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

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and developing effective prevention and treatment strategies. Recent works combine imaging and genetic data, and leverage the strengths of both modalities to achieve better classification results. In this work, we propose MCA-GCN, a Multi-stream Cross-Attention and Graph Convolutional Network-based classification method for AD patients. It first constructs a brain region-gene association network based on brain region fMRI time series and gene SNP data. Then it integrates the absolute and relative positions of the brain region time series to obtain a new brain region time series containing temporal information. Then long-range and local association features between brain regions and genes are sequentially aggregated by multi-stream cross-attention and graph convolutional networks. Finally, the learned brain region and gene features are input to the fully connected network to predict AD types. Experimental results on the ADNI dataset show that our model outperforms other methods in AD classification tasks. Moreover, MCA-GCN designed a multi-stage feature scoring process to extract high-risk genes and brain regions related to disease classification.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer's disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer's disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages, 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepen the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

Association between calculated remnant cholesterol levels and incident risks of Alzheimer's disease among elderly patients with type 2 diabetes: a real-world study

Objective

Alzheimer's disease (AD) is a leading cause of dementia, with a rising global burden. Remnant cholesterol (RC), a component of triglyceride-rich lipoproteins, has been implicated in cardiovascular diseases and metabolic disorders, but its role in AD remains unclear. This study investigated the association between RC levels and the risk of AD among elderly patients with type 2 diabetes (T2D) in a real-world clinical setting.

Methods

We conducted a retrospective cohort study using electronic medical records from Gongli Hospital of Shanghai Pudong New Area, covering the period from 2013 to 2023. The study included 15,364 elderly patients aged 65-80 years with T2D. RC levels were calculated using the equation. The primary outcome was the diagnosis of AD, validated by neurologists using ICD-10-CM code G30. Cox proportional hazards models were employed to estimate hazard ratios (HRs) for AD across quartiles of RC levels, adjusting for potential confounders.

Results

Over a mean follow-up of 3.69 ± 1.33 years, 312 new cases of AD were identified. A U-shaped relationship was observed between RC levels and AD risk, with the lowest risk associated with RC levels between 0.58-0.64 mmol/L. Both lower (<0.52 mmol/L) and higher (≥0.77 mmol/L) RC levels were linked to increased AD risk. Compared to the reference group (Q2: 0.52-0.64 mmol/L), the adjusted HRs (95% CI) for the lowest and highest quartiles were 1.891 (1.368-2.613) and 1.891 (1.363-2.622), respectively. Each 1 mmol/L increase in RC was associated with a 3.47-fold higher risk of AD (HR=4.474, 95% CI 2.330-8.592).

Conclusion

RC levels may serve as a predictive biomarker for AD risk, with both extremes posing a higher risk. Future studies should explore the mechanistic pathways and potential interventions targeting RC to prevent AD in high-risk populations.

Oligodendrocytes, the Forgotten Target of Gene Therapy

If the billions of oligodendrocytes (OLs) populating the central nervous system (CNS) of patients could express their feelings, they would undoubtedly tell gene therapists about their frustration with the other neural cell populations, neurons, microglia, or astrocytes, which have been the favorite targets of gene transfer experiments. This review questions why OLs have been left out of most gene therapy attempts. The first explanation is that the pathogenic role of OLs is still discussed in most CNS diseases. Another reason is that the so-called ubiquitous CAG, CBA, CBh, or CMV promoters-widely used in gene therapy studies-are unable or poorly able to activate the transcription of episomal transgene copies brought by adeno-associated virus (AAV) vectors in OLs. Accordingly, transgene expression in OLs has either not been found or not been evaluated in most gene therapy studies in rodents or non-human primates. The aims of the current review are to give OLs their rightful place among the neural cells that future gene therapy could target and to encourage researchers to test the effect of OL transduction in various CNS diseases.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer's disease: a magnetoencephalography study

