bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between white matter hyperintensities and cognitive dysfunction in Alzheimer’s, vascular and mixed dementias
Authors: Lee Hyunwoo , Wiggermann Vanessa , Rauscher Alexander , Beg Mirza Faisal , Popuri Karteek , Tam Roger , Lam Kevin , Liu‐Ambrose Teresa , Dao Elizabeth , Keridy Walid Ahmed Al , Jacova Claudia , Sossi Vesna , Pettersen Jacqueline , Benavente Oscar R. , Hsiung Ging‐Yuek Robin 
Published: December 2020

AbstractBackgroundIncreased burden of white‐matter hyperintensities (WMHs) on MRI is common among different types of dementia. WMHs can be caused by various underlying etiologies and are non‐specific markers of tissue abnormality. Therefore, global measures such as WMH burdens may not distinguish between degrees of cognitive impairment. We explored the associations between lobar‐specific WMH volumes and cognitive dysfunction across different dementia subtypes.MethodN=57 participants (7 Alzheimer’s disease [AD]/5 subcortical vascular dementia [SVaD]/7 mixed dementia [MixD]/38 vascular cognitive impairment [VCI]; Sex:34M/23F; Age:73±8yrs; pooled from three independent studies) were scanned on 3T MRI. WMHs were segmented on 3D‐FLAIR (MixD/AD/SVaD) or dual‐echo PD/T2‐weighted (VCI) images. T1‐weighted images were processed with Freesurfer 6.0. Left and right frontal, temporal, occipital and parietal lobes plus basal ganglia volumes were constructed using the Freesurfer segmentation outputs. We calculated the proportion of WMH burden within each lobe, i.e. the ratio of lobar WMH volume over the lobar volume. The Montreal Cognitive Assessment (MoCA) was used as a measure of cognitive function. We used a general linear model to determine the association between the WMH/lobar volume ratios and the MoCA scores. Covariates included age, sex, and presence of AD‐related (yes for MixD/AD, no for rest) and vascular‐related conditions (yes for SVaD/VCI/MixD, no for AD). Additionally, we assessed the relationship between the MoCA scores and the ratio of total WMH burden over the whole‐brain parenchymal volume.ResultThe model yielded a significant association between the MoCA scores and WMH/lobar ratios within the left (p=0.031) and the right (p=0.035) frontal lobes, with higher ratios predicting lower MoCA scores. The effect was not significant within the temporal, occipital, parietal lobes and the basal ganglia. The effect was insignificant at the whole‐brain level (p=0.08).ConclusionWe found a significant association between the frontal lobe WMH burden and the MoCA scores, especially sensitive to frontal executive functions. This suggests that region‐specific assessments of WMHs may provide improved imaging‐cognitive correlations over the global WMH burden. Future studies with more consistent imaging protocols and broader cognitive/clinical assessments are warranted to further characterize the relationship between WMHs and cognitive impairment.
Multimodal screening for neurodegeneration in preclinical Alzheimer’s disease using EEG, APOE4 genotype, neuropsychological and MRI data
Authors: Gaubert Sinead , Raimondo Federico , Houot Marion , Corsi Marie‐Constance , Naccache Lionel , Sitt Jacobo Diego , Hermann Bertrand , Oudiette Delphine , Gagliardi Geoffroy Pierre , Habert Marie‐Odile , Dubois Bruno , Fallani Fabrizio De Vico , Bakardjian Hovagim , Epelbaum Stéphane ,   Alzheimer’s Disease Neuroimaging Initiative, MEMENTO Study Group, and INSIGHT‐preAD Study Group
Published: December 2020

