bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large scale, voxel‐to‐voxel correlation analysis of tau positron emission tomography signal to histological pathology in non‐Alzheimer tauopathies
Authors: Chen Yuheng , Alegro Maryana , Morales Dulce Ovando , Joie Renaud , Shankar Anubhav , Burr Alex , Lee Wing Hung , Poon Kinsoon , Kantamneni Namrata , Colmignoli Stefano , Eser Rana A. , Miller Bruce L. , Rabinovici Gil D. , Grinberg Lea Tenenholz 
Published: December 2020

AbstractBackgroundTau pathology strongly correlates with neuronal loss and clinical decline across tauopathies, emphasizing the need to detect and monitor tau pathology in living patients. Positron Emission Tomography (PET) using radiotracers like Flortaucipir ([18F]AV‐1451) enables good visualization of AD tau pathology. However, the sensitivity and specificity of this tracer to visualize tau deposits in non‐AD tauopathies remain poorly understood. PET‐to‐autopsy validation has been challenging because of issues with tissue deformation and large‐scale pathological analysis. Here, we present i) an interdisciplinary approach combining immunohistochemistry, computer vision, and convolution neural network (CNN) to 3D‐reconstructed, billion‐pixel digital pathology tau images of human brain hemispheres, co‐register them to PET and ii) preliminary PET‐to‐pathology voxel‐to‐voxel correlation analyses.MethodWe employed a pipeline developed in‐house combining whole‐brain processing, free‐floating immunohistochemistry and computer‐based algorithms for 2D and 3D registration. SlideNet, our CNN for segmenting tau inclusions in digital images (Figure 1A), aims at generating quantitative, 3D‐tau‐maps. We applied SlideNet to images of whole hemisphere sections in case #1 with Cortico‐Basal‐Degeneration and case #6 with Progressive‐Supranuclear‐Palsy labeled by immunohistochemistry (CP‐13 antibody, Ser202) (Figure 2A), and the demographics of cases are shown in Table 1. We registered the resulting heatmaps (Figure 1B) to MRI and PET coordinates (Figure 3), enabling voxel‐to‐voxel comparisons.ResultThe CNN output probability map containing three categories: tau in neurons, tau in glia/processes, and background (Figure 2B). After precision‐recall statistical thresholding at 0.7 (Figure 4A), the resulting tau segmentation shows in Figure 2C. The area‐under‐curve on receiver‐operating‐characteristic graph was 0.84 in testing and validation datasets from the two cases we processed (Figure 4B), suggesting that our CNN selects tau inclusions with strong class separation. We registered the histology‐based, quantitative 3D‐tau‐map and PET scan onto the 3D MRI space (Figures 1C‐D). Preliminary correlation analysis a coefficient of 0.182 (p<0.001) between low resolution tau map and PET scan (Figure 5).ConclusionCNN segmentation of pathology combined with whole‐brain processing and high‐performance computer vision enables voxel‐to‐voxel correlations analysis between histological tau and tau PET tracer binding, a keystep toward early detection of tauopathies. Ongoing regional analysis will provide more granular correlations, especially in areas known for off‐target binding, and we are currently finalizing the result in another 6 cases (Table 1).
