bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of blood‐based epigenetic age acceleration with cognitive impairment and brain outcomes by cardiovascular disease among women
Authors: Shadyab Aladdin H. , McEvoy Linda K. , Horvath Steve , Whitsel Eric A. , Rapp Stephen R. , Espeland Mark A. , Resnick Susan M. , Chen Jiu‐Chiuan , Chen Brian H. , Li Wenjun , Hayden Kathleen M. , Manson JoAnn E. , Bao Wei , Kusters Cynthia D.J. , LaCroix Andrea Z.
Published: December 2021

Background

DNA methylation‐based epigenetic age acceleration (AgeAccel), an aging biomarker indicating faster biological aging relative to chronological age, is associated with many age‐related phenotypes. Blood‐based AgeAccel has been associated with lower cognitive function in several studies. However, there are limited data on associations of AgeAccel with mild cognitive impairment (MCI), dementia, and brain structure. Further, no prior study investigated whether associations of AgeAccel with cognitive and brain outcomes vary by cardiovascular disease (CVD), despite evidence linking vascular pathologies to both AgeAccel and dementia.

Method

Participants were 615 older women from a case‐cohort study of CVD in the Women’s Health Initiative (WHI) Clinical Trials and Observational Study who also enrolled in the WHI Memory Study (n=327 with incident CVD during follow‐up and n=288 without incident CVD). AgeAccel was measured in blood collected at baseline when women had no CVD or cognitive impairment. Women were followed for up to 25 years to determine centrally adjudicated diagnoses of MCI and dementia according to DSM‐IV criteria. A subset (n=90) had structural brain MRI scans performed ∼8 years after baseline. Associations between four measures of AgeAccel (intrinsic AgeAccel [IEAA], extrinsic AgeAccel [EEAA], AgeAccelPheno, and AgeAccelGrim) and risks for MCI/dementia incidence were estimated using Cox proportional hazards regression models. Associations of AgeAccel with brain volumes were examined using multiple linear regression models. Models were stratified by incident CVD status and adjusted for chronological age and other sociodemographic characteristics, lifestyle behaviors, and clinical factors.

Result

Among women who developed CVD, a 1‐year increase in IEAA was associated with higher risk of MCI (HR=1.10; 95% CI=1.03‐1.18), higher risk of combined MCI/dementia (HR=1.07; 95% CI=1.01‐1.13), and higher ventricular volume (in cm3; β=1.04; 95% CI=0.15‐1.94). A 1‐year increase in AgeAccelPheno was associated with higher risk of combined MCI/dementia (HR=1.05; 95% CI=1.00‐1.10) in women with incident CVD. The other two measures of AgeAccel were not associated with any outcome among women with incident CVD. No associations of AgeAccel with cognitive impairment or brain volumes were observed among women who did not develop CVD.

Conclusion

Blood‐based AgeAccel may be a marker of cognitive and brain health among women with underlying vascular pathologies.
Glitter in the darkness? Non‐fibrillar β‐amyloid plaque components significantly impact the β‐amyloid PET signal
Authors: Biechele Gloria , Monasor Laura Sebastian , Wind Karin , Blume Tanja , Parhizkar Samira , Arzberger Thomas , Sacher Christian , Beyer Leonie , Eckenweber Florian , Gildehaus Franz Josef , von Ungern‐Sternberg Barbara , Willem Michael , Bartenstein Peter , Cumming Paul , Rominger Axel , Herms Jochen , Lichtenthaler Stefan F , Haass Christian , Tahirovic Sabina , Brendel Matthias
Published: December 2021

Background

β‐amyloid PET (Aβ‐PET) is an important tool for quantification of amyloidosis in the brain of suspected Alzheimer’s disease (AD) patients and transgenic AD mouse models. Despite the excellent correlation of Aβ‐PET with gold standard immunohistochemical assessments, the relative contributions of fibrillar and non‐fibrillar Aβ components to the in vivo Aβ‐PET signal remain unclear. Thus, we obtained two murine cerebral amyloidosis models that present with distinct Aβ plaque compositions and performed regression analysis between immunohistochemistry and Aβ PET to determine the biochemical contributions to Aβ‐PET signal in vivo.

Method

We investigated groups of AppNL‐G‐F
and APPPS1 mice three, six and 12 months of age by longitudinal [18F]‐florbetaben Aβ‐PET and with immunohistochemical analysis of the fibrillar and total Aβ burdens. We then applied group level inter‐modality regression models using age and genotype matched sets of fibrillar/ non‐fibrillar Aβ data (predictors) and Aβ‐PET results (outcome) for both transgenic models. An independent group of double‐hit APPPS1 mice with dysfunctional microglia due to knock‐out of triggering receptor expression on myeloid cells 2 (Trem2‐/‐) served for validation and evaluation of translational impact.

Result

Neither fibrillar nor non‐fibrillar Aβ content alone sufficed to explain the Aβ‐PET findings in either transgenic AD model (Figure 1). A regression model compiling fibrillar and non‐fibrillar Aβ together with the estimate of individual heterogeneity and age at scanning could explain a 93% of variance of the Aβ‐PET signal (p<0.001; Figure 2). Fibrillar Aβ burden had a 16‐fold higher contribution to the Aβ‐PET signal when compared to non‐fibrillar Aβ. However, given the relatively greater abundance of non‐fibrillar Aβ, we estimate that non‐fibrillar Aβ produced 79±25% of the net in vivo Aβ‐PET signal in AppNL‐G‐F
mice, and 25±12% in the APPPS1 mice. Corresponding results in groups of APPPS1/Trem2‐/‐ and APPPS1/Trem2+/+ mice validated the calculated regression factors and revealed that the altered fibrillarity due to Trem2 knockout impacts the Aβ‐PET signal (Figure 3).

