326 with acquired prosopagnosia, 235 (72.1%) were categorized as degenerative, 91 (27.9%) as non-degenerative. The most common degenerative diagnoses were posterior cortical atrophy, primary prosopagnosia syndrome, Alzheimer's disease dementia and semantic dementia, with each diagnosis accounting for >10% of this group. The most common non-degenerative diagnoses were infarcts (ischaemic and haemorrhagic), epilepsy-related and primary brain tumours, each accounting for >10%. We identified a group of patients with non-degenerative transient prosopagnosia in which facial recognition loss improved or resolved over time. These patients had migraine-related prosopagnosia, posterior reversible encephalopathy syndrome, delirium, hypoxic encephalopathy and ischaemic infarcts. On [18F] fluorodeoxyglucose-PET, the temporal lobes proved to be the most frequently affected regions in 117 patients with degenerative prosopagnosia, while in 82 patients with non-degenerative prosopagnosia, MRI revealed the right temporal and right occipital lobes as most affected by a focal lesion. The most common pathological findings in those with degenerative prosopagnosia were frontotemporal lobar degeneration with hippocampal sclerosis and mixed Alzheimer's and Lewy body disease pathology. In this large case series of patients diagnosed with prosopagnosia, we observed that facial recognition loss occurs across a wide range of acquired degenerative and non-degenerative neurological disorders, most commonly in males with developmental prosopagnosia. The right temporal and occipital lobes, and connecting fusiform gyrus, are key areas. Multiple different pathologies cause degenerative prosopagnosia.

Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

Deciphering the clinico-radiological heterogeneity of dysexecutive Alzheimer's disease

Dysexecutive Alzheimer's disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors ("eigenbrains") accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. "left-dominant," "right-dominant," "bi-parietal-dominant," and "heteromodal-diffuse." Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain-behavior relationships relevant to clinical practice and disease physiology.

Posterior cortical atrophy: clinical, neuroimaging, and neuropathological features

INTRODUCTION

Posterior Cortical Atrophy (PCA) is a neurodegenerative disorder characterized by impairment of higher-order visual processing in the setting of progressive atrophy of the parietal and occipital lobes. The underlying pathology is variable but most commonly Alzheimer's disease. The majority of individuals develop symptoms before 65 years of age; however, delayed diagnosis is common due to misattribution of symptoms to ocular rather than cortical pathology.

AREAS COVERED

The purpose of this review is to provide readers with an in-depth analysis of Posterior Cortical Atrophy syndrome, including clinical, imaging, pathological, and genetic features, management, and treatments.

EXPERT OPINION

Most patients present initially with a relatively pure visuoperceptual-visuospatial syndrome, though other cognitive domains become affected over time. Structural neuroimaging demonstrates parieto-occipital or temporo-occipital predominant atrophy. Cerebrospinal fluid Alzheimer's disease biomarkers, or amyloid/tau PET imaging can help evaluate for underlying Alzheimer's disease, which is the most common underlying neuropathology. The cornerstone of management is focused on nonpharmacologic measures. Early etiologic diagnosis is important with the arrival of disease-modifying therapies, especially for Alzheimer's disease.

Diagnosis and Management of Posterior Cortical Atrophy

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.

Neurofunctional Correlates of Activities of Daily Living in Patients with Posterior Cortical Atrophy

BACKGROUND

Research on posterior cortical atrophy (PCA) has focused on cognitive decline, especially visual processing deficits. However, few studies have examined the impact of PCA on activities of daily living (ADL) and the neurofunctional and neuroanatomic bases of ADL.

OBJECTIVE

To identify brain regions associated with ADL in PCA patients.

METHODS

A total of 29 PCA patients, 35 typical Alzheimer's disease (tAD) patients, and 26 healthy volunteers were recruited. Each subject completed an ADL questionnaire that included basic and instrumental subscales (BADL and IADL, respectively), and underwent hybrid magnetic resonance imaging and 18F fluorodeoxyglucose positron emission tomography. Voxel-wise regression multivariable analysis was conducted to identify specific brain regions associated with ADL.

