bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness of a virtual health coach intervention for caregivers of persons with frontotemporal degeneration
Authors: Massimo Lauren M , Hirschman Karen , Fisher Lauren , Sharkey Michelle , Aryal Subhash , Riegel Barbara 
Published: June 2023

AbstractBackgroundFrontotemporal degeneration (FTD) is a common cause of young‐onset dementia. A tremendous burden is placed on caregivers who often sacrifice their own self‐care needs in order to manage the cognitive decline and subsequent functional impairments of their loved one, contributing to extraordinarily high levels of stress and depression. Health coaching is a support intervention that is personalized and multidimensional, providing support for coping with stress while fostering self‐care. Here we report on the efficacy of a virtual health coach intervention (Virtual Caregiver Coach for You; ViCCY) aimed at increasing self‐care behaviors and reducing stress in FTD caregivers.MethodIn this pilot randomized control trial, 30 caregivers of persons with behavioral variant FTD (bvFTD) were randomly assigned to either the intervention group (ViCCY) which included 10 coaching sessions over 6 months plus targeted health information through the internet or the control group receiving standard care augmented with the targeted health information through the internet. Caregiver self‐report measures on self‐care (primary outcome), stress, depression and coping were collected at enrollment, 3 and 6 months. Using linear mixed‐effects models, we evaluated differences in change over time between the intervention and control groups.ResultsThere were no significant differences in baseline characteristics between the groups. There was a significant group‐by‐time interaction for self‐care monitoring (t58 = 2.37, p = 0.0210) and self‐care confidence (t58 = 2.32, p = 0.0239) on the Self‐Care Inventory, demonstrating that caregivers who received the ViCCY intervention improved in their self‐care over time. The intervention group also reported lower self‐neglect over time compared to the control group (Health Care Self Neglect Scale: t58 = ‐2.38, p = 0.0417). No differences were observed between the groups on the secondary outcome measures of stress, depression and coping.ConclusionsbvFTD caregivers are a unique population who defer their own self‐care in response to the stress of caregiving. This RCT shows promise for remotely delivered health coaching as an innovative way to increase support that is urgently needed to reduce poor outcomes in this distressed and underserved caregiver population.
Disentangling the heterogeneity of brain atrophy in Alzheimer’s Disease
Authors: Liu Hangfan , Toledo Jon B. , Grothe Michel J. , Rashid Tanweer , Launer Lenore J. , Shaw Leslie M. , Snoussi Haykel , Heckbert Susan R. , Weiner Michael W. , Trojanowski John Q , Seshadri Sudha , Habes Mohamad 
Published: June 2023

AbstractBackgroundAlzheimer’s disease (AD) is the most common cause of dementia. In AD patients, structural MRI analyses have identified various brain atrophy patterns, which are likely the result of differences in regional pathology distribution and variable presence of co‐pathologies.MethodWe included 496 ADNI participants and applied our robust collaborative clustering approach (RCC) to the MRI scans of the Aβ+ participants. RCC removes noise while simultaneously clustering subjects and features. Group comparisons were performed applying Kruskal‐Wallis analyses for quantitative variables and chi‐square tests for categorical variables (Table 1). Power transformations were used to normalize the distribution of quantitative variable for linear regression analyses. APOE ε4 presence and white matter hyperintensity (WMH) volume were included as predictors. P‐values <0.05 were considered statistically significant.ResultWe identified three distinct atrophy clusters among the Aβ+ participants (Table 1). A “limbic predominant” atrophy cluster showed greater cingulate and right hippocampus, inferior and middle temporal lobe atrophy than the cognitively unremarkable (CU) Aβ‐ reference group (Figure 1). The diffuse atrophy cluster showed greater parieto‐occipital‐temporal atrophy. The “hippocampus sparing” atrophy cluster showed a similar neocortical atrophy pattern except for less atrophy in the temporal lobe. The limbic predominant cluster participants had a higher APOE ε4 prevalence than the hippocampal sparing cluster participants (Table 1, 2). The three clusters had higher baseline ADAS‐Cog13 and flortaucipir SUVR binding in all the Braak staging‐defined areas than the CU Aβ‐ reference group (Table 3). The diffuse atrophy cluster had higher ADAS‐Cog13 scores, florbetapir composite scores, and flortaucipir SUVRs in all the Braak staging‐defined areas than the two other atrophy clusters. The hippocampal sparing atrophy cluster participants were older and had higher WMH volumes than the limbic predominant atrophy cluster and the CU Aβ‐ reference group participants.ConclusionOur clustering method discovered three reproducible atrophy patterns in AD, including typical and atypical presentations. This finding likely reflects different combinations of co‐pathology leading to the development of AD, some of which are captured with MRI. Mainly, WMH contributed to the heterogeneity of AD and the atypical neocortical variant.
Prediction of brain amyloidosis: a multi‐site machine learning analysis
Authors: Fan Shuyang , Ha Sung Min , Xiao Pan , Schindler Suzanne E. , Gordon Brian A. , Raji Cyrus A. , Fagan Anne M. , Xiong Chengjie , Cruchaga Carlos , Aschenbrenner Andrew J. , Hassenstab Jason J. , Benzinger Tammie L.S. , Bateman Randall J. , Morris John C , Ances Beau M. , Sotiras Aristeidis 
Published: June 2023

