Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

15 Years of Longitudinal Genetic, Clinical, Cognitive, Imaging, and Biochemical Measures in DIAN

This manuscript describes and summarizes the Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs), highlighting the wealth of longitudinal data, samples, and results from this human cohort study of brain aging and a rare monogenic form of Alzheimer's disease (AD). DIAN Obs is an international collaborative longitudinal study initiated in 2008 with support from the National Institute on Aging (NIA), designed to obtain comprehensive and uniform data on brain biology and function in individuals at risk for autosomal dominant AD (ADAD). ADAD gene mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes are deterministic causes of ADAD, with virtually full penetrance, and a predictable age at symptomatic onset. Data and specimens collected are derived from full clinical assessments, including neurologic and physical examinations, extensive cognitive batteries, structural and functional neuro-imaging, amyloid and tau pathological measures using positron emission tomography (PET), flurordeoxyglucose (FDG) PET, cerebrospinal fluid and blood collection (plasma, serum, and whole blood), extensive genetic and multi-omic analyses, and brain donation upon death. This comprehensive evaluation of the human nervous system is performed longitudinally in both mutation carriers and family non-carriers, providing one of the deepest and broadest evaluations of the human brain across decades and through AD progression. These extensive data sets and samples are available for researchers to address scientific questions on the human brain, aging, and AD.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease

Background

Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse.

Objective

To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression.

Methods

We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy.

Results

The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups.

Conclusion

These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

Toward individualized connectomes of brain morphology

The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.

Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging

Background

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive.

Objective

We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI).

Methods

Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model.

Results

In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions).

Conclusions

Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.

Axonal damage and astrocytosis are biological correlates of grey matter network integrity loss: a cohort study in autosomal dominant Alzheimer disease

Brain development and maturation leads to grey matter networks that can be measured using magnetic resonance imaging. Network integrity is an indicator of information processing capacity which declines in neurodegenerative disorders such as Alzheimer disease (AD). The biological mechanisms causing this loss of network integrity remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and network integrity in mutation carriers (N=219) and noncarriers (N=136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Aβ, Tau, synaptic (SNAP-25, neurogranin) and neuronal calcium-sensor protein (VILIP-1) preceded grey matter network disruptions by several years, while inflammation related (YKL-40) and axonal injury (NfL) abnormalities co-occurred and correlated with network integrity. This suggests that axonal loss and inflammation play a role in structural grey matter network changes.

Key points

Abnormal levels of fluid markers for neuronal damage and inflammatory processes in CSF are associated with grey matter network disruptions.The strongest association was with NfL, suggesting that axonal loss may contribute to disrupted network organization as observed in AD.Tracking biomarker trajectories over the disease course, changes in CSF biomarkers generally precede changes in brain networks by several years.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants

Recent developments in image analysis have enabled an individual’s brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum

Background

Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship.

Methods

Seven hundred and eight participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling.

Results

We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants.

Conclusions

Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage.

Funding

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).

The human connectome in Alzheimer disease - relationship to biomarkers and genetics

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

Tau-related grey matter network breakdown across the Alzheimer's disease continuum

BACKGROUND

Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer's disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum.

METHODS

We included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions.

RESULTS

Across the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology.

CONCLUSIONS

Our data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.

Associations Between Amyloid and Tau Pathology, and Connectome Alterations, in Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

The roles of amyloid-β and tau in the degenerative process of Alzheimer's disease (AD) remain uncertain. [18F]AV-45 and [18F]AV-1451 PET quantify amyloid-β and tau pathology, respectively, while diffusion tractography enables detection of their microstructural consequences.

OBJECTIVE

Examine the impact of amyloid-β and tau pathology on the structural connectome and cognition, in mild cognitive impairment (MCI) and AD.

METHODS

Combined [18F]AV-45 and [18F]AV-1451 PET, diffusion tractography, and cognitive assessment in 28 controls, 32 MCI, and 26 AD patients.

RESULTS

Hippocampal connectivity was reduced to the thalami, right lateral orbitofrontal, and right amygdala in MCI; alongside the insula, posterior cingulate, right entorhinal, and numerous cortical regions in AD (all p < 0.05). Hippocampal strength inversely correlated with [18F]AV-1451 SUVr in MCI (r = -0.55, p = 0.049) and AD (r = -0.57, p = 0.046), while reductions in hippocampal connectivity to ipsilateral brain regions correlated with increased [18F]AV-45 SUVr in those same regions in MCI (r = -0.33, p = 0.003) and AD (r = -0.31, p = 0.006). Cognitive scores correlated with connectivity of the right temporal pole in MCI (r = -0.60, p = 0.035) and left hippocampus in AD (r = 0.69, p = 0.024). Clinical Dementia Rating Scale scores correlated with [18F]AV-1451 SUVr in multiple areas reflecting Braak stages I-IV, including the right (r = 0.65, p = 0.004) entorhinal cortex in MCI; and Braak stages III-VI, including the right (r = 0.062, p = 0.009) parahippocampal gyrus in AD.

CONCLUSION

Reductions in hippocampal connectivity predominate in the AD connectome, correlating with hippocampal tau in MCI and AD, and with amyloid-β in the target regions of those connections. Cognitive scores correlate with microstructural changes and reflect the accumulation of tau pathology.