bluuebunny/crossref_metadata_2025_split huggingface dataset:
Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study
Authors: Sewell Kelsey R , Rainey‐Smith Stephanie , Villemagne Victor L , Peiffer Jeremiah J , Sohrabi Hamid R , Taddei Kevin , Ames David , Maruff Paul , Laws Simon M , Masters Colin L. , Rowe Christopher , Martins Ralph N , Erickson Kirk I. , Brown Belinda M 
Published: December 2022

AbstractBackgroundPhysical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required.MethodsParticipants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition.ResultsHigher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022).ConclusionThese results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.
Identification of a dual Aβ‐tau disaggregator for the treatment of Alzheimer’s disease
Authors: Han Seung Hoon , Kim Koeun , Park Suji , Jeong Jaehoon , Ko Yeon Uk , Kim Hye‐Ju , Kim Seong Muk 
Published: December 2022

AbstractBackgroundThe accumulation of misfolded protein aggregates is connected with neurodegenerative diseases such as Alzheimer’s disease (AD), and Parkinson’s disease. Because of the accumulation of amyloid β (Aβ) plaques and tau tangles (NFTs) in the brain, neurons slowly degenerate and lose their functions in AD. Recently, many clinical trials for AD therapy with single‐target drugs, especially targeted for Aβ or tau aggregates, have failed. As a therapeutic strategy for AD, we therefore propose to introduce a multifunctional anti‐AD agent to disaggregate the Aβ plaques and NFTs.MethodHit compound was selected with disaggregation of Aβ and tau aggregates in various concentrations and half maximal effective concentration by thioflavin T (ThT) fluorescence assay. In vitro study, we examined the effects of our hit compound on the accumulation of intracellular Aβ or tau in over‐expressing cells. In vivo study, we administrated orally our hit compound to AD mouse model for 12 weeks (100mg/kg by weekly). Analysis of Aβ and tau was performed by molecular biological and histological experiments.ResultOur results show that the hit compound had dual disaggregation capacity for Aβ and tau aggregates. We observed significant decrease of Aβ and NFTs in hit compound treated AD mice compared to vehicle treated. Also, we found the restoration of cognitive decline in compound treated AD mice.ConclusionThe study demonstrate that our hit compound was identified as dual functional drug for Aβ and NFTs disaggregator. These findings may help to design the next generations of dual or selective disaggregators.
Association between multimorbidity, subjective memory decline and cognitive performance in older adults.
Authors: Souza‐Talarico Juliana Nery , Santos Jair Lício Ferreira , Buck Harleah , Duarte Yeda Aparecida de Oliveira 
Published: December 2022

AbstractBackgroundHigh multimorbidity rates co‐existing with Alzheimer’s disease (AD) have been interpreted as a consequence of dementia progression. Little is known about multimorbidity’s relationship with cognition as an underlying factor capable of accelerating the progression and onset of the disease. Here we examined the relationship between multimorbidity, cognitive performance and subjective memory decline in older adults in a large representative dataset.MethodsCross‐sectional data from 1,103 individuals (60.1% female), between 60 – 97 years old, enrolled in the Health, Wellbeing and Aging Study (SABE), a Brazilian population‐cohort study, were assessed for multimorbidity and cognitive performance. Multimorbidity was defined as the co‐occurrence of at least two chronic conditions. Cognitive performance was determined using the MMSE, verbal fluency (number of animals per minute), and backward digital span. For the subjective memory performance, participants were asked whether their memory for daily activities was better, the same, or worse compared to the last ten years.ResultsMultimorbidity was present in 36.8% of the participants, with the highest rates observed in the participants with subjective memory decline (p < 0.001). Multivariate analysis adjusted for age, sex, education and depression symptoms showed that the higher the multimorbidity rate, the lower the MMSE (B = ‐0.365; p = 0.025) and the digit span scores (B = ‐0.232; p = 0.003; F = 68.3; p < 0.001).ConclusionIn older adults, multimorbidity was associated with higher subjective memory decline, lower short‐term memory, and global cognitive performance. These findings support future longitudinal studies investigating multimorbidity in moderating dementia pathology and progression.
Bead‐based electrochemical gap sensors for detection of tau protein in blood to diagnosis and monitoring the tauopathy
Authors: Kwon SunSang , Yun Mijin , Lee Soo Hyun 
Published: December 2022