Understanding the nature and onset of neurophysiological changes, and the selective vulnerability of central hub regions in the functional network, may aid in managing the growing impact of Alzheimer's disease on society. However, the precise neurophysiological alterations occurring in the pre-clinical stage of human Alzheimer's disease remain controversial. This study aims to provide increased insights on quantitative neurophysiological alterations during a true early stage of Alzheimer's disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional and whole-brain neurophysiological changes in a unique cohort of 11 cognitively unimpaired individuals with pathogenic mutations in the presenilin-1 or amyloid precursor protein gene and a 1:3 matched control group (n = 33) with a median age of 49 years. We examined several quantitative magnetoencephalography measures that have been shown robust in detecting differences in sporadic Alzheimer's disease patients and are sensitive to excitation-inhibition imbalance. This includes spectral power and functional connectivity in different frequency bands. We also investigated hub vulnerability using the hub disruption index. To understand how magnetoencephalography measures change as the disease progresses through its pre-clinical stage, correlations between magnetoencephalography outcomes and various clinical variables like age were analysed. A comparison of spectral power between mutation carriers and controls revealed oscillatory slowing, characterized by widespread higher theta (4-8 Hz) power, a lower posterior peak frequency and lower occipital alpha 2 (10-13 Hz) power. Functional connectivity analyses presented a lower whole-brain (amplitude-based) functional connectivity in the alpha (8-13 Hz) and beta (13-30 Hz) bands, predominantly located in parieto-temporal hub regions. Furthermore, we found a significant hub disruption index for (phase-based) functional connectivity in the theta band, attributed to both higher functional connectivity in 'non-hub' regions alongside a hub disruption. Neurophysiological changes did not correlate with indicators of pre-clinical disease progression in mutation carriers after multiple comparisons correction. Our findings provide evidence that oscillatory slowing and functional connectivity differences occur before cognitive impairment in individuals with autosomal dominant mutations leading to early onset Alzheimer's disease. The nature and direction of these alterations are comparable to those observed in the clinical stages of Alzheimer's disease, suggest an early excitation-inhibition imbalance, and fit with the activity-dependent functional degeneration hypothesis. These insights may prove useful for early diagnosis and intervention in the future.

Comparative efficacy of intermittent theta burst stimulation and high-frequency repetitive transcranial magnetic stimulation in amnestic mild cognitive impairment patients

Intermittent theta burst stimulation, a derivative of repetitive transcranial magnetic stimulation, has been applied to improve cognitive deficits. However, its efficacy and mechanisms in enhancing cognitive function in patients with amnestic mild cognitive impairment compared with traditional repetitive transcranial magnetic stimulation paradigms remain unclear. This study recruited 48 amnestic mild cognitive impairment patients, assigning them to intermittent theta burst stimulation, repetitive transcranial magnetic stimulation, and sham groups (5 times/wk for 4 wk). Neuropsychological assessments and functional magnetic resonance imaging data were collected pre- and post-treatment. Regarding efficacy, both angular gyrus intermittent theta burst stimulation and repetitive transcranial magnetic stimulation significantly improved general cognitive function and memory compared to the sham group, with no significant difference between the 2 treatment groups. Mechanistically, significant changes in brain activity within the temporoparietal network were observed in both the intermittent theta burst stimulation and repetitive transcranial magnetic stimulation groups, and these changes correlated with improvements in general cognitive and memory functions. Additionally, intermittent theta burst stimulation showed stronger modulation of functional connectivity between the hippocampus, parahippocampal gyrus, and temporal regions compared to repetitive transcranial magnetic stimulation. The intermittent theta burst stimulation and repetitive transcranial magnetic stimulation can improve cognitive function in amnestic mild cognitive impairment patients, but intermittent theta burst stimulation may offer higher efficiency. Intermittent theta burst stimulation and repetitive transcranial magnetic stimulation likely enhance cognitive function, especially memory function, by modulating the temporoparietal network.

Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=-0.12, CI=-0.14,-0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =-0.18, CI=-0.20,-0.15, P<0.00001).

Disturbances of thalamus and prefrontal cortex contribute to cognitive aging: A structure-function coupling analysis based on KL divergence

Normal aging is accompanied by changes in brain structure and function associated with cognitive decline. Structural and functional abnormalities, particularly the prefrontal cortex (PFC) and subcortical regions, contributed to cognitive aging. However, it remains unclear how the synchronized changes in structure and function of individual brain regions affect the cognition in aging. Using 3D T1-weighted structural data and movie watching functional magnetic resonance imaging data in a sample of 422 healthy individuals (ages from 18 to 87 years), we constructed regional structure-function coupling (SFC) of cortical and subcortical regions by quantifying the distribution similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF). Further, we investigated age-related changes in SFC and its relationship with cognition. With aging, increased SFC localized in PFC, thalamus and caudate nucleus, decreased SFC in temporal cortex, lateral occipital cortex and putamen. Moreover, the SFC in the PFC was associated with executive function and thalamus was associated with the fluid intelligence, and partially mediated age-related cognitive decline. Collectively, our results highlight that tighter structure-function synchron of the PFC and thalamus might contribute to age-related cognitive decline, and provide insight into the substrate of the thalamo-prefrontal pathway with cognitive aging.

Mitophagy-associated programmed neuronal death and neuroinflammation

Mitochondria are crucial organelles that play a central role in cellular metabolism and programmed cell death in eukaryotic cells. Mitochondrial autophagy (mitophagy) is a selective process where damaged mitochondria are encapsulated and degraded through autophagic mechanisms, ensuring the maintenance of both mitochondrial and cellular homeostasis. Excessive programmed cell death in neurons can result in functional impairments following cerebral ischemia and trauma, as well as in chronic neurodegenerative diseases, leading to irreversible declines in motor and cognitive functions. Neuroinflammation, an inflammatory response of the central nervous system to factors disrupting homeostasis, is a common feature across various neurological events, including ischemic, infectious, traumatic, and neurodegenerative conditions. Emerging research suggests that regulating autophagy may offer a promising therapeutic avenue for treating certain neurological diseases. Furthermore, existing literature indicates that various small molecule autophagy regulators have been tested in animal models and are linked to neurological disease outcomes. This review explores the role of mitophagy in programmed neuronal death and its connection to neuroinflammation.

Early Cortical Microstructural Changes in Aging Are Linked to Vulnerability to Alzheimer's Disease Pathology

BACKGROUND

Early identification of Alzheimer's disease (AD) risk is critical for improving treatment success. Cortical thickness is a macrostructural measure used to assess neurodegeneration in AD. However, cortical microstructural changes appear to precede macrostructural atrophy and may improve early risk identification. Currently, whether cortical microstructural changes in aging are linked to vulnerability to AD pathophysiology remains unclear in nonclinical populations, who are precisely the target for early risk identification.

METHODS

In 194 adults, we calculated magnetic resonance imaging-derived maps of changes in cortical mean diffusivity (microstructure) and cortical thickness (macrostructure) over 5 to 6 years (mean age: time 1 = 61.82 years; time 2 = 67.48 years). Episodic memory was assessed using 3 well-established tests. We obtained positron emission tomography-derived maps of AD pathology deposition (amyloid-β, tau) and neurotransmitter receptors (cholinergic, glutamatergic) implicated in AD pathophysiology. Spatial correlational analyses were used to compare pattern similarity among maps.

RESULTS

Spatial patterns of cortical macrostructural changes resembled patterns of cortical organization sensitive to age-related processes (r = -0.31, p < .05), whereas microstructural changes resembled the patterns of tau deposition in AD (r = 0.39, p = .038). Individuals with patterns of microstructural changes that more closely resembled stereotypical tau deposition exhibited greater memory decline (β = 0.22, p = .029). Microstructural changes and AD pathology deposition were enriched in areas with greater densities of cholinergic and glutamatergic receptors (ps < .05).

