Jones, David T.

AbstractBackgroundDysexecutive Alzheimer's disease (dAD) is a relatively early‐onset variant of AD primarily degrading core executive functions in the absence of predominant behavioral symptoms. Clinical observations suggest substantial variability in clinical, cognitive and neuroimaging profiles within this syndrome. In this presentation, we will discuss our ongoing work disentangling this heterogeneity using unsupervised machine learning techniques.MethodWe collected clinical, neuropsychological and multimodal imaging (MRI, FDG‐PET, amyloid‐PET, tau‐PET) data on 52 dAD patients assessed in our behavioral neurology clinic at Mayo Clinic Rochester. We first performed a spectral clustering analysis based on FDG‐PET to delineate latent patterns of network degeneration (referred as “eigenbrains”) and assessed the relationships between these eigenbrains and clinical and cognitive symptomatology. We then performed a hierarchical clustering on these eigenbrains to derive data‐driven subtypes of dAD. We compared clinical and cognitive data between subtypes, and then compared the imaging profiles to 52 amyloid‐negative age‐ and sex‐matched controls.ResultSix eigenbrains explained approximatively 48% of the variance in FDG‐PET patterns and primarily reflected heteromodal to primary motor/sensory, left‐right hemispheric asymmetry, and anterior‐to‐posterior gradients of macro‐scale cortical organization (Fig 1). These eigenbrains differentially related to reported age at symptom onset, degree of clinical impairment, and performance on a wide range of cognitive domains (executive functions, episodic memory, and visuospatial). Hierarchical clustering revealed four dAD subtypes (diffuse, biparietal, left‐hemisphere, right‐hemisphere). These subtypes differed in reported age at symptom onset and cognitive profile, where the heteromodal‐diffuse subtype exhibited an overall worse clinical picture and the biparietal had a milder profile compared to other subtypes, which was not explained by disease duration. Additionally, spatial patterns of tau distribution and neurodegeneration overlapped with patterns of FDG‐PET hypometabolism in each subtype, whereas patterns of amyloid deposition were similar across subtypes (Figs 2 & 3).ConclusionAlmost half of the variance in macro‐scale patterns of hypometabolism in this dAD cohort was accounted for by six eigenbrains. These eigenbrains captured the inter‐individual variability in age at symptom onset and cognitive impairment. Four dAD subtypes derived from these eigenbrains revealed meaningful differences in clinical, cognitive, and imaging profiles. Recognizing this heterogeneity has significant clinical implications for diagnosis, prognosis, and symptom monitoring.

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.

AbstractBackgroundSlow walking speed is associated with numerous negative health outcomes including global cognitive decline.1 More detailed gait analysis can differentiate among different dementia subtypes,10 but the predictive power of baseline gait metrics for domain‐specific cognitive decline needs further characterization.MethodWe conducted a follow up study on 534 participants of the Mayo Clinic Study of Aging who underwent evaluations every 15 months (median follow‐up = 5.4 years). We analyzed quantitative gait metrics, acquired with a pressure‐sensing walkway (GAITRite® instrument), and cognitive scores using a linear mixed effects model to identify correlations between z‐scored baseline gait metrics (cadence, swing time, stance time, velocity, double support time (DST), stride length, stride length standard deviation (SD), stride time SD, swing time SD, stance time SD, DST SD) and cognitive domain z‐scores (memory, attention, language, visuospatial, global) at baseline and over time. Models controlled for age, sex, and probable REM sleep behavior disorder at baseline.ResultThe table includes both baseline associations and difference in slopes (Δ), relative to the average person, in cognitive domains. These slopes ranged from about ‐0.05 for visuospatial to ‐0.12 for attention.ConclusionIncreased gait velocity and stride length are associated with less severe domain‐specific and global cognitive decline while increased swing time SD and DST are associated with more severe rates of decline in some domains. These findings suggest that baseline gait characteristics could be a helpful predictor of future cognitive changes.References:1. Savica R, Wennberg AMV, Hagen C, et al. Comparison of Gait Parameters for Predicting Cognitive Decline: The Mayo Clinic Study of Aging. Journal of Alzheimer's disease : JAD. 2017;55(2):559. doi:10.3233/JAD‐1606972. Mc Ardle R, del Din S, Galna B, Thomas A, Rochester L. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & Posture. 2020;76:372–376. doi:10.1016/j.gaitpost.2019.12.028

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

AbstractBackgroundResting state functional connectivity (rsfMRI) studies have shown functional network disruptions in logopenic progressive aphasia (LPA) (reduced connectivity within the left temporal language network) and posterior cortical atrophy (PCA) (reduced connectivity within ventral visual network). However, little is known about how these networks compare and how connectivity differs within and between networks in these atypical Alzheimer’s Disease (AD) phenotypes.MethodFifty‐nine amyloid‐positive PCA patients and eighty‐five amyloid‐positive LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore the default mode network (DMN), salience, sensorimotor, left language, visual and memory networks, which were generated by averaging multiple regions‐of‐interest (ROI) within each network. The data was analyzed at both the voxel‐level and network‐level (ROI‐ROI).ResultVoxel‐level differences in functional connectivity between PCA and LPA were observed in all networks (p<0.05; cluster size, FWE=0.05). PCA showed reduced connectivity in the occipital lobe in visual and memory networks, parietal lobe in language and DMN, and sensorimotor strip in sensorimotor network, particularly on the right, compared to LPA. Conversely, LPA showed reduced connectivity in the frontal cortex in salience, memory, visual and sensorimotor networks, inferior parietal lobe in visual network, and anterior temporal pole and sensorimotor cortex in the language network, compared to PCA. Between network differences (p<0.05; cluster size, FDR=0.05) were observed between groups, with PCA showing reduced connectivity between the visual and sensorimotor networks, while LPA showed reduced connectivity between the language and sensorimotor networks. The DMN was anti‐correlated to the language network in PCA, while it was positively correlated to the language network in LPA. In both phenotypes, the DMN was anticorrelated with the salience network. The visual network in PCA showed connections to all networks, while in LPA, it was only connected to the salience, language and DMN.ConclusionThe atypical AD phenotypes were associated with multiple disruptions in connectivity, both within and between brain networks. Phenotype specific differences in connectivity patterns were noted in visual network for PCA and language network for LPA.