A time‐embedding network model captures dynamic longitudinal pathology changes in a dominantly inherited Alzheimer disease population
Authors: McCullough Austin A , Gordon Brian A , McDade Eric , Bateman Randall J , Morris John C , Benzinger Tammie LS ,   Dominantly Inherited Alzheimer Network (DIAN)
Published: December 2020

AbstractBackgroundPopulation‐level analyses of autosomal dominant Alzheimer disease (ADAD) cohorts have revealed a roadmap of neuropathological and volumetric changes that increase over disease‐time and culminate in severe cognitive impairment. Traditional statistical analysis approaches struggle to integrate these cohort‐level dynamics with patient data to inform disease trajectory predictions at the individual level. Current machine learning approaches excel at this integration of population and individual level information, but require much larger sample sizes than current Alzheimer disease research cohorts can provide. The current work introduces a time‐embedding neural network in order to accurately represent known disease‐time dependent patterns among neuroimaging variables. The network is trained to predict ADAD carrier status to demonstrate the ability of this framework to make actionable predictions within ADAD populations.Method468 participants from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer Network (DIAN) were selected for analysis. Structural MRI, [11C] Pittsburgh Compound B (PiB) PET, and [18F] Fluorodeoxyglucose (FDG) PET region of interest imaging data was utilized (Table 1). DIAN estimated years to symptom onset (EYO) was used as disease‐time. The time‐embedding network learns EYO‐dependent representations of all imaging data in both mutation carrier and non‐carrier groups. These representations are combined with control variables and passed to a decoder network trained to output carrier status predictions (Figure 1).ResultThe time‐embedding network captures three main longitudinal pathology dynamics that correspond to known longitudinal PiB PET, FDG PET, and volumetric pathology changes in ADAD populations (Figure 2A). The network was successfully trained to predict ADAD carrier status in individual participants (Figure 2B). Prediction accuracy increases as the time of prediction moves closer to onset of clinical symptoms (Figure 2C).ConclusionThe time‐embedding network model is able to accurately represent nonlinear time‐dependent processes in a low‐dimensional, sparsely sampled embedding. This embedding provides an interpretable framework that is consistent with known longitudinal pathology changes when applied to an ADAD population, and can be trained to make accurate longitudinal predictions with currently available data sets.
Ratio of serum proBDNF to BDNF and its association with cognitive performance and brain morphometry in mild cognitive impairment
Authors: Cechova Katerina , Angelucci Francesco , Markova Hana , Nikolai Tomas , Matuskova Veronika , Laczó Jan , Nedelska Zuzana , Vyhnalek Martin , Hort Jakub 
Published: December 2020

AbstractBackgroundBrain‐derived neurotrophic factor (BDNF) is the neurotrophin well‐known for its role in neurogenesis, synaptic plasticity and enhancement of long‐term potentiation (LTP). Its precursor proBDNF exhibits opposite biological function – inhibits LTP and increases long‐term depression (LTD) in the brain. Altered proBDNF/BDNF ratio in favor of the pro‐form may lead to enhancement of LTD and reduction of LTP in hippocampal neurons with consequent synaptic dysfunction and memory deficits. To explore this hypothesis, we investigated the associations of serum proBDNF, mature BDNF and its ratio (proBDNF/BDNF) with cognitive performance and structural changes of AD‐related brain regions in individuals with amnestic mild cognitive impairment (aMCI).Method95 individuals with aMCI (age = 68.5 ± 7.3; MMSE = 26.1 ± 2.4) were recruited from the Czech Brain Aging Study. All participants underwent clinical and laboratory evaluation (with APOE and BDNF genotyping), comprehensive neuropsychological examination and brain MRI (1.5T). FreeSurfer 5.3 software was used to measure left and right hippocampal volume and left and right entorhinal and parahippocampal thickness. Serum levels of proBDNF and BDNF were measured by sandwich enzyme‐linked immunosorbent assay (ELISA).ResultA negative correlation was found between serum proBDNF/BDNF ratio and verbal memory (r = ‐0. 25, p = 0.04) and executive function (r = ‐0. 30, p = 0.02) but not with other cognitive composite scores (ps > 0.26). A trend of positive correlation was found between serum BDNF levels and verbal memory domain (r = 0.23, p = 0.08). No other correlation between BDNF and proBDNF levels and cognitive performance was significant. Correlations of proBDNF and BDNF levels and its ratio with volume and thickness of AD‐related brain regions were not significant.ConclusionHigher proBDNF/BDNF ratio is associated with lower cognitive performance in verbal memory and executive function but not with structural brain changes in aMCI individuals. Our findings suggest that proBDNF/BDNF ratio may be a more reliable blood biomarker of cognitive change than BDNF and proBDNF levels alone.