CONCLUSIONS

Patterns of cortical microstructural changes were more AD-like than patterns of macrostructural changes, which appeared to reflect more general aging processes. Microstructural changes may better inform early risk prediction efforts as a sensitive measure of vulnerability to pathological processes prior to overt atrophy and cognitive decline.

Fingerprints of brain disease: connectome identifiability in Alzheimer's disease

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.

Role of the central nervous system in cell non-autonomous signaling mechanisms of aging and longevity in mammals

Multiple organs orchestrate the maintenance of proper physiological function in organisms throughout their lifetimes. Recent studies have uncovered that aging and longevity are regulated by cell non-autonomous signaling mechanisms in several organisms. In the brain, particularly in the hypothalamus, aging and longevity are regulated by such cell non-autonomous signaling mechanisms. Several hypothalamic neurons have been identified as regulators of mammalian longevity, and manipulating them promotes lifespan extension or shortens the lifespan in rodent models. The hypothalamic structure and function are evolutionally highly conserved across species. Thus, elucidation of hypothalamic function during the aging process will shed some light on the mechanisms of aging and longevity and, thereby benefiting to human health.

Multimodal analysis of disease onset in Alzheimer's disease using Connectome, Molecular, and genetics data

Alzheimer's disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer's disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Recent Progress in Brain Network Models for Medical Applications: A Review

Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.

Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns

Importance

Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia.

Objective

To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes.

Design, Setting, and Participants

Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited.

Main Outcomes and Measures

A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined.

Results

Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: β = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI.

Conclusions and Relevance

These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

Monoclonal antibodies and aptamers: The future therapeutics for Alzheimer's disease

Alzheimer's disease (AD) is considered the most common and prevalent form of dementia of adult-onset with characteristic progressive impairment in cognition and memory. The cure for AD has not been found yet and the treatments available until recently were only symptomatic. Regardless of multidisciplinary approaches and efforts made by pharmaceutical companies, it was only in the past two years that new drugs were approved for the treatment of the disease. Amyloid beta (Aβ) immunotherapy is at the core of this therapy, which is one of the most innovative approaches looking to change the course of AD. This technology is based on synthetic peptides or monoclonal antibodies (mAb) to reduce Aβ levels in the brain and slow down the advance of neurodegeneration. Hence, this article reviews the state of the art about AD neuropathogenesis, the traditional pharmacologic treatment, as well as the modern active and passive immunization describing approved drugs, and drug prototypes currently under investigation in different clinical trials. In addition, future perspectives on immunotherapeutic strategies for AD and the rise of the aptamer technology as a non-immunogenic alternative to curb the disease progression are discussed.

Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of Alzheimer's Disease

Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Functional connectivity favors aberrant visual network c-Fos expression accompanied by cortical synapse loss in a mouse model of Alzheimer's disease

While Alzheimer's disease (AD) has been extensively studied with a focus on cognitive networks, sensory network dysfunction has received comparatively less attention despite compelling evidence of its significance in both Alzheimer's disease patients and mouse models. We recently found that neurons in the primary visual cortex of an AD mouse model expressing human amyloid protein precursor with the Swedish and Indiana mutations (hAPP mutations) exhibit aberrant c-Fos expression and altered synaptic structures at a pre-amyloid plaque stage. However, it is unclear whether aberrant c-Fos expression and synaptic pathology vary across the broader visual network and to what extent c-Fos abnormality in the cortex is inherited through functional connectivity. Using both sexes of 4-6-month AD model mice with hAPP mutations (J20[PDGF-APPSw, Ind]), we found that cortical regions of the visual network show aberrant c-Fos expression and impaired experience-dependent modulation while subcortical regions do not. Interestingly, the average network-wide functional connectivity strength of a brain region in wild type (WT) mice significantly predicts its aberrant c-Fos expression, which in turn correlates with impaired experience-dependent modulation in the AD model. Using in vivo two-photon and ex vivo imaging of presynaptic termini, we observed a subtle yet selective weakening of excitatory cortical synapses in the visual cortex. Intriguingly, the change in the size distribution of cortical boutons in the AD model is downscaled relative to those in WT mice, suggesting that synaptic weakening may reflect an adaptation to aberrant activity. Our observations suggest that cellular and synaptic abnormalities in the AD model represent a maladaptive transformation of the baseline physiological state seen in WT conditions rather than entirely novel and unrelated manifestations.

