Graff‐Radford, Jonathan

OBJECTIVE Idiopathic normal pressure hydrocephalus (iNPH) results in significant morbidity in the elderly with symptoms of dementia, gait instability, and urinary incontinence. In well-selected patients, ventriculoperitoneal shunt (VPS) placement often results in clinical improvement. Most postshunt assessments of patients rely on subjective scales. The goal of this study was to assess the utility of remote activity monitoring to provide objective evidence of gait improvement following VPS placement for iNPH. METHODS Patients with iNPH were prospectively enrolled and fitted with 5 activity monitors (on the hip and bilateral thighs and ankles) that they wore for 4 days preoperatively within 30 days of surgery and for 4 days within 30 days postoperatively. Monitors collected continuous data for number of steps, cadence, body position (upright, prone, supine, and lateral decubitus), gait entropy, and the proportion of each day spent active or static. Data were retrieved from the devices and a comparison of pre- and postoperative movement assessment was performed. The gait data were also correlated with formal clinical gait assessments before and after lumbar puncture and with motion analysis laboratory testing at baseline and 1 month and 1 year after VPS placement. RESULTS Twenty patients fulfilled the inclusion and exclusion criteria (median age 76 years). The baseline median number of daily steps was 1929, the median percentage of the day spent inactive was 70%, the median percentage of the day with a static posture was 95%, the median gait velocity was 0.49 m/sec, and the median number of steps required to turn was 8. There was objective improvement in median entropy from pre- to postoperatively, increasing from 0.6 to 0.8 (p = 0.002). There were no statistically significant differences for any of the remaining variables measured by the activity monitors when comparing the preoperative to the 1-month postoperative time point. All variables from motion analysis testing showed statistically significant differences or a trend toward significance at 1 year after VPS placement. Among the significantly correlated variables at baseline, cadence was inversely correlated with percentage of gait cycle spent in the support phase (contact with ground vs swing phase). CONCLUSIONS This pilot study suggests that activity monitoring provides an early objective measure of improvement in gait entropy after VPS placement among patients with iNPH, although a more significant improvement was noted on the detailed clinical gait assessments. Further long-term studies are needed to determine the utility of remote monitoring for assessing gait improvement following VPS placement.

AbstractIntroductionPresence of apolipoprotein E (APOE) ε4 has shown greater predisposition to medial temporal involvement in posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA). Little is known about its influence on memory network connectivity, a network comprised of medial temporal structures.MethodsFifty‐eight PCA and 82 LPA patients underwent structural and resting state functional magnetic resonance imaging (MRI). Bayesian hierarchical linear models assessed the influence of APOE ε4 on within and between‐network connectivity for five networks.ResultsAPOE ε4 carriers showed reduced memory and language within‐network connectivity in LPA and increased salience within‐network connectivity in PCA compared to non‐carriers. Between‐network analysis showed evidence of reduced DMN connectivity in APOE ε4 carriers, with reduced DMN‐to‐salience and DMN‐to‐language network connectivity in PCA, and reduced DMN‐to‐visual network connectivity in LPA.DiscussionThe APOE genotype influences brain connectivity, both within and between‐networks, in atypical Alzheimer's disease. However, there was evidence that the modulatory effects of APOE differ across phenotype.HIGHLIGHTS APOE genotype is associated with reductions in within‐network connectivity for the memory and language networks in LPA APOE genotype is associated with reductions in language‐to‐visual connectivity in LPA and PCA APOE genotype has no effect on the memory network in PCA

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.

Background: The National Institute on Aging-Alzheimer’s Association Research Framework proposes defining Alzheimer’s disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear. Objective: To determine whether cerebrospinal fluid (CSF)-neurodegenerative biomarkers (N) map differentially to Alzheimer’s disease pathology measured by Aβ 42 (an indicator of amyloidosis, [A]), p-tau (an indicator of tau deposition, [T]), and MRI vascular pathology indicators (measured by white-matter integrity, infarcts, and microbleeds [V]). Methods: Participants were from Mayo Clinic Study of Aging (MCSA) with CSF measures of NfL, Ng, t-tau, Aβ 42 , and p-tau and available MRI brain imaging. Linear models assessed associations between CSF neurodegeneration (N) markers, amyloid markers (A), tau (T), and vascular pathology (V). Results: Participants ( n = 408) had a mean age of 69.2±10.7; male, 217 (53.2%); cognitively unimpaired, 359 (88%). All three neurodegeneration biomarkers correlated with age ( p < 0.001 for NfL and t-tau, p = 0.018 for Ng). Men had higher CSF-NfL levels; women had higher Ng ( p < 0.001). NfL and t-tau levels correlated with infarcts ( p = 0.009, p = 0.034 respectively); no biomarkers correlated with white-matter integrity. N biomarkers correlated with p-tau levels (T, p < 0.001). Higher Aβ 42 levels associated with higher N-biomarker levels but only among cognitively unimpaired (A, p < 0.001). Conclusion: The influence of vascular pathology in the general population on CSF (N) biomarkers is modest, with greater influence of infarcts than white-matter disruption. Neurodegeneration markers more closely correlated with tau than amyloid markers.