AbstractBackgroundEarly biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease (AD) (Jack et al., 2018). Electroencephalography (EEG) is a non‐invasive and cheap technique that would be an interesting screening tool for the preclinical stage of AD.MethodWe included participants from the INSIGHT‐preAD cohort, which is an ongoing single‐center multimodal observational study designed to identify risk factors and markers of progression to clinical AD in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint (Dubois et al., 2018). We divided the subjects into four groups, according to their amyloid status (on 18F‐florbetapir PET) and neurodegeneration status (on 18F‐fluorodeoxyglucose PET). We analysed 314 baseline 256‐channel high‐density eyes‐closed 1‐minute resting‐state EEG recordings. We extracted 10 quantitative EEG biomarkers, including spectral measures, algorithmic complexity and functional connectivity metrics. We evaluated three different classifiers (random forest, decision tree and logistic regression) to predict amyloid and neurodegeneration status from multimodal data, combining EEG, ApoE4 genotype, demographic, neuropsychological and MRI data. We divided the dataset into a training set (n=258) and validation set (n=46). We used a 5‐fold cross‐validation on the training set to identify the optimal combination of features.ResultAs recently published (Gaubert et al., 2019), the most prominent effects of neurodegeneration on EEG metrics were localized in fronto‐central regions with an increase in high‐frequency oscillations (higher beta and gamma power) and a decrease in low‐frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted Symbolic Mutual Information in theta band. In the machine learning analysis, among the different features, EEG was the most strongly predictive of neurodegeneration. Similar predictive performance was obtained when reducing the number of electrodes from 256 to 8. EEG biomarkers combined with hippocampal volumetry had a high negative predictive value (91%) and high sensitivity (82%) to predict neurodegeneration. The combination of ApoE4 genotype with demographic data was most strongly predictive of amyloid status.ConclusionEEG could be a valuable tool to screen for or rule out neurodegeneration in elderly memory complainers. This procedure has been patented under the following PCT number: PCT/EP2019/086629.
Cohousing for people living with dementia: The Modena experience
Authors: Fabbo Andrea , Neviani Francesca , Valentini Laura , Luppi Emanuela , Bertolotti Marco 
Published: December 2020

AbstractBackgroundSocial isolation, caregiving burden and costs, loss of quality of life related to increasing disability, loss of agency and personhood, are some of the main unmet needs for people with dementia (PwD) and their caregivers. Objective: In order to support people with dementia and their caregivers we implemented a cohousing model. 5 PwD were encouraged to live together. Caregivers were supported in the care according to person centred approach.MethodParticipation in the project (supported by Modena City Council and local Alzheimer association, had been on a voluntary basis. The only exclusion criteria for PwD had been bedridden .Each PwD has his own bedroom with personal belongings and furniture, while the dining and living room are furnished according to prosthetic environment principles. Caregivers turn up in order to both maintain familial environment and reduce global care burden, while formal caregivers, provided by social service and employed by relatives, contribute to PwD care. According to carers expressed needs, voluntaries and an occupational therapist have been engaged to weekly involve PwD in leisure activities and psychosocial intervention. A granted psychologist is available to support Caregivers. A cost analysis, PwD and caregiver quality of life evaluation and caregiving burden were checked. Two control groups of PwD living at home with relatives or family assistant were considered in order to evaluated this cohousing model effectiveness. The first control group was supported by local dementia association. PwD were follow by a memory clinic in cohousing group and in both control groups.ResultA significant cost reduction was found in cohousing group (1879 €/month vs 2502€/month and 2662€/month in control groups). Moreover caregiving burden reduction and an improved PwD and caregivers quality of life were found in cohousing group.ConclusionThis is the first cohousing experience for PwD, in our Country. Relatives improved caregiving experience, reduced financial burden, isolation and time spent for assistance. PwD reduced the risk of institutionalization and improved their quality of life. According to this study results another cohousing experience was open 6 month ago by our community social services.
Daily stressful experiences, depressive symptoms, and behavioral responses of ADRD family caregivers
Authors: Puga Frank , Wang Danny , Pickering Carolyn E 
Published: December 2020

