Cortical atrophy pattern-based subtyping predicts prognosis of amnestic MCI: an individual-level analysis

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space

Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease. Through this representation learning, we trained a clustered latent space with distinct atrophy patterns and clinical characteristics in AD, and replicated the findings in prodromal Alzheimer's disease. Moreover, we discovered that these clusters are not solely attributed to subtypes but also reflect disease progression in the latent space, representing the core dimensions of heterogeneity, namely progression and subtypes. Furthermore, longitudinal latent space analysis revealed two distinct disease progression pathways: medial temporal and parietotemporal pathways. The proposed approach enables effective latent representations that can be integrated with individual-level cognitive profiles, thereby facilitating a comprehensive understanding of AD heterogeneity.

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes

BACKGROUND

The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach.

METHODS

Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained.

RESULTS

Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637).

CONCLUSIONS

Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

Rapid olfactory decline during aging predicts dementia and GMV loss in AD brain regions

INTRODUCTION

Longitudinal multivariable analyses are needed to determine if the rate of olfactory decline during normal cognition predicts subsequent Alzheimer's disease (AD) diagnoses and brain dysmorphology.

METHODS

Older adults (n = 515) were assessed annually for odor identification, cognitive function and dementia clinical diagnosis (max follow-up 18 years). Regional gray matter volumes (GMV) were quantified (3T MRI) in a cross-sectional subsample (n = 121). Regression models were adjusted for APOE-ε4 genotype, dementia risk factors and demographics.

RESULTS

Faster olfactory decline during periods of normal cognition predicted higher incidence of subsequent MCI or dementia (OR 1.89, 95% CI: 1.26, 2.90, p < 0.01; comparable to carrying an APOE-ε4 allele) and smaller GMV in AD and olfactory regions (β = -0.11, 95% CI -0.21, -0.00).

DISCUSSION

Rapid olfactory decline during normal cognition, using repeated olfactory measurement, predicted subsequent cognitive impairment, dementia, and smaller GMVs, highlighting its potential as a simple biomarker for early AD detection.

HIGHLIGHTS

Rate of olfactory decline was calculated from olfactory testing over ≥3 time points. Rapid olfactory decline predicted impaired cognition and higher risk of dementia. Neurodegeneration on 3T magnetic resonance imaging was identical in those with olfactory decline and Alzheimer's disease.

Four Distinct Subtypes of Alzheimer's Disease Based on Resting-State Connectivity Biomarkers

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses.

METHODS

We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network.

RESULTS

Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns.

CONCLUSIONS

These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease

Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.

Clinical relevance of brain atrophy subtypes categorization in memory clinics

INTRODUCTION

The clinical relevance of brain atrophy subtypes categorization in non-demented persons without a priori knowledge regarding their amyloid status or clinical presentation is unknown.

METHODS

A total of 2083 outpatients with either subjective cognitive complaint or mild cognitive impairment at study entry were followed during 4 years (MEMENTO cohort). Atrophy subtypes were defined using baseline magnetic resonance imaging (MRI) and previously described algorithms.

RESULTS

Typical/diffuse atrophy was associated with faster cognitive decline and the highest risk of developing dementia and Alzheimer's disease (AD) over time, both in the whole analytic sample and in amyloid-positive participants. Hippocampal-sparing and limbic-predominant atrophy were also associated with incident dementia, with faster cognitive decline in the limbic predominant atrophy group. Lewy body dementia was more frequent in the hippocampal-sparing and minimal/no atrophy groups.

DISCUSSION

Atrophy subtypes categorization predicted different subsequent patterns of cognitive decline and rates of conversion to distinct etiologies of dementia in persons attending memory clinics.

Clinically Available Software for Automatic Brain Volumetry: Comparisons of Volume Measurements and Validation of Intermethod Reliability

OBJECTIVE

To compare two clinically available MR volumetry software, NeuroQuant® (NQ) and Inbrain® (IB), and examine the inter-method reliabilities and differences between them.

MATERIALS AND METHODS

This study included 172 subjects (age range, 55-88 years; mean age, 71.2 years), comprising 45 normal healthy subjects, 85 patients with mild cognitive impairment, and 42 patients with Alzheimer's disease. Magnetic resonance imaging scans were analyzed with IB and NQ. Mean differences were compared with the paired t test. Inter-method reliability was evaluated with Pearson's correlation coefficients and intraclass correlation coefficients (ICCs). Effect sizes were also obtained to document the standardized mean differences.

RESULTS

The paired t test showed significant volume differences in most regions except for the amygdala between the two methods. Nevertheless, inter-method measurements between IB and NQ showed good to excellent reliability (0.72 < r < 0.96, 0.83 < ICC < 0.98) except for the pallidum, which showed poor reliability (left: r = 0.03, ICC = 0.06; right: r = -0.05, ICC = -0.09). For the measurements of effect size, volume differences were large in most regions (0.05 < r < 6.15). The effect size was the largest in the pallidum and smallest in the cerebellum.

CONCLUSION

Comparisons between IB and NQ showed significantly different volume measurements with large effect sizes. However, they showed good to excellent inter-method reliability in volumetric measurements for all brain regions, with the exception of the pallidum. Clinicians using these commercial software should take into consideration that different volume measurements could be obtained depending on the software used.

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development

Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. Volumetric decreases in gray matter have been demonstrated in corticolimbic circuits involved in emotion regulation. MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.

Addressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers

It has been 10 years since machine learning was first applied to neuroimaging data in psychiatric disorders to identify diagnostic and prognostic markers at the level of the individual. Proof of concept findings in major depression have since been extended in international samples and are beginning to include hundreds of samples from multisite data. Neuroimaging provides the unique capability to detect an acute depressive state in major depression, while we would not expect perfect classification with current diagnostic criteria which are based solely on clinical features. We review developments and the potential impact of heterogeneity, as well as homogeneity, on classification for diagnosis and prediction of clinical outcome. It is likely that there are distinct biotypes which comprise the disorder and which predict clinical outcome. Neuroimaging-based biotypes could aid in identifying the illness in individuals who are unable to recognise their illness and perhaps to identify the treatment resistant form early in the course of the illness. We propose that heterogeneous symptom profiles can arise from a limited number of neural biotypes and that apparently heterogeneous clinical outcomes include a common baseline predictor and common mechanism of treatment. Baseline predictors of clinical outcome reflect factors which indicate the general likelihood of response as well as those which are selective for a particular form of treatment. Irrespective of the mechanism, the capacity for response will moderate the outcome, which includes inherent models of interpersonal relationships that could be associated with genetic risk load and represented by patterns of functional and structural neural correlates as a predictive biomarker. We propose that methods which directly address heterogeneity are essential and that a synergistic combination could bring together data-driven inductive and symptom-based deductive approaches. Through this iterative process, major depression can develop from being syndrome characterized by a collection of symptoms to a disease with an identifiable pathophysiology.