ObjectiveThis study was undertaken to assess cross‐sectional and longitudinal [18F]‐flortaucipir positron emission tomography (PET) uptake in pathologically confirmed frontotemporal lobar degeneration (FTLD) and to compare FTLD to cases with high and low levels of Alzheimer disease (AD) neuropathologic changes (ADNC).MethodsOne hundred forty‐three participants who had completed at least one flortaucipir PET and had autopsy‐confirmed FTLD (n = 52) or high (n = 58) or low ADNC (n = 33) based on Braak neurofibrillary tangle stages 0–IV versus V–VI were included. Flortaucipir standard uptake value ratios (SUVRs) were calculated for 9 regions of interest (ROIs): an FTLD meta‐ROI, midbrain, globus pallidum, an AD meta‐ROI, entorhinal, inferior temporal, orbitofrontal, precentral, and medial parietal. Linear mixed effects models were used to compare mean baseline SUVRs and annual rate of change in SUVR by group. Sensitivity and specificity to distinguish FTLD from high and low ADNC were calculated.ResultsBaseline uptake in the FTLD meta‐ROI, midbrain, and globus pallidus was greater in FTLD than high and low ADNC. No region showed a greater rate of flortaucipir accumulation in FTLD. Baseline uptake in the AD‐related regions and orbitofrontal and precentral cortices was greater in high ADNC, and all showed greater rates of accumulation compared to FTLD. Baseline differences were superior to longitudinal rates in differentiating FTLD from high and low ADNC. A simple baseline metric of midbrain/inferior temporal ratio of flortaucipir uptake provided good to excellent differentiation between FTLD and high and low ADNC (sensitivities/specificities = 94%/95% and 71%/70%).InterpretationThere are cross‐sectional and longitudinal differences in flortaucipir uptake between FTLD and high and low ADNC. However, optimum differentiation between FTLD and ADNC was achieved with baseline uptake rather than longitudinal rates. ANN NEUROL 2022;92:1016–1029

Background: Observational studies suggested that dementia risk in patients with rheumatoid arthritis (RA) is higher than in the general population. Objective: To examine the associations of RA with cognitive decline and dementia, and neuroimaging biomarkers of aging, Alzheimer’s disease, and vascular pathology in adult participants in the Mayo Clinic Study of Aging (MCSA). Methods: Participants with RA were matched 1:3 on age, sex, education, and baseline cognitive diagnosis to participants without RA. RA cases with MRI were also matched with non-cases with available MRI. All available imaging studies (i.e., amyloid and FDG PET, sMRI, and FLAIR) were included. The study included 104 participants with RA and 312 without RA (mean age (standard deviation, SD) 75.0 (10.4) years, 33% male and average follow-up (SD) 4.2 (3.8) years). Results: Groups were similar in cognitive decline and risk of incident dementia. Among participants with neuroimaging, participants with RA (n = 33) and without RA (n = 98) had similar amyloid burden and neurodegeneration measures, including regions sensitive to aging and dementia, but greater mean white matter hyperintensity volume relative to the total intracranial volume (mean (SD)% : 1.12 (0.57)% versus 0.76 (0.69)% of TIV, p = 0.01), and had higher mean (SD) number of cortical infarctions (0.24 (0.44) versus 0.05 (0.33), p = 0.02). Conclusion: Although cognitive decline and dementia risk were similar in participants with and without RA, participants with RA had more abnormal cerebrovascular pathology on neuroimaging. Future studies should examine the mechanisms underlying these changes and potential implications for prognostication and prevention of cognitive decline in RA.

BackgroundFocally enlarged sulci (FES) are areas of proposed extraventricular fluid entrapment that may occur within idiopathic normal pressure hydrocephalus (iNPH) with radiographic evidence of disproportionately enlarged subarachnoid‐space hydrocephalus (DESH), and should be differentiated from atrophy.PurposeTo evaluate for change in FES size and pituitary height after shunt placement in iNPH.Study typeRetrospective.SubjectsA total of 125 iNPH patients who underwent shunt surgery and 40 age‐matched controls.Field Strength/Sequence1.5 T and 3 T. Axial T2w FLAIR, 3D T1w MPRAGE, 2D sagittal T1w.AssessmentFES were measured in three dimensions and volume was estimated by assuming an ellipsoid shape. Pituitary gland height was measured in the mid third of the gland in iNPH patients and controls.Statistical testsWilcoxon signed‐rank test for comparisons between MRI measurements; Wilcoxon rank sum test for comparison of cases/controls. Significance level was P < 0.05.ResultsFifty percent of the patients had FES. FES volume significantly decreased between the pre and first postshunt MRI by a median of 303 mm3 or 30.0%. Pituitary gland size significantly increased by 0.48 mm or 14.4%. FES decreased significantly by 190 mm3 or 23.1% and pituitary gland size increased significantly by 0.25 mm or 6% between the first and last postshunt MRI.Data ConclusionDecrease in size of FES after shunt placement provides further evidence that these regions are due to disordered cerebrospinal fluid (CSF) dynamics and should not be misinterpreted as atrophy. A relatively smaller pituitary gland in iNPH patients that normalizes after shunt is a less‐well recognized feature of altered CSF dynamics.Evidence Level3Technical EfficacyStage 2