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

Multifaceted impact of specialized neuropeptide-intensive neurons on the selective vulnerability in Alzheimer's disease

INTRODUCTION

Widespread disruption of neuropeptide (NP) networks in Alzheimer's disease (AD) and disproportionate absence of neurons expressing high NP-producing, coined as HNP neurons, have been reported for the entorhinal cortex (EC) of AD brains. Hypothesizing that functional features of HNP neurons are involved in the early pathogenesis of AD, we aim to understand the molecular mechanisms underlying these observations.

METHODS

Multiscale and spatiotemporal transcriptomic analysis was used to investigate AD-afflicted and healthy brains. Our focus encompassed NP expression dynamics in AD, AD-associated NPs (ADNPs) trajectories with aging, and the neuroanatomical distribution of HNP neuron.

RESULTS

Findings include that 1) HNP neurons exhibited heightened metabolic needs and an upregulation of gene expressions linked to protein misfolding; 2) dysfunctions of ADNP production occurred in aging and mild cognitive decline; 3) HNP neurons co-expressing ADNPs were preferentially distributed in brain regions susceptible to AD.

DISCUSSION

We identified potential mechanisms that contribute to the selective vulnerability of HNP neurons to AD. Our results indicate that the functions of HNP neurons predispose them to oxidative stress and protein misfolding, potentially serving as inception sites for misfolded proteins in AD.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

The subcortical default mode network and Alzheimer's disease: a systematic review and meta-analysis

The default mode network is a central cortical brain network suggested to play a major role in several disorders and to be particularly vulnerable to the neuropathological hallmarks of Alzheimer's disease. Subcortical involvement in the default mode network and its alteration in Alzheimer's disease remains largely unknown. We performed a systematic review, meta-analysis and empirical validation of the subcortical default mode network in healthy adults, combined with a systematic review, meta-analysis and network analysis of the involvement of subcortical default mode areas in Alzheimer's disease. Our results show that, besides the well-known cortical default mode network brain regions, the default mode network consistently includes subcortical regions, namely the thalamus, lobule and vermis IX and right Crus I/II of the cerebellum and the amygdala. Network analysis also suggests the involvement of the caudate nucleus. In Alzheimer's disease, we observed a left-lateralized cluster of decrease in functional connectivity which covered the medial temporal lobe and amygdala and showed overlap with the default mode network in a portion covering parts of the left anterior hippocampus and left amygdala. We also found an increase in functional connectivity in the right anterior insula. These results confirm the consistency of subcortical contributions to the default mode network in healthy adults and highlight the relevance of the subcortical default mode network alteration in Alzheimer's disease.

Insights from the neural guidance factor Netrin-1 into neurodegeneration and other diseases

Netrin-1 was initially discovered as a neuronal growth cue for axonal guidance, and its functions have later been identified in inflammation, tumorigenesis, neurodegeneration, and other disorders. We have recently found its alterations in the brains with Alzheimer's disease, which might provide important clues to the mechanisms of some unique pathologies. To provide better understanding of this promising molecule, we here summarize research progresses in genetics, pathology, biochemistry, cell biology and other studies of Netrin-1 about its mechanistic roles and biomarker potentials with an emphasis on clinical neurodegenerative disorders in order to expand understanding of this promising molecular player in human diseases.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease

BACKGROUND

Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.

