Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks

Multilayer Integration of Networks Toolbox (MINT)

We present MINT (Multilayer Integration of Networks Toolbox), a Python package for multimodal data integration and community detection. MINT includes data standardization, Similarity Network Fusion, Generalized Louvain clustering, visualization, cross-validation, and modality selection optimization, capturing complex relationships among disease markers. We applied MINT to two multimodal datasets spanning the Alzheimer's disease (AD) spectrum: a primary cohort of 206 participants and a validation cohort of 143 participants, including structural magnetic resonance imaging (MRI), amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), cognition, and genetics. We hypothesized that modeling intra- and inter-modality associations would improve AD prediction and identify preclinical cases. Across both datasets, MINT identified PET and CSF as optimal modalities and detected two communities: one AD-dominant and one cognitively normal-dominant (CN). Sensitivity and specificity for CN and AD were 84.38% (95% CI: 73.14-92.24) and 92.65% (95% CI: 83.67-97.57). The AD-dominant community exhibited poorer cognition and higher genetic risk and AD pathology (p < 0.001). CN individuals in this group showed elevated amyloid (p=0.009), tau (p=0.004), and ptau (p < 0.001) compared to AD individuals in the CN-dominant group. MINT can identify biologically relevant subgroups, predict disease progression, and serves as a powerful tool for uncovering complex relationships across heterogeneous and multifactorial disorders.

A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity; however, translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity. We organized the current methodology into 1) a subtyping clustering strategy for patients with AD, and we also subdivided it into subtyping analysis based on cross-sectional multimodal neuroimaging profiles and the identification of long-term disease progression from short-term datasets; 2) a stratified strategy that integrates neuroimaging measures with biomarkers; and 3) individual-specific abnormal patterns based on the normative model. Then, we evaluated the characteristics of these studies along 2 dimensions: 1) the understanding of pathology and 2) clinical application. We systematically address the limitations, challenges, and future directions of research into AD heterogeneity. Our goal is to enhance the neuroimaging heterogeneity framework for AD, thereby facilitating its transition from bench to bedside.

Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network

BACKGROUND

Mild cognitive impairment (MCI) is a heterogeneous disorder with significant individual variabilities in clinical and biological features. Abnormal inter-regional structural covariance suggests disruption of the brain structural network in MCI. Most studies have examined group-level structural covariance alterations while ignoring individual-level differences. Hence, we aimed to investigate the heterogeneity of MCI using individual differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted images of 596 MCI patients and 309 cognitively normal (CN) were collected from the ADNI database as discovery dataset, and 122 MCI and 117 CN from the OASIS-3 dataset as validation cohort. We constructed each patient's IDSCN using regional gray matter volume and applied K-means clustering analysis to identify MCI subtypes based on significantly altered covariance edges. Then, clinical features, brain structure, and gene expression profiles were evaluated for each subtype.

RESULTS

In the ADNI dataset, MCI patients exhibited significant alterations in structural covariance edges, mainly involving the hippocampus, parahippocampal gyrus, and amygdala. Two robust MCI subtypes were identified. Subtype 1 showed faster disease progression relative to subtype 2, which was validated in the independent OASIS-3 dataset. Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal ion transport and neuron projection development. Finally, correlation analysis and functional annotation further revealed that the affected edges were related to cognitive performance and implicated in memory and emotion terms.

CONCLUSIONS

In summary, these findings offer new perspectives into understanding the heterogeneity of MCI and facilitate strategies for future precision medicine.

The whole-brain structural and functional connectome in Alzheimer's disease spectrum: A multimodal Bayesian meta-analysis of graph theoretical characteristics

Alzheimer's disease (AD) spectrum is increasingly recognized as a progressive network-disconnection syndrome. Neuroimaging studies using graph theoretical analysis (GTA) have reported alterations in the topological properties of whole-brain structural and functional connectomes in both preclinical AD and AD patients, though findings remain inconsistent. This study aimed to identify robust changes in multimodal GTA metrics across the AD spectrum through a comprehensive literature search and Bayesian random-effects meta-analyses. The analysis included 53 studies (37 functional and 17 structural), involving 1649 AD patients, 1455 preclinical AD patients, and 1771 healthy controls (HC). Results revealed lower structural network integration (evidenced by higher characteristic path length and/or normalized characteristic path length) and segregation (evidenced by lower clustering coefficient and local efficiency) in AD and preclinical AD patients compared to HC. Functional network segregation was also lower in AD patients, while preclinical AD showed preserved functional topology despite structural changes. Moderator analyses identified potential methodological moderators, including neuroimaging technique, node and edge definitions, and network type, although further validation is needed. These findings support the progressive disconnection hypothesis in the AD spectrum and suggest that structural network alterations may precede functional network changes. Furthermore, the results help clarify inconsistencies in previous studies and highlight the utility of graph-based metrics as biomarkers for staging AD progression.

