bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative accuracy remains after shortening of dynamic [18F]flortaucipir PET protocol
Authors: Tuncel Hayel , Visser Denise , Yaqub Maqsood , Timmers Tessa , Wolters Emma E. , Ossenkoppele Rik , Van Berckel Bart N.M. , Boellaard Ronald , Golla Sandeep S.V. 
Published: December 2020

AbstractBackgroundTau pathology can be assessed by [18F]flortaucipir positron emission tomography (PET). Dynamic PET protocols allow for accurate quantification of [18F]flortaucipir specific binding. However, dynamic acquisitions can be challenging or burdensome to patients given the long required scan duration of 130 min, especially in persons with cognitive impairment due to Alzheimer’s disease (AD). Therefore, the current study assessed the effect of shorter scan protocols for [18F]flortaucipir PET on its quantitative accuracy.MethodFor this study, two study samples were used: 10 Alzheimer disease (AD) patients and 10 healthy controls (HC) with arterial blood sampling, and 8 AD patients and 6 HCs with a test‐retest scan protocol but without arterial sampling. All subjects underwent a 130‐minute dynamic [18F]flortaucipir PET scan with a 20 min break after 60 minutes. First, the second part of the scan was shortened to 80‐120, 80‐110 and 80‐100 minutes p.i.. Second, the first part of the scan was shortened to 0‐50, 0‐40, and 0‐30 min p.i.. To fill the gap of missing time frames in the reference region (i.e. grey matter cerebellum) time activity curves, four different interpolation methods were tested: 2T4k_VB model using population‐based input‐function (2T4k_POP‐IP), cubic, linear and exponential interpolations. Regional BPND values estimated using SRTM and/or RPM and different scan protocols were compared to the respective values estimated using the original scan acquisition. In addition, test‐retest repeatability (TRT) of the RPM BPND values estimated using the optimal shortest scan duration was assessed.ResultRPM BPND values obtained using a shortened second part of the PET scan had negligible effect on the accuracy of parameter estimates (Table 1). The RPM BPND values using 0‐30/80‐100 min with 2T4k_POP‐IP interpolation had the best correlation with original DVR‐1, SRTM and RPM BPND (r2=0.95, slope=0.91; r2=0.97, slope=0.88; r2=0.99, slope=0.96). The TRT remained similar using the shortest scan duration (Figure 1).ConclusionThe current study demonstrated that accurate [18F]flortaucipir quantification can be obtained using a scan protocol of 0‐30/80‐100 min p.i.. With this reduction in total scan duration the scan will be less burdensome while remaining quantitatively accurate.
Complex morphometric effects of sex and aging on subcortical brain structures (N = 9,872)
Authors: Ching Christopher , Abaryan Zvart , Zhu Alyssa , Bright Joanna , Jahanshad Neda , Thompson Paul M 
Published: December 2020

AbstractBackgroundMuch remains to be understood about subcortical brain alterations across the lifespan and how such changes are related to sex and genetic risk for dementia. Here we analyze the effect of sex, age and APOE genotype on subcortical volume and high‐resolution morphometry in a large population‐based sample. We hypothesized that shape analysis would reveal complex age and sex effects not revealed by gross volumetric analysis and that each copy of the APOE 4 alele would be associated with smaller hippocampal volume.MethodT1‐weighted brain MRI data (N=9,872) from the UK Biobank were processed using FreeSurfer v5.3 to derive bilateral hippocampus, thalamus, putamen, pallidum, amygdala, caudate, nucleus accumbens, lateral ventricle and intracranial volumes (ICV). The ENIGMA Shape Analysis Pipeline was used to compute local thickness and surface area (SA) metrics for up to 2,502 vertices along each structure’s surface (Figure 1). Linear regression was used to model age (44‐79 yrs), sex, age‐by‐sex interactions, and APOE genotype (Table 1) associations with gross volumes and shape metrics while adjusting for age2, ICV, years of education and body mass index, and adjusting for multiple comparisons.ResultOlder individuals had generally lower gross volumes and larger lateral ventricles (Table 2). Males tended to have larger volumes compared to females (Table 2), though shape analysis revealed subregions that were larger in females (Figure 2). In the full cohort, a significant age‐by‐sex interaction showed males had lower volumes with increasing age. However, in individuals over 60, this interaction was significantly attenuated (Table 2). Shape analysis revealed age‐by‐sex interactions to be more complex, with females displaying subregions of lower thickness and SA with increasing age. No effect of APOE genotype nor interaction between age, sex and genotype were observed, possibly due to small samples of APOE e2/2 and e4/4 genotypes.ConclusionHere we show that age and sex effects on subcortical structures may be more complex when modeled using local shape morphometric features. While males tended to lose volume faster than females over the full age span, these effects were weaker at more advanced ages (>60), indicating potentially complex and differential brain aging patterns in men and women.
Comparison of fasting blood sugar, HbA1c, vitamin B12, homocysteine and vitamin D in two parallel, aging study cohorts from rural and urban India: SANSCOG study and TLSA
Authors: Sundarakumar Jonas , Karunakaran Smitha , ME Sumathi , R Kalyani , Sivakumar Palanimuthu T. , Rao Naren P. , Ravindranath Vijayalakshmi ,   SANSCOG and TLSA Investigators
Published: December 2020

