bluuebunny/crossref_metadata_2025_split huggingface dataset:
Amyloid pathology, small‐vessel disease, atrophy, and cognition in normal adults with type 2 diabetes
Authors: Lesman‐Segev Orit H. , Golan Sapir , Ravona‐Springer Ramit , Livny Abigail , Lin Hung‐Mo , Ouyang Yuxia , Zadok Maya , Hoffmann Chen , Domachevsky Liran , Beeri Michal Schnaider 
Published: February 2022

AbstractBackgroundVarious factors contribute to cognitive decline in older adults with type 2 diabetes (T2D). We assessed the association between imaging metrics of brain pathology (including global atrophy, small vessel disease, and amyloid deposition) with cognitive functioning in older adults with T2D.MethodCommunity‐dwelling, cognitively normal, older adults with T2D from the Israel Diabetes and Cognitive Decline Study with brain MRI and/or F18‐Flutemetamol amyloid‐PET were included. Gray matter (GM) and white matter hyperintensities (WMH) volume were extracted from high‐resolution T1‐weighted and fluid‐attenuated inversion recovery (FLAIR), respectively, using Statistical Parametric Mapping (SPM8). Amyloid PET was visually read and quantified using standardized uptake value ratio (SUVR) with the cerebellar gray and white matter as reference region. Global mean and lobar SUVR were extracted. Global cognitive functioning was assessed by neuropsychological testing and summarized into four domains including executive functions, attention, memory, and language, and an average of the four domains comprised global cognition. Linear regression models were used in a stepwise manner controlling for age, sex, education, HbA1c, and duration of T2D. MRI measures were also adjusted for intracranial volume.Result236 participants, 232 with MRI and 52 with amyloid‐PET were included, mean age 75 [66‐89], 39% females, 11 (21.1%) amyloid positive (Table 1). On average, participants had good glycemic control (mean HbA1C=6.7). An increase in global SUVR was associated with lower global cognition (Estimate=‐1.23, p=0.02). Additional adjustment for GM volume did not alter the results (Table 2). These results were driven by associations of SUVR with executive functioning (‐1.32, p=0.01), though a trend toward the same direction was also seen with attention (‐0.95, p=0.09), language (‐0.86, p=0.13), and memory (‐0.85, p=0.14) (Figure 1). Amyloid in all lobes contributed significantly to the associations (Table 2). No association was found between WMH volume and any of the cognitive outcomes (p‐values>0.21).ConclusionIn our cohort of normal cognitive older adults with well controlled T2D, amyloid load but not small vessel disease nor glycemic control was associated with cognitive functioning. This association was not mediated by GM volume suggesting other neurobiological substrates underlying the effect of amyloid on cognition in this population.
Identifying multimodal imaging‐driven subtypes in mild cognitive impairment using deep multiview learning
Authors: Feng Yixue , Kim Mansu , Liu Kefei , Saykin Andrew J. , Moore Jason H. , Long Qi , Shen Li 
Published: February 2022

AbstractBackgroundMultimodal neuroimaging data can provide complementary information that a single modality cannot about neurodegenerative diseases such as Alzheimer's disease (AD). Deep Generalized Canonical Correlation Analysis (DGCCA) is able to learn a shared feature representation from different views of data by applying non‐linear transformation using neural network. We utilize DGCCA to extract maximally correlated components from multi‐modal neuroimaging data to identify potential imaging‐driven MCI subtypes.MethodWe study 308 Mild Cognitive Impairment (MCI) participants (195 early MCI and 113 late MCI) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), each with voxel level features from FDG PET, amyloid PET (AV45) and structural MRI processed using voxel‐based morphometry (VBM). Six experimental settings are designed to compare single modality with multiview methods ‐ GCCA and DGCCA, see Figure 1. Agglomerative clustering was used to generated 2 clusters with features from each experiment. To investigate differences between the clusters, Wilcoxon rank‐sum tests are conducted on 11 baseline AD biomarkers including 5 cognitive assessments and 6 brain volume measures, from the ADNI QT‐PAD dataset http://www.pi4cs.org/qt‐pad‐challenge.ResultAmong the two multiview methods, DGCCA is able to explain 68.57% variance with 20 features, while GCCA explains 68.66% variance with 94 features. To evaluate the potential subtypes from clustering, the Calinski‐Harabasz (CH) score, Silhouette score and adjusted mutual information (AMI) score are computed, see Table 1. AV45 generates the best defined clusters, where DGCCA generates clusters with quality comparable to single modality features. In our QT analysis, clusters from FDG and DGCCA features show differential measure in all biomarkers where DGCCA learns from multimodal data, see Figure 2.ConclusionDGCCA is able to learn maximally correlated features from multimodal neuroimaging data with reduced dimensionality, and explain more variance than its linear counterpart GCCA. Cluster analysis shows these imaging‐driven MCI subtypes are different from the currently diagnosis with differential QT measures, by incorporating complementary information from 3 imaging modalities. DGCCA shows to be an effective feature learning method, and this multiview learning framework can identify potentially novel MCI subtypes to facilitate early detection of AD.