AbstractBackgroundRecently, there is a need for a method for detecting biomarkers in blood that can be collected relatively easily for diagnosis and monitoring of dementia symptoms in Alzheimer's disease (AD) patients. Tau protein, a typical hallmark of AD, has six isoforms, and is divided into four repeating microtubule binding domains (4R) and three microtubule binding domains (3R) depending on whether exon 10 is included. According to the balance of 3R‐ and 4R‐tau, tauopathy can be distinguished. It is expected to be able to differentiate normal (NC) from AD and other dementias by measuring the tau protein in the blood.MethodIn this study, an electrochemical sensor with a novel structure was employed to detect tau in the plasma of living patients. The nanogap sensor consists of a gap electrode and a SU‐8 microwell for loading a single magnetic bead (Figure 1). A sample was prepared by reacting the surface of the magnetic beads with an antibody (total tau or anti 3R‐ or 4R‐tau antibody) and an antibody functionalized with a secondary O‐glycosylated tau (O‐g tau) antibody or phosphorylated tau (p‐tau) antibody.ResultAs a result of O‐g/p‐tau measurement, the impedance change by O‐g tau was large in NC (n=4), whereas the impedance change by p‐tau was large in AD (n=13). Patients with idiopathic Parkinson's disease (IPD) (n=3) also had more impedance changes due to p‐tau (Figure 2). As a result of measuring 3R‐ or 4R‐tau protein, the impedance change by 4R‐tau in NC was larger than that by 3R‐tau, whereas the opposite result was shown in AD. similar to the previous results, the impedance change by 3R‐tau was larger in IPD (Figure 3),ConclusionWe used a novel electrochemical gap sensor to detect tau protein in blood samples collected from living patients. As a result, it is expected to be able to differentiate NC from tauopathy and other dementias by measuring the tau protein in the blood (Figure 4).
QEEG‐based differentiation between Alzheimer’s disease with or without alpha synucleinopathy
Authors: Park Ukeob , Kang Seung Wan , Baik Kyoungwon , Ye Byoung Seok 
Published: December 2022

AbstractBackgroundAs existence of alpha synucleniopathy could affect progression of cognitive impairment in Alzheimer’s disease (AD), it’s very important to differentiate comorbidity of alpha synucleniopathy in AD to predict progression. To develop affordable EEG‐based discriminating machine‐learning(ML) algorithm for existence of alpha synucleniopathy, we explore differences between AD with vs without it.MethodBased on pattern of cognitive impairment and 3 types of PET scan [18F‐Florbetaben brain amyloid‐beta, FDG, DAT‐PET] , dementia due to Alzheimer’s disease [pure ADD] or Lewy Body (pure LBD) or mixed type (AD with LB disease) were clinically classified.We measured 19ch resting state EEG based on international 10‐20. Quantitative analysis of EEG was done by iSyncBrain®.ResultPure ADD showed the characteristics that general EEG slowing with low total power, relative delta enhancement and desynchronized alpha with low amplitude. Pure LBD showed the pattern of slow alpha peak frequency with intact synchronization, relatively higher EEG total power and frontotemporal theta enhancement. Compared to pure ADD, ADD with LB showed mixed pattern of ADD and LDB. Total power was higher, alpha wave showed intact synchronization but theta power was rather enhanced at frontal(Fz; p‐value < 0.05) and bilateral temporal(T3,5, T4,6; p‐value < 0.05) area that was reported as characteristic pattern of progressive type of AD by previous studies.ConclusionThese findings imply that ADD could have different EEG oscillation characteristics according to the Lewybody disease. Next step, we will develop the machine‐learning algorithm to discriminate the Lewybody disease based on specific EEG features and validate it.
Tau Pathology induces Whole‐Brain Subcritical Brain Dynamics across the spectrum of Alzheimer’s Disease
Authors: Kobeleva Xenia , Ritter Petra , Patow Gustavo , Deco Gustavo 
Published: December 2022

