Performance of [(18)F]flutemetamol amyloid imaging against the neuritic plaque component of CERAD and the current (2012) NIA-AA recommendations for the neuropathologic diagnosis of Alzheimer's disease

INTRODUCTION: Performance of the amyloid tracer [(18)F]flutemetamol was evaluated against three pathology standard of truth (SoT) measures including neuritic plaques (CERAD "original" and "modified" and the amyloid component of the 2012 NIA-AA guidelines). METHODS: After [(18)F]flutemetamol imaging, 106 end-of-life patients who died underwent postmortem brain examination for amyloid plaque load. Blinded positron emission tomography scan interpretations by five independent electronically trained readers were compared with pathology measures. RESULTS: By SoT, sensitivity and specificity of majority image interpretations were, respectively, 91.9% and 87.5% with "original CERAD," 90.8% and 90.0% with "modified CERAD," and 85.7% and 100% with the 2012 NIA-AA criteria. DISCUSSION: The high accuracy of either CERAD criteria suggests that [(18)F]flutemetamol predominantly reflects neuritic amyloid plaque density. However, the use of CERAD criteria as the SoT can result in some false-positive results because of the presence of diffuse plaques, which are accounted for when the positron emission tomography read is compared with the 2012 NIA-AA criteria

Operations with the digital orbit feedback system in the NSLS x-ray ring

The digital filtering and eigenvector decomposition-based orbit correction is performed by two dedicated HP 742/743 rt micros which communicate with Motorola CPU based orbit-measuring and orbit-correction systems. The correction algorithm in the DFbk was orthogonalized with respect of the analog global harmonic feedback. Operational results concerning improvements in the noise suppression at low frequencies and especially in the dc drift as well as in the orbit stability are shown. Efforts are underway to improve the resolution of the orbit measuing system and the sampling rate using 16 bit 400 kHz ADC`s which will allow orbit sampling with high resolution at 4 kHz frequency

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups

Detection of early Alzheimer's disease in MCI patients by the combination of MMSE and an episodic memory test

BACKGROUND: Mild cognitive impairment (MCI) is a heterogeneous clinical entity that comprises the prodromal phase of Alzheimer's disease (Pr-AD). New biomarkers are useful in detecting Pr-AD, but they are not universally available. We aimed to investigate baseline clinical and neuropsychological variables that might predict progression from MCI to AD dementia. METHODS: All patients underwent a complete clinical and neuropsychological evaluation at baseline and every 6 months during a two-year follow-up period, with 54 out of 109 MCI patients progressing to dementia (50 of them progressed to AD dementia), and 55 remaining as stable MCI (S-MCI). RESULTS: A combination of MMSE and California Verbal Learning Test Long Delayed Total Recall (CVLT-LDTR) constituted the best predictive model: subjects scoring above 26/30 on MMSE and 4/16 on CVLT-LDTR had a negative predictive value of 93.93% at 2 years, whereas those subjects scoring below both of these cut-off scores had a positive predictive value of 80.95%. CONCLUSIONS: Pr-AD might be distinguished from S-MCI at baseline using the combination of MMSE and CVLT-LDTR. These two neuropsychological predictors are relatively brief and may be readily completed in non-specialist clinical settings

Validity of a novel computerized cognitive battery for mild cognitive impairment

BACKGROUND: The NeuroTrax Mindstreams computerized cognitive assessment system was designed for widespread clinical and research use in detecting mild cognitive impairment (MCI). However, the capability of Mindstreams tests to discriminate elderly with MCI from those who are cognitively healthy has yet to be evaluated. Moreover, the comparability between these tests and traditional neuropsychological tests in detecting MCI has not been examined. METHODS: A 2-center study was designed to assess discriminant validity of tests in the Mindstreams Mild Impairment Battery. Participants were 30 individuals diagnosed with MCI, 29 with mild Alzheimer's disease (AD), and 39 healthy elderly. Testing was with the Mindstreams battery and traditional neuropsychological tests. Receiver operating characteristic (ROC) analysis was used to examine the ability of Mindstreams and traditional measures to discriminate those with MCI from cognitively healthy elderly. Between-group comparisons were made (Mann-Whitney U test) between MCI and healthy elderly and between MCI and mild AD groups. RESULTS: Mindstreams outcome parameters across multiple cognitive domains significantly discriminated among MCI and healthy elderly with considerable effect sizes (p < 0.05). Measures of memory, executive function, visual spatial skills, and verbal fluency discriminated best, and discriminability was at least comparable to that of traditional neuropsychological tests in these domains. CONCLUSIONS: Mindstreams tests are effective in detecting MCI, providing a comprehensive profile of cognitive function. Further, the enhanced precision and ease of use of these computerized tests make the NeuroTrax system a valuable clinical tool in the identification of elderly at high risk for dementia

Better cardiovascular health is associated with slowed clinical progression in autosomal dominant frontotemporal lobar degeneration variant carriers

IntroductionCardiovascular health is important for brain aging, yet its role in the clinical manifestation of autosomal dominant or atypical forms of dementia has not been fully elucidated. We examined relationships between Life's Simple 7 (LS7) and clinical trajectories in individuals with autosomal dominant frontotemporal lobar degeneration (FTLD).MethodsTwo hundred forty-seven adults carrying FTLD pathogenic genetic variants (53% asymptomatic) and 189 non-carrier controls completed baseline LS7, and longitudinal neuroimaging and neuropsychological testing.ResultsAmong variant carriers, higher baseline LS7 is associated with slower accumulation of frontal white matter hyperintensities (WMHs), as well as slower memory and language declines. Higher baseline LS7 associated with larger baseline frontotemporal volume, but not frontotemporal volume trajectories.DiscussionBetter baseline cardiovascular health related to slower cognitive decline and accumulation of frontal WMHs in autosomal dominant FTLD. Optimizing cardiovascular health may be an important modifiable approach to bolster cognitive health and brain integrity in FTLD.HighlightsBetter cardiovascular health associates with slower cognitive decline in frontotemporal lobar degeneration (FTLD). Lifestyle relates to the accumulation of frontal white matter hyperintensities in FTLD. More optimal cardiovascular health associates with greater baseline frontotemporal lobe volume. Optimized cardiovascular health relates to more favorable outcomes in genetic dementia

Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study

BackgroundFrontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged &lt;65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown.ObjectiveThis study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD.MethodsParticipants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age.ResultsThe CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P&lt;.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P&lt;.001 in all cases).ConclusionsThese findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change