METHODS

This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan-Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.

RESULTS

The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.

CONCLUSIONS

This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease

This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64 years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28 years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06 years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebral small-vessel disease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.

Odor identification score as an alternative method for early identification of amyloidogenesis in Alzheimer's disease

A simple screening test to identify the early stages of Alzheimer's disease (AD) is urgently needed. We investigated whether odor identification impairment can be used to differentiate between stages of the A/T/N classification (amyloid,  tau, neurodegeneration) in individuals with amnestic mild cognitive impairment or AD and in healthy controls. We collected data from 132 Japanese participants visiting the Toranomon Hospital dementia outpatient clinic. The odor identification scores correlated significantly with major neuropsychological scores, regardless of apolipoprotein E4 status, and with effective cerebrospinal fluid (CSF) biomarkers [amyloid β 42 (Aβ42) and the Aβ42/40 and phosphorylated Tau (p-Tau)/Aβ42 ratios] but not with ineffective biomarkers [Aβ40 and the p-Tau/total Tau ratio]. A weak positive correlation was observed between the corrected odor identification score (adjusted for age, sex, ApoE4 and MMSE), CSF Aβ42, and the Aβ42/40 ratio. The odor identification score demonstrated excellent discriminative power for the amyloidogenesis stage , according to the A/T/N classification, but was unsuitable for differentiating between the p-Tau accumulation and the neurodegeneration stages. After twelve odor species were analyzed, a version of the score comprising only four odors-India ink, wood, curry, and sweaty socks-proved highly effective in identifying AD amyloidogenesis, showing promise for the screening of preclinical AD.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

NMNAT2 supports vesicular glycolysis via NAD homeostasis to fuel fast axonal transport

BACKGROUND

Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer's, Parkinson's, and Huntington's disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose long-projecting axons are often vulnerable in neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring axonal ATP levels for axonal transport, critical for axonal function.

METHODS

We generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting a NAD hydrolase, sterile alpha and TIR motif-containing protein 1 (SARM1), prevented axonal deficits caused by NMNAT2 loss. This study used a combination of techniques, including genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos.

RESULTS

We provide in vivo evidence that NMNAT2 in glutamatergic neurons is required for axonal survival. Using in vivo and in vitro studies, we demonstrate that NMNAT2 maintains the NAD-redox potential to provide "on-board" ATP via glycolysis to vesicular cargos in distal axons. Exogenous NAD+ supplementation to NMNAT2 KO neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate both in vitro and in vivo that reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons.

CONCLUSION

NMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2  = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2  = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2  = 0.346) and Aβ42/Aβ40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2  = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

Association of Loneliness with Functional Connectivity MRI, Amyloid-β PET, and Tau PET Neuroimaging Markers of Vulnerability for Alzheimer's Disease

Background

Loneliness has been declared an "epidemic" associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimer's disease (AD).

Objective

To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia.

Methods

In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions.

Results

Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest.

Conclusions

In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum

BACKGROUND

White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations.

METHODS

We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥ 55 years, including 276 cognitively normal older adults (CN), 142 with mild cognitive impairment (MCI), and 87 AD patients, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University.

RESULTS

Compared to CN, AD and MCI subjects showed significantly higher RD, MD, and AxD values (all p-values < 0.001) and significantly lower FA values (left p ≤ 0.002, right p ≤ 0.015) after Bonferroni adjustment for multiple comparisons. Most tests of cognition and mood (p < 0.001) as well as higher medial temporal amyloid burden (p < 0.001) were associated with poorer WM integrity in the CBH after Bonferroni adjustment.

CONCLUSION

These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.

Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network

Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this work, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights into detecting AD-related abnormal neural circuits.

Alterations via inter-regional connective relationships in Alzheimer's disease

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-β deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-β PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aβ-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aβ-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.