AbstractBackgroundPosition emission tomography (PET) imaging and cerebrospinal fluid (CSF) analysis are the gold standards for evaluating brain amyloidosis in vivo, but their utility is limited by invasive procedures, high costs of PET, and limited accessibility to PET facilities. Prior research has used machine learning to assess amyloidosis based on non‐invasive and easily accessible information such as cognitive test scores and genetic data, but past accounts have typically used a single cohort to train and test their models, which may undermine their generalizability. Importantly, most studies have imputed amyloidosis of symptomatic participants without evaluating their models on cognitively unimpaired (CU) individuals. Accordingly, we aimed to develop algorithms that estimate risks for amyloidosis and validate them on CU participants from an external cohort.MethodData from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 987) and the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) (n = 688) were used. The examined predictors for amyloidosis were demographic factors, body mass index (BMI), apolipoprotein E (APOE) genotype, and performance on cognitive tests (Table 1). Random forest models were trained with nested cross‐validation (CV) on the ADNI cohort before testing on the Knight ADRC cohort for a reliable assessment of generalizability. The outer CV loop enabled unbiased measurement of prediction performance, and the inner loop hyperparameter optimization.ResultThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.81 [0.79, 0.84] on the ADNI cohort (Table 2), and its performance generalized well to the Knight ADRC participants. When only CU participants are considered, performance was overall lower than that for the full cohort, which we expect given that they have less over pathology. The model was nonetheless able to achieve an AUC of 0.78 [0.75, 0.81] on the Knight ADRC CU participants.ConclusionMachine learning models can predict brain amyloidosis with a moderate degree of accuracy on asymptomatic individuals and generalize well to external cohorts. Our models can be used to screen for individuals likely to be Aβ PET‐positive, thus reducing preclinical AD trial recruitment cost by avoiding unnecessary PET scans.
Levels of blood memory CD8 T cells that induce AD‐like pathology in mice correlate with decreased Aβ42 and increased total Tau in CSF of AD patients
Authors: Wheeler Christopher J. , Van Dam Debby , Vermeiren Yannick , De Reu Hans , De Deyn Peter Paul 
Published: June 2023

AbstractBackgroundWe previously engineered mice to accumulate elevated levels of age‐related, antigen‐specific memory CD8 T cells as in humans. These mice spontaneously develop all major hallmarks of AD with aging. Analogous T cells reactive to an epitope on Amyloid Precursor Protein (APP) were found in aging humans, accumulated in AD brain, and decreased in AD blood. Here, we examine whether their levels are associated with established AD biomarkers in CSF.MethodAntigen‐specific CD8 T cells in blood were quantified by flow cytometric analysis after staining with anti‐CD8, anti‐KLRG1 and APP peptide‐HLA‐A2 multimers in normal aging, MCI with or without AD biomarkers, and confirmed late‐onset AD patient cohorts (n = 50). Aβ1‐42, total Tau, and pTau181 were quantified in CSF, and cognitive performance assessed by MMSE.ResultPercentage of APP‐specific memory CD8 T cells was significantly decreased with dementia (0.68 + 0.29% for MMSE <25; 1.63 + 0.32% for MMSE >24), reaching minimal levels in AD blood, which precluded meaningful correlations in AD patients. Nevertheless, increasing levels of APP‐specific memory CD8 T cells in blood from all subjects combined corresponded with increased CSF Aβ1‐42 (r = 0.373, P < 0.0004) and decreased total Tau but not pTau181 or MMSE (r = ‐0.234, P < 0.0292; r = ‐0.208, P < 0.0531; and r = ‐0.009, P < 0.9410, respectively). Decreases in the larger parental (KLRG1+) memory CD8 T cell population also correlated with decreased CSF Aβ1‐42 in AD patients alone (r = 0.511, P = 0.003 for % KLRG1+ of CD8 T cells; r = 0.682, P < 0.00009 for % KLRG1+CD8+ cells; n = 31).ConclusionOur results reveal that age‐related memory CD8 T cells in blood decrease with dementia, and in proportion to decreased CSF Aβ1‐42 and total Tau in all patients, while their parental KLRG1+ population is directly proportional to Aβ1‐42 in AD patients only. This validates our previous findings that loss of APP‐specific memory CD8 T cells from blood accurately tracks the AD continuum, and support the notion that antigen‐specific memory CD8 T cells are associated with the earliest detectable pathologic changes in AD.
Activation of D1 receptor improved learning and memory and synaptic plasticity in Alzheimer’s model
Authors: Bakhtiarzadeh Fatemeh , Shahpasand Koorosh , Fathollahi Yaghoub , Najafi_Zadeh Javad Mir 
Published: June 2023