SNAP25 reflects amyloid‐ and tau‐related synaptic damage: Associations between PET, VBM and cerebrospinal fluid biomarkers of synaptic disfunction in the Alzheimer’s disease spectrum
Authors: Benedet Andréa Lessa , Ashton Nicholas J. , Pascoal Tharick A. , Brinkmalm Ann , Nilsson Johanna , Kvartsberg Hlin , Mathotaarachchi Sulantha , Savard Mélissa , Therriault Joseph , Tissot Cécile , Chamoun Mira , Zetterberg Henrik , Blennow Kaj , Rosa‐Neto Pedro 
Published: December 2020

AbstractBackgroundIt is widely accepted that synaptic dysfunction is an early core feature of Alzheimer’s disease (AD). Cerebrospinal fluid (CSF) biomarkers of synaptic damage have already been associated with amyloid and tau pathologies as well as with cognitive decline in the AD spectrum. However, to the best of our knowledge, no study has yet shown the relationship between CSF biomarkers and PET biomarkers at the voxel‐level for these pathologies. Thus, we aim to investigate the association between several biomarkers of synaptic damage and amyloid and tau, in the AD spectrum, using data from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort.Method163 participants (23 young controls, 77 normal controls, 37 mild cognitive impairment, 22 AD and 9 FTD) were evaluated with cross‐sectional CSF biomarkers, [18F]MK6240 and [18F]AZD4694 PET as well as white(WM) and grey matter(GM) MRI voxel‐based morphometry (VBM). For PET, SUVRs were determined using the cerebellar cortex as reference tissue. The CSF biomarkers were quantified by in‐house immunoassays for neurogranin and neuromodulin (GAP‐43), whereas SNAP‐25 and synaptotagmin‐1 (SYT1) were quantified using an immunoprecipitation mass spectrometry method. Linear models were applied to evaluate group differences in CSF biomarker concentrations, adjusting by age and sex. Voxel‐wise linear regressions were also implemented, using VoxelStats, to examine the association between CSF‐ and imaging‐based measures.ResultComparison between groups showed increased concentrations of the CSF biomarkers across disease stages (Figure 1). At the voxel level, consistent associations were found between CSF biomarkers and PET amyloid in AD‐related regions, with the SNAP‐25 biomarkers demonstrating the strongest correlations (Figure 2). Regions with clear associations between tau PET and CSF biomarkers were the inferior temporal, frontal and inferior‐parietal cortices. Again, associated regions were common to all biomarkers, with the strongest correlations found with the SNAP‐25 biomarkers (Figure 3). CSF biomarkers also correlated with GM, but not WM, VBM, being the associated areas in regions commonly affected by AD pathophysiology (Figure 4). Further analyses with SV2A PET should corroborate current findings.ConclusionThese results suggest that CSF SNAP‐25 reflects synaptic degeneration and loss in cortical regions affected by amyloid and tau pathology throughout the continuum.
Higher percent of energy intake as carbohydrates has a negative relationship with cognitive performance in APOE4 carriers
Authors: Sullivan Debra K , Taylor Matthew K , Burns Jeffrey M 
Published: December 2020

AbstractBackgroundExpression of the APOE4 allele is a known genetic risk factor of Alzheimer’s Disease. There is speculation that dietary carbohydrate intake may also influence AD risk. The purpose of this study was to evaluate the cross‐sectional relationship between dietary carbohydrate intake and cognitive performance stratified by APOE status in a population of cognitively normal, older adults.MethodBaseline data from 62 cognitively normal older adults participating in a nutrition intervention study (Nutrition Interventions for Cognitive Enhancement study; NICE study) were analyzed. Dietary intake was obtained by 3‐day diet records and entered into the Nutrition Data System for Research. Fasting blood draws were attained and APOE status was determined by Quest Diagnostics®. A cognitive battery was conducted by a trained psychometrician. Individual test z‐scores were calculated to form domains of memory (Logical Memory Tests I and II and the Free and Cued Selective Recall Test), attention (Digit Span Forward and Backward and Stroop Number Letter), visuospatial processing (Stroop Color, Block Design, Trailmaking A, and Digit Symbol Substitution Test), and executive function (Trailmaking B and Category Fluency Animals/Vegetables). We constructed ordinary least squares regression models to assess the relation between proportion of carbohydrate as energy and cognition controlled for age and gender in an APOE‐dependent manner. Statistical analyses were performed using R (v. 3.6.2; R Foundation, Vienna, Austria). Statistical significance was set at p < 0.05.ResultParticipants were 85% female and had a mean age of 72.1±4.9 years. APOE status was 32% APOE4 carriers (n=20) and 68% non‐carriers. There was no difference in age, sex or dietary macronutrient intake between carriers and non‐carriers. Within APOE4 carriers, higher percent of energy as carbohydrate was related to poorer memory (β̞=‐0.64, p=0.002) and executive function (β̞=‐0.51, p=0.02). Within the non‐carriers, there was no relationship between percent carbohydrate and cognitive performance.ConclusionAPOE4 carriers with higher percent dietary carbohydrate intake had worse performance on cognitive functions tests. These results support the concept that dietary carbohydrate may be an important modifiable risk factor for AD. These findings will be further investigated utilizing the final sample size of the NICE Study (n=200).