Conclusion

Taken together, the in vivo Aβ‐PET signal derives from the composite of fibrillar and non‐fibrillar Aβ plaque components. While fibrillar Aβ has inherently higher PET tracer binding, the greater abundance of non‐fibrillar Aβ plaque in AD model mice contributes importantly to the PET signal.
Is A+T‐ Alzheimer’s disease or not? A combined CSF and pathology study
Authors: Vromen Ellen M. , de Boer Sterre C.M. , Teunissen Charlotte E. , Rozemuller Annemieke J.M. , Visser Pieter Jelle , Bouwman Femke H. , Tijms Betty M. ,   for the Alzheimer's Disease Neuroimaging Initiative
Published: December 2021

Background

The biological definition of Alzheimer’s disease (AD) based on cerebrospinal fluid (CSF) biomarkers requires both abnormal amyloid (A) and p‐tau (T). However, previous studies showed that up to 32% of individuals with autopsy‐confirmed AD show normal p‐tau levels in CSF in vivo. We studied in two independent cohorts how often individuals with abnormal CSF amyloid and normal p‐tau levels had autopsy‐confirmed AD, and whether these individuals differed in pathological amyloid and tau scores compared to individuals with both abnormal CSF amyloid and p‐tau levels.

Method

In total we included 118 individuals with CSF collection and autopsy from the Amsterdam Dementia Cohort (ADC) (n=75, mean±SD age 62±9 years, 26 (35%) female, 37 (49%) with AD at autopsy) and from ADNI (n=43, mean±SD age 77±7 years, 9 (21%) female, 31 (72%) with AD at autopsy). The mean time between CSF collection and death was 4.5±2.9 years in ADC and 5.1±2.5 years in ADNI. Biomarker profiles were based on dichotomized CSF Aβ42 and p‐tau (ApT) (ADC: Innotest ELISA, ADNI: Elecsys). We assessed the diagnostic accuracy of ApT profiles to detect autopsy‐confirmed AD, and investigated differences in degree of AD neuropathology (based on A‐score for Thal stage, B‐score for Braak stage and C‐score for CERAD score; available in n=36 (48%) ADC individuals and all ADNI individuals) between A+pT+ and A+pT‐ individuals with and without autopsy‐confirmed AD.

Result

All individuals with an A+pT+ profile had AD at autopsy (27/27 in ADC, 23/23 in ADNI). Of individuals with an A+pT‐ profile 7/13 (54%) individuals in ADC and 5/8 A+pT‐ (64%) in ADNI had AD at autopsy. In both cohorts, CSF amyloid status showed the highest accuracy to detect autopsy‐confirmed AD (86‐88%) and the addition of CSF p‐tau status did not improve this (81%‐87%). Comparing individuals on A, B and C neuropathological scores, we observed no differences in degree of AD neuropathology between A+pT‐ and A+pT+ individuals with autopsy‐confirmed AD. A+pT‐ individuals without AD at autopsy had low neuropathological scores.

Conclusion

Our results indicate that a normal p‐tau concentration in the presence of an abnormal amyloid marker does not exclude the presence of AD at autopsy.
Preventing cognitive decline in older adults with mild cognitive impairment using integrated Korean and Western treatments: Initial results
Authors: Lee Ji Hyun , Kim Do Yeun , Kim Man Gi , Kang Hakmook , Shin Hye‐Won , Kim Kwong Ki , Koo Byung Soo
Published: December 2021

Background

Despite the rapidly rising incidence of cognitive impairment, no current medications have proven effective for mild cognitive impairment (MCI). The Integrated dementia clinic at Dongguk University Ilsan Hospital is a nationally recognized Korean‐Western (KW) clinic that provides the integrated medical service via co‐consultation by both neurologists and Korean medicine neuropsychologists.

Objective

The purpose of this study is to examine whether the integrated KW treatment prevents cognitive decline in patients with MCI as measured by changes in the Mini Mental State Examination (MMSE) scores over time.

Method

We retrospectively examined the medical records of patients from May 2015 to June 2020. Of 19 patients who were diagnosed as MCI, twelve MCI patients underwent MMSE at their initial visit to the clinic and at least once more over a period of up to 5 years. These twelve MCI patients divided into two groups: MMSE improvement group vs. MMSE worsening group. We performed the Wilcoxon rank sum test to compare the group achieving improvement in MMSE to decrease in MMSE. We also used the same test to assess the difference in the median length of follow‐up between the two groups.

Result

The prevalence of MCI seen during the review period was 23% (19/82). The baseline characteristics of patients with MCI were shown in Table 1. Among those twelve MCI patients who received integrated KW treatment, 67 % (8/12) of them achieved improvement in MMSE score, which was statistically significant (p‐value = 0.008) compared to those showing a decrease in MMSE score while the length of follow‐up was not significantly different between the two groups. The detailed treatment and changes in MMSE presented in Table 2. For treatment from Western medicine, anticonvulsants were most commonly prescribed, and donepezil was given to 63%. Acupuncture (100%), Moxibustion (100%), and herbal medicine (Gagam‐sohabhwangwon: 6/8,62.5 %) were most prescribed Korean medicine.