An exploratory study of caregiver burden amongst urban upper class adults in India during COVID‐19
Authors: Mondal Shreosi , Tandon Ravina , Dasgupta Jayashree 
Published: February 2022

AbstractBackgroundThe COVID pandemic has impacted caregivers of older adults globally and families have been wary of approaching hospitals due to increased risk of exposure. We explored reasons for help seeking and caregiver burden amongst a sample of consecutive urban upper class adults who approached a non‐hospital based organization providing elderly support in Delhi, India.MethodsA detailed assessment was carried out by trained care specialists using a clinical interview, Hindi Mental Status examination, evaluation of independence in activities of daily living and caregiver burden using the Zarit Burden Interview (ZBI). The assessment was conducted online or in person depending on comfort of the family. Descriptive analysis was carried out to understand the caregiver profile and reasons for seeking help.ResultsOf the 39 adults assessed, majority had a primary diagnosis of dementia (80%). 10% had other psychiatric illness and 10% other neurological illness. 87% families preferred an in person assessment, stating that an online assessment would not be possible with their family member. Primary caregivers were mostly female (80%) and older adults (46%). The main reason for seeking help was inability to manage challenging behaviors at home (97%), followed by concerns about increased cognitive impairments during COIVD (80%), mood related disturbances (41%) and difficulties with activities of daily living (41%). Assessment using ZBI revealed 82% primary caregivers experienced mild to moderate caregiver burden, however, they felt that they were doing their duty and none spontaneously reported a need to seek any form of support for themselves.ConclusionCurrently India does not have a public health system that supports dementia care and public awareness about management is limited. Our findings highlight that caregivers experience significant stress particularly managing challenging behaviors. Most caregivers are female older adults in whom caregiver burden may be more due to factors like role‐strain and limited independence. In absence of a supportive public health system, our findings also highlight the important role that non‐hospital based organizations play in bridging a gap for dementia care delivery, particularly during these COIVD times.