Utility of ioflupane‐SPECT with multimodal imaging in dementia with Lewy bodies
Authors: Miyagawa Toji , Przybelski Scott A. , Maltais Daniela D. , Min Paul H. , Jordan Lennon , Lesnick Timothy G. , Chen Qin , Graff‐Radford Jonathan , Jones David T. , Savica Rodolfo , Knopman David S. , Petersen Ronald C. , Fields Julie A. , Machulda Mary M. , Forsberg Leah K. , Allen Laura A. , Parisi Joseph E. , Murray Melissa E. , Ferman Tanis J. , Dickson Dennis W. , Boeve Bradley F. , Kantarci Kejal , Lowe Val J. 
Published: December 2020

AbstractBackgroundMultiple imaging modalities have been individually shown to be useful in diagnosing Dementia with Lewy Bodies (DLB). Reduced striatonigral uptake on ioflupane‐SPECT reflects striatonigral dopamine dysfunction observed in DLB, while other imaging modalities assess different aspects of DLB, which potentially work complementarily when used together. We assessed how well ioflupane‐SPECT differentiates DLB from Alzheimer’s Disease Dementia (ADem) and whether adding another imaging modality to ioflupane‐SPECT would provide additional value.MethodIoflupane‐SPECT, MRI, FDG‐PET, and PiB‐PET were assessed on 35 DLB and 14 ADem patients (including 12 patients with eventual autopsy confirmation). Striatonigral dopamine transporter uptake was evaluated semi‐quantitatively with ioflupane‐SPECT using DaTQUANT software (GE Healthcare) to calculate z‐scores of putamen uptake. Hippocampal volume was calculated with structural MRI, cingulate island sign (CIS) ratio with FDG‐PET, and global cortical PiB retention with PiB‐PET.ResultLower DaTQUANT z‐scores of putamen were observed in DLB patients compared to ADem patients (c‐statistic 0.896, p<0.001). Ioflupane‐SPECT showed higher c‐statistic in differentiating DLB from ADem than hippocampal volume on MRI (c‐statistic 0.718, p=0.034), CIS on FDG‐PET (c‐statistic 0.869, p=0.003), or global cortical PiB retention on PiB‐PET (c‐statistic 0.869, p=0.002), but some overlap between two diagnostic groups was still observed with the single imaging modality. Among these imaging modalities, ioflupane‐SPECT was the only modality correlated with the Unified Parkinson Disease Rating Scale (UPDRS) part III, while only PiB‐PET correlated with Montreal Cognitive Assessment (MoCA) test score. Adding another imaging modality to ioflupane‐SPECT enhanced c‐statistics (+MRI c‐statistic 0.931, p<0.001; +FDG‐PET c‐statistic 0.957, p=0.005; +PiB‐PET c‐statistic 0.955, p=0.007), and ioflupane‐SPECT in combination with both FDG‐PET and PiB‐PET showed the highest c‐statistic of 0.974 (p=0.043).ConclusionIoflupane‐SPECT is an excellent imaging modality to identify DLB by detecting striatonigral dopamine dysfunction which is strongly associated with motor impairment in DLB. Correlative neuroimaging with MRI, FDG‐PET, or PiB‐PET may add small incremental value in differentiation of DLB and ADem. Supported by NIH grants (AG016574, AG006786, AG015866, NS100620, AG062677), grant from GE Healthcare, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, the Deal Family Foundation, and the Little Family Foundation.