AbstractBackgroundCognitive dysfunction in Alzheimer’s Disease (AD) is caused by dysfunctional brain region interactions. Although functional connectivity alterations in AD have been described in several studies, their associations to tau pathology have not been completely understood. Using whole‐brain network modeling we can mechanistically associate the functional impact of tau on brain function in silico.MethodUsing tau PET and fMRI data from the ADNI3 study, we linked regional tau burdens to fMRI activity using a Hopf dynamical model of brain activity using linear regressions. The Hopf model faithfully recreates oscillatory brain activity and adjust the bifurcation of the regional oscillations to either to a noisy (subcritical) or a more regular (supracritical) regime (Deco et al., 2017). The similarity of simulated and empirical time series was assessed by comparing the resulting (dynamical) functional connectivity differences using root mean squared error and Kolmogorov‐Smirnov statistics (as described in Kobeleva et al., 2021). We compared the resulting bifurcation parameters of the Hopf model between healthy controls with no SUVR above 2, healthy controls with at least one region with a SUVR above 2 (considered as at‐risk subjects with a high tau burden) and patients with mild cognitive impairment (MCI).ResultWe achieved a good fit of the tau‐dependent model of brain activity, ensuring a realistic representation of brain activity in the model. Mean bifurcation parameters differed across the three groups (controls with low and high tau burden, and MCI patients). Specifically, we found increasingly more subcritical bifurcation parameters in controls with high tau burden and MCI patients, quantitatively associating a higher tau burden associated with noisier brain oscillations.ConclusionOur results show that biology‐inspired models of AD contain parameters with clinically relevant information. We observed a shift to a more subcritical regime in subjects with high tau and even more so in MCI, showing that tau is associated with nosier brain oscillations, possibly contributing to impaired information transmission and observed cognitive dysfunction. These results constitute a missing link to the branching process theory of cortical network function in AD and contribute to understanding impaired cortical excitability in AD.
QEEG‐based discernment of dementia pathologies through machine learning: Lewy body and Alzheimer’s disease
Authors: Jeong Taegyun , Park Ukeob , Kang Seung Wan 
Published: December 2022

AbstractBackgroundAlzheimer’s disease is the most common cause of dementia, which destroys nerve cells in the brain through the formation and accumulation of amyloid plaques. However, various other pathologies of dementia also exist, such as Lewy body pathology where the destruction is caused by the accumulation of Lewy body peptides. Differing dementia pathologies carry distinguishing symptoms and treatment methods; hence we cannot disregard the importance of correct identification of the pathology.Currently, the correct pathology can be identified through positron emission tomography (PET), which is expensive and results in exposure to harmful ionizing radiation. Therefore, the present study aims to overcome the disadvantages of current screening methods and distinguish the quantitative electroencephalography (QEEG) of two dementia pathologies through machine learning.MethodEEG data employed in the present study were recorded at the 19 electrode locations defined by the international 10‐20 system, in eyes‐closed resting‐state condition. The acquired data were then re‐referenced to a common average reference. Hereafter, bandpass filtering at 0.1‐45.5Hz, bad epoch rejection and independent component analysis (ICA) was performed to remove noise components from the data. Standardized low resolution brain electromagnetic tomography (sLORETA) was further used for mathematical estimation of source cortical activity, yielding data for 68 cortical regions based on the Desikan‐Killiany atlas.Gamma wave (30‐45Hz) features were excluded from the data due to is vulnerability to electromyography. Further feature reduction was also performed, through the designated criteria on p‐value threshold (0.05) and feature importance threshold determined by shapely values. The final dataset (N = 104; 30 ADD, 74 DLB) was established, where 20% of the data (N =21; 6 ADD, 15 DLB) were randomly selected and excluded as test data for model verification.ResultThe final machine learning model trained and tested through the dataset yielded test accuracy at 85.7% with ADD sensitivity at 83.3%, DLB sensitivity at 86.7%. The 5 fold cross validation accuracy was at 82.1%.ConclusionThe model developed in the present study yielded a promising classification performance, through EEG data which is cheap and harmless to record. Such QEEG‐based classification models may sufficiently replace the PET in future, resolving current disadvantages.
Longitudinal evolution of neuropathological substrates of Alzheimer’s disease in obese patients
Authors: Debatisse Justine , Schindler Louise , Nowell Joseph , Edison Paul 
Published: December 2022