Patient‐tailored Alzheimer’s disease staging based on integrated biomarker timelines
Authors: yeon Kim Chae , Cho Hanna , Lee Wha Jin , Kim Sung‐Woo , Lyoo Chul Hyoung , Seong Joon‐Kyung 
Published: February 2022

AbstractBackgroundConsiderable evidence confirms the accumulation of fibrillar Amyloid‐β(Aβ) and aggregates of hyperphosphorylated tau as early Alzheimer’s Disease (AD) biomarker changes. However, the relationships between amyloid and tau within AD pathogenesis are not fully understood. Estimating the integrated order of these two biomarkers abnormality and identifying associations between two biomarkers in vivo could facilitate accurate prediction of patient‐tailored prognosis in early disease stage.MethodWe recruited 74 healthy controls, 75 MCI patients, and 38 AD patients with T1‐weighted magnetic resonance image (MRI) and two PET scans ([18F]Flortaucipir for tau and [18F]Florbetaben for Aβ) with about two‐year follow‐up data. All regional Aβ and regional tau were aligned simultaneously in the order of posterior probabilities of biomarkers becoming abnormal using the DEBM algorithm. All participants were then labeled with disease staging score based on the integrated biomarker timelines. We then explored the relationship between the disease score at baseline and the patient’s diagnosis in follow and annual change of CDR‐SB scores.ResultFirstly, the integrated biomarker ordering shows a partially overlapped sequence of the Thal stage (in terms of the Aβ biomarker) and the Braak stage (in terms of the tau biomarker). Tau accumulates first within a region called the entorhinal cortex, then spreads out of that region. In the neocortical region (except the entorhinal cortex), the aggregation of amyloid precedes that of tau. Secondly, MCI patients’ stage at baseline matches well with the diagnosis labels at the same time point, and also significantly correlates to the change of CDR‐SB scores. Furthermore, MCI patients with higher disease stage scores at baseline have significantly higher chance to convert to AD at follow‐up.ConclusionIn this study, we proposed in vivo temporal alterations of both regional amyloid and regional tau simultaneously. All biomarkers exhibited analogous trends to previous research. Amyloid aggregation generally occurs at early stages of the disease and seems to affect not tau seeding but initial tau spreading. Patient staging based on the integrated order of regional biomarker abnormality can help prediction of AD prognosis and future cognition change in early disease stage.
Cortical microstructure is associated with tau burden and predicts cognitive decline and clinical progression in healthy older adults
Authors: Rodriguez‐Vieitez Elena , Montal Victor , Sepulcre Jorge , Lois Cristina , Hanseeuw Bernard , Vilaplana Eduard , Schultz Aaron P. , Properzi Michael J , Scott Matthew R. , Amariglio Rebecca E. , Papp Kate V. , Marshall Gad A , Fortea Juan , Johnson Keith A. , Sperling Reisa A. , Vannini Patrizia 
Published: February 2022

AbstractBackgroundNon‐invasive biomarkers of neuronal injury may be useful to detect cognitively‐normal individuals at risk of developing AD. A recent diffusion‐weighted imaging (DWI) method has allowed to assess microstructural properties of the brain's grey matter by means of cortical mean diffusivity (cMD) (Montal et al. Alz&Dem 14(3):340, 2018). Increased cMD is thought to reflect early neuronal injury that may precede macrostructural neurodegeneration. Here, we aimed to investigate the association of cMD with in vivo amyloid‐β and tau pathology in cognitively‐normal individuals, and to determine whether cMD can predict cognitive decline and clinical progression.Method196 cognitively‐normal participants from the Harvard Aging Brain Study (72.5±9.4 years; Table 1) underwent concurrent DWI, T1w‐MRI, 11C‐PIB‐PET, 18F‐flortaucipir (FTP)‐PET imaging and cognitive assessments at baseline. PIB‐PET was quantified using non‐PVC‐corrected Logan‐DVR, and FTP‐PET using PVC‐corrected SUVr; PET data were normalized to cerebellar grey‐matter. Longitudinal measures of Preclinical Alzheimer Cognitive Composite (PACC5) were available for n=186 individuals over a 3.72±1.96 yr follow‐up interval. Surface‐based image analysis (FreeSurfer 6.0) was performed to investigate vertex‐wise relationships between cMD and global amyloid‐β (PIB‐FLR), as well as tau (entorhinal, inferior‐temporal FTP). We assessed whether regional cMD predicted longitudinal PACC5 using linear mixed‐effects models, and progression to MCI using Cox proportional‐hazards regression.ResultEntorhinal and inferior‐temporal tau, but not amyloid‐β, were associated with increases in surface‐based cMD in AD‐signature clusters (Fig. 1A); illustrated for middle‐temporal gyrus cMD (Fig. 1B). Correcting for demographics and baseline PACC5, increased cMD within AD‐signature regions‐of‐interest predicted the rate of subsequent cognitive decline, which remained significant after correcting for PIB‐FLR and regional cortical thickness; there was a synergistic interaction between cMD and PIB‐FLR on the slope of PACC5 decline (Fig. 2). From n=163 participants with clinical follow‐up data, 11/163 (6.7%) converted to MCI within 3.3±1.5 years. Dichotomized regional cMD (top‐tertile group being "high cMD") predicted conversion to MCI, illustrated for the middle‐temporal gyrus, hazard ratio HR[95% CI] = 5.21 [1.14‐23.75], p=0.03, corrected by age, sex and education (Fig. 3).ConclusioncMD is a promising technique to quantify cortical microstructural changes possibly related to tau‐induced neuronal injury. Its ability to predict short‐term cognitive decline and clinical progression suggests utility in clinical trials.