Intranasal amyloid model of Alzheimer's disease - potential opportunities and challenges

Amyloid beta 1-42 (Aβ1-42) peptide is one of the most studied disease-related amyloidogenic peptides implicated in the pathophysiology of Alzheimer's disease (AD). Despite significant scientific breakthroughs in the recent past, the existing non-transgenic animal models do not demonstrate accurate pathology of AD progression. This review has presented a concise mechanistic understanding of the intranasal amyloid-based animal model of AD, along with its advantages, challenges, and major limitations. Furthermore, discussions on how to combat these challenges to pave the road toward developing novel therapeutics for AD, have also been included. Preclinical exploration of repeated intranasal amyloid-beta exposure would certainly aid the translational development of a robust animal model of AD. This will also provide a better understanding of disease progression and pathology in the intranasal animal model.

Investigating neuropathological correlates of hyperactive and psychotic symptoms in dementia: a systematic review

Introduction

Behavioral and Psychological Symptoms of Dementia (BPSD) are common neuropsychiatric manifestations that complicate the clinical course of dementia and impact caregiving. Among these, the Hyperactivity-Impulsivity-Irritiability-Disinhibition-Aggression-Agitation (HIDA) and Psychosis (P) domains are particularly challenging to manage. Despite their prevalence, their underlying mechanisms and neuropathological correlates, remain poorly understood. This systematic review aims to elucidate the neuropathological basis of the HIDA and psychosis domains, exploring whether distinct proteinopathies and neural circuit dysfunctions are associated with these symptoms.

Methods

The review follows PRISMA guidelines, with a systematic search conducted across MEDLINE, CENTRAL, and EMBASE databases. Inclusion criteria involved studies exploring the neuropathology of the HIDA and psychosis domains in individuals with dementia. Records were screened using PICO software, and data quality was assessed using the Newcastle-Ottawa Scale (NOS) and CARE guidelines. A narrative synthesis was conducted due to heterogeneity in the data.

Results

From 846 records identified, 37 studies met inclusion criteria. Of the 18,823 cases analyzed, the most common diagnoses were Alzheimer's Disease (83.44%), Dementia with Lewy Bodies (5.37%), and Frontotemporal Dementia (13.40%). HIDA-P symptoms were distributed across all clinical diagnoses, with agitation (14.00%), delusions (11.60%), disinhibition (7.61%), and hallucinations (6.83%) being the most frequently reported behaviors. The primary neuropathological diagnosis was Alzheimer's Disease Neuropathologic Change (ADNC), present predominantly in intermediate to severe forms. The neuropathological analysis revealed the co-occurrence of multiple proteinopathies, particularly TAUopathy, TDP-43 pathology, and Lewy-related pathology (LRP), with the latter, in association with ADNC, reported in 15 studies.

Discussion

HIDA-P symptoms were linked with overlapping involvement of different neural circuits, particularly the amygdala and the broader limbic system. Evidence suggests that TAUopathy and multiple proteinopathies in key brain regions, such as amygdala, are central to the development of these symptoms. In contrast, the contribution of beta-amyloid and vascular damage appears marginal in the genesis of HIDA and psychotic symptoms. No behavioral symptom is pathognomonic of a specific proteinopathy; rather, the topography and severity of lesions plays a more decisive role than their single molecular composition.

Systematic review registration

INPLASY2024100082.

Curation and validation of electronic medical record-based dementia diagnoses in the VA Million Veteran Program

BACKGROUND

The age distribution and diversity of the VA Million Veteran Program (MVP) cohort make it a valuable resource for studying the genetics of Alzheimer's disease (AD) and related dementias (ADRD).

OBJECTIVE

We present and evaluate the performance of several International Classification of Diseases (ICD) code-based classification algorithms for AD, ADRD, and dementia for use in MVP genetic studies and other studies using VA electronic medical record (EMR) data. These were benchmarked relative to existing ICD algorithms and AD-medication-identified cases.

