bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dementia, fall risk and routine clinical care: Opportunities to enhance care by incorporation of examiner‐independent analytics — Computerized cognitive evaluation and quantified digital gait analysis including dual tasking
Authors: Rosenfeld Yulia , Kaczmarek Olivia , Chee Jordan , Bumstead Barbara , Zarif Myassar , Anand Bhupinder , Ofori Edward , Gudesblatt Mark 
Published: February 2022

AbstractBackgroundDementia is an encompassing term referring to conditions characterized by progressive cognitive impairment (CI) that impairs ability to function independently and simultaneously results in increased risk of falling. Falls are common and their consequence costly in cognitively impaired individuals. Clinical reports suggest that falls and gait performance during dual tasking (DT) are particularly related to executive function. Additional reports propose that DT gait abilities predict future fall risk and that interventions designed to improve DT will decrease future fall risk. Analysis of DT gait function to non‐DT gait function has been shown to be superior in discerning those at higher fall risk, in comparison to previous measures. Clinically, CI varies in degree and combination of cognitive deficits and gait disturbance but clinician recognition of such varied types/degrees of CI is likely problematic.MethodsCross sectional analysis of PwDaD who underwent both validated computerized cognitive assessment battery (CAB) and digital gait analysis in the course of routine patient care and were screened for risk of falls.Results120 PwDaD, 57.5% female, average age 74+/‐ 10 years. T‐tests were performed between gait domains scored from PKMAS for PwDaD groups for step length, stride length, stride width, velocity and total double support. All individual gait domains showed a significant change in score for t‐tests performed between preferred walking speed (PWS) and DT gait function (p<0.01). Regression analysis of PwDaD cognitive profile against DT velocity was performed as well. CAB regressions of multiple cognitive domain scores against dual task velocity which were significant included: global summary score, executive function, visuo‐spatial and motor skills’ scores (p<0.05).ConclusionThe addition of a cognitive factor to gait, i.e. dual task, demonstrates a decrease in gait parameter functions in comparison to PwDaD gait at PWS. Earlier objective patient centric identification of increased fall risk provides clinicians an opportunity to effectively and pre‐emptively intervene to reduce such increased disability consequence and costs associated with this disease related impact.
Comparative analysis of cerebrospinal fluid markers and multimodal imaging in predicting Alzheimer’s disease progression
Authors: Romano Michael F. , Balachandra Akshara , Zhou Xiao , Jadick Michalina , Qiu Shangran , Nijhawan Diya , Chin Sang P. , Au Rhoda , Kolachalama Vijaya B. 
Published: February 2022

AbstractBackgroundCerebrospinal fluid (CSF) levels of p‐tau, t‐tau, and Aβ42 are widely accepted in vivo biomarkers for Alzheimer’s Disease (AD). However, lumbar puncture comes with limitations, including severe infection, headache, bleeding at the site, a lack of spatial information about regions of neurodegeneration, and substantial inter‐lab variability. A less invasive method based on imaging for assessing AD progression can lead to insights on AD subtypes, decrease lumbar puncture‐related morbidity, and lower the psychological burden on misdiagnosed patients.MethodWe developed deep learning models composed of a fully convolutional network (FCN) linked with a multi‐layer perceptron (MLP) to predict the 2‐year risk of AD progression in individuals with mild cognitive impairment (MCI) using T1‐weighted MRI, fluorodeoxyglucose (FDG) PET, and florbetapir (amyloid) PET images, respectively, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n=328). Images were all co‐registered within‐subject, normalized to MNI space, and skull‐stripped utilizing SPM‐12 prior to incorporating them in the model. A fusion model incorporating all three imaging modalities was evaluated to utilize the additive benefits of each imaging modality. An MLP utilizing only the above three CSF biomarkers was trained for comparison.ResultOur FCN‐MLP model trained on FDG data alone achieved the highest accuracy of all three single imaging modality models. The model based on the amyloid scans alone was the most sensitive of all three single modalities, though it was the least specific. The MLP model constructed from CSF biomarkers was more specific than all imaging modalities. The fusion model had the highest accuracy out of all models, comparable sensitivity to the amyloid model, and similar specificity to the CSF model. It also had a higher F1 score than all other models.ConclusionOur fusion model predicts AD progression risk with superior accuracy and sensitivity, and a comparable specificity to a model constructed from CSF biomarkers alone. The FCN‐MLP framework could be extended to incorporate non‐invasive features that are easily obtainable in a memory clinic to develop more accurate risk models. Future studies can focus on augmenting the deep learning model performance by including tau‐based PET images as development of more disease specific tau tracers continues to evolve.
Neurochemical levels measured by 1H MR spectroscopy are associated with amyloid‐β and tau deposition in cognitively unimpaired older adults
Authors: Kara Firat , Joers James M. , Deelchand Dinesh K. , Park Young Woo , Przybelski Scott A. , Lesnick Timothy G. , Knopman David S. , Lowe Val J. , Vemuri Prashanthi , Machulda Mary M. , Mielke Michelle M. , Jack Clifford R. , Petersen Ronald C. , Öz Gülin , Kantarci Kejal 
Published: February 2022