Ability of tau‐PET, phospho‐tau217, NfL and cortical thickness to predict short‐term cognitive decline in early symptomatic Alzheimer’s disease
Authors: Smith Ruben , Cullen Nicholas , Leuzy Antoine , Janelidze Shorena , Palmqvist Sebastian , Dage Jeffrey L , Borroni Edilio , Klein Gregory , Ossenkoppele Rik , Mattsson‐Carlgren Niklas , Stomrud Erik , Hansson Oskar 
Published: February 2022

AbstractBackgroundBiomarkers for the prediction of cognitive decline in patients with mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) and mild AD dementia are needed for inclusion of suitable patients in clinical trials. We therefore assessed the ability of tau‐PET, blood and cerebrospinal fluid (CSF) phospho‐Tau217 (pTau217), CSF neurofilament light (NfL) and cortical thickness to predict cognitive decline early symptomatic AD.Method73 amyloid‐β positive (CSF Aβ42/Aβ40<0.752) participants with MCI and mild AD dementia (MMSE ≥ 22) were assessed at baseline with cognitive testing, blood and cerebrospinal fluid (CSF)‐sampling, MRI and [18F]RO948 PET and were followed longitudinally with annual cognitive testing over 24 months. Slopes of change in MMSE and preclinical Alzheimer cognitive composite (PACC) were calculated over two years. We used linear regression models to assess the ability of biomarkers (plasma pTau217, CSF‐pTau217, temporal meta‐ROI [18F]RO948 PET standardized uptake value ratios (SUVR), CSF‐NfL and MRI “AD‐signature” cortical thickness) to predict cognitive decline both individually and in combination. The linear regression models were adjusted for age, sex, education, and baseline MMSE.ResultAll tested biomarkers individually showed significant associations with cognitive decline using both MMSE and PACC as outcome variables. For predicting longitudinal change in MMSE using individual biomarkers, [18F]RO948 SUVR showed the highest R2‐value (0.39, 95% CI [0.28‐0.67]) and best model fit (lowest Akaike Information Criterion score)(Table 1). Adding other biomarkers yielded a slightly higher R2‐value (0.44 [0.33‐0.63]), but did not improve the model fit. In a sensitivity analysis we performed a similar analysis in a subset of participants having undergone longitudinal testing with a modified PACC test battery (n=53). Results again showed the best performance for [18F]RO948 SUVRs, when tested alone, but with improved model fit and R2‐values with the addition of cortical thickness and CSF‐NfL data (Table 2).ConclusionTau‐PET imaging outperformed CSF, plasma and MRI measures when predicting cognitive decline in prodromal AD and mild AD dementia. Specifically, tau‐PET provided the best prediction alone for change in MMSE and in combination with NfL and cortical thickness for change in PACC, indicating that it would be a suitable biomarker for inclusion in clinical trials.