AbstractBackgroundFamily caregivers of individuals living with Alzheimer’s disease or related dementias (ADRDs) are at a higher risk for serious mental illness, specifically depression. Caring for individuals living with ADRDs can be stressful due to the behavioral symptoms of dementia (BSD). Over time, stressors, such as BSDs, can contribute to the onset of depressive symptoms. Few studies have examined BSDs as contextual triggers contributing to variations in daily mental health symptom experiences of caregivers. Further, it is unclear how behavioral adaptations to caregiving‐specific stressors, such as engaging in elder abuse and neglect (EAN), impact caregiver mental health. In this study, we identified specific individual vulnerabilities, daily environmental stressors, and caregiver behaviors that increase the daily odds of experiencing depressive symptoms.MethodUsing a micro‐longitudinal design and ecological momentary assessment methods, we examined daily environmental stressors (BSDs), caregiver behaviors (i.e., EAN events), and individual vulnerabilities (i.e., demographics, early life adversity, social isolation, social support, and coping strategies) that impact daily odds of experiencing depressive symptoms. Data were collected from a national sample of family caregivers (N=50) who completed daily diaries over a 21‐day period (n=841). Multi‐level modeling was used to test a series of hypotheses on the relationship between daily environmental stressors, caregiver behaviors, individual vulnerabilities, and depression symptom experiences.ResultFamily caregivers were more likely to report depressive symptoms on a given day when a BSD was present and perceived as bothersome (OR = 1.22, 95% CI: 1.09‐1.43, p = 0.002). Additionally, when an EAN event occurred on a given day, caregivers were more likely to report depressive symptoms (OR = 1.27, 95% CI: 1.02‐1.58, p = 0.034) . Individual‐level factors also interacted with BSD, EAN events, and daily depressive symptoms.ConclusionThe results from this study help elucidate factors that contribute to variations in the daily mental health symptom experiences of ADRD caregivers. The interactions between BSDs, EAN events, and depression identified in this study will inform interventions to better support family caregivers and improve mental health outcomes for an at‐risk population.
Cortical plasticity assessment predicts decline in patients with mild cognitive impairment
Authors: Lorenzo Francesco Di , Motta Caterina , Bonnì Sonia , Caltagirone Carlo , Martorana Alessandro , Koch Giacomo 
Published: December 2020

AbstractBackgroundAlzheimer`s disease (AD) is characterized by loss of synaptic connections, cell death and disruption of structural and functional networks. One of the most consistent findings is the impairment of cortical plasticity, especially Long Term Potentiation (LTP) mechanisms. Recently, the use of new diagnostic criteria allowed to considered AD as a clinico‐biological entity identifiable in vivo on the presence of biomarkers. In light of these new criteria, aim of the current work is to investigate cortical plasticity in patients with hippocampal type memory impairment admitted for the first time in the memory clinic and stratified according to CSF biomarkers profile; moreover we followed patients up to a period of three years to explore the relationship between neurophysiological, neuropsychological and CSF biomarkers and clinical progression.MethodSeventy‐three patients were recruited and followed up for 36 months. They underwent CSF sampling and Transcranial Magnetic Stimulation to investigate LTP and intracortical circuits. According to the new AD criteria we divided patients in three groups: 1) Mild Cognitive Impaired (MCI) patients (n=21); Prodromal AD (PROAD) patients (n=24); AD Dementia (ADD) patients (n=28).ResultIn neurophysiological evaluations only iTBS protocol was different among the different groups showing a paradoxical reversal of LTP for ADD and PROAD and a poor response for MCI patients. ProAD worsened faster than MCI. Regression analyses showed that LTP impairment was related to clinical progression. Kaplan‐Meyer analyses showed that patients expressing the worst LTP values were the ones to progress faster in a 3 year time.ConclusionThe new criteria based on the presence of biomarkers and dementia allow us to identify patients at a prodromal stage that will develop dementia due to AD. LTP impairment drives the clinical progression in patients at prodromal stages confirming its pivotal role in determining cognitive decline. These results pave the way for the identification of new therapeutic targets such as synaptic plasticity modulators.
Multilevel and network analysis in patients with mild cognitive impairment: Cognitive assessment as a predictor of biomarkers
Authors: Fernández Rodrigo S , Clarens Maria Florencia , Mendez Patricio Alexis Chrem , Crivelli Lucia , Allegri Ricardo F 
Published: December 2020