Conclusions

This study demonstrates, for the first time, that integrated KW treatment positively affects cognitive function in MCI patients with increases in MMSE scores over time. Further research with a larger data set, needs to be conducted to validate the effectiveness of integrative approach.
Biological activity identification of newly synthesized plumbagin‐lipoic acid conjugate: In vitro and in silico analyses of interaction with acetylcholinesterase and butyrylcholinesterase
Authors: Sakayanathan Penislusshiyan , Loganathan Chitra , Iruthayaraj Ancy , Thayumanavan Palvannan
Published: December 2021

Background

Identification of novel compounds with inhibitory potential against cholinesterases (ChEs) such as acetylcholinesterase (AChE) and butrylcholinesterase (BChE) is considered important strategy to overcome Alzheimer’s disease (AD). The ratio of major globular tetrameric form of AChE (G4) to the minor lower species (G1; monomers and G2; dimers) is decreased in the AD brain. Also, in recent time, the involvement of BChE in progression of AD is recognized. Hence, it is imperative to understand the inhibitory potential of any new compound against BChE and various globular forms of AChE to identify as lead molecule to further explored for AD treatment. Keeping this view, plumbagin (PLU) and lipoic acid (LA) conjugate was synthesized and characterized for its inhibition against AChE and BChE. Further in silico analysis was carried out to get an insight into the interaction of PLU‐LA with the AChE and BChE.

Method

Newly synthesized PLU‐LA conjugate by esterification reaction was characterized by nuclear magnetic resonance spectroscopy (NMR) and mass spectroscopy (MS). The synthesized PLU‐LA was studied for the inhibition against BChE and different molecular forms of AChE. Kinetic analysis was carried out to determine the type of inhibition and inhibitor constant. In silico analysis such as molecular docking and molecular dynamics simulation was carried out using AutoDock and Desmond, respectively.

Result

PLU‐LA synthesized was confirmed by NMR and MS analysis. PLU‐LA inhibited the different molecular forms (G2 and G4) of AChE at micromolar concentration. Kinetic analysis revealed the type of inhibition with low Ki value. Molecular docking analysis revealed the orientation of PLU‐LA at the active site of human AChE and BChE forming interaction with catalytic site amino acid. Molecular dynamics simulation confirmed the stable binding of PLU‐LA with ChEs.

Conclusion

PLU‐LA was synthesized and it’s inhibition against ChEs (AChE and BChE) is understood. In silico analysis highlighted the interaction of PLU‐LA within the active site gorge of the ChEs.
Metabolic syndrome and its effect in dementia: Not your typical memory loss symptoms
Authors: Rodriguez‐Santiago María A. , Jiménez‐Velazquez Ivonne Z. , Maldonado Aleyda , Valentin Eric Miranda , Rivera Horacio Serrano
Published: December 2021

Background

Alzheimer Disease (AD) is the most prevalent form of dementia and the fourth cause of death in Puerto Rico (1). While perhaps there are no treatments for AD, treating modifiable risk factors offers opportunities for prevention of dementia concomitantly. Objective: Primary end point was to find an association between the Metabolic Syndrome (MetS), its individual component, and dementia in a puertorrican population ≥ 65 years old. The secondary end point was to determine which aspects of cognition (Memory, Orientation, Judgment, Community affairs, Home/hobbies and Personal care) are mostly affected.

Method

Cross sectional study with 135 puertorricans ≥ 65 y/o with family history of Early Onset Alzheimer’s Disease (EOAD) who were divided by having MetS (n=47) vs without (n=88). MetS was defined by the National Cholesterol Education Program criteria. Waist circumference measurement was adapted with body mass index ≥ 30 kg/m2. Cognition function was assessed by the Clinical Dementia Rating (CDR) Scale.

Result

Our study revealed no significant statistical association between MetS and moderate dementia in the participants ≥65 years old with family history of EOAD using the CDR Scale. Nonetheless, BMI ≥ 30 kg/m2 in participants with MetS was found to have a significant statistical association, or a trend towards significance, with moderate dementia in 5 out of 6 domains of cognitive performance, as per CDR ≥2: Memory (p= 0.066), Orientation (p=0.21), Community‐interactions (p= 0.040), Judgment (p=0.040) and Hobbies/Home (p= 0.099).

Conclusion

MetS had no statistical association with dementia. However, BMI ≥30 kg/m2 was associated with dementia, mostly affecting orientation, judgment and community tasks, not necessarily the typical memory loss. BMI’s ideal goal needs to be achieved and must be considered a priority in the patient’s primary care management. Clinically, ≥65 y/o puertorrican patients with MetS should be screened for dementia assessing the capacities to perform activities of daily living (ADL’s) not only memory tests, as part of preventive medicine.
Automated prescreening of MCI through deep learning models based on wearable inertial sensors data
Authors: Lee Hyeonil , Shahzad Ahsan , Kim Kiseon
Published: December 2021

Background

During mild cognitive impairment (MCI), a precursor of dementia, often the mild symptoms go unnoticed and are assumed as normal aging signs by elderly people, resulting in late diagnosis and progression to dementia. Recently, several studies investigated the potential of behavioral analysis (ADL assessment, speech analysis etc.) based biomarkers for automated early screening of MCI in home settings. In this work, different deep learning models with walking data have been examined and a 2D‐CNN‐GRU model for automatic diagnosis of MCI based on wearable inertial sensors gait data is proposed.

