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AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) occurs before age 65 and has more diverse disease presentations than late‐onset AD. To improve our understanding of phenotypic heterogeneity among EOAD individuals, we analyzed cognitive scores using data‐driven statistical analysis.MethodBaseline cognitive data from 286 sporadic EOAD individuals from the Longitudinal EOAD study (LEADS) were transformed to z‐scores using data from 95 cognitively normal (CN) individuals. Cognitive composites were generated for domains of memory, language, speed/attention, visuospatial, and executive function. Residuals from linear regression models on Z‐scores adjusted for age, sex, and education were obtained. Cluster analysis using the Ward method on the cognitive domain residuals was performed and scree plot using the pseudo T‐squared determined the optimal number of clusters for the EOAD sample. We also compared gray matter density (GMD) of each EOAD cluster to CN participants using voxel‐wise multiple linear regressions.ResultsThree clusters of cognitive performance were identified from the EOAD sample. Disease duration was not significantly different across clusters. Using a z‐score of ‐1.5 SD as the impairment threshold, all clusters were impaired across most domains (Table 1). Cluster‐3 was more impaired than cluster‐2 in all domains (Table 2; all p<.0001), and in all domains except episodic memory compared to cluster‐1 (all p<.01). Cluster‐1 (n = 71; 85.9% amnestic) was most impaired in executive function, visuospatial, and speed/attention. Cluster‐2 (n = 133; 88.7% amnestic) was most impaired in episodic memory. Cluster‐3 (n = 82; 69.5% amnestic) was most impaired in executive function, visuospatial, and speed/attention (Table 1). 3D‐comparisons showed all EOAD clusters had reduced GMD compared to CN. Cluster‐1 and cluster‐3 both showed widespread atrophy, with cluster‐3 being more severe. Cluster‐2 showed the most atrophy in the temporal and parietal lobes (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among sporadic EOAD individuals. Cluster‐3 appeared to reflect widespread impairment, and cluster‐2 represented an amnestic‐only presentation. Despite comparable disease duration, some EOAD patients progress faster, while some are more resilient. 3D‐comparisons showed neurodegenerative changes affecting brain regions responsible for respective impaired cognitive functions in each cluster (e.g., cluster‐2 is primarily amnestic‐impaired and has temporoparietal atrophy). Future work should explore amyloid‐PET and tau‐PET burden.

AbstractBackgroundSubjective cognitive concerns are common in functionally intact adults, potentially indicating future cognitive decline. Remote smartphone cognitive testing holds promise for objectively tracking cognition in individuals reporting complaints. In our initial exploration of the link between subjective cognitive complaints and digital clinical outcomes, we examined participants' self‐reported cognitive complaints' association with smartphone tests on memory and executive functioning.MethodsParticipants, without self‐reported MCI or dementia, were recruited from the UCSF Brain Healthy Registry (BHR) to complete cognitive tasks on their personal smartphone with the ALLFTD Mobile App. Participants also completed the 12‐item Everyday Cognition Screening Tool (ECog‐12), assessing subjective cognitive impairment, online as part of BHR. Average ECog‐12 scores, including Memory and Executive Functioning subdomains, were calculated. Correlations between scores from online ECog‐12 scores and app‐based smartphone tasks were examined. Regression analyses, adjusting for age, education, and sex, were performed to evaluate the relationship between ECog‐12 and smartphone tasks (Stroop & Flanker) measuring executive function and memory.ResultsOut of 2,283 invited BHR participants, 456 (mean age: 64.6 years (SD=11.8) and education: 16.3 years (SD=2.4)) completed smartphone tasks (81.1% completion rate). Age was negatively associated with executive functioning and memory tasks (β’s=‐0.51 to ‐0.54, p’s<0.05), while higher education correlated with better performance (β’s=0.10‐0.14, p’s<0.05). Sex was not significantly associated with performance on any task (β’s=‐0.04 to 0.09, p’s>0.05). Subjective cognitive concerns were linked to tasks of executive functioning (Stroop: β=‐0.18, p=0.002) and memory (β=‐0.14, p=0.02). Endorsement of executive functioning items on ECog‐12 associated with Stroop performance (β=‐0.16, p=0.005), and memory‐specific complaints were related to memory performance (β=‐0.1, p=0.09). The relationship with flanker scores was not significant (β=‐0.08, p=0.17). Similar patterns emerged with the online ECog‐12; online and app‐based scores are strongly correlated (β=0.75, p<0.05).ConclusionsIn this cognitively intact sample, subjective cognitive concerns were linked to poorer performance on ALLFTD Mobile App tasks assessing executive functioning and memory. These findings suggest that unsupervised remote smartphone testing merits further consideration to monitor cognition in individuals with subjective cognitive concerns. Subsequent studies in deeply phenotyped cohorts with follow‐up assessments are essential for understanding the clinical relevance of these results.

AbstractBackgroundDiagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies.MethodSeventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism.ResultThirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n=5), ii) anterior temporal and orbitofrontal (n=11), iii) occipito‐parietal (n=6), and iv) lateral frontal and parietal (n=14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles.ConclusionFifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.

AbstractBackgroundProgressive supranuclear palsy (PSP) can present with different clinical variants which show distinct, but partially overlapping, patterns of neurodegeneration and tau deposition in a PSP network of regions, including cerebellar dentate, superior cerebellar peduncle, midbrain, thalamus, basal ganglia, and frontal lobe. We sought to determine whether disruptions in functional connectivity within this PSP network measured using resting‐state functional MRI (rs‐fMRI) differed between PSP‐Richardson’s syndrome and the cortical and subcortical variants of PSP.MethodStructural MRI and rs‐fMRI scans were collected for 40 PSP‐RS, 24 PSP‐cortical (12 speech and language; 10 corticobasal syndrome; 2 frontal) and 36 PSP‐subcortical (18 parkinsonism; 11 progressive gait freezing; 6 postural instability; 1 oculomotor) participants who met the Movement Disorder Society PSP clinical criteria (Table 1). Ninety‐six participants underwent flortaucipir‐PET scans. MRIs were processed using CONN Toolbox. Functional connectivity between seeds placed throughout the PSP network was compared between each PSP group and 83 healthy controls, and between the PSP groups, covarying for age and sex.ResultsConnectivity was reduced throughout the network in PSP‐RS compared to controls (Figure 1A), involving cerebellar dentate, midbrain nuclei, subthalamic nuclei, basal ganglia, thalamus, and frontal regions. Frontal regions showed reduced connectivity to other regions in the network in PSP‐cortical, including substantia nigra and superior cerebellar peduncle. Disruptions in connectivity in PSP‐subcortical were less pronounced, with the strongest disruption between the pallidum and putamen. When PSP groups were compared to each other (Figure 1B), PSP‐RS had lower connectivity from the thalamus and cerebellar dentate to the cortex compared to PSP‐subcortical and from the thalamus to substantia nigra than PSP‐cortical. PSP‐subcortical had lower connectivity from the subthalamic nucleus to caudate than PSP‐RS and from the subthalamic nucleus to substantia nigra than PSP‐cortical and higher connectivity from the thalamus to the prefrontal cortex than both the other variants. Voxel‐based analyses replicated these patterns. There was moderate evidence that tau uptake in the PSP network regions influenced connectivity between these regions in PSP.ConclusionsPatterns of disrupted functional connectivity revealed both variant‐specific and shared disease pathways among PSP variants, providing insight into the disease heterogeneity beyond patterns of atrophy.

AbstractBackgroundLarge‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age≥65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).Method731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans.ResultAmyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR=10.1 versus 2.4, p=0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR≤0.5; Figure 1D‐E).ConclusionThe lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

AbstractBackgroundLarge‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age = 65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).Method731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans.ResultAmyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR = 10.1 versus 2.4, p = 0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR = 0.5; Figure 1D‐E).ConclusionThe lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.

AbstractBackgroundDiagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies.MethodSeventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism.ResultThirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n = 5), ii) anterior temporal and orbitofrontal (n = 11), iii) occipito‐parietal (n = 6), and iv) lateral frontal and parietal (n = 14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles.ConclusionFifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.