Accumulation of amyloid‐β alters white matter integrity in cognitively unimpaired middle‐aged females
Authors: Xia Ying , Fazlollahi Amir , Dore Vincent , Bourgeat Pierrick , Raniga Parnesh , Martin Nick , Salvado Olivier , Rose Stephen , Lupton Michelle K. , Breakspear Michael , Fripp Jurgen ,   PISA (Prospective Imaging Study of Aging: Genes, Brain and Behaviour)
Published: February 2022

AbstractBackgroundThe APOE‐ε4 allele is the strongest known genetic risk factor for Alzheimer’s disease (AD) and is associated with higher amyloid‐β (Aβ) deposition. White matter (WM) microstructural damages are known to occur early at the preclinical stage of AD. Although stable associations between APOE‐ε4 status and WM alternations have been reported in patients with AD and mild cognitive impairment, few studies have described WM differences in cognitively unimpaired (CU) subjects at risk of AD. We aim to investigate associations of APOE‐ε4 status and Aβ load with WM microstructural changes in mid‐age CU individuals.Method151 CU participants from the Prospective Imaging Study of Ageing (PISA) were included (aged 44‐66, 78% female, 77 APOE‐ε4 carriers (ε4+) and 74 non‐carriers (ε4‐)) and underwent Aβ PET and 3T MRI scans including multi‐shell diffusion‐weighted images. Aβ load was quantified in Centiloid (CL) on PET and Aβ+ was defined as CL ≥ 20. Tract based spatial statistics (TBSS) analysis was performed on fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) to assess WM alternations associated with APOE‐ε4 status and/or Aβ load. All analyses were corrected for age and for multiple comparisons.ResultA significant effect of gender, but not APOE‐ε4 or Aβ status was observed on WM integrity in the full cohort. When considering females only, APOE‐ε4 carriers showed regional increased MD in several right WM tracts. However, these differences were no longer significant when correcting for Aβ status. In contrast, significant WM alternations associated with Aβ+ status were found when correcting for APOE‐ε4 status. In this female‐only subset, widespread WM microstructural damages were observed in Aβ+ε4+ subjects (N=10) compared to Aβ‐ε4+ (N=51) and Aβ‐ε4‐ (N=54) subjects. No significant differences were observed between Aβ‐ε4+ and Aβ‐ε4‐ subjects. Similar differences were also observed in the global diffusion metrics of WM tracts.ConclusionThese findings suggest that WM alternations associated with Aβ accumulation appear in mid‐life CU female subjects. Future studies in larger cohorts enriched for gender, APOE and Aβ status are required to clarify the relative contributions of APOE‐ε4 and Aβ status on WM microstructural damage.
Telephone support for dementia family caregivers during the COVID‐19 pandemic: Experiential differences in spouse and adult‐child caregivers
Authors: Lee Jung‐Ah , Ju Eunae , Tom Courtney , Tran Tiffany Marie 
Published: February 2022

AbstractBackgroundDue to the COVID‐19 pandemic, family caregivers stayed home with their person with dementia (PWD) continuously for 24/7. They faced limited social activity with lack of essential services such as adult daycare and in‐home services. Social restrictions can worsen cognitive deterioration and increase behavioral problems of PWD, causing increased caregiver burden, distress, and loneliness. Caregiver research team developed a telephone support intervention with trained university students who provided emotional support, and COVID‐19 information. The objective of the study is to identify the different needs in caring between spousal caregivers and adult‐child caregivers for community‐dwelling PWD during this pandemic.MethodsFamily caregivers were recruited through our previous home‐visit family caregiver study. Trained bilingual university students conducted telephone support calls in English, Spanish, Korean or Vietnamese as preferred by caregiver participants, once a week during the 3 month of the early pandemic period. Students summarized each conversation in a call log for debriefing by a gerontology faculty. Thematic analysis using call logs was conducted and coded by two independent raters by using Dedoose, a qualitative data analysis tool. Similarities and differences between spousal caregivers and adult‐child caregivers were identified.Results14 spousal caregivers (11 were wives and 3 were husbands, mean age 67.4) and 11 adult‐child caregivers (daughters, mean age 49) participated in the study. Three themes were identified: (1) spousal caregivers had high sense of being left alone and isolated while adult‐child caregivers felt a greater responsibility for the family including PWD care, (2) both groups showed increased stress but for spouse caregivers it was due to worsening PWD behaviors, for adult‐child caregivers it was the restriction on social interaction and concerns about losing jobs, (3) similarities in adapting to the COVID‐19 safety recommendations and incorporating it into everyday life.ConclusionThe findings showed an increase in stress and anxiety in both groups but the differences were: for spouse high level of loneness, difficulty with PWD behaviors; for adult‐child caregivers greater burden due to family and reduced social life. Both groups followed COVID‐19 safety protocol and highly appreciated the telephone‐based emotional support during the pandemic.