AbstractBackgroundResearch on identification of risk factors and biomarkers for dementia to facilitate early intervention and prevention has emerged as an important research priority, globally. Previously known risk factors of dementia, mostly from research on the western population could potentially differ vastly in occurrence in the Indian population.MethodSrinivaspura Aging, Neuro Senescence and COGnition (SANSCOG) study and Tata Longitudinal Study on Aging (TLSA) are parallel, prospective, community‐based, cohort studies in India – rural and urban, respectively – for comprehensive evaluation of risk and protective factors associated with cognitive changes due to normal ageing, dementia and other related disorders. Both the study cohorts comprise of cognitively healthy individuals aged ≥ 45 years. However, they have distinct population characteristics. Participants in the SANSCOG study are recruited from the villages of Srinivaspur Taluk in Kolar District in southern India, through an area sampling strategy, whereas, those in TLSA are recruited through convenience sampling, from urban Bangalore, India. In addition to the detailed clinical and neurocognitive evaluations, fasting peripheral blood is collected in serum, EDTA and fluoride vacutainers and sent to a laboratory for biochemical measurements. Preliminary analysis was been done on biochemical parameters such as fasting blood sugar (FBS), HbA1c, vitamin B12, homocysteine and vitamin D.ResultSignificantly higher percentage of the urban cohort had fasting blood glucose in the diabetic and prediabetic ranges. HbA1c values were also substantially higher in the urban cohort, which also had a much higher percentage of persons with abnormal total cholesterol (≥ 200 mg/dL). However, Vitamin B12 deficiency (< 200 pg/mL) was significantly more in the rural than in the urban cohort. Conversely, Vitamin D deficiency (< 20 ng/mL) was less in the rural than in the urban cohort. The urban cohort had a significantly higher percentage of persons with abnormally high serum homocysteine levels (≥ 15 µmol/L).ConclusionThe two study cohorts differed significantly in their biochemical measurements – the urban cohort having a higher proportion of participants with abnormal values of FBS and HbA1c, total cholesterol and serum homocysteine. Vitamin B12 deficiency was seen more in the rural cohort and vitamin D deficiency more in the urban cohort.
Longitudinal hippocampal atrophy is associated with an amyloid‐independent entorhinal tauopathy and an amyloid‐dependent neocortical tauopathy
Authors: Hanseeuw Bernard , Jacobs Heidi I.L. , Schultz Aaron P. , Buckley Rachel F. , Properzi Michael J. , Becker Alex , Farrell Michelle E. , Scott Matthew R. , Hampton Olivia L. , Sanchez Justin S. , Quiroz Yakeel T. , Chhatwal Jasmeer P. , Price Julie C. , Sperling Reisa A. , Johnson Keith A. 
Published: December 2020