AbstractBackgroundDiscussion surrounding the nomenclature of the “nonfluent/agrammatic” spectrum of progressive speech‐language disorders has largely focused on the clinical‐pathological and neuroimaging correlations, with some attention paid to the prognostication afforded by differentiating clinical phenotypes. Progressive apraxia of speech (AOS), with or without agrammatic aphasia, is generally associated with an underlying tauopathy; however, patients have offered a unique perspective on the importance of distinguishing between difficulties with speech and language that extends beyond pathological specificity. This study aimed to provide insight into the experience of patients with primary progressive AOS (PPAOS), with particular attention to their diagnostic journey.MethodA qualitative research approach was used. Structured interviews were conducted with three prosodic and one phonetic predominant PPAOS patients, judged representative of our research cohort, who were previously received other diagnoses, including primary progressive aphasia (PPA). A current PPAOS diagnosis and willingness to discuss the details of their diagnostic workup and reflect upon their experience were the inclusion criteria. One patient preferred to provide written responses. Each interview was reviewed, and the collective contents were examined using thematic analysis.ResultThe primary themes were identity, community, and planning. Patients expressed: 1) the importance of the alignment of AOS with their difficulties, specifically that they had no difficulty with thinking of words or with comprehension (i.e., no aphasia); 2) not sharing attributes with PPA patients and their observed clinical presentations; and 3) the desire to capitalize upon their retained skills for treatment, for example “I intend to retain my ‘voice’ even as I lose my speaking ability.”ConclusionUnderstanding the experiences of PPAOS patients may inform future iterations of progressive speech‐language spectrum diagnostic criteria and the consistency of their use, along with intervention recommendations. The specificity of a PPAOS diagnosis is meaningful to patients as it relates to their identity, sense of community, and approach to future planning. Further work will evaluate the experience of patients with a broader spectrum of progressive speech‐language disorders along with their care partners. More work is underway to outline the predictive value of an AOS diagnosis, relative to co‐occurring aphasia severity, to inform better prognostication.

AbstractBackgroundSubjective cognitive concerns are common in functionally intact adults, potentially indicating future cognitive decline. Remote smartphone cognitive testing holds promise for objectively tracking cognition in individuals reporting complaints. In our initial exploration of the link between subjective cognitive complaints and digital clinical outcomes, we examined participants' self‐reported cognitive complaints' association with smartphone tests on memory and executive functioning.MethodsParticipants, without self‐reported MCI or dementia, were recruited from the UCSF Brain Healthy Registry (BHR) to complete cognitive tasks on their personal smartphone with the ALLFTD Mobile App. Participants also completed the 12‐item Everyday Cognition Screening Tool (ECog‐12), assessing subjective cognitive impairment, online as part of BHR. Average ECog‐12 scores, including Memory and Executive Functioning subdomains, were calculated. Correlations between scores from online ECog‐12 scores and app‐based smartphone tasks were examined. Regression analyses, adjusting for age, education, and sex, were performed to evaluate the relationship between ECog‐12 and smartphone tasks (Stroop & Flanker) measuring executive function and memory.ResultsOut of 2,283 invited BHR participants, 456 (mean age: 64.6 years (SD = 11.8) and education: 16.3 years (SD = 2.4)) completed smartphone tasks (81.1% completion rate). Age was negatively associated with executive functioning and memory tasks (ß’s = ‐0.51 to ‐0.54, p’s<0.05), while higher education correlated with better performance (ß’s = 0.10‐0.14, p’s<0.05). Sex was not significantly associated with performance on any task (ß’s = ‐0.04 to 0.09, p’s>0.05). Subjective cognitive concerns were linked to tasks of executive functioning (Stroop: ß = ‐0.18, p = 0.002) and memory (ß = ‐0.14, p = 0.02). Endorsement of executive functioning items on ECog‐12 associated with Stroop performance (ß = ‐0.16, p = 0.005), and memory‐specific complaints were related to memory performance (ß = ‐0.1, p = 0.09). The relationship with flanker scores was not significant (ß = ‐0.08, p = 0.17). Similar patterns emerged with the online ECog‐12; online and app‐based scores are strongly correlated (ß = 0.75, p<0.05).ConclusionsIn this cognitively intact sample, subjective cognitive concerns were linked to poorer performance on ALLFTD Mobile App tasks assessing executive functioning and memory. These findings suggest that unsupervised remote smartphone testing merits further consideration to monitor cognition in individuals with subjective cognitive concerns. Subsequent studies in deeply phenotyped cohorts with follow‐up assessments are essential for understanding the clinical relevance of these results.