AbstractBackgroundThe extracellular aggregation of the amyloid protein and hyperphosphorylated tau in neurons are biomarkers of Alzheimer's disease. Their use the clinical settings allowed to improve the diagnosis, in patients with cognitive impairment. The objective of this study was to evaluate the extent to which multivariate and network analysis of a neuropsychological battery are sensitive to predict and classify patients with mild cognitive impairment with positive (MCI +) and negative (MCI‐) amyloid markers.MethodPatients with MCI‐ (n = 32) and MCI + (n = 54), performed a standard neuropsychological battery (UDS2). A multivariate Analysis of Variance (MANOVA) was performed to compare the performance of each group, followed by a Linear Discriminant Analysis analysis (LDA) to detect the tests that best classify groups. Moreover, an analysis of networks and centrality measures was carried out, with the objective of characterizing the way in which the different tests characterize the groupsResultThe MANOVA revealed that MCI‐ and MCI + groups significantly differ in their overall neuropsychological performance (P <0.001). Likewise, the LDA showed that a function (P <0.05) explains 60% of the variance (R2) and accurately classified group membership in 71% of patients. Network analysis showed that, the groups were similar in the number of connections, but differ in their type and strength. Finally, these analyzes suggested, that the tests of orientation, verbal‐immediate memory, logical‐deferred memory and semantic fluency, would be those that best separate groups.ConclusionThe neuropsychological evaluation was capable of 1) Separating the MCI‐ and MCI + groups according to their relative performance; 2) Accurately characterize how the neuropsychological tests are associated with each other and 3) Predict whether MCI patients have positive / negative amyloid biomarkers.
Elevated levels of synaptic protein GAP‐43 associate with brain tauopathy, atrophy and cognition in Alzheimer’s disease
Authors: Dhiman Kunal , Villemagne Victor LL , Eratne Dhamidhu , Graham Petra L , Fowler Christopher J , Bourgeat Pierrick , Li Qiao‐Xin , Collins Steven , Bush Ashley I , Rowe Christopher C , Ames David , Masters Colin L , Blennow Kaj , Zetterberg Henrik , Martins Ralph N , Gupta Veer Bala 
Published: December 2020

AbstractBackgroundSynaptic dysfunction is an early pathophysiological event, which associates with cognitive aberrations in Alzheimer’s disease (AD). Growth associated protein 43 (GAP‐43) is a nervous tissue specific protein, which plays a crucial role in axon formation, growth and synaptic plasticity. The current study evaluated the utility of cerebrospinal fluid (CSF) GAP‐43 in diagnosing AD, and its association with neuropathological and cognitive changes in AD, using CSF samples of participants from the Australian Imaging, Biomarkers and Lifestyle study of ageing (AIBL).MethodThe study participants (n=218) were clinically classified into healthy controls (HC, n=156), mild cognitive impairment (MCI, n=34) and AD (n=28). CSF levels of core CSF biomarkers (amyloid beta‐42, Ab42; total‐tau, T‐tau; phosphorylated‐tau, P‐tau) were assessed using commercial enzyme linked immunosorbent assays (ELISA). CSF GAP‐43 levels were assessed using a sandwich ELISA, based on the monoclonal antibody NM4 (Fujirebio, Ghent, Belgium) and a polyclonal GAP‐43 antibody (ABB‐135, Nordic Biosite, Täby, Sweden). Association of GAP‐43 with core CSF biomarkers was assessed via a linear correlation analysis. Multiple regression analyses were undertaken to assess the utility of GAP‐43 for predicting baseline amyloid load (measured by standardized uptake value ratio, SUVR), brain atrophy (measured by hippocampal volume) and cognition (measured by Mini‐Mental State Examination, MMSE and Clinical Dementia Rating Sum of Boxes, CDR‐SB).ResultGAP‐43 levels were significantly elevated in AD compared to HC (p=0.001) and MCI (p= 0.036) and distinguished AD from HC with an area under the receiver operating characteristic curve (AUC) of 0.72. CSF GAP‐43 levels were significantly correlated with CSF T‐tau (r=0.84, p<0.001) and P‐tau (r=0.87, p<0.001), but not with CSF Ab42 (r=0.05, p=0.470). CSF levels of GAP‐43 significantly predicted baseline hippocampal volume (b =−0.05, standardized b=−0.16, p=0.027), and cognition as measured by MMSE (b =−0.28, standardized b =−0.14, p=0.039) and CDR‐SB (b =0.28, standardized b =0.22, p=0.001), after controlling for covariates. CSF GAP‐43 levels also significantly predicted baseline amyloid (b =0.05, standardized b=0.24, p<0.001).ConclusionAs an AD biomarker, CSF GAP‐43 could be used to track the stage‐ and state‐associated synaptic aberrations that occur in the AD brain, and to understand the impact of stage‐associated synaptic aberrations on cognition and brain atrophy.
Deferiprone to delay dementia (the 3D trial)
Authors: Ayton Scott , Woodward Michael , Ellis Kathryn A. , Lim Yen Ying , Maruff Paul T. , Desmond Patricia M. , Salvado Olivier , Fazlollahi Amir , Rowe Christopher C. , Churilov Leonid , Bush Ashley I. ,   3D Trial Investigators
Published: December 2020