Method

Sixty subjects (30 Cognitively Normal‐CN and 30 MCI) recruited from National Research Center of Dementia Database, Gwangju, South Korea, underwent comprehensive medical assessments by Chosun hospital doctors. The gait data of each participant was recorded using Shimmer3 inertial sensors under dual‐task settings. The subjects wore 7 shimmer3 sensors at five different body locations, i.e. waist, each thigh, each shank and each foot and performed 5 trials of dual‐task walking. For each experiment trials, the subject walked straight 10m and returned 10m while naming animals, food, fruits, colors and plants. Out of 10 meters, the central 6m stable walking signals were extracted and used for post processing. A total of eight features utilized for deep learning models’ training were, angular velocity along each axis of tri‐axial gyroscope sensor, 3‐axis acceleration data, and the signal vector magnitude of both sensors. In order to learn the gait pattern and time series characteristics for each step, the proposed model was built using six Convolutional Neural Networks‐(CNNs) as input for three Gated Recurrent Units‐(GRUs). Finally, the Leave One subject Out (LOO) Cross‐Validation (CV) was used to report the model's performance.

Result

As compared to individual representative deep learning models, the proposed combined model 2D‐CNN‐GRU proved to be more effective in MCI detection by learning discriminating walk signal characteristics and achieved Sensitivity 83.33%, Accuracy 73.33 % and Specificity 63.34% (Refer Table below).

Conclusion

This research work is the pioneer to investigate deep learning approaches for MCI diagnostic research using inertial sensor data and has the potential to facilitate early detection of MCI in home settings.
Differentiation of dementia with Lewy bodies from normal elderly and Alzheimer’s disease: A DLB Resemblance Atrophy Index
Authors: Liu Wanting , Luo Yishan , Shi Lin , Mok Vincent C.T. ,   National Alzheimer’s Coordinating Center
Published: December 2021

Background

It is sometimes challenging for clinicians to differentiate Dementia with Lewy Bodies (DLB) from normal elderly and Alzheimer’s disease (AD), particularly at the early stage. Since structural difference in multiple brain regions exists between DLB and normal elderly and AD, we aimed to derive a single severity index by combining these multiple MRI features from machine learning and explored the performance of this index (DLB resemblance atrophy index [DLB‐RAI]) in differentiating mild DLB from normal elderly and AD.

Method

61 DLB subjects, 128 AD subjects (mean clinical dementia rating [CDR]=0.852 vs 0.953, p=0.248) and 128 normal controls (NC) with age matched were recruited from the National Alzheimer’s Coordinating Center (NACC) database. All MRI images were postprocessed by an MRI‐based volumetry segmentation tool (AccuBrain®). We analyzed the whole brain structural difference between DLB and AD and NC subgroups. All brain features were utilized to derive a DLB‐RAI. We obtained the optimal cutoff of DLB‐RAI and used 10‐fold cross‐validation method to validate the performance of DLB‐RAI among the three subgroups.

Result

The optimal cutoff of DLB‐RAI in the differentiation of mild DLB from NC and AD was 0.5. The accuracy of DLB‐RAI (≥0.5) in distinguishing DLB from NC was 83.0% (AUC=0.87, Figure 1) with an optimal sensitivity of 0.83 and specificity of 0.86, while the accuracy in differentiating between DLB and AD was 75.10% (AUC=0.77, Figure 2). Amygdala, caudate, putamen, midbrain, medulla and cerebellum were significantly different between DLB and AD (p < 0.05).

Conclusion

DLB‐RAI may aid the differential diagnosis of mild DLB, normal elderly and AD. It may have the potential to be utilized in the clinical practice.
Differential discordance of informant‐ and self‐rated depression and apathy in Black, Hispanic, and White participants in the NACC dataset
Authors: Wyman Mary F. , Burke Shanna L , Paukner Mitchell J. , Umucu Emre , Naseh Mitra , Grudzien Adrienne , Zuelsdorff Megan , Walaszek Art , Chappell Richard J. , Gleason Carey E.
Published: December 2021

Background

Evidence points to racial and ethnic differences in prevalence of neuropsychiatric syndromes (NPS) and interpretation of symptoms by patients and family. Although Alzheimer’s and related dementia (ADRD) research and clinical assessment relies heavily on family report, agreement of self and informant NPS ratings has not been examined in racially diverse cohorts. We used cross‐sectional data from the National Alzheimer’s Coordinating Center (NACC) Uniform Dataset to assess concordance in depression and apathy by race/ethnicity, across cognitive status categories.

Methods

The analytic dataset included 28,165 non‐Hispanic Whites (NHW), 5177 non‐Hispanic Blacks (NHB), and 2943 Hispanics age 45 and older, with baseline cognitive status of normal cognition, mild cognitive impairment (MCI), impaired‐not‐MCI, or dementia. Rates of depression and apathy were calculated using self‐report (Geriatric Depression Scale; GDS‐12 and GDS‐3 subscales) and informant report (Neuropsychiatric Inventory‐Questionnaire (NPI‐Q)). Next, using previously‐validated cutpoints on GDS subscales, concordance of dichotomously‐coded self‐ and informant ratings was examined by race/ethnicity within each cognitive status category.