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder with complex etiology. Among the neurotransmitter abnormalities that have been investigated in AD, the dopaminergic system has been intensively studied as a key neurotransmitter system involved with emotion and cognition. The dopaminergic system undergoes several changes during the neuropathological aging process. In general, some studies suggested that dopamine plays a major role in synaptic plasticity mechanisms. Interestingly, in AD, the dysfunction of dopaminergic transmission has been hypothesized as a new player in the pathophysiology of AD. On the other hand, stimulation of dopaminergic input to the hippocampus, ventral tegmental area, improved working and spatial memory in mice model of Alzheimer’s disease. In this study we assessed the effect of dopamine receptors on learning and memory and synaptic plasticity in Alzheimer’s like disease mice model.MethodOne month after bilaterally intra hippocampal injection of cis‐ P tau, adult male mice received D1 and D2 receptor antagonist, after 5 minute, induced high‐frequency ventral tegmental area stimulation for 30 min/ 5 day, and animals were assessed for changes in learning and memory. Short term and long term memory was assessed respectively by Y maze and Barnes maze and also synaptic plasticity was studied by in vitro evoked‐ field potential recording.ResultHippocampal cis‐ P tau injection as a new mice model of Alzheimer’s disease produced spatial and working memory impairment and also disrupted long term potentiation in striatum radiatum of hippocampus. Applying VTA‐DBS ameliorated memory impairment, with no effects in the control group. Further, in vitro evoked field potential recording showed a restoration of long term potentiation (LTP).Using D2 receptor antagonist, has no effect on learning and memory improvement after stimulation of ventral tegmental area but using D1 receptor antagonist impaired learning and memory. Applying dopamine on hippocampal slice restored LTP and D1 receptor antagonist had no effect on LTP disruption, but D2 receptor antagonist restore LTP.ConclusionThese results indicate that ventral tegmental area stimulation might mitigate memory dysfunction and synaptic plasticity in cis‐P tau accumulation by D1 receptor and phasic release of dopamine.
Alzheimer’s disease risk alleles of PICALM gene may accelerate cognitive (memory and attention) performance in middle‐aged healthy APOE‐ε4 carriers
Authors: Dzianok Patrycja , Kublik Ewa 
Published: June 2023

AbstractBackgroundThe APOE (allele ε4) and PICALM (SNPs rs541458: allele T; and rs3851179: allele G) genes were recognized as risk factors for late‐onset Alzheimer’s disease (AD). However, effects of these risk genes on the cognitive functions of healthy individuals are still not determined and their possible interactions have never been studied. We investigated the preclinical influence of both genes on brain cognitive functions in a healthy middle‐aged population.MethodSeventy three healthy adults (50‐63 y/o) completed computer tasks measuring attention, cognitive control and memory. MSIT task included an easy‐control condition and an attentionally demanding condition with combined cognitive conflicts. Sternberg’s task included an easy condition (there is a need to memorize 4 letters strings) and a hard‐one (with 8 letters strings). APOE (rs429358/rs7412) and PICALM (rs3851179 and rs541458) alleles were determined using a traditional Sanger sequencing protocol. The data was divided according to the genetic tests results (control group: absence of ε4 and less‐risky PICALM alleles; the double risk group: ε4‐carriers, and homozygous carriers of high‐risk PICALM alleles; single risk group: ε4‐carriers and less‐risky PICALM alleles). ANOVA was used to determine the effects of genetic variants on task performance (reaction time (RT) and accuracy for easy/hard MSIT and Sternberg conditions).ResultResults revealed that while the groups did not differ in accuracy or RTs for easy conditions, they did differ significantly on hard conditions (p<0.05). The single‐risk group showed longer RTs than the control group in both tasks. The effect was preserved when performing the analysis of covariance (ANCOVA) with the age as a covariate.ConclusionAD risk genes, APOE and PICALM, can affect cognitive abilities even in healthy individuals. APOE‐ε4‐carriers needed longer time to process difficult stimuli but surprisingly an addition of PICALM risk alleles seemed to reverse this tendency. Further studies are needed to unambiguously determine the effects of the PICALM and its interactions with other risk genes (including APOE) in modulating the cognitive abilities of still healthy individuals.[Supported by National Science Centre, Poland, no. 2018/31/N/HS6/03551.]
The eighteen months’ follow‐up, second version, after the intervention using a rehabilitation program of physical and cognitive rec‐Xercise (REPCREC) for the elderly with mild cognitive impairment (MCI)
Authors: Kanemaru Akiko , Kato Takayuki , Kawaji Yoshie , Honda Takuya , Makita Ayaka , Goto Takahiro , Uno Shizuna , Uemura Honoka , Higuchi Wakana , Shimizu Aya , Enomoto Yoji , Kanemaru Kazutomi 
Published: June 2023

