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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.

AbstractPrior studies have shown communication‐related participation restrictions in patients with degenerative disease do not always match clinician judgment of symptom severity. Relatedly, there is a growing body of literature documenting discrepancies between patients with dementia and care partner perception of participation restrictions. However, it is not known how care partner perceptions of communication participation restrictions match or diverge from the patient's experience, which was the topic of this study.Toward that end, 28 patients with apraxia of speech and/or dysarthria associated with primary progressive apraxia of speech, nonfluent/agrammatic primary progressive aphasia, or progressive supranuclear palsy, all types of frontotemporal dementia, were seen for speech‐language examinations. The patients and their care partners were asked to independently complete the Communication Participation Item Bank (CPIB), short form, which is a 10‐question survey about communication in different contexts; higher scores reflect less interference from their condition on communication. Care partners were instructed to complete the form with their perception of the patient's restrictions. The total CPIB score (maximum 30) and difference scores were calculated and visualized.The data (Figure) suggest mismatches exist in care partner and patient ratings of communication participation restrictions. Of 28 patients, only 1 pair had identical scores, each rating severe interference in all communication contexts. 57% of patients rated communication as less impacted (higher scores) compared to their family member, leaving 39% of family members rating communication as less impacted than their loved one's perception. This study lays the foundation for future research to include patient and care partner ratings when examining the benefit of impairment‐based therapy, utilization of compensatory speech strategies, effectiveness of such strategies, and impact of other augmentative or alternative means of communication. The CPIB may be useful to document reduced psychosocial impact of disease and diminished burden and burnout on care partners with different intervention options. Future studies will explore possible differences between FTD phenotypes and, more specifically, whether 1) the motor speech disorder (MSD) disorder is primary or secondary to other motor impairment, 2) the nature or severity of the MSD, or 3) concomitant cognitive‐communication impairment predict the existence of and/or direction of a discrepancy.

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.

ImportancePhysical activity is associated with cognitive health, even in autosomal dominant forms of dementia. Higher physical activity is associated with slowed cognitive and functional declines over time in adults carrying autosomal dominant variants for frontotemporal lobar degeneration (FTLD), but whether axonal degeneration is a potential neuroprotective target of physical activity in individuals with FTLD is unknown.ObjectiveTo examine the association between physical activity and longitudinal neurofilament light chain (NfL) trajectories in individuals with autosomal dominant forms of FTLD.Design, Setting, and ParticipantsThis cohort study included individuals from the ALLFTD Consortium, which recruited patients from sites in the US and Canada. Symptomatic and asymptomatic adults with pathogenic variants in one of 3 common genes associated with FTLD (GRN, C9orf72, or MAPT) who reported baseline physical activity levels and completed annual blood draws were assessed annually for up to 4 years. Genotype, clinical measures, and blood draws were collected between December 2014 and June 2019; data were analyzed from August 2021 to January 2022. Associations between reported baseline physical activity and longitudinal plasma NfL changes were assessed using generalized linear mixed-effects models adjusting for baseline age, sex, education, functional severity, and motor symptoms.ExposuresBaseline physical activity levels reported via the Physical Activity Scale for the Elderly. To estimate effect sizes, marginal means were calculated at 3 levels of physical activity: 1 SD above the mean represented high physical activity, 0 SD represented average physical activity, and 1 SD below the mean represented low physical activity.Main Outcomes and MeasuresAnnual plasma NfL concentrations were measured with single-molecule array technology.ResultsOf 160 included FTLD variant carriers, 84 (52.5%) were female, and the mean (SD) age was 50.7 (14.7) years. A total of 51 (31.8%) were symptomatic, and 77 carried the C9orf72 variant; 39, GRN variant; and 44, MAPT variant. Higher baseline physical activity was associated with slower NfL trajectories over time. On average, NfL increased 45.8% (95% CI, 22.5 to 73.7) over 4 years in variant carriers. Variant carriers with high physical activity demonstrated 14.0% (95% CI, −22.7 to −4.3) slower NfL increases compared with those with average physical activity and 30% (95% CI, −52.2 to −8.8) slower NfL increases compared with those with low physical activity. Within genotype, C9orf72 and MAPT carriers with high physical activity evidenced 18% to 21% (95% CI, −43.4 to −7.2) attenuation in NfL, while the association between physical activity and NfL trajectory was not statistically significant in GRN carriers. Activities associated with higher cardiorespiratory and cognitive demands (sports, housework, and yardwork) were most strongly correlated with slower NfL trajectories (vs walking and strength training).Conclusions and RelevanceIn this study, higher reported physical activity was associated with slower progression of an axonal degeneration marker in individuals with autosomal dominant FTLD. Physical activity may serve as a primary prevention target in FTLD.

AbstractBackgroundGlobular glial tauopathy (GGT) has been associated with frontotemporal dementia syndromes; little is known about the clinical and imaging characteristics of GGT and how they differ from other non‐globular glial 4‐repeat tauopathies (N4GT) such as progressive supranuclear palsy (PSP) or corticobasal degeneration (CBD).MethodsFor this case–control study the Mayo Clinic brain banks were queried for all cases with an autopsy‐confirmed diagnosis of GGT between 1 January 2011 and 31 October 2021. Fifty patients with N4GT (30 PSP, 20 CBD) were prospectively recruited and followed by the Neurodegenerative Research Group at Mayo Clinic, Minnesota. Magnetic resonance imaging was used to characterize patterns of gray/white matter atrophy, MR‐parkinsonism index, midbrain volume, and white matter hyperintensities.18F‐Fluorodeoxyglucose‐, 11C Pittsburg compound‐, and 18F‐flortaucipir‐positron emission tomography scans were reviewed.ResultsTwelve patients with GGT were identified: 83% were women compared to 42% in NG4T (p = 0.02) with median age at death 76.5 years (range: 55–87). The most frequent clinical features were eye movement abnormalities, parkinsonism, behavioral changes followed by pyramidal tract signs and motor speech abnormalities. Lower motor neuron involvement was present in 17% and distinguished GGT from NG4T (p = 0.035). Primary progressive apraxia of speech was the most frequent initial diagnosis (25%); 50% had a Parkinson‐plus syndrome before death. Most GGT patients had asymmetric frontotemporal atrophy with matching hypometabolism. GGT patients had more gray matter atrophy in temporal lobes, normal MR‐parkinsonism index, and larger midbrain volumes.ConclusionsFemale sex, lower motor neuron involvement in the context of a frontotemporal dementia syndrome, and asymmetric brain atrophy with preserved midbrain might be suggestive of underlying GGT.

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

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