Results

Hispanics were slightly younger and reported fewer years of formal education, while NHB were more likely to be female (Table 1). Self‐rated apathy and depression levels were higher in Hispanic participants compared to their non‐Hispanic counterparts (p<.0001). NHB and NHW did not differ on apathy scores, but NHW endorsed more depression compared to NHB (p=.0006). Rates of informant‐rated apathy and depression were highest in the Hispanic group and lowest in NHB (all comparisons p<.0001). Across cognitive status categories, informants were more likely to identify depression compared to self‐ratings (Figure 1). However, self‐reported rates of apathy were higher than informant report, except in the dementia group (Figure 2). Overall, discordance of report was statistically significant in all race/ethnic groups. Interestingly, reports of depression in Hispanic participants (n=198) adjudicated as impaired/not MCI were not discordant.

Conclusions

In this diverse NACC cohort, discordance between informant and self‐report of depression and apathy was revealed across race/ethnicity and cognitive status, but the direction of discordance differed by symptom. Future research should examine the impact of other demographic characteristics on observed differences, explore use of different cutpoints, and examine neuropsychiatric symptoms in additional racial groups.
Vision‐based gait analysis system utilizing deep learning algorithms in idiopathic normal‐pressure hydrocephalus patients
Authors: Kang Kyunghun , Jeong Sungmoon , Yu Hosang , Park Jaechan
Published: December 2021

Background

Idiopathic normal‐pressure hydrocephalus (INPH) is a rare neurological disorder. It is an idiopathic adult‐onset syndrome involving nonobstructive enlargement of cerebral ventricles, and it is known by its symptoms of cognitive impairment, gait disturbance, and urinary dysfunction. While INPH can present with any of these classic clinical symptoms in varying degrees, the most frequent and important INPH clinical feature is gait disturbance. A vision‐based gait analysis method using monocular videos was proposed to estimate temporo‐spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision‐based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision‐based gait analysis in INPH patients.

Method

Gait data from 46 patients were simultaneously collected from the vision‐based system utilizing deep learning algorithms and the GAITRite system.

Result

There was a strong correlation in 11 gait parameters between our vision‐based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision‐based gait analysis system were correlated with FAB scores.

Conclusion

Vision‐based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision‐based gait analysis for INPH patients.
Compromised immunity rather than specific microbe may increase vulnerability to Alzheimer’s disease, and vaccines with beneficial off‐target effects may support immunity and prevent AD
Authors: Ukraintseva Svetlana , Yashkin Arseniy Pavlovich , Akushevich Igor , Arbeev Konstantin , Duan Matt , Tropsha Alex , Stallard Eric , Yashin Anatoliy
Published: December 2021

Background

Diverse pathogens (viral, bacterial, fungal) were linked to Alzheimer’s disease (AD) suggesting that compromised immunity may play role in AD. Chronic infections could be signs of weakened immune system favoring AD risk, while vaccines with beneficial off‐target effects might support immunity and protect against AD.

Methods

We estimated effects of diagnosed herpes simplex, zoster (shingles), pneumonia, recurrent mycoses, and vaccinations against shingles and pneumonia, on AD onset in pseudorandomized sample of Health and Retirement Study participants aged 75+.

Results

Herpesviruses and recurrent mycoses increased AD risk by 25%‐42%. Pneumococcal and shingles vaccines received between ages 65‐75 lowered consequent AD risk by 15%‐21%.

Conclusion

Herpes and fungal infections increased AD risk, while vaccinations against shingles and pneumonia were protective, supporting the idea that AD is linked to compromised immunity rather than to specific microbe, and repurposing vaccines with beneficial off‐target effects on immune robustness/resilience might be useful for AD prevention. The study was supported by NIH grants 2RF1AG046860, 1R01AG062623 and 1R01AG063971.
Exploring the unmet needs facing individuals with Alzheimer’s disease and related dementias during the COVID‐19 pandemic
Authors: Benjenk Ivy , Chen Jie
Published: December 2021

Background

Many adults with ADRD rely on support from friends, family members, social service agencies, and their communities to meet their basic needs. It is unclear if COVID‐19 mitigation policies have made it more difficult for individuals with ADRD to receive this support. The objective of this study was to explore the types of unmet needs experienced by adults with ADRD in the United States during the COVID‐19 pandemic as well as the characteristics of adults with ADRD that are experiencing unmet needs.

Method

We performed a cross‐sectional analysis of the Medicare Current Beneficiary Survey (MCBS) summer and fall 2020 COVID‐19 supplemental surveys. The MCBS uses complex survey design to provide a nationally representative sample of Medicare beneficiaries. There were 20,800 survey participants in total and 4.07% have received a diagnosis with ADRD. Logistic regression was used to examine the characteristics of those reporting one or more unmet need.

Result

16.61% Medicare beneficiaries with ADRD reported having one or more unmet needs during the COVID‐19 pandemic (vs. 16.52% Medicare beneficiaries without ADRD, p=0.956). While beneficiaries with and without ADRD had no difference in unmet needs surrounding rent/mortgage, food, supplies, and medications, adults with ADRD had higher rates of unmet need for healthcare services (8.86% vs. 5.77%, p=0.008). Adults with ADRD were more likely to report having an unmet need between March‐June than between July‐November (18.10% vs. 7.84%, p<0.001). Among those with ADRD, individuals with depression (AOR=2.25, p=0.001) and multimorbidity (AOR=1.79, p=0.030) reported significantly higher unmet need, after controlling for age, sex, race/ethnicity, insurance, and geographic location. As compared with individuals without ADRD, individuals with co‐existing ADRD and depression (p<0.001) or co‐existing ADRD and multimorbidity (p<0.001) reported higher likelihood of having unmet needs.