AbstractBackgroundObesity is a metabolic condition with increasing prevalence all over the world. Patients with a body mass index (BMI) higher than 30 are considered obese. Obesity is also a risk factor for chronic morbidities such as type 2 diabetes and may increase the risk of developing Alzheimer's disease (AD). Not much is known about longitudinal changes in Alzheimer’s pathology in these patients. We hypothesised that BMI would predict longitudinal pathological alterations derived from imaging parameters.Method252 participants (n=107 obese non‐diabetic patients with BMI>30) cognitively normal (n=80) and with mild cognitive impairment (n=172) were evaluated from the Alzheimer’s Disease Neuroimaging Initiative (Mean age = 73.6 years, SD = 8.0). All participants had two T1‐weighted MPRAGE MRI scans within two‐years (Mean gap=718 days, SD=107). Regional cortical thickness was measured on T1‐MPRAGE using FreeSurfer v.6.0. Individual gray matter density maps were computed using voxel‐based morphometry (VBM) and regional values were sampled in various cortical regions. Regional cortical thickness and gray matter density are expressed as delta values between baseline and followup (Value followup ‐ Value Baseline). Spearman’s correlation coefficients were calculated to assess the relationship between BMI and imaging biomarkers.ResultIn the whole cohort, we found that BMI is significantly negatively correlated with longitudinal changes in cortical thickness in anterior cingulate (ACC, p=0.0073, r=‐0.17) and posterior cingulate (PCC, p=0.0147, r=‐0.15) cortical thickness. We also found that BMI is significantly negatively correlated with longitudinal changes in frontal (p=0.0184, r=‐0.15), parietal (p=0.0057, r=‐0.18) and temporal (p=0.0141, r=‐0.16) gray matter density. When looking only at MCI participants, we found similar results with even stronger correlations. BMI is significantly negatively correlated with delta anterior cingulate (ACC, p=0.0008, r=‐0.25) and posterior cingulate (PCC, p=0.004, r=‐0.22) cortical thickness.ConclusionIn this cohort of obese and non‐obese participants cognitively normal and with MCI, we found that obesity was associated with a longitudinal 2‐years worsening of macrostructural changes. Higher BMI was associated with a higher reduction of cortical thickness and with a decreased gray matter density in several brain regions.
Colocalization of cortical diffusivity changes and microglial activation in dementia
Authors: Debatisse Justine , Leng Fangda , Brooks David J , Edison Paul 
Published: December 2022

