Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort

Structural Connectome Analysis using a Graph-based Deep Model for Age and Dementia Prediction

We tackle the prediction of age and mini-mental state examination (MMSE) score based on structural brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple branches, thereby disentangling the input node and graph features. The novelty of our work lies in the model architecture, especially the connectivity attention module, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. Through our experiments, we validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease

OBJECTIVE

To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.

METHODS

A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.

RESULTS

Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).

CONCLUSION

The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

Imaging of brain clearance pathways via MRI assessment of the glymphatic system

Glymphatic clearance dysfunction may play an important role in a variety of neurodegenerative diseases and the progression of ageing. However, in vivo imaging of the glymphatic system is challenging. In this study, we describe an MRI method based on chemical exchange saturation transfer (CEST) of the Angiopep-2 probe to visualize the clearance function of the glymphatic system. We injected rats with Angiopep-2 via the tail vein and performed in vivo MRI at 7 T to track differences in Angiopep-2 signal changes; we then applied the same principles in a bilateral deep cervical lymph node ligation rat model and in ageing rats. We demonstrated the feasibility of Angiopep-2 CEST for visualizing the clearance function of the glymphatic system. Finally, a pathological assessment was performed. Within the model group, the deep cervical lymph node ligation group and the ageing group showed higher CEST signal than the control group. We conclude that this new MRI method can visualize clearance in the glymphatic system.

Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease

Introduction: The diagnosis of Alzheimer's disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect the structural network in the pre-clinical stage. We aimed to assess differences in topological properties in cognitively normal (CN) individuals with varying levels of amyloid and tau pathology, as well as their association with AD pathology burden. Methods: A total of 68 CN individuals were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A-T- (n = 19), A+T- (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A-T- group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, p = 0.02), while no difference in global efficiency was found across the three groups. At the local level, the A+T+ group showed disrupted topological properties in the left hippocampus compared with the A-T- group, characterized by lower local efficiency (p < 0.01) and a lower clustering coefficient (p = 0.014). Conclusions: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation of the topological properties of the structural network in pre-clinical AD.

Alzheimer's disease: an axonal injury disease?

Alzheimer's disease (AD) is the primary cause of dementia and is anticipated to impose a substantial economic burden in the future. Over a significant period, the widely accepted amyloid cascade hypothesis has guided research efforts, and the recent FDA approval of an anti- amyloid-beta (Aβ) protofibrils antibody, believed to decelerate AD progression, has further solidified its significance. However, the excessive emphasis placed on the amyloid cascade hypothesis has overshadowed the physiological nature of Aβ and tau proteins within axons. Axons, specialized neuronal structures, sustain damage during the early stages of AD, exerting a pivotal influence on disease progression. In this review, we present a comprehensive summary of the relationship between axonal damage and AD pathology, amalgamating the physiological roles of Aβ and tau proteins, along with the impact of AD risk genes such as APOE and TREM2. Furthermore, we underscore the exceptional significance of axonal damage in the context of AD.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

White matter dementia then… and now

White matter dementia (WMD) is a concept introduced in 1988 to highlight the importance of white matter pathology in producing cognitive dysfunction and dementia. Whereas gray matter, particularly the cerebral cortex, has been primarily investigated in the dementias, subcortical pathology has long been correlated with cognitive loss, and a corticocentric perspective cannot account for the full range of neurobehavioral disorders. Within the subcortical regions, white matter is prominent, accounting for about half the volume of the adult brain, and many white matter diseases, injuries, and intoxications can produce cognitive dysfunction so severe as to justify the term dementia. Recognition of this novel syndrome relied heavily on the introduction of magnetic resonance imaging (MRI) that permitted in vivo visualization of white matter lesions. Neuropsychological studies clarified the clinical presentation of WMD by identifying a profile dominated by cognitive slowing and executive dysfunction, and a precursor syndrome of mild cognitive dysfunction was proposed to identify early cognitive impairment that may later evolve to WMD. As knowledge advanced, the role of white matter in structural connectivity within distributed neural networks was elucidated. In addition, highlighting the frequent commingling of gray and white matter involvement, white matter pathology was associated with neurodegenerative diseases such as Alzheimer's disease and chronic traumatic encephalopathy, with potentially transformative clinical implications. In particular, preventive measures and treatments exploiting white matter restoration and plasticity are gaining much attention. Today, WMD has matured into a concept that not only integrates knowledge from across the spectrum of clinical neuroscience, but also informs new investigations into many perplexing disorders and enables a more complete understanding of brain-behavior relationships.