AbstractBackgroundTo prevent or slow down the age‐related cognitive decline is an important issue. Therefore we have conducted intervention study with our rehabilitation program of physical and cognitive Rec‐Xercise (REPCREC) for the elderly with MCI.MethodSeventy‐six cases, out of seventy‐nine enrolled individuals, have undergone our evaluation and intervention program (REPCREC). The subjects have been divided into four groups; group A the cognitive REPCREC, group B physical REPCREC, group C both cognitive and physical REPCREC and group D control. The design of this study is a non‐randomized controlled trial. Intervention groups have received the each session once per week up to twelve times. Before and after the intervention, and 3‐, 6‐, 12‐ & 18‐months after the intervention, we have evaluated the following items: physical function, GDS, POMS2, neuropsychological tests [MMSE, RBMT, TMT, Ravenʼs Colored Progressive Matrices(RCPM), Rey‐Osterrieth Complex Figure Test(ROCF), story retention, coding and Miyake’s paired verbal associate learning test(MPVALT)]. A one‐day booster program has been provided at the time of evaluation. For statistical analysis, Fisher’s exact test and Kruscal‐Wallis test for the baseline data, and linear mixed model(LMM) analysis have been performed.ResultThe median age is 79, median MMSE scare is 27.5, and the numbers of each group are A18, B24, C27, and D7 cases. At the baseline, no difference between groups except POMS‐F (p = 0.026). LMM analysis revealed the significant differences: 1) before REPCREC; RCPM of group A (p = 0.013), story retention delayed (p = 0.001) of group A (p = 0.011)▪C (p = 0.037), story retention immediate (p = 0.022), MPVALT (non‐related) (p = 0.048) and ROCF (recall) (p = 0.006), 2) after REPCREC; coding (p = 0.011) of group A (p = 0.049)▪C (p = 0.032) and ROCF(recall) (p = 0.018), 3) 3 months after REPCREC; TMTB (p = 0.031) of group A (p = 0.032) and story retention delayed of group A (p = 0.011).ConclusionThe program REPCREC showed the better effects after REPCREC on non‐verbal intelligence, visuospatial abilities, memory, attention, planning, working memory etc. [RCPM, ROCF (recall), MPVALT] in especially the cognitive REPCREC (group A). And group A showed the significant improvement in TMTB and story retention delayed, even three months after REPCREC. That may indicate the prolonged efficacy of the REPCREP in shift of attention and coherent memory for MCI
Levels of plasma neurofilament light and pTau‐181 predict for steeper cognitive decline in Typical AD and Hippocampal‐sparing compared to Limbic‐predominant AD subtype
Authors: Bucci Marco , Naudet Léa Delphine Christelle , Nordberg Agneta K 
Published: June 2023