RESULTS

General cognitive status was similar between PCA and tAD patients; however, the former had lower total ADL scores and BADL and IADL scores. All three scores were associated with hypometabolism in bilateral parietal lobes (especially bilateral superior parietal gyri) at the whole-brain level, PCA-related hypometabolism level, and PCA-specific hypometabolism level. A cluster that included the right superior parietal gyrus showed an ADL×group interaction effect that was correlated with the total ADL score in the PCA group (r = -0.6908, p = 9.3599e-5) but not in the tAD group (r = 0.1006, p = 0.5904). There was no significant association between gray matter density and ADL scores.

CONCLUSION

Hypometabolism in bilateral superior parietal lobes contributes to a decline in ADL in patients with PCA and can potentially be targeted by noninvasive neuromodulatory interventions.

Phenotypic subtypes of progressive dysexecutive syndrome due to Alzheimer's disease: a series of clinical cases

Diagnostic criteria for a progressive dysexecutive syndrome due to Alzheimer's disease (dAD) were proposed. Clinical observations suggest substantial variability in the clinico-radiological profiles within this syndrome. We report a case series of 6 patients with dAD highlighting this heterogeneity. Average age at diagnosis was 57.3 years, and patients were followed annually with clinical, cognitive, and multimodal imaging assessments for an average of 3.7 years. Cases were divided based into three subtypes based on their pattern of FDG-PET hypometabolism: predominantly left parieto-frontal (ldAD), predominantly right parieto-frontal (rdAD), or predominantly biparietal (bpdAD) (n = 2 for each). Prominent executive dysfunction was evidenced in all patients. ldAD cases showed greater impairment on measures of verbal working memory and verbal fluency compared to other subtypes. rdAD cases showed more severe alterations in measures of visual abilities compared to language-related domains and committed more perseverative errors on a measure of cognitive flexibility. bpdAD cases presented with predominant cognitive flexibility and inhibition impairment with relative sparing of working memory and a slower rate of clinical progression. rdAD and bpdAD patients developed neuropsychiatric symptoms, whereas none of the ldAD patients did. For each subtype, patterns of tau deposition relatively corresponded to the spatial pattern of FDG hypometabolism. dAD cases could be differentiated from two clinical cases of atypical AD variants (language and visual) in terms of clinical, cognitive and neuroimaging profiles, suggesting that dAD subtypes represent clinical entities separable from other variants of the disease. The recognition of distinct dAD phenotypes has clinical relevance for diagnosis, prognosis, and symptom management.

Posterior Cortical Atrophy: Primary Occipital Variant

BACKGROUND

Posterior Cortical Atrophy (PCA) is one of the atypical Alzheimer's Disease (AD) variants, characterized by predominant visuospatial and visuoperceptual deficits, with established dorsal and ventral subtypes. A third primary occipital (caudal) variant has been suggested. We aimed to determine its demographics, clinical manifestations, and biomarker findings.

METHODS

Fifty-two PCA patients were investigated. Patients underwent neuropsychological assessment, MRI imaging and FDG-, amyloid- and tau-PET scans. Normalized regional FDG-PET values were represented as z-scores relative to a control population. Patients were divided into "primary occipital" and "other PCA" subgroups according to FDG-PET defined criteria, with primary occipital defined as patients in which the z-scores for occipital subregions were at least one standard deviation lower (i.e., more abnormal) than the z-scores in all other brain regions. Global amyloid-PET, temporoparietal FDG-PET and temporal tau-PET regions-of-interest (ROI) were calculated.

RESULTS

Nine patients were classified as primary occipital; they were older (p=0.034) and had more years of education (p=0.007) than other PCA patients. Primary occipital group performed worse on the Ishihara test for color perception (p<0.001), while other PCA patients performed worse on the Western Aphasia Battery (WAB) praxis scale (p=0.005). Overall neuropsychiatric symptom burden was lower in the primary occipital group (p<0.001). The FDG-PET meta-ROI was higher in the primary occipital subtype (p=0.006), but no differences were observed in amyloid and tau-PET.

CONCLUSIONS

Our findings suggest primary occipital PCA is characterized by an older age-at-onset, more color perception dysfunction, less severe ideomotor apraxia and less hypometabolism in temporal-parietal meta-ROI compared to established phenotypes.