AbstractBackgroundHippocampal atrophy is observed in Alzheimer’s disease (AD), but also in older adults with no evidence of amyloid‐β (Aβ) plaques. To understand the pathophysiology of hippocampal volume (HV) loss, we investigated the associations between longitudinal HV, age, genotype, Aβ, and tau in clinically normal (CN) participants from the Harvard Aging Brain Study.MethodSerial MRI (HV [1.3‐7.0y], PiB‐PET (Aβ, [1.9‐8.5y]), and Flortaucipir‐PET (tau, [0.8‐6.0y]) measures were obtained from 128CN participants (72 [56%] females, 38 [30%] e4 carriers, median age at baseline: 71.3, follow‐up duration: 5.1y). Participants had a median of 3MRIs [2‐5], 3PiB‐PET [2‐5], and 2Flortaucipir‐PET [2‐4]. Longitudinal HVs were processed using Freesurfer v.6 and adjusted for intracranial volume. PiB was measured in a neocortical aggregate, Flortaucipir in inferior temporal (IT) and entorhinal cortex (EC). PET data were expressed as PVC‐SUVr scaled to subcortical white matter. We predicted imaging data over time with random intercept and slope in linear mixed‐models and extracted PiB, FTP, and HV slopes for each subject. Baseline PET and slope data were entered in age‐adjusted linear regressions to evaluate their associations with HV slope.ResultFaster HV loss was observed at older ages (Figure 1), and marginally in e4 carriers (Table, #1‐2). It was also associated with higher baseline PiB levels (#3). The PiB association was stronger than the one of e4 status (#3). HV slope did not correlate with PiB slope (#4). HV loss was associated with baseline EC‐FTP (#5) and baseline IT‐FTP (#6). The association between HV loss and EC‐FTP was also observed in the low‐PiB only (#7), but not the one with IT‐FTP (#8). IT‐FTP, but not EC‐FTP, interacted with baseline PiB to predict HV slope (Figure 2‐3). FTP slopes measures were associated with HV slope, above and beyond the baseline association (#9‐10). Altogether PET data explained 40% of the variance in HV slope; but faster HV loss was still associated with age after taking PET measures into account.ConclusionIn preclinical AD, hippocampal atrophy is associated with an Aβ‐independent entorhinal tau accumulation and an Aβ‐dependent neocortical tau accumulation. It is also likely associated with pathological processes that our biomarkers did not measure.
Dextromethorphan for the treatment of agitation in dementia: A pilot study
Authors: Hwang Tzung‐Jeng , Chen Jiahn‐Jyh , Lin Yi‐Ting , Chan Hung‐Yu 
Published: December 2020

AbstractBackgroundAgitation is highly prevalent in patients with dementia and off‐label use of antipsychotic agents for agitation is common. But antipsychotic agents are clearly associated with increased adverse effects. A recent study found dextromethorphan (DXM) and low dose quinidine may be effective for the management of agitation in patients with Alzheimer disease (AD).MethodsThis is an open‐label, 10‐week study. Eligible dementia patients (AD or VaD) were enrolled. These patients received DXM 60 mg/day from Day 1 through Day 7. If the symptoms of agitation were not reduced, the dose might be gradually increased to 240 mg/day. The primary endpoint was score change in agitation domain of the Neuropsychiatric Inventory (NPI), and secondary endpoints include change on NPI total score and subscores, CGI, and MMSE.ResultsA total of 16 patients were recruited. The mean (SD) of age was 79.3 (9.9) years, with male to female ratio 1:1. The mean (SD) of MMSE was 9.7 (9.7). At baseline, CGI‐Severity for agitation was 4 to 6 (mean=4.9), The mean and SD of behavioral disturbances were: 8.4 (3.5) for NPI agitation score, 8.1 (4.3) for NPI irritability score, 7.6 (4.8) for NPI aberrant motor behavior, and 58.8 (33.7) for NPI total score. At the endpoint, the mean dose of DXM was 150 mg/day. There was a significant decrease of the score of behavioral disturbance: 3.8 (3.0) for NPI agitation score (p<0.001), 4.2 (3.9) for NPI irritability score (p<0.001), 2.3 (3.9) for NPI aberrant motor behavior (p<0.036), and 24.4 (19.6) for NPI total score (p<0.001). The CGI‐I on agitation revealed 69% (n=11) with much/very much improved, 25% (n=4) minimal improved, and 6% (n=1) minimally worse. There were some mild to moderate adverse effects reported, including malaise, anemia, drowsiness, skin itching, and increased appetite. No severe adverse effects were found.ConclusionsThe results of this preliminary study suggest DXM 60‐240 mg/day seem quite efficacious for the treatment of agitation in dementia and without severe adverse reactions. It may be suitable for short‐term use for control of agitation in elderly dementia patients.
Multimodal modelling of the heterogeneity of Alzheimer’s disease
Authors: Garcia Mar Estarellas , Young Alexandra L. , Schott Jonathan M. , Alexander Daniel C. ,   Centre for Medical Image Computing
Published: December 2020