METHODS

We used chart review of n = 103 MVP participants to evaluate diagnostic utility of the algorithms. Suitability for genetic studies was examined by assessing association with APOE ε4, the strongest genetic AD risk factor, in a large MVP cohort (n = 286 K).

RESULTS

The newly developed MVP-ADRD algorithm performed well, comparable to the existing PheCode dementia algorithm (Phe-Dementia) in terms of sensitivity (0.95 and 0.95) and specificity (0.65 and 0.70). The strongest APOE ε4 associations were observed in cases identified using MVP-ADRD and Phe-Dementia augmented with medication-identified cases (MVP-ADRD or medication, p = 3.6 ×10-290; Phe-Dementia or medication, p = 1.4 ×10-290). Performance was improved when cases were restricted to those with onset age ≥60.

CONCLUSIONS

We found that our MVP-developed ICD-based algorithms had good performance in chart review and generated strong genetic signals, especially after inclusion of medication-identified cases. Ultimately, our MVP-derived algorithms are likely to have good performance in the broader VA, and their performance may also be suitable for use in other large-scale EMR-based biobanks in the absence of definitive biomarkers such as amyloid-PET and cerebrospinal fluid biomarkers.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Spatiotemporal Dysregulation of Neuron-Glia Related Genes and Pro-/Anti-Inflammatory miRNAs in the 5xFAD Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD), the leading cause of dementia, is a multifactorial disease influenced by aging, genetics, and environmental factors. miRNAs are crucial regulators of gene expression and play significant roles in AD onset and progression. This exploratory study analyzed the expression levels of 28 genes and 5 miRNAs (miR-124-3p, miR-125b-5p, miR-21-5p, miR-146a-5p, and miR-155-5p) related to AD pathology and neuroimmune responses using RT-qPCR. Analyses were conducted in the prefrontal cortex (PFC) and the hippocampus (HPC) of the 5xFAD mouse AD model at 6 and 9 months old. Data highlighted upregulated genes encoding for glial fibrillary acidic protein (Gfap), triggering receptor expressed on myeloid cells (Trem2) and cystatin F (Cst7), in the 5xFAD mice at both regions and ages highlighting their roles as critical disease players and potential biomarkers. Overexpression of genes encoding for CCAAT enhancer-binding protein alpha (Cebpa) and myelin proteolipid protein (Plp) in the PFC, as well as for BCL2 apoptosis regulator (Bcl2) and purinergic receptor P2Y12 (P2yr12) in the HPC, together with upregulated microRNA(miR)-146a-5p in the PFC, prevailed in 9-month-old animals. miR-155 positively correlated with miR-146a and miR-21 in the PFC, and miR-125b positively correlated with miR-155, miR-21, while miR-146a in the HPC. Correlations between genes and miRNAs were dynamic, varying by genotype, region, and age, suggesting an intricate, disease-modulated interaction between miRNAs and target pathways. These findings contribute to our understanding of miRNAs as therapeutic targets for AD, given their multifaceted effects on neurons and glial cells.

Trajectory of brain-derived amyloid beta in Alzheimer's disease: where is it coming from and where is it going?

Alzheimer's disease (AD) is a progressive neurological disorder that primarily impacts cognitive function. Currently there are no disease-modifying treatments to stop or slow its progression. Recent studies have found that several peripheral and systemic abnormalities are associated with AD, and our understanding of how these alterations contribute to AD is becoming more apparent. In this review, we focuse on amyloid‑beta (Aβ), a major hallmark of AD, summarizing recent findings on the source of brain-derived Aβ and discussing where and how the brain-derived Aβ is cleared in vivo. Based on these findings, we propose future strategies for AD prevention and treatment, from a novel perspective on Aβ metabolism.

The Relationship Between First Presenting Neuropsychiatric Symptoms in Older Adults and Autopsy-Confirmed Memory Disorders

OBJECTIVES

Although dementia is typically considered a disease of cognitive decline, almost all patients present with neuropsychiatric symptoms (NPS) at some stage of their disease. Few studies have assessed the timing of NPS onset in relation to pathological diagnoses of neurodegenerative diseases. We sought to examine the association between the first presenting clinically significant NPS in aging individuals and neuropathological diagnoses of memory disorders.