But will they use it? Factors influencing sustained use of a digital memory notebook application by individuals with mild cognitive impairment
Authors: Schmitter‐Edgecombe Maureen , Brown Katelyn , Faust Emily , Holder Larry , Cook Diane , Cunningham Reanne , Sumida Catherine 
Published: December 2020

AbstractBackgroundAdvances in technology have fueled new opportunities to improve external compensatory devices. The Digital Memory Notebook (DMN) is an iOS tablet application (app) developed in collaboration with older individuals with mild cognitive impairment (MCI) to support functional independence. To understand users for whom DMN training was beneficial, we examined data from a pilot clinical trial for factors that influenced sustained use of this compensatory tool.MethodTwenty‐five participants (M age = 72; M education = 16; 48% female) with MCI learned to use the DMN app in 5‐6 (two‐hour) training sessions delivered within one‐month. Participant use of the DMN app was then monitored through real‐time collection of data usage metrics for an additional three months. Based on app use during the third‐month post‐training, participants were divided into DMN user (N = 13; M daily distinct uses = 4.5) and non‐user (N = 12) groups. Demographics, clinician ratings, baseline clinical measures and DMN usage metrics were all examined.ResultNo group differences emerged in demographics, pre‐morbid ability estimate (High Average), or clinician ratings of motivation, comprehension, insight and competency during training sessions. DMN non‐users exhibited lower scores on standardized measures of global cognitive status (Low Average vs. Average) and memory (Borderline vs. Low Average), and self‐reported poorer everyday memory. DMN usage metrics captured during training revealed no group differences in week one (sessions 1‐2). By week two (session 3), DMN non‐users showed fewer daily distinct uses of the DMN (4 vs. 8). During weeks 3 and 4, DMN non‐users continued to show fewer daily distinct uses as well as fewer interactions with all sections of the app (i.e., calendar, daily event scheduling, notes and journal). Non‐users also demonstrated poorer use of advanced app functions during training (e.g., high priority scheduling, reminder alarms, picture use, prospective memory use).ConclusionIndividuals with MCI who were actively using the DMN three‐months post‐intervention were less cognitively impaired, demonstrated greater DMN use as early as week two of training, and made use of advanced functions. Next steps include increasing DMN use through early adaptive learning and continued automated booster training.
Development of a clinical practice model of music therapy to address psychosocial functioning for persons with dementia
Authors: Reschke‐Hernandez Alaine E 
Published: December 2020

AbstractBackgroundEmerging research and anecdotal evidence suggest that music therapy and related music interventions may effectively target affective, psychosocial, and behavioral goals for individuals with dementia. However, such research has been criticized for its lack of methodological rigor as well as insufficient detail regarding theoretical foundation of the intervention, characteristics of the music itself, interventionist training, and protocol adherence.MethodThe purpose of this research was to develop a Clinical Practice Model of music therapy for individuals with dementia. The model was developed in four phases. In the first phase, I applied the Therapeutic Function of Music Worksheet (Hanson‐Abromeit, 2005) to current practice with dementia patients to foster understanding of how music itself contributes to the efficacy of therapeutic music interventions. I used text analysis software to facilitate interpretation of the completed worksheet. Next, I performed an exhaustive literature review of music and non‐music strategies for working with this population, 1970‐2017. I collected anecdotal strategy suggestions from dementia care specialists. I then informally piloted each strategy in my clinical practice. The results of the first two steps yielded thematic categories of strategies grounded in research evidence and clinical wisdom. In step three, a panel of dementia care experts validated these themes and associated strategies. This process ensured all aspects of evidence‐based practice were present in the model. Finally, I tested a music therapy intervention, grounded in the model, through a randomized clinical crossover trial.ResultThe model that emerged from this research is theoretically grounded in the biopsychosocial model of healthcare and Kitwood’s personhood framework. It includes an organizational schema for applying intervention strategies within six thematic areas: cognition, attention, familiarity, audibility, structure, and autonomy.ConclusionThe Clinical Practice Model offers a foundation for holistically considering individual’s needs and strengths and guidance for systematic intervention design and research. It predicts that an intervention built upon this schema will influence social‐affective responses, wellbeing, and in turn, psychosocial symptoms of dementia. While the model is likely to evolve as new knowledge is gained, it has great implications for research, clinical practice, and education in music therapy and related healthcare professions.