AbstractBackgroundThere is growing evidence that microglial activation plays a crucial role in the early pathological changes reported in Alzheimer’s disease (AD). Multimodal neuroimaging can be used to detect micro‐ and macrostructural alterations together with molecular imaging to quantify metabolism and proteins expression. Diffusivity map, reflecting neuronal integrity, can be derived from diffusion‐weighted imaging, and is thought to be increased in AD following pathological alterations. We hypothesised that changes in diffusivity could be colocalized with microglial activation at different stages of the disease.Method56 patients (mean age=70.4, SD=8.5) cognitively normal (CN, n=15), with mild cognitive impairment (MCI, n=28) and with AD (n=12) were included in this study. All participants underwent diffusion tensor imaging (DTI), amyloid PET ([18F]flutemetamol) and TSPO PET ([11C]PBR28). DTI data were processed with FSL v.6.0 to compute mean diffusivity (MD) maps. Standard uptake ratio (SUVR) values were calculated in SPM12 to identify the amyloid deposition and microglial activation within cortical regions. Amyloid status (Aβ‐positive/Aβ‐negative) was determined from processed data using a cerebellar gray matter ratio with a binding value greater than the CN mean+2SD. Z score maps of MD and [11C]PBR28 SUVR were computed using CN amyloid negative participants as reference. Z score maps were then correlated using SPM8 and the BPM toolbox. Significance was set at a threshold of p<0.05 with an extended threshold of 50 voxels.ResultWe found a significant positive correlation clusters between MD and PBR28‐SUVR Z score maps (higher diffusivity correlated with higher microglial activation) in MCI amyloid negative subjects in the temporal lobe (coordinates: 28 ‐3 ‐32, corrected p<0.001, k=7363). In MCI amyloid positive subjects, we found positive correlation in the parietal lobe (coordinates: ‐47 ‐41 39, corrected p=0.008, k=5712).ConclusionWe found colocalized diffusivity and microglial changes in the early stage of the disease, in the temporal lobe of MCI amyloid negative participants and in later stages of the disease, in the parietal lobe of MCI amyloid positive participants. These results suggest the potential of DTI diffusivity and microglial activation imaging in detecting early neuronal damage occurring in dementia.
Executive functioning and attention in amnestic mild cognitive impairment are related to widespread grey matter integrity
Authors: Lejko Nena , Enriquez‐Geppert Stefanie , Reesink Fransje E , De Deyn Peter Paul , Spikman Jacoba M , Aleman André 
Published: December 2022

AbstractBackgroundMemory problems that are characteristic of amnestic mild cognitive impairment (aMCI) are often accompanied by difficulties in attention and executive functions (EF). Studies have consistently reported atrophy of the hippocampus and its association with memory decline in aMCI. However, it is less clear whether differences in grey matter volume (GMV) also contribute to difficulties in attention and EF. To that end, we investigated whether GMV is related to performance on a series of cognitive tests in people with aMCI.MethodGMV of 29 people with aMCI (mean age = 73.8 ± 7.6 Y, 5 F) was measured with voxel‐based morphometry and regressed against measures of task‐switching (Trail Making B‐A), inhibition (Stroop interference score), working memory (Symbol Digit Modalities), and attention (Vienna Reaction Time S1, S2, S3). Analyses were controlled for age and total intracranial volume. Because of the small sample, we used a lenient significance threshold of p < 0.001 (uncorrected) and cluster size (k) >= 20.ResultBetter task performance was related to higher GMV in widespread cortical regions. Task‐switching was mainly related to GMV in the bilateral insula (k = 924, 456, Z = 4.87, 3.79) and prefrontal areas (k = 846, Z = 4.35). Inhibition was related to volume in left posterior areas (k = 302, 300, Z = 3.93, 3.62), and working memory to volume in left temporal, parietal, and frontal cortices (k = 21‐88, Z = 3.28‐3.67). Attention to visual stimuli was associated with GMV of cerebellar (k = 20‐281, Z = 3.36‐3.59) and parieto‐occipital regions (k = 20‐76, Z = 3.28‐4.17), and attention to auditory stimuli mainly with prefrontal regions (k = 113, 49, Z = 3.96, 3.53). Interestingly, slower reactions to complex stimuli were related to higher GMV in occipital gyri (k = 28, Z = 3.56).ConclusionOur results suggest that EF and attention in people with aMCI are related to integrity of regions beyond those normally implicated in memory decline (e.g. the hippocampus). As investigation into aMCI is crucial for preventing disease progression, our findings should be further investigated by studies with a control group of healthy older adults.