AbstractBackgroundProton MR Spectroscopy (1H MRS) provides non‐invasive measurements of alterations in brain biochemistry associated with neuronal and glial abnormalities in neurodegeneration. 1H MRS with along with automated voxel placement (AVP) improves the precision of 1H MRS metabolite measurements. Our objective was to investigate the association of 1H MRS metabolites obtained by the advanced sLASER single voxel 1H MRS protocol with early tau deposition as measured by AV‐1451 PET and Aβ deposition as measured by 11C‐Pittsburgh compound B (PiB) PET in cognitively unimpaired older adults.MethodCognitively unimpaired adults with median (range) age of 77 (66, 94) from the population‐based Mayo Clinic Study of Aging (MCSA) participated in the single‐voxel 1H MRS study (n=40). All participants had an MRI, 11C‐PiB and AV‐1451 PET examinations. The posterior cingulate cortex (PCC) and the left hippocampus (LHC) were chosen for the 1H MRS voxel locations because of their vulnerability to early AD pathology. Right hippocampus was not studied due to time constraints of the MR examination. 1H MRS was performed at 3T (Siemens) using an optimized sLASER protocol with AVP. Standardized uptake value ratios (SUVR) were calculated for global cortical and regional (PCC, LHC) PiB and AV‐1451. Associations between log transformed SUVRs and the concentration of MRS‐measured metabolites (µmol/g) were assessed using age‐adjusted Pearson correlations.ResultThe median (range) global PiB SUVR was 1.42 (1.20‐2.81) and AV‐1451 SUVR was 1.20 (0.94, 1.46). In the PCC, higher AV‐1451 SUVR was associated with lower N‐acetylaspartate (NAA) (r=‐0.375; p=0.019) and glutamate (r=‐0.354; p=0.027) concentrations. Higher global PIB SUVR was associated with higher myo‐inositol in the LHC (r=0.361; p=0.039).ConclusionLower levels of NAA, a marker of neuronal health, and the neurotransmitter glutamate are associated with local tau pathology in the PCC, suggesting early neuronal dysfunction in relation to tau accumulation. The association of hippocampal myo‐inositol, a putative glial marker, with the global cortical Aβ pathology suggests that myo‐inositol elevation may be an indicator of neuroinflammatory response to Aβ related pathology.
Automatic identification of MCI progressors significantly reduces the number of subjects in clinical trials
Authors: Shafiee Neda , Dadar Mahsa , Ducharme Simon , Collins Louis 
Published: February 2022