AbstractBackgroundPotential therapeutic targets for Alzheimer’s disease (AD) outside of β‐amyloid are of increased interest. Brain iron has been reported to predict accelerated AD progression when measured using fluid biomarkers (CSF ferritin), quantitative susceptibility mapping MRI, or directly in post‐mortem tissue, and is one potential therapeutic target. Brain iron may contribute to neurodegeneration by becoming pro‐oxidant (e.g. ferroptosis), or by promoting an inflammatory phenotype of microglia. Deferiprone is an orally bioavailable, BBB‐penetrant moderate iron chelating drug approved for the peripheral iron overload in β‐thalassemia. Deferiprone reported promising evidence for disease modification of Parkinson’s disease in two phase II studies; here we report the status of a phase IIb clinical trial of deferiprone for AD.MethodsThe primary outcome of this trial is to compare the cognitive performance in a neuropsychological test battery of subjects with mild Alzheimer’s disease (confirmed using β‐amyloid PET) treated for 12 months with either deferiprone (15 mg/kg) administered orally twice a day or a matching placebo. Secondary outcomes include adverse events (safety analysis), change in brain iron burden measured by quantitative susceptibility mapping MRI (target engagement), and brain volume changes (secondary efficacy measure). 171 participants will be recruited and randomized in a 2:1 ratio (drug:placebo).ResultsTen clinical trial sites have been established in Victoria (6), New South Wales (3), and Western Australia (1). The first patient was randomized in March 2018. As of January 2020, 101 patients have been screened and 45 randomized. 18 patients have completed the treatment protocol of 1 year.ConclusionsThe outcome of this trial is scheduled to report in 2021.
Longitudinal changes in resting‐state EEG are correlated with cognitive decline in Alzheimer’s disease and related dementia
Authors: Meghdadi Amir H. , Berka Chris , Richard Christian , Karic Marija Stevanovic , Rupp Greg , Mcconnell Marissa , Poole Jared , Fink Abigail , Hamilton Joanne M , Salat David H 
Published: December 2020

AbstractBackgroundResting‐state EEG measures such as spectral power in Theta (3‐7 Hz) and Alpha (8‐13 Hz) bands have been previously linked to cognitive decline in Alzheimer’s disease (AD) and other dementias. However, routine use of EEG in clinical settings has not been widely practiced. In this ongoing longitudinal study, we demonstrate how changes in EEG measures after 1‐year are correlated with actual changes in cognitive decline as measured by MMSE score of cognitive assessment.MethodFive minutes of eyes‐closed resting‐state EEG were recorded during both an initial and one‐year follow‐up visits from Healthy Controls (n=67), individuals with Mild Cognitive Impairment (n=16), Alzheimer’s Disease (n=4), Dementia with Lewy‐body (n=1), Parkinson’s disease dementia (n=1), and subjects reporting memory problems without a clear diagnosis (n=7). A generalized‐linear‐model (GLM) was used to regress the relationship between predictors (MMSE and age at initial visit as well as longitudinal changes in 9 EEG measures that were selected a priori) and the outcome variable (actual changes in MMSE score after 1‐year). EEG measures included Theta‐Alpha Ratio (TAR) and relative power at 5 Hz (theta5) at temporal channels as well as relative power at 10Hz at POz channel.ResultMMSE scores fell in 32% of participants and improved in 8% (Figure 1). A GLM using logarithmic link‐function with 11 predictors and 21 terms (EEG predictors and their interaction with age) was fitted to the data (F=4.24, p=2.44x10‐6). The most significant (p<0.01) predictors were age, theta5, TAR and their interaction with age, all at channel T6. Predicted and actual decline in MMSE were highly correlated (r=0.73, p=10‐5) (Figure 2). A simpler model with 12 terms (no interaction between predictors) resulted in (F=4.34, p=3.98x10‐5).ConclusionPredictive power of EEG in modeling cognitive decline was demonstrated in a cohort of patients with known or possible dementia diagnosis as well as controls. These results support the utility of EEG as a biomarker of cognitive decline. These markers could supplement other neuropathological biomarkers (such as beta‐amyloid and tau), particularly because changes in cognition may not necessarily happen at the same rate as pathological changes.