Grey matter network topology is altered in Alzheimer's disease and these alterations are related to cognitive decline. Understanding the biological underpinnings of loss of brain connectivity may provide insights into mechanisms related to developing Alzheimer's dementia (i.e. dementia A+). We investigated which biological processes as measured in CSF proteomics were associated with loss of brain connections across the Alzheimer's disease continuum. We included 347 individuals with abnormal CSF amyloid [mean age ± standard deviation (SD) 66 ± 8; 98 cognitively unimpaired-A+, 88 mild cognitive impairment-A+, 161 dementia A+] and 146 cognitively unimpaired individuals with normal CSF amyloid (mean age ± SD 62 ± 8) and available T1w MRI-scans and CSF proteomic data (3097 proteins using tandem mass tag spectrometry) from the Amsterdam Dementia Cohort. We used an automated pipeline to construct grey matter networks from 3D-T1 sequences and for each network, calculated the small-worldness coefficient, which we previously found to be robustly related to cognitive decline. Linear models were applied to test associations between CSF protein levels and connectivity measures using an interaction term for clinical stage while controlling for connectivity density, age and sex. We validated our results in data from the Alzheimer's disease Neuroimaging Initiative (ADNI). Pathway enrichment analysis was performed for proteins associated with loss of brain connectivity (P < 0.05) using the Gene Ontology database. Individuals across the Alzheimer's disease continuum had lower small-worldness coefficients compared with controls (ANOVA P < 0.001). In amyloid positive individuals, higher levels of 222 proteins and lower levels of 482 proteins were associated with lower small-worldness coefficients and were enriched for innate immune system and neuroplasticity pathways, respectively. Stratified for disease stage, most protein associations with lower small-worldness coefficients were found in mild cognitive impairment A+ (n = 527 proteins) and dementia A+ (n = 799 proteins) with considerable overlap (n = 239 proteins). Proteins in these stages were enriched for complement activation and synaptic integrity. In cognitive unimpairment A+, we found proteins enriched for processes involved in apoptosis. We did not find any enriched biological processes in controls. Repeating analyses in ADNI indicated that similar biological processes were associated with altered grey matter network connectivity. Higher CSF levels of proteins involved in immune responses and lower levels of proteins related to neuroplasticity were associated with lower small-worldness coefficients across the Alzheimer's disease continuum. This suggests that preserving cognitive function in the presence of amyloid and prevention of dementia A+ may require therapies that strengthen synapses and targets the innate immune system in addition to amyloid and tau.

Alzheimer's disease (AD) emerges from multi-scale interactions between molecular pathology and disruptions in large-scale brain network dynamics. Understanding how these processes co-evolve and relate to disease stages is essential for advancing complex systems models of aging and AD, and for developing system-informed interventions. However, progress has been limited by a lack of large-scale longitudinal data. To address this, we examined the longitudinal relationship between subsystems of the default mode network (DMN) (posterior DMN, ventral DMN, anterior dorsal DMN) using task-free functional MRI (fMRI) and amyloid positron emission tomography (PET) imaging in a large longitudinal cohort spanning the clinico-biological spectrum of AD (n = 1,451; 2,763 time points) using mixed-effect models. We also assessed whether patterns of DMN connectivity predicted conversion to amyloid positivity, mild cognitive impairment (MCI), and dementia using Cox proportional hazards models. Our findings reveal a dynamic interplay between amyloid accumulation and connectivity within and between DMN subsystems, with both hyper- and hypoconnectivity emerging across DMN subsystems in association with increasing amyloid burden. Importantly, survival models showed that DMN connectivity patterns predicted conversion to critical stages of the disease, including not only conversion to MCI and dementia, but also conversion to amyloid positivity in otherwise clinically unimpaired individuals who were amyloid negative at baseline. These associations were independent of age, APOE4 status, sex, education, and in-scanner motion. These results support a model in which breakdowns in tightly regulated feedback loops governing DMN physiology represent a core systems-level pathophysiology of AD. Notably, this functional dyshomeostasis precedes detectable amyloidosis on imaging. Future studies should focus on the development of robust biomarkers of brain function that can be applied at the individual level, which could in turn help support the development of therapeutic approaches targeting system-level pathophysiology.