AbstractBackgroundAccurately predicting the progression rate in patients with mild cognitive impairment and mild dementia due to AD enables enrichment of trial populations. Here, we evaluate the potential use of our prognostic model (Abstract #54217) as a screening tool for enrichment in clinical trials in early AD.MethodData included 312 patients with mild AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and were chosen based on the Clinical Dementia Rating (CDR=0.5) and the Mini‐Mental State Exam (MMSE, range 24‐30) scores. We classified subjects into stable and progressive (defined by at least a 2‐point increase in CDR‐SB) using our automatic classification tool. Required sample sizes to detect a reduction in the mean annual rate of cognitive decline based on CDR‐SB score were estimated. We corrected for the annualized decline due to normal aging so as not to overestimate the benefit of enrichment when computing the treatment effect. We estimated and compared sample sizes for two groups of subjects: Using data from the mild AD subjects in the ADNI dataset that fit the selection criteria above (the unenriched group) and using only the subset of those ADNI MCI subjects identified as pMCI using baseline data and the classifier described above (the enriched group).ResultFigure 1 shows the required sample sizes for different treatment effects for both unenriched and enriched MCI cohorts. Using the unenriched group of MCI subjects, power analysis shows that 1075 subjects are required in a 2‐year (764 subjects for a 3‐year) trial of therapy with a hypothesized 25% effect size (80% power and 5% significance level) to reduce cognitive decline. When using the enriched cohort of MCI subjects, only 279 subjects are required for a 2‐year (373 for 3‐year) trial (solid lines in Fig. 3). These results demonstrate that enrichment using baseline HC, MoCA, ADAS‐13 and MMSE yields a 3.8‐fold decrease in the sample size for a 2‐year study (2.8‐fold decrease for a 3‐year study).ConclusionEnriching clinical trial cohorts of subjects in the early stages of AD with a higher chance of functional and cognitive decline will improve efficiency with smaller, faster, less expensive trials.
Effective feature learning of multi‐modal genetic and neuroimaging data for prediction of future conversion to Alzheimer’s disease: A machine learning based study
Authors: Mirabnahrazam Ghazal , Ma Da , Lee Sieun , Popuri Karteek , Cao Jiguo , Wang Lei , Galvin James E. , Beg Mirza Faisal 
Published: February 2022

AbstractBackgroundDue to the high dimensionality of Single Nucleotide Polymorphism (SNP) data, current imaging genetic studies of Dementia of Alzheimer’s Type (DAT) usually only select a limited number of candidate SNPs that are reported in the literature, while there might be potentially important AD‐related risk factors in the remaining genes. In this study, we harness a robust feature selection technique that utilizes all available SNPs in the human genome to identify the relevant MRI and genetic features while overcoming feature redundancy.MethodA total of 543 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that had both MRI and genetic data available are included in the study. These subjects are divided into seven novel stratified groups [Table 1] based on their longitudinal clinical diagnosis. A subject is categorized as DAT+ if it has a follow‐up diagnosis of DAT, and is categorized as DAT‐ otherwise. We utilize a 2‐stage probabilistic multi‐kernel classifier. In the first stage, using 10‐fold cross‐validation: In each fold we select discriminative features by applying Fisher's Exact test on SNP data and Welch's t‐test on MRI data using subjects in the subgroups with the most certain longitudinal diagnosis (sNC in DAT‐ and sDAT in DAT+). In the second stage, using only the most frequently selected features above, we retrain on 80% of sNC and sDAT, validate on the remaining 20%, and test on subjects in the other subgroups with milder longitudinal diagnosis results.ResultOur study showed that although genetic features have a lower prediction power than MRI features, combining both modalities can improve the prediction of future conversion to AD. For subjects in pNC group, MRI features fail to indicate potential risk of developing AD while genetic data can accurately identify the risk [Figure 1]. Our feature selection method has successfully identified significant AD risk factors for both modalities.ConclusionOur study demonstrated that using effective feature selection methods eliminate the need for heuristic selection of AD‐related genes used for machine‐learning studies for image genetics. Our proposed method reveals genetic risk factors already mentioned in literature as well as some novel risk factors that could advance clinical diagnostics in the future.
The relationship between brain‐age association and prediction: The impact of parameter selection
Authors: Zeighami Yashar , Evans Alan C. 
Published: February 2022

AbstractBackgroundAssociation and prediction studies of brain age target the relationships between neuroanatomical changes across lifespan and behavioural or pathological phenotypes. However, the relationship between the two analyses has not been directly examined. Here we use brain resels (i.e. resolution elements) (Worsley et al. 1996) to directly compare age association and brain‐age prediction as a function of cortical thickness smoothing and parcellation parameters.MethodData included 608 healthy subjects (Age = 53.5218.07; Age range = 18‐88, 308 Female subjects) subjects with T1‐weighted MRI from the Cambridge Centre for Ageing and Neuroscience (https://www.cam‐can.org/index.php?content=dataset). CIVET pipeline (http://www.bic.mni.mcgill.ca/ServicesSoftware/CIVET) was used to extract cortical surfaces and calculate cortical thickness across the brain. We used 6 different diffusion based smoothing kernels (0, 5, 10, 20, 30, and 40 mm) and 5 different parcellation levels: 100, 200, 400, and 1000 parcels from Schaefer et al., 2018 multi‐level functional parcellation atlas as well as the entire cortex. For each level of smoothing and parcellation, age‐related correlations were calculated. Brain age was predicted using a linear regression model with 10‐fold cross validation, with principal components of cortical thickness data as predictors. To directly compare the correlation and prediction results, we calculated the brain resels for each smoothing parcellation parameter.ResultFigure 1.A shows the correlation patterns across the brain as a function of smoothing and parcellation. Figure 2 shows age prediction error as a function of smoothing kernel, parcellation level, and number of principal components included. Larger smoothing kernels and brain parcels result in higher correlation values (Figure 1.B), but lower prediction accuracy (Figure 2). Figures 3.A and 3.B show mean age‐correlation value and age‐prediction error as a function of number of resels.ConclusionOur results demonstrate an opposite relationship between brain‐age association values and brain‐age prediction accuracy, and quantify how smoothing and parcellation parameters affect each of these analyses. These results highlight the importance of parameter selection for each analysis type and how they might affect the final results, and have significant implications for brain aging studies.
Optimal parameters for propagating gamma brain waves using flickering lights in human
Authors: Park Yeseung , Lee Kanghee , Kim Sang‐Su , Kim Do‐Won , Park Jaehyeok , Yoo Seunghyup , Kim Ki Woong 
Published: February 2022