AbstractBackgroundRecent studies suggest plasma neurofilament light (p‐NFL) and phosporylated‐Tau‐181 (p‐pTau181) are valuable biomarkers for detecting faster cognitive decline in patients with Alzheimer’s Disease (AD) dementia. Recently, AD subtyping has been used to subgroup patients into AD phenotypes with possibly different cognitive prognoses. The aim of this study was to investigate the interaction between p‐NFL and p‐pTau‐181 and different AD subtypes to predict decline in episodic memory.MethodIn total, 357 non‐AD (Aß‐) (Cognitive Normal and Mild Cognitive Impairment) and 73 AD subjects (Aß+, according to mainly Amyloid‐PET or ‐CSF) were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Subjects had at least one plasma sample and one MRI scan. The latter was used to subtype AD patients (Risacher et al. 2017). AD subjects were subtyped in Limbic‐predominant (18%), typical AD (67%) and Hippocampal‐sparing (15%) subtypes. The ADNI_MEM composite score was used as proxy for cognition. Linear mixed models were tested to predict cognitive decline up to 4.5 years of follow up (27.7±13.1 months) including the interaction between time, AD subtype and p‐NFL and p‐pTau‐181. Age, gender, education, and ApoE4 status were included in the models. Models were selected incrementally through model comparison (AIC, BIC). The final model included both p‐NFL and p‐pTau‐181.ResultThe AD subtypes did not differ in cognition (‐1±0.6 for the whole group) or age (74±8 ys for the whole group), but in gender. Higher p‐NFL levels in patients with Hippocampal‐sparing and Typical AD subtypes showed faster decline in cognition compared to non‐AD patients (p<0.05, both), while Limbic‐predominant did not. For p‐pTau‐181, the interaction with Limbic‐predominant was significant and the model showed stronger separation of cognitive decline across all AD subtypes than p‐NFL. Combining both biomarkers improved the model but only the interactive effects of pTau‐181 for Hippocampal‐sparing and typical AD subtypes were significant (p<0.05, both).ConclusionIn the Limbic‐predominant AD subtype the levels of the two plasma biomarkers p‐NFL and p‐pTau‐181 did not predict for progressive cognitive decline (episodic memory), suggesting that this AD subtype with late disease onset (contrary to the Hippocampal‐sparing subtype), cannot be early detected with plasma p‐NFL and p‐pTau‐181.
Longitudinal trajectories of self‐reported subjective cognitive decline in relation to objective cognitive performance in a population‐based cohort
Authors: Street Rebecca E , Lu Kirsty , Nicholas Jennifer M , Keuss Sarah E , Pavisic Ivanna M , James Sarah‐Naomi , Baker John , Buchanan Sarah M , Wagen Aaron Z , Storey Mathew , Harris Matthew J , Parker Thomas D , Lane Christopher A , Keshavan Ashvini , Freiberger Tamar , Murray‐Smith Heidi , Cash David M , Coath William , Wong Andrew , Richards Marcus , Fox Nick C , Crutch Sebastian J , Schott Jonathan M 
Published: June 2023

AbstractBackgroundSubjective Cognitive Decline (SCD) may represent the onset of cognitive decline before impairment on standard cognitive tests occurs (Jessen et al., 2014). Previous cross‐sectional studies were shown to have limited ability to capture objective differences in cognitive performance between groups with and without SCD (Cacciamani et al., 2017). Understanding the interplay between changes in subjective memory ratings and objective cognitive function over time may have utility in identifying people at risk of dementia.Method370 cognitively normal (CN) individuals with complete cognitive data (Table 1 for characteristics) from Insight 46, a longitudinal neuroscience sub‐study of the MRC National Survey for Health and Development (the British 1946 Birth Cohort), underwent cognitive, clinical, and physical assessments, and neuroimaging (combined MRI/18F‐Florbetapir‐PET). SCD was measured using MyCog (Rami et al., 2014) a validated tool, from the SCD‐Questionnaire, where higher scores indicate greater subjective complaints. Linear regression models were used to test whether longitudinal cognitive change as measured using the Preclinical Alzheimer Cognitive Composite (PACC) and a visual short‐term memory binding task were influenced by baseline MyCog and whether there are parallel associations between rates of change in MyCog and longitudinal cognitive change. Covariates were sex, age at baseline visit, childhood cognitive ability, educational attainment, and adult socioeconomic position.ResultBaseline MyCog scores did not predict rates of change in PACC or visual short‐term memory binding outcome measures (Table 2). Rates of MyCog change alone did not predict rate of change in PACC, localisation error, total correct number of trials or proportion of misbinding errors (Table 3). However, we observed an interaction effect with baseline amyloid‐b status, whereby greater rates of subjective memory concerns predicted faster rates of decline in the proportion of misbinding errors in amyloid‐b positive participants only (Table 3; Figure 1).ConclusionThese findings suggest that higher amyloid‐b burden in CN individuals with subjective memory complaints is associated with faster rates of decline in visual working memory. Corroborating previous reports that feature binding tasks can objectify subtle cognitive deficits found in SCD (Koppara et al., 2015) and serve as a promising tool for identifying people at risk of dementia.