Abstract Staging the severity of Alzheimer’s disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer’s clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1–2, 3–4, 5–6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1–42 and Aβ1–40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78–0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72–0.85 versus C = 0.73–0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer’s disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Abstract 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.

AbstractIntroductionEmpathy relies on fronto‐cingular and temporal networks that are selectively vulnerable in behavioral variant frontotemporal dementia (bvFTD). This study modeled when in the disease process empathy changes begin, and how they progress.MethodsFour hundred thirty‐one individuals with asymptomatic genetic FTD (n = 114), genetic and sporadic bvFTD (n = 317), and 163 asymptomatic non‐carrier controls were enrolled. In sub‐samples, we investigated empathy measured by the informant‐based Interpersonal Reactivity Index (IRI) at each disease stage and over time (n = 91), and its correspondence to underlying atrophy (n = 51).ResultsEmpathic concern (estimate = 4.38, 95% confidence interval [CI] = 2.79, 5.97; p < 0.001) and perspective taking (estimate = 5.64, 95% CI = 3.81, 7.48; p < 0.001) scores declined between the asymptomatic and very mild symptomatic stages regardless of pathogenic variant status. More rapid loss of empathy corresponded with subcortical atrophy.DiscussionLoss of empathy is an early and progressive symptom of bvFTD that is measurable by IRI informant ratings and can be used to monitor behavior in neuropsychiatry practice and treatment trials.

AbstractBackgroundAmyloidosis‐driven tau deposition has been a significant focus of imaging research in Alzheimer’s disease (AD). However, the 3R/4R tau seen in AD can be observed in the transentorhinal/entorhinal regions (Braak I/II) in the absence of amyloidosis. The underlying drivers of this phenomenon are not well understood. Our goal was to examine amyloid‐independent pathways of longitudinal Tau‐PET SUVR progression in the entorhinal cortex (ERC) using a broad range of risk and protective factors.MethodWe identified 449 participants from the population‐based sample of Mayo Clinic Study of Aging >50 years old with baseline amyloid PET and at least two serial tau PET scans (mean number of scans=2.3; mean follow‐up time=3.2 years). Using an amyloid SUVR cut‐point of 1.48, we categorized participants into A+ (n=167; mean age=77.5 years; 56% male; baseline ERC tau=1.20; 83% cognitively unimpaired) and A‐ (n=282; mean age=67.5; 56% male; baseline ERC tau=1.07; 92% cognitively unimpaired). We used an ERC tau SUVR cut‐point of 1.21 to determine T+. We fit mixed effects and growth curve models with age, sex, baseline amyloid, risk factors (midlife activities, sleep, stress, education/occupation, cardiovascular metabolic conditions) as predictors and longitudinal ERC tau as the outcome. We fit separate models in A‐, A+, and all participants.ResultsFor models of all participants and A+ participants, the only predictors of higher rates of ERC tau deposition were increasing age and amyloid SUVR (p<0.05). In the A‐ models (figure below), amyloid SUVR and age were predictors of baseline, but not longitudinal ERC tau. However, a greater number of cardiovascular metabolic conditions predicted a higher rate of ERC tau accumulation (0.007 (0.002), p<0.05). Only 13% of the 23 A‐T+ participants were APOE4 carriers (compared to 31% in the overall sample), suggesting the role of non‐APOE4 pathways to tau deposition.ConclusionOur results support previous evidence that significant amyloidosis is causal to higher rates of tau deposition. However, in those without significant amyloidosis, cardiovascular metabolic conditions predicted higher rates of ERC tau accumulation. Further work is warranted to investigate the mechanisms through which genetics, comorbidities, and lifestyle may play a role in early tau deposition.