Psychiatric disorders and dementia share overlapping clinical and genetic factors (e.g., SNCA, CLU, and APOE ), and both conditions implicating the prefrontal cortex (PFC). We examined the association between genetic liability for psychiatric disorders and PFC thickness in Alzheimer's disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and cognitively healthy controls. We analyzed 2,538 individuals (1,258 AD, 169 DLB, 238 FTD, and 873 controls) from the Amsterdam Dementia Cohort. 3D T1w images were processed with FreeSurfer (version 7.1.1) and labels for PFC correspond to those from the Desikan-Kiliany atlas. The segmentation of all images were visually quality checked. Polygenic risk scores (PRS) for depression (MDDPRS), bipolar disorder (BDPRS), schizophrenia (SCZPRS), and autism (ASDPRS) were calculated using LDpred2, with weights from independent GWAS. Linear mixed models were used to test associations between PRS and PFC thickness, using PRS*hemisphere, PRS*diagnostic group, age, sex, scanner model, and total gray matter volume as fixed effects and hemisphere as random effect. No significant main effects were observed between PRS and PFC thickness. Hemisphere did not influence the relationship for BDPRS, SCZPRS, or ASDPRS. MDDPRS interacted with hemisphere in relation to PFC, showing positive associations in the left hemisphere and negative in right, although no association reached significance. SCZPRS interacted with DLB case-control status in relation to lateral orbitofrontal cortex (OFC) thickness, showing that higher SCZPRS associated with reduced lateral OFC thickness in DLB patients (B=-0.05, p  = 0.003), but not in controls (B=0.003, p  = 0.6). Additionally, ASDPRS interacted with FTD in relation to PFC regions, although stratified models did not reach significance. The finding that SCZPRS is associated with reduced lateral OFC thickness in DLB patients suggests a potential genetic basis for psychotic symptoms in DLB and contributes to understanding the neurobiological overlap between DLB and schizophrenia.

BACKGROUND AND OBJECTIVES:Blood-based biomarkers are reliable indicators of Alzheimer disease (AD)-related pathology in cognitively normal individuals. However, it remains unclear how changes in these biomarkers relate to emerging brain atrophy. We aimed to investigate longitudinal associations between blood-based biomarkers and brain atrophy, and the temporal sequence of these processes. METHODS:In the prospective observational Subjective Cognitive Impairment Cohort based in Amsterdam, individuals with subjective cognitive decline recruited from a memory clinic underwent repeated blood sampling (484 samples, follow-up 5 ± 3 years) and MRI scans (457 scans, follow-up 5 ± 3 years). We measured blood-based biomarkers (Aβ42/40 ratio [Aβ42/40], phosphorylated tau [pTau217], glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) using the SIMOA platform. AD-signature brain volumes (5 temporal, 4 parietal, and 2 frontal regions) were determined using the FreeSurfer pipeline. We used linear mixed models to examine associations between baseline or slope of biomarker and brain volume changes. In an exploratory analysis, extrapolated pTau217 trajectories were compared with hippocampal volume trajectories to estimate the temporal gap between these changes. RESULTS:A total of 167 individuals were included (49 amyloid-positive and 118 amyloid-negative), with amyloid-positive individuals being older (age: 66 ± 8 years, 57.1% female) than amyloid-negative individuals (61 ± 8 years, 33.9% female). Baseline levels and longitudinal increases in GFAP showed associations with higher rates of atrophy across nearly all AD-signature regions (β range -0.02 to -0.00, 95% CI range -0.04 to -0.00, pFDR < 0.05). Baseline pTau217 was associated with atrophy across several medial temporal regions (β range -0.03 to -0.01, 95% CI range -0.04 to -0.00, pFDR < 0.05), whereas increases in pTau217 were only associated with atrophy primarily in temporal regions (β range -0.02 to -0.01, 95% CI range -0.02 to -0.00), although the latter associations were only trend-level after FDR correction (pFDR = 0.077). Baseline concentrations, but not increases in NfL, showed associations with temporal regions (β range -0.02 to -0.01, 95% CI range -0.04 to -0.00, pFDR < 0.05). By contrast, baseline Aβ42/40 showed only associations with hippocampal atrophy (0.01 [0.00-0.03]), but not after FDR correction. Using baseline and (extrapolated) trajectories over time, we estimated that changes in pTau217 preceded hippocampal atrophy by 19.8 (10.7-43.7) years. DISCUSSION:Blood-based biomarkers capture distinct aspects of brain atrophy, with pTau217 primarily indicating atrophy in medial temporal regions and GFAP more widespread neurodegeneration. This supports the complementary use of both markers for early identification and monitoring in preventive trials and clinical care.