Conclusion

Many adults with ADRD experienced unmet need during the first 4 months of the COVID‐19 pandemic. We also found that the ADRD patients with depression and complex health needs who require greatest number of healthcare services were more likely to voice unmet needs. The residual impact of unmet needs during the early pandemic months and on‐high risk subpopulations is currently unknown.
Associations between longitudinal neuropsychiatric symptoms and biomarkers of beta‐amyloid, tau, neurodegeneration, and cognitive decline
Authors: Johansson Maurits , Stomrud Erik , Johansson Per , Svenningsson Anna L , Palmqvist Sebastian , Janelidze Shorena , van Westen Danielle , Mattsson‐Carlgren Niklas , Hansson Oskar
Published: December 2021

Background

A better understanding of drivers behind neuropsychiatric symptoms (NPS) in neurocognitive diseases, such as Alzheimer’s disease, can potentially guide drug development. Cross‐sectional studies have related apathy, depression and anxiety to beta‐amyloid‐ (Aβ) and tau‐pathology, neurodegeneration as well as cognitive deficits already at preclinical stages. However, longitudinal studies are scarce and often small. We investigated effects of neuropathology and cognition on the longitudinal trajectories of NPS in a large group of cognitively unimpaired subjects (CU).

Method

The sample originated from the Swedish BioFINDER study. CU subjects with at least one NPS assessment during the biennial follow‐up of maximum 8 years were included (n=356). The Apathy Evaluation Scale (AES) and the Hospital Depression and Anxiety Scale (HADS) assessed apathy as well as anxious and depressive symptoms, respectively. The Mini Mental State Examination (MMSE) and a modified Preclinical Alzheimer Cognitive Composite (mPACC) measured cognition longitudinally. Plasma P‐tau217, CSF Aβ42/Aβ40 ratio and CSF neurofilament light (NfL) were quantified at baseline using immunoassays. Magnetic Resonance Imaging quantified white matter lesions (WML). Subject‐specific cognitive change per year (slope) was obtained by linear regression models. Linear mixed effect models were used to test associations between longitudinal NPS and other measures (including longitudinal cognition).

Results

Subjects with lower levels of CSF Aβ42/40 (β=‐0.060, p=0.01) or higher plasma P‐tau217 (β=0.253, p=0.015) at study start demonstrated accelerated levels of apathy over time. On a trend level, a high load of WML predicted over time high levels of apathy (β=0.183, p=0.053). Increased levels of longitudinal anxiety were predicted by the interaction between time and lower CSF Aβ42/40 (β=‐0.283, p=0.005) or higher CSF NfL (β=0.054, p=0.024). Furthermore, more rapid decline in mPACC was associated with more rapid progression of apathy (β=‐0.478, p=<0.001) and anxiety (β=‐0.066, p=<0.023). Similar results were obtained for longitudinal associations between MMSE and apathy (β=‐0.673, p=<0.001).

Conclusion

These results suggests that the longitudinal trajectories of apathy and anxiety already in CU subjects are related to key hallmarks of Alzheimer´s disease, including Aβ‐ or tau‐pathology at baseline, and further are parallel with cognitive decline over time.
The auditory mismatch negativity reflects accelerated aging in adults with Down’s syndrome
Authors: Avancini Chiara ,   Cambridge Intellectual and Developmental Disabilities Research Group
Published: December 2021

Background

Alzheimer’s disease (AD) affects nearly the entirety of the Down’s Syndrome population (DS) with a prevalence of 75% by the time they reach their 60s. The early symptoms of dementia in DS may reflect frontal lobe vulnerability to amyloid deposition. The Mismatch Negativity (MMN) is a frontocentral event‐related potential (ERP) component elicited by auditory violations. Predictive coding postulates that the brain creates neural predictions about future events by compiling statistical regularities from incoming stimuli. Within the MMN network, the MMN results from the failure of higher frontotemporal nodes to predict the incoming sensory input, which results in changes of coupling between the sensory and the frontotemporal cortices. In the typically developing (TD) population, the MMN response has been found to decrease with age. With a cross‐sectional and longitudinal study, we assessed whether the MMN reflects premature neurological aging in DS and whether it predicts cognitive decline one year later.

Method

The MMN ERP and the MMN Global‐Field Power (GFP) were elicited with an auditory global‐local task and recorded with high‐density EEG in 36 DS adults and in 39 age‐matched TD controls. Cognitive impairment was measured in DS with the neuropsychological battery CAMCOG‐DS. One year later, 34 adults with DS underwent a follow‐up administration of the CAMCOG‐DS. Linear models tested whether the MMN predicted age in the two groups. Linear and curvilinear models tested whether the MMN obtained at the first time‐point was predictive of cognitive decline one year later as measured by changes in CAMCOG‐DS scores.

Result

In the cross sectional phase, an interaction between the MMN amplitude, both expressed in ERP and GFP, and group was found. In particular, MMN amplitude predicted age in DS but not in TD. On the other hand, neither amplitude nor latency of the MMN predicted cognitive decline one year later.