AbstractBackgroundSensory stimulation with 40 Hz flickering light could entrain gamma oscillations leading to decreased amyloid β burden. However, the optimum parameters of a light source to promote brain gamma activity in humans have not suggested yet. We performed the study to investigate the optimal intensity and frequency of the flickering light stimulus (FLS) in young adults.MethodWe measured electroencephalography (EEG) during the FLS presented. We compared the event‐related synchronization (ERS) values of entrained gamma waves between four different luminance intensities (10, 100, 400, and 700 cd/m2) at 10 different flickering frequencies from 32Hz to 50Hz with an interval of 2Hz. Each frequency condition was comprised of 10 times repetition of 2‐sec with inter‐stimulus interval (ISI). We also examined the severity of six adverse effects in both experiments. Using the Granger causality, we compared the propagation of gamma waves in visual cortex to other brain regions between different luminance intensities and flickering frequencies.ResultEntrained gamma activity started after the FLS onset, lasted during the FLS, and diminished after the FLS offset which was observed most highly at parietal area and steadily decreased from the parietal to the frontal area. Lights of higher luminance intensities (700cd/m2 and 400cd/m2) entrained stronger and more widely spread gamma waves than those of lower luminance intensities (100cd/m2 and 10cd/m2). Lights flickering at 34Hz ∼ 38Hz entrained stronger and more widely spread beyond visual cortex than those flickering at 40Hz ∼ 50Hz.ConclusionOptimal frequency of FLS for entraining brain gamma activity in human may be different from those in animals. Higher luminance with lower gamma frequency stimulus are effective to propagate the gamma activity to the frontal area.
MRI longitudinal and cross‐sectional monitoring of amyloid pathology in non‐human primates
Authors: Genovese Thomas S. , Ramadane Dina , Kingsley Charles , Nehete Pramod N. , Patel Akash G. , Leung Jonathan , Tibensky Miroslav , Rusinek Henry , Gray Stanton B. , Williams Lawrence E. , Zaim‐Wadghiri Youssef , Wisniewski Thomas , Scholtzova Henrieta 
Published: February 2022