Blood-based biomarkers have been established as reliable markers of Alzheimer's disease (AD)-related pathology in individuals with subjective cognitive decline (SCD). It remains unclear how these biomarkers and their longitudinal changes are associated with gray matter atrophy in SCD. This longitudinal study investigates the relationship between (changes in) blood-based biomarkers and changes in cortical thickness and hippocampal volume in individuals with SCD. We included 167 individuals with SCD (49 amyloid-positive [A+] and 118 amyloid-negative [A−]) who underwent biennial blood sampling ( n  = 484) and repeated imaging over a follow-up period of 4.6±2.7 years. Blood-based biomarkers (Aβ 42/40 , pTau217, GFAP, and NfL) were measured using the SIMOA platform. AD-signature cortical thickness and hippocampal volume were determined using the longitudinal FreeSurfer pipeline. We used two linear mixed models to investigate the associations between baseline biomarker levels or biomarker slopes, and changes in AD-signature cortical thickness and hippocampal volume over time. SCD A+ showed greater decreases over time in AD-signature cortical thickness (β Time*Amyloid status :-0.04±0.01) and hippocampal volume (β:-0.07±0.01) compared to SCD A−; both p  <0.05). Higher baseline GFAP and increases in GFAP over time were associated with greater decreases in AD-signature cortical thickness (β:-0.02±0.01; β:-0.33±0.10) and hippocampal volume over time (β:-0.02±0.01; β:-0.43±0.12; all p  <0.01). Higher baseline pTau217 and increases in pTau217 were associated with greater decreases in hippocampal volume (β:-0.03±0.01; β:-0.97±0.44; p  <0.05), but not in AD-signature cortical thinning. Higher baseline NfL was associated with greater decreases in AD-signature cortical thickness (β:-0.01±0.01) and hippocampal volume (β:-0.02 ± 0.01; p  <0.05) over time, but changes in NfL were not. Neither baseline nor changes in Aβ 42/40 were associated with atrophy. These findings suggest that gray matter atrophy in individuals with SCD is associated with AD-related pathology as measured by blood-based biomarkers, especially GFAP. This highlights the potential of blood-based biomarkers as monitoring tools for structural brain changes and disease progression.

Neuroinflammation plays a key role in Alzheimer’s disease (AD) pathophysiology, but it is not clear how neuroinflammation contributes to disease progression. We aim to investigate the role of neuroinflammation on longitudinal cognition and survival in a unique cohort with PET imaging of translocator protein (TSPO) binding tracer [11C]PK11195 and long-term follow-up. We hypothesized that higher [11C]PK11195 binding would be associated with faster cognitive decline and higher mortality. 19 participants with AD dementia, 9 participants with MCI due to AD, and 21 healthy controls (HC) with historical dynamic [11C]PK11195 PET data were included. Principal component analysis was performed to identify relevant [11C]PK11195 patterns. An additional AD ROI consisting of temporal and parietal regions was investigated. [11C]PK11195 scores in the principal components (PCs) and AD ROI were compared between groups using ANOVA. Longitudinal MMSE covering a period up to 11 years was used to measure cognitive decline. We used linear mixed models with random subject-specific intercepts and slopes corrected for age, sex and syndrome diagnosis to investigate the association of neuroinflammation with cognition in MCI and AD. Survival data were available for all MCI and AD participants, up to 15.7 years after PET. To examine the influence of neuroinflammation on survival time, we used age, sex, and syndrome diagnosis adjusted cox proportional-hazards models. Two PCs were retained. PC1 explained 55.4