Conclusion

Our study shows that in our cohort the MMN amplitude reflects accelerated aging in DS but not in TD, suggesting that MMN amplitude may be a sensitive marker of early onset AD in the DS population. On the other hand, neither MMN amplitude or latency predicts cognitive decline one year later.
Evaluating digital device technology in Alzheimer’s disease via artificial intelligence
Authors: Sood Meemansa , Harms Robbert , Bügler Maximilian , Tarnanas Ioannis , Coello Neva , Fröhlich Holger
Published: December 2021

Background

Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre‐symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR‐AD (www.radar‐ad.org) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living.

Method

An example of a panel of digital technology RADAR‐AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re‐ran VAMBN.

Result

Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub‐item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms.

Conclusion

Our results indicate that there is a significant dependency between digital readouts and clinical scores such as MMSE and FAQ. Therefore, DM’s may have the potential to act as a vital measure in the diagnosis of AD in a pre‐symptomatic stage. Our next steps will focus on evaluating the diagnostic benefit of DMs compared to the questionnaire‐based FAQ.
Cost of illness of apathy in Alzheimer’s disease
Authors: Kruse Christopher , Maier Franziska , Bürger Katharina , Dodel Richard , Fellgiebel Andreas , Frölich Lutz , Klöppel Stefan , Kornhuber Johannes , Laske Christoph , Peters Oliver , Priller Josef , Schneider Anja , Spottke Annika , Teipel Stefan J , Arnim Christine , Wiltfang Jens , Jessen Frank
Published: December 2021

Background

Within a 12‐week multicentre randomised controlled trial, standardised data on resource use of patients and their caregivers was collected using the Resource Utilization in Dementia (RUD) instrument. The aim of this study is to evaluate the insufficiently known influence of apathy on resource utilisation in Alzheimer's patients and their caregivers.

Method

107 patients were included in the study. Costs were collected from a societal perspective over a four‐week period and examined depending on the Apathy Evaluation Scale‐Clinician Version (AES‐C). Using the Mann‐Whitney U and Kruskal‐Wallis tests, potential cost drivers were identified in a univariate analysis. Afterwards potential cost drivers for total costs, patient costs and caregiver costs were analysed using a generalised linear model (GLM) with a γ‐distributed dependent variable and log link function.

Result

The mean total costs were €3,069. The mean patient costs were €359 and were mainly related to professional services, hospitalisation and medication. The mean caregiver costs were €2,711 and were largely caused by informal care with a share of €2,509. Within the sensitivity analysis, informal care costs decreased to €1,742 and €903, respectively. The multiple regression analysis showed that Apathy, represented by the AES‐C, is a cost driving factor for total costs and caregiver costs, however not for patient costs.

Conclusion

Previous studies have often presented costs as a function of cognitive impairment. The present data show that apathy also has a significant influence on resource utilisation. At the same time, however, it was also shown that this influence was mainly caused by the increased resource use of the caregivers and that the resource use of the patients did not show significant changes.
Cerebrospinal fluid neurogranin in non‐amnestic and amnestic Alzheimer’s disease
Authors: Cousins Katheryn A.Q. , Burke Sarah E. , Ballinger Samantha , Irwin David J. , Shaw Les M. , Wolk David A. , Trojanowski John Q. , Lee Eddie B. , Grossman Murray
Published: December 2021

Background

Neurogranin (Ng) is a postsynaptic protein involved in long‐term potentiation and is largely expressed in temporal, parietal, and limbic regions (Lista et al., 2017; Kvartsberg et al., 2019). Increased Ng in cerebrospinal fluid (CSF) follows synaptic dysfunction, and is thought to be a marker specific to Alzheimer’s disease (AD) pathology (Portelius et al., 2018; Clarke et al., 2019). Even so, non‐amnestic variants of AD (naAD) show altered CSF biomarker levels compared to amnestic AD (aAD) (Wellington et al., 2018; Cousins et al., 2020). Here, we compared CSF Ng across aAD, naAD, and frontotemporal dementia (FTD). Neuroimaging tested if Ng differences across naAD and aAD are in part due to regional differences in atrophy.

Method

Patient inclusion criteria were available CSF Ng and Aβ1‐42, and either a non‐amnestic (naAD, FTD) or amnestic (aAD) clinical diagnosis. naAD patients (n=34) had likely AD pathology, determined by autopsy (n=3) or CSF Aβ1‐42≤192 pg/mL (Shaw et al., 2009). FTD patients (n=60) had likely non‐AD pathology, determined by autopsy (n=21) or CSF Aβ1‐42>192 pg/mL. aAD patients (n=62) had autopsy‐confirmed AD pathology and an amnestic phenotype. Neuroimaging was available for 29 aAD, 24 naAD, and 50 FTD. Mindboggle Atlas defined regions (Klein and Tourville, 2012) and grey matter volume (GMV) estimated atrophic severity. Regressions 1.) identified regions associated with Ng and 2.) compared atrophy across aAD and naAD; age at MRI, time between MRI and CSF, and sex were covariates. We report regions that were significant in both analyses (p<0.01).

Result

An ANCOVA (covarying for age at CSF and sex) showed that CSF Ng differed by group (p=4.5x106). Post‐hoc pairwise comparisons showed aAD had higher Ng than naAD (p=0.016) and FTD (p=2.6x106), with no difference between naAD and FTD (p=0.23). Neuroimaging showed increased Ng correlated with lower GMV in left fusiform (p=0.0062), precuneus (p=0.0076), and superior parietal lobule (p=0.0073), and naAD had higher GMV than aAD in these three regions (all p<0.0007).