AbstractBackgroundThe potential for using MRI technologies to visualize amyloid deposits has been increasingly recognized. The present study utilized a New World non‐human primate (NHP) model of amyloid pathology, squirrel monkey (SQM), which unlike other NHPs, develops age‐dependent abundant cerebral amyloid angiopathy (CAA). The transverse relaxation rate (R2*) has a linear correlation with iron content, a component of amyloid deposits and microhemorrhages. Here we assessed whether the region of interest (ROI)‐based quantitative R2* assessment is able to detect differences in SQM neuropathology between varying age cohorts as well as longitudinally. An additional focus was to examine the presence of naturally occurring ARIA‐E like pathologies in geriatric SQMs.MethodMulti‐gradient echo (MGE) MRI datasets were acquired in three different SQM age cohorts at various stages of Aβ pathology development. The R2* maps were generated (Firevoxel parametric map tool) from each subject to enable ROI‐based quantitative R2* analyses. DAB‐enhanced histochemical iron stain (Meguro method), along with 6E10/4G8 immunohistochemistry, was used to identify iron‐positive Aβ deposits. Histological examination of neuroinflammation and microhemorrhages (Perls’ iron stain) was performed. T2‐w FLAIR images were acquired to assess the presence of ARIA‐E.ResultMGE imaging was capable of depicting brain pathology differences between three SQM age cohorts (ages 5, 16, 21). Absolute quantitation of R2* values, defined by ROI circumventing the cortex, revealed significant age‐related differences in selected brain regions, with the highest R2* counts detected in 21yr old monkeys. The ability to longitudinally track pathology‐related increase in R2* values was further validated by MRIs performed at specific intervals post‐initial scans in our geriatric monkeys. Iron‐positive Aβ deposits and microhemorrhages detected by MRI were confirmed via matched histological sections. Subsequent histological evaluation demonstrated disease‐stage differences in microgliosis and astrocytosis. Moreover, ARIA‐E like T2‐w signal hyperintensities were detected in our oldest cohort. Neuropathological correlates of ARIA‐E‐related changes were characterized histologically.ConclusionThe current study highlights the utility of MRI methodologies to monitor age‐dependent progression of Aβ‐related pathology, as well as the importance of the SQM model for future translational research on advancing therapeutic interventions targeting amyloid depositions.
Imaging measures of molecular pathology and neurodegeneration in dominantly‐inherited and sporadic early‐onset Alzheimer’s Disease
Authors: Iaccarino Leonardo , Llibre‐Guerra Jorge J. , Edwards Lauren , Mundada Nidhi , Strom Amelia , Tsoy Elena , Li Yan , Gordon Brian A. , Schindler Suzanne E. , Fagan Anne M. , Benzinger Tammie L.S. , Wang Guoqiao , Hassenstab Jason , Morris John C. , Xiong Chengjie , Perrin Richard J. , Soleimani‐Meigooni David N. , Miller Zachary A. , Kramer Joel H. , Rosen Howard J. , La Joie Renaud , Miller Bruce L. , Bateman Randall J. , Rabinovici Gil D. 
Published: February 2022

AbstractBackgroundApproximately 5% of patients with Alzheimer’s Disease (AD) develop symptoms before age 65, with this early‐onset presentation attributed to either sporadic (sEOAD) or dominantly‐inherited (DIAD) etiology. Both sEOAD and DIAD are characterized by brain amyloid‐beta (Aβ) accumulation and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated.MethodWe included 108 sEOAD Aβ‐PET positive cases from the UCSF Alzheimer’s Disease Research Center (mean±standard deviation age 59.7±5.4 years) and 86 DIAD cases from the Dominantly Inherited Alzheimer Network (DIAN) study (age 45.5±9.5 years) with symptom onset before 65 years (see Table 1). All cases were symptomatic (clinical dementia rating [CDR] global ≥0.5) and underwent standard clinical and cognitive measures, 11C‐PiB‐PET (Aβ), 18F‐FDG‐PET (glucose metabolism) and structural MRI. PET scans were processed to obtain Standardized Uptake Value Ratio (SUVR, PIB50‐70, FDG30‐60) images. Warped PET SUVR and modulated, warped gray matter images were compared across the two cohorts with SPM12 after smoothing to ∼8mm3, covarying for age, CDR sum of boxes, years of education, APOE e4 status, and respective global biomarker score. A‐priori statistical threshold was set at p<0.001 uncorrected, with a Family Wise Error p<0.05 correction at the cluster‐level.ResultsEOAD participants were older, more educated, had greater cognitive impairment, had higher Aβ burden and were more likely to be APOE ε4 carriers (see Table 1). sEOAD participants exhibited higher relative PIB‐PET binding in anterior temporal and frontal (mostly white matter) regions, as well as lower relative FDG‐PET binding and sMRI‐based volumes in temporoparietal regions. DIAD participants showed higher relative PIB‐PET binding in the medial occipital, thalami, basal ganglia (caudate nuclei and putamen) and medial/dorsal frontal regions. DIAD participants showed lower relative FDG‐PET binding mostly in the frontal and parietal white matter (see Figure 1).ConclusionAβ pathology in DIAD was more likely to affect subcortical systems, whereas both Aβ pathology and neurodegeneration tended to be relatively more cortical and extensive in sEOAD. These results highlight EOAD heterogeneity and show that sporadic and inherited AD exhibit distinct regional vulnerabilities in neurodegeneration as well as differing topography and magnitude of biomarker abnormalities.