BACKGROUND AND OBJECTIVES:Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex diagnostic tasks by augmenting user capabilities, but workflow integration poses many challenges. We propose that a modeling framework based on fluorodeoxyglucose PET (FDG-PET) imaging can address these challenges and form the basis of an effective CDSS for neurodegenerative disease. METHODS:This retrospective study focused on FDG-PET images in a discovery cohort drawn from 3 research studies plus routine clinical patients. When selecting research study participants, the inclusion criterion was the availability of an FDG-PET image from within 2.5 years of diagnosis with 1 of 9 specific neurodegenerative syndromes or designation as unimpaired. Participants from disease groups were recruited from the clinical patient population while unimpaired participants came primarily from a population study. The discovery cohort was used to develop a clinical decision support framework we call StateViewer, which applies a neighbor matching algorithm to detect the presence of 9 different neurodegenerative phenotypes. The ML performance of this framework was evaluated in the discovery cohort by nested cross-validation and externally validated in the Alzheimer's Disease Neuroimaging Initiative. Potential for clinical integration was demonstrated in a radiologic reader study focused on differentiating posterior cortical atrophy from Lewy body dementia. RESULTS:The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female. Our model framework was able to detect the presence of 9 different neurodegenerative phenotypes with a sensitivity of 0.89 ± 0.03 and an area under the receiver operating characteristic curve of 0.93 ± 0.02. In the radiologic reader study, readers using our model were found to have 3.3 ± 1.1 times greater odds of making a correct diagnosis than readers using a current standard-of-care workflow. DISCUSSION:Our proposed framework provides strong classification performance with high interpretability, and it addresses many of the challenges that face clinical integration of ML-based decision support tools. One limitation of this study is a uniform discovery cohort that is not representative of other patient populations in some regards.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

The grey matter of the brain develops and declines in coordinated patterns during the lifespan. Such covariation patterns of grey matter structure can be quantified as grey matter networks, which can be measured with magnetic resonance imaging. In Alzheimer's disease, the global organization of grey matter networks becomes more random, which is captured by a decline in the small-world coefficient. Such decline in the small-world value has been robustly associated with cognitive decline across clinical stages of Alzheimer's disease. The biological mechanisms causing this decline in small-world values 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 small-world coefficient in mutation carriers (N = 219) and non-carriers (N = 136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Amyloid beta, Tau, synaptic (Synaptosome associated protein-25, Neurogranin) and neuronal calcium-sensor protein (Visinin-like protein-1) preceded loss of small-world coefficient by several years, while increased levels in CSF markers for inflammation (Chitinase-3-like protein 1) and axonal injury (Neurofilament light) co-occurred with decreasing small-world values. This suggests that axonal loss and inflammation play a role in structural grey matter network changes.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice-versa. We included data from patients with genetic, biomarker, and/or autopsy evidence for fronto-temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto-temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer’s disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group-wise differences in clinical/cognitive features and 18 Fluorodeoxyglucose-positron emission tomography (FDG-PET) patterns. This was followed by a spectral covariance decomposition between FDG-PET images to yield latent patterns of relative metabolism (“eigenbrains”). These eigenbrains were subsequently linked to clinical/cognitive data and meta-analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain-based diagnostic predictions. dAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo-parietal areas in aAD, and fronto-temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG-PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3). This study suggests distinct macro-scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data-driven techniques to inform the classification of degenerative diseases.