DESIGN

This retrospective longitudinal cohort study utilized the National Alzheimer's Coordinating Center (NACC) dataset, which includes participant data from 37 Alzheimer's Disease Research Centers collected between 2005 and 2022.

PARTICIPANTS

Participants (N = 5,416) aged 45 years or older with Clinical Dementia Rating-Global ratings of less than or equal to 1 were included in this analysis. A total of 4,033 (74.5%) participants presented with at least one NPS at any NACC visit.

MEASUREMENTS

To measure first NPS, the NACCBEHF variable was used, a clinician-rated variable defined as "the predominant symptom that was first recognized as a decline in the subject's behavior." Neuropathologic variables included assessments of Alzheimer's Disease, Frontotemporal Dementia, Lewy Body Dementia, Cerebral Amyloid Angiopathy, Hippocampal Sclerosis, and Cerebrovascular Disease.

RESULTS

Presentation with any clinically significant first NPS was associated with several neuropathological diagnoses including Alzheimer's Disease, Frontotemporal Lobar Dementia with TDP-43 pathology, and Lewy Body Dementia. While specific first NPS were associated with Frontotemporal Dementia neuropathology (personality change and disinhibition) and Lewy Body Dementia neuropathology (psychosis and REM behavior disturbance), Alzheimer's Disease neuropathology was associated with the majority of NPS.

CONCLUSIONS

Since neuropsychiatric symptoms are frequently the first presenting symptom of dementia, their associations with well-defined neuropathological diagnoses may help clinicians predict the subtype of future dementias.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

MicroRNA (miRNA) Complexity in Alzheimer's Disease (AD)

AD is a complex, progressive, age-related neurodegenerative disorder representing the most common cause of senile dementia and neurological dysfunction in our elderly domestic population. The widely observed heterogeneity of AD is a reflection of the complexity of the AD process itself and the altered molecular-genetic mechanisms operating in the diseased human brain and CNS. One of the key players in this complex regulation of gene expression in human pathological neurobiology are microRNAs (miRNAs) that, through their actions, shape the transcriptome of brain cells that normally associate with very high rates of genetic activity, gene transcription and messenger RNA (mRNA) generation. The analysis of miRNA populations and the characterization of their abundance, speciation and complexity can further provide valuable clues to our molecular-genetic understanding of the AD process, especially in the sporadic forms of this common brain disorder. Current in-depth analyses of high-quality AD and age- and gender-matched control brain tissues are providing pathophysiological miRNA-based signatures of AD that can serve as a basis for expanding our mechanistic understanding of this disorder and the future design of miRNA- and related RNA-based therapeutics. This focused review will consolidate the findings from multiple laboratories as to which are the most abundant miRNA species, both free and exosome-bound in the human brain and CNS, which miRNA species appear to be the most prominently affected by the AD process and review recent developments and advancements in our understanding of the complexity of miRNA signaling in the hippocampal CA1 region of AD-affected brains.

The inhibition of ORMDL3 prevents Alzheimer's disease through ferroptosis by PERK/ATF4/HSPA5 pathway

Alzheimer's disease (AD) is a neurodegenerative disease with high incidence and widespread attention. There is currently no clear clarification of the pathogenesis. However, ORMDL3 causes ferroptosis in AD, and the potential mechanisms remain unclear. So, this study explore the function of ORMDL3 on ferroptosis in AD and its potential regulatory mechanisms. APPswe/PS1dE9 mice and C57BL/6 mice were induced into the mice model. The murine microglial BV-2 cells also were induced into the vitro model. In serum samples of AD patients, ORMDL3 mRNA expression levels were upregulated. The serum ORMDL3 levels expression was positively related to the ADL score or MoCA score in AD patients. The serum ORMDL3 expression level was positively related to MMSE score or Hcy levels in AD patients. The mRNA expression of ORMDL3 in the hippocampal tissue of the mice model of AD was upregulated at one, four and eight months. The protein expression of ORMDL3 was upregulated in the mice model of AD. ORMDL3 promoted Alzheimer's disease, and increased oxidative response and ferroptosis in a model of AD. PERK/ATF4/HSPA5 pathway is one important signal pathway for the effects of ORMDL3 in a model of AD. Collectively, these data suggested that ORMDL3 promoted oxidative response and ferroptosis in a model of AD by the PERK/ATF4/HSPA5 pathway, which might be a novel target spot mechanism of ferroptosis in AD and may serve as a regulator of AD-induced ferroptosis.