Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease

Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks

In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused-l 0 $$ {l}_0 $$ penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Personal Mastery Attenuates the Association between Greater Perceived Discrimination and Lower Amygdala and Anterior Hippocampal Volume in a Diverse Sample of Older Adults

There is limited research investigating whether perceived discrimination influences brain structures that subserve episodic memory, namely the hippocampus and amygdala. Our rationale for examining these regions build on their known sensitivity to stress and functional differences along the long-axis of the hippocampus, with the anterior hippocampus and amygdala implicated in emotional and stress regulation. We defined perceived discrimination as the unfair treatment of one group by a dominant social group without the agency to respond to the event. A potential moderator of perceived discrimination is personal mastery, which we operationally defined as personal agency. Our primary goals were to determine whether perceived discrimination correlated with amygdala and anterior hippocampal volume, and if personal mastery moderated these relationships. Using FreeSurfer 7.1.0, we processed T1-weighted images to extract bilateral amygdala and hippocampal volumes. Discrimination and personal mastery were assessed via self-report (using the Experiences of Discrimination and Sense of Control questionnaires, respectively). Using multiple regression, greater perceived discrimination correlated with lower bilateral amygdala and anterior hippocampal volume, controlling for current stress, sex, education, age, and intracranial volume. Exploratory subfield analyses showed these associations were localized to the anterior hippocampal CA1 and subiculum. As predicted, using a moderation analysis, personal mastery attenuated the relationship between perceived discrimination and amygdala and anterior hippocampal volume. Here, we extend our knowledge on perceived discrimination as a salient psychosocial stressor with a neurobiological impact on brain systems implicated in stress, memory, and emotional regulation, and provide evidence for personal mastery as a moderating factor of these relationships.

Multi-Scale Label-free Human Brain Imaging with Integrated Serial Sectioning Polarization Sensitive Optical Coherence Tomography and Two-Photon Microscopy

The study of neurodegenerative processes in the human brain requires a comprehensive understanding of cytoarchitectonic, myeloarchitectonic, and vascular structures. Recent computational advances have enabled volumetric reconstruction of the human brain using thousands of stained slices, however, tissue distortions and loss resulting from standard histological processing have hindered deformation-free reconstruction of the human brain. The development of a multi-scale and volumetric human brain imaging technique that can measure intact brain structure would be a major technical advance. Here, we describe the development of integrated serial sectioning Polarization Sensitive Optical Coherence Tomography (PSOCT) and Two Photon Microscopy (2PM) to provide label-free multi-contrast imaging, including scattering, birefringence and autofluorescence of human brain tissue. We demonstrate that high-throughput reconstruction of 4×4×2cm3 sample blocks and simple registration of PSOCT and 2PM images enable comprehensive analysis of myelin content, vascular structure, and cellular information. We show that 2μm in-plane resolution 2PM images provide microscopic validation and enrichment of the cellular information provided by the PSOCT optical property maps on the same sample, revealing the sophisticated capillary networks and lipofuscin filled cell bodies across the cortical layers. Our method is applicable to the study of a variety of pathological processes, including demyelination, cell loss, and microvascular changes in neurodegenerative diseases such as Alzheimer's disease (AD) and Chronic Traumatic Encephalopathy (CTE).

Investigating the relationship between sleep disturbances and white matter hyperintensities in older adults on the Alzheimer's disease spectrum

Background

While studies report that sleep disturbance can have negative effects on brain vasculature, its impact on cerebrovascular disease such as white matter hyperintensities (WMHs) in beta-amyloid positive older adults remains unexplored.

Methods

Linear regressions, mixed effects models, and mediation analysis examined the crosssectional and longitudinal associations between sleep disturbance, cognition, and WMH burden, and cognition in normal controls (NCs), mild cognitive impairment (MCI), and Alzheimer's disease (AD) at baseline and longitudinally.

Results

People with AD reported more sleep disturbance than NC and MCI. AD with sleep disturbance had more WMHs than AD without sleep disturbances. Mediation analysis revealed an effect of regional WMH burden on the relationship between sleep disturbance and future cognition.

Conclusion

These results suggest that WMH burden and sleep disturbance increases from aging to AD. Sleep disturbance decreases cognition through increases in WMH burden. Improved sleep could mitigate the impact of WMH accumulation and cognitive decline.

Plasma p-tau181 shows stronger network association to Alzheimer's disease dementia than neurofilament light and total tau

INTRODUCTION

We examined the ability of plasma hyperphosphorylated tau (p-tau)181 to detect cognitive impairment due to Alzheimer's disease (AD) independently and in combination with plasma total tau (t-tau) and neurofilament light (NfL).

METHODS

Plasma samples were analyzed using the Simoa platform for 235 participants with normal cognition (NC), 181 with mild cognitive impairment due to AD (MCI), and 153 with AD dementia. Statistical approaches included multinomial regression and Gaussian graphical models (GGMs) to assess a network of plasma biomarkers, neuropsychological tests, and demographic variables.

RESULTS

Plasma p-tau181 discriminated AD dementia from NC, but not MCI, and correlated with dementia severity and worse neuropsychological test performance. Plasma NfL similarly discriminated diagnostic groups. Unlike plasma NfL or t-tau, p-tau181 had a direct association with cognitive diagnosis in a bootstrapped GGM.

DISCUSSION

These results support plasma p-tau181 for the detection of AD dementia and the use of blood-based biomarkers for optimal disease detection.

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.

Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer's Disease

BACKGROUND

Lipidomics may provide insight into biochemical processes driving Alzheimer's disease (AD) pathogenesis and ensuing clinical trajectories.

OBJECTIVE

To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression.

METHODS

We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aβ42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression.

RESULTS

In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aβ 42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion.

CONCLUSION

Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual's metabolic status and may support identifying different risks of clinical progression.

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.

Perspectives for New and More Efficient Multifunctional Ligands for Alzheimer's Disease Therapy

Despite tremendous research efforts at every level, globally, there is still a lack of effective drugs for the treatment of Alzheimer's disease (AD). The biochemical mechanisms of this devastating neurodegenerative disease are not yet clearly understood. This review analyses the relevance of multiple ligands in drug discovery for AD as a versatile toolbox for a polypharmacological approach to AD. Herein, we highlight major targets associated with AD, ranging from acetylcholine esterase (AChE), beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), glycogen synthase kinase 3 beta (GSK-3β), N-methyl-d-aspartate (NMDA) receptor, monoamine oxidases (MAOs), metal ions in the brain, 5-hydroxytryptamine (5-HT) receptors, the third subtype of histamine receptor (H3 receptor), to phosphodiesterases (PDEs), along with a summary of their respective relationship to the disease network. In addition, a multitarget strategy for AD is presented, based on reported milestones in this area and the recent progress that has been achieved with multitargeted-directed ligands (MTDLs). Finally, the latest publications referencing the enlarged panel of new biological targets for AD related to the microglia are highlighted. However, the question of how to find meaningful combinations of targets for an MTDLs approach remains unanswered.