Decreased image self-similarity of repeated FDG-PET scans has been suggested to indicate increased risk for disease progression. Here, we compared several self-similarity measures based on conventionally used voxel- and ROI-based approaches and those based on patterns of cortical metabolism. From the MCSA/ADRC, we included 376 cognitively unimpaired (CU) individuals and those with a clinical diagnosis of MCI (n = 55) or AD (n = 20) who had two FDG-PET images within 2-3 years and clinical follow-up available. We compared image self-similarity for three approaches: voxel, ROI and “eigenbrain” (Fig.1). For the voxel approach, FDG-PET images were normalized to template space, grey matter masked, and intensity normalized to the pons to create standard uptake value ratios (SUVR) for each voxel. From these images, mean SUVR were extracted for 122 ROIs (MCALT-ADIR122) for the ROI approach. For the “eigenbrain” approach, the SUVR images were transformed to a latent space embedding composed of 253 components (“eigenbrains”) and reflecting metabolism patterns, that was generated in an independent sample of 7008 FDG-PET images. For each approach, image self-similarity between baseline and follow-up values (voxel, ROI or eigenbrain) was determined using three metrics (Pearson correlation, cosine, Euclidean distance). We computed AUC to determine which approach and metric would best discriminate between groups. We used Cox regression analyses to assess whether the metrics could predict disease progression. Comparing each distance metric across approaches, AUC was highest in differentiating clinical diagnoses using self-similarity measures based on eigenbrains, then voxels, then ROIs (e.g., Euclidean distance CU vs AD AUC eigenbrain: 0.86, voxel: 0.75, ROI: 0.71; Fig.1). Across all approaches, Euclidean distance always discriminated best between classes. Eigenbrain-based Euclidean distance was associated with risk for progression to MCI in CU (HR [95%CI]: 1.25 [1.05, 1.49]), and eigenbrain-based cosine and Euclidean distances were associated with risk for progression to AD in MCI (1.61 [1.1, 2.34] and 1.53 [1.01, 2.31], respectively), while we found no associations for voxel- or ROI-based distance measures. Eigenbrain-based measures outperformed other, more conventional approaches in discriminating clinical diagnoses, and were associated with risk for disease progression. Our results suggest that decreased self-similarity in metabolism patterns informs on disease stage and prognosis.

Predictive models of tau-PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer’s disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task-free fMRI) and global organization (from FDG-PET) to predict regional tau-PET across AD phenotypes. We included 430 cognitively unimpaired (CU) individuals (214 A+T-, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer’s Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as “Mayo cohort”). The connectivity approach consisted of assessing the relationship between a region’s tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG-PET images that did not overlap with the Mayo cohort, 2) projecting FDG-PET images of the Mayo cohort onto the low-dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau-PET in a linear model and determining optimal parameters based on predicted R2 values, and 4) using the parameters from the model built in Step 3 to predict tau-PET in the ADNI cohort (n = 86) (Fig.2). The connectivity-based approach yielded adjusted R2 values ranging from 0.19-0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R2 and predicted R2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out-of-sample median R2 in the ADNI cohort was 0.41 (range 0.10-0.60) (Fig.3). An unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome-based one to predict regional tau-PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large-scale physiology of the brain underlying specific mental functions rather than local connectivity factors.

AbstractFrom a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer’s disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer’s disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer’s disease from the normative cohort at the patient level, as well as between Alzheimer’s disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer’s disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer’s disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer’s disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer’s disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer’s disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer’s disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

Lower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics. Data from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI-scans (median of 6 scans over 1.5 follow-up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D-T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations ( p <0.05) were identified and examined for their biological relevance according to Gene Ontology. Connectivity density declined over time with -0.8% per year (p MCI = 0.001; figure 1), which did not differ from controls (p dif = 0.15). Gamma declined faster in MCI than in controls (b MCI = -0.016 vs. b CN = -0.007, p dif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; p FDR = 7.00E-07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; p FDR = 1.40E-02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; p FDR = 9.42E-05; figure 3). There were no protein-connectivity relationships in controls. Proteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

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.