Conclusion

Despite likely AD pathology, naAD patients had significantly lower Ng than aAD, which may be explained in part by regional expression of Ng and differences in disease distribution between naAD and aAD.
Template impact in nucleus basalis segmentation
Authors: Conklin Chris , Scott David , Bracoud Luc , Vuksanovic Vesna , Wischik Claude M , Suhy Joyce , Staff Roger ,   the Alzheimer's Disease Neuroimaging Initiative
Published: December 2021

Background

The Nucleus Basalis of Meynert (NB), located within the basal forebrain sublenticular region, is a cholinergic center with neurons providing cholinergic afferents to the entire neocortex. As posterior NB provides cholinergic innervation to the temporal pole and superior temporal cortex, this correlates well with memory loss and language impairment in AD.

NB volume can serve as a potential emerging biomarker using volumetric MRI. One method consists in using DARTEL normalization to register images to a basal forebrain atlas. Our aim is to study the influence of template content and determine what is sufficient in practice for accurate evaluation, in the context of an on‐going multi‐center trial where the final dataset would not be available until study end.

Methods

96 good quality 3D T1‐weighted datasets were randomly chosen among screening scans of an on‐going AD trial (TRx‐237‐039/NCT03446001), with equal distribution of datasets from GE, Philips and Siemens, 1.5T/3T scanners (16 each). Separate DARTEL templates were created from the full list of 96 subjects, from subsets of 24, 48 and 72 subjects, and from independent sets of 20 and 96 random ADNI AD subjects to assess comparability.

Processing followed a standard SPM12 DARTEL approach. 3DT1 images were initially segmented to create gray/white matter tissue maps and subsequently registered to create a template image. Derived flow fields were applied to warp and smooth all subjects to MNI space while preserving regional signal. Final subject maps were resliced to match a basal forebrain ROI.

Volumes were compared with two‐sided paired t‐tests. Correlations across volumes were assessed with Pearson’s coefficient. Mean absolute percent difference to full 96‐template was calculated.

Results

Volumes obtained from either template were all highly and significantly correlated to each other (r>0.99, p<0.001). T‐tests showed significant differences (p<0.001), respectively resulting in mean absolute differences to the full template of 2.3% for the 24‐template, 0.8% for the ADNI‐20‐template, 1.6% for the ADNI‐96‐template and 0.3% for all others.

Conclusion

Template choice does impact measurements significantly. Choosing a sufficiently large sample is important when building the template, to avoid biasing overall results. A common template is essential for proper comparison of separate cohorts with this method.
An MRI multi‐scanner neuroimaging data harmonization study using RAVEL and ComBat
Authors: Torbati Mahbaneh Eshaghzadeh , Minhas Davneet S , Ahmad Ghasan E , O'Connor Erin E , John Muschelli , Laymon Charles M , Zixi Yang , Cohen Ann D , Aizenstein Howard J , Klunk William E , Christian Bradley T , Crainiceanu Ciprian , Hwang Seong Jae , Tudorascu Dana L
Published: December 2021

Background

Large‐scale multi‐site neuroimaging studies provide higher power for statistical analyses. However, these aggregated datasets are susceptible to unwanted variability resulting from scanner and acquisition protocol differences. To address this problem, a group of intensity normalization and harmonization methods have been developed recently. This study used a paired 1.5T‐3T MRI dataset to evaluate an intensity normalization procedure, RAVEL (Fortin et al., 2016), a data harmonization method, ComBat (Johnson et al., 2007), and a pipeline combining the two.

Method

Sixteen healthy elderly participants (median 77.5 years (range=70‐79), 25% (n=4) males) underwent 1.5T and 3T T1‐weighted MRI, three months apart. These images (RAW) were used as input for RAVEL (an intensity normalization technique) and/or ComBat (a harmonization procedure) to characterize the cross‐scanner harmonization of FreeSurfer‐extracted summary measures (cortical thickness and/or volume) relevant to Alzheimer’s disease (Zhang et al., 2001). In setting‐1 (RAVEL), measures were extracted from RAVEL‐normalized images. In setting‐2 (ComBat), measures were extracted from RAW images and then harmonized using ComBat. In setting‐3 (RAVEL‐ComBat), ComBat was applied to the measures resulted from setting‐1. Paired t‐tests were used to evaluate cross‐scanner differences of measures in each of these sets. The measures with statistically significant differences were considered with potential scanner differences and decreasing such differences to lose significance, was considered to achieve harmonization.

Result

Table 1 shows the means of the differences and their 95% CI for statistical significance, indicating 14 measures with potential scanner differences in RAW (blue/yellow under RAW). Out of these 14 measures, RAVEL‐Combat harmonized 13 measures (green under RAVEL‐ComBat) compared to 8 and 9 harmonized measures by RAVEL and ComBat, respectively. For example, for the cortical thickness of the Inferior Temporal Left, the mean of differences for RAW was 0.25mm, 95%CI:(0.16mm, 0.34mm), indicating a statistically significant difference (blue). This difference has been reduced to 0.08mm, 95%CI (‐0.05mm, 0.21mm) using RAVEL‐ComBat (green) and no longer statistically significant. We observed that, both, RAVEL‐ComBat and RAVEL slightly increased the variability of the differences.

Conclusion

A combination of intensity normalization (RAVEL) and data harmonization (ComBat) performs best in terms of harmonizing data across 1.5T and 3T MRI within the same cohort.