Dysexecutive Alzheimer's disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors ("eigenbrains") accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. "left-dominant," "right-dominant," "bi-parietal-dominant," and "heteromodal-diffuse." Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain-behavior relationships relevant to clinical practice and disease physiology.

Many patients with amyotrophic lateral sclerosis (ALS; motor neuron disease) use natural or traditional therapies of unproven benefit. One such therapy is ginseng root. However, in some other disease models, ginseng has proven efficacious. Ginseng improves learning and memory in rats, and reduces neuronal death following transient cerebral ischemia. These effects of ginseng have been related to increases in the expression of nerve growth factor and its high affinity receptor in the rat brain, and antioxidant actions, inter alia. Since such actions could be beneficial in ALS as well, we studied the effect of ginseng (Panax quinquefolium), 40 and 80 mg/Kg, in B6SJL-TgN(SOD1-G93A)1Gur transgenic mice. The ginseng was given in drinking water, from age 30d onwards. We measured the time to onset of signs of motor impairment, and survival. There was no difference between the two ginseng groups (n=6, 6) in either measure. However, compared to controls (n=13), there was a prolongation in onset of signs (116d vs. 94d, P<0.001), and survival (139d vs. 132d, P<0.05). These experiments lend support to the use of ginseng root in ALS. Future experiments using this model could examine for symptomatic effects of ginseng, measure the effect of specific ginsenosides (which differ between ginseng species), and elucidate their mechanisms of action.

In vivo tau-positron emission tomography (PET) is an attractive biomarker for Alzheimer’s disease (AD) diagnosis and treatment. However, tau-PET is less widely available than other modalities. In this study, we tested cross-modality synthesis of tau-PET brain images from fluorodeoxyglucose F-18 (FDG)-PET using a deep convolutional neural network (CNN). Participants (n=1,192) who had brain FDG-PET with 18 F-FDG and tau-PET with Flortaucipir (F-18-AV-1451) were included for training and testing. This cohort spanned normal aging (ages 26-98), pre-clinical, and clinical AD and related disorders including the FTD and DLB spectrum. External validation was done using ADNI (n=288). The PET scans were co-registered to the corresponding MRI and subsequently warped to Mayo Clinic Adult Lifespan Template (MCALT) space. Tau-PET images were SUVR-normalized to the cerebellar crus, and FDG to the pons. A 3D dense-U-net model was utilized as an architecture. Cross-validation experiments were conducted using 5-fold validations (60% training set, 20% validation set, and 20% test set) with mean squared error as the loss function. Our dense-U-net model successfully synthesized tau-PET from metabolic images with good correlation and low prediction error for regional SUVRs (Figure 1A-C). The model showed a robust prediction ability, performing accurately in an independent, external ADNI cohort (Figure 1D-F). The model-imputed tau-PET significantly improved performance in classifying tau positivity (mean AUROC(±SD)=0.78±0.04 and 0.85±0.03 for FDG-PET and synthesized tau-PET, respectively) and diagnostic groups (cognitively unimpaired with abnormal amyloid-PET vs. cognitively impaired with abnormal amyloid-PET) compared to the original input FDG data (mean AUROC(±SD)=0.89±0.04, 0.85±0.05 0.91±0.04 for actual tau-PET, FDG-PET and synthesized tau-PET, respectively) (Figure 2), suggesting enhanced clinical utility for metabolic images. The ADNI cohort also showed similar results (for tau positivity: AUROC=0.66 and 0.78 for FDG-PET and synthesized tau-PET, respectively; for CU A+ vs. CI A+: AUROC=0.86, 0.62, and 0.73 for actual tau-PET, FDG-PET, and synthesized tau-PET; Figure 3). We showed that using a CNN model to predict tau-PET from FDG-PET is feasible. The synthesized tau-PET can augment the value of FDG-PET, facilitating the multi-modal diagnosis of AD.