AbstractBackgroundData‐driven modelling techniques, such as Subtype and Stage Inference (SuStaIn) (Young, et al., 2018), have been developed to disentangle the temporal and phenotypic heterogeneity of neurodegenerative diseases such as Alzheimer’s disease (AD) from widely available cross‐sectional datasets such as ADNI. This study builds on early work with SuStaIn by using multimodal data and missing‐data modelling to give a more detailed picture of the landscape of sporadic AD subtypes.MethodSuStaIn was applied to cross‐sectional cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and positron emission tomography (PET) metrics, and cognitive scores from ADNI to identify multimodal disease subtypes with distinct progression patterns. The dataset consists of 789 subjects that passed quality control (182 cognitively normal (CN), 86 subjective memory concern (SMC), 241 early mild cognitive impairment (MCI), 163 late MCI and 117 AD). Amyloid‐negative (CSF Abeta < 192pg/ml) subjects were chosen as control subjects. A K‐Nearest Neighbour (KNN) algorithm was used to impute missing values (3% of the data), allowing for subtyping and staging of the full dataset.ResultFigure 1 visualises the subtype progression patterns, with the precise model output being shown in Figure 2. Four different subtypes can be inferred from the data: 1) Typical AD with late tau: First changes are seen in CSF and PET measures of b‐amyloid, followed by cognitive scores impairment and finally, brain atrophy. 2) Typical AD: Changes in b‐amyloid closely followed by an increase in tau, both using CSF and PET based biomarkers. Cognitive scores and subcortical areas are affected next. 3) Cortical AD: Cortical atrophy is followed by subcortical atrophy and then increase in CSF and PET amyloid. 4) Cognitive decline with hippocampal atrophy: Mainly defined by a decrease in cognitive tests performance, followed by atrophy of the hippocampus and the amygdala.ConclusionThis study provides a deeper insight into the heterogeneity of AD, revealing four subtypes with differing trajectories of biomarker abnormalities. Unravelling this heterogeneity will be a critical step in developing personalised medicine, allowing for improved power for clinical trials and better information for patients.
CT‐based brain segmentation and volumetry using deep learning methods
Authors: Srikrishna Meera , Pereira Joana B , Heckemann Rolf A , Volpe Giovanni , Zettergren Anna , Kern Silke , Westman Eric , Skoog Ingmar , Schöll Michael 
Published: December 2020

AbstractBackgroundComputed tomography (CT) is the most commonly used brain examination tool for the initial assessment of neurodegenerative diseases such as dementia disorders. It is widely available, affordable and provides short scan times, however, it is mainly used for visual assessment of brain integrity and exclusion of co‐pathologies while magnetic resonance imaging (MRI) is the preferred modality for the extraction of regional brain volumetric measures, obtained by brain segmentation into tissue classes and anatomical regions. We developed an automated, deep learning‐based approach to segment CT scans into intracranial volume (ICV), grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF).Method139 CT and T1‐weighted MRI scans acquired on the same day in participants of the Gothenburg H70 Birth Cohort Study (70% female) were included in this study. SPM12 was used to extract GM, WM and CSF tissue class labels and Pincram for creating ICV masks from the MRIs. These labels were used as training input and as the reference criteria for subsequent volumetric and similarity comparisons. In a training subcohort (n=120), CT scans were co‐registered to their corresponding MRI. A U‐Net deep learning model was developed using the Keras software package which underwent two training sessions: 1. Predict ICVs in CT and extract brain; 2. Predict GM, WM and CSF volumes with extracted CT images as input, preserving the predictions in individual space. MRI labels were incorporated during the training sessions including 120 datasets.ResultNineteen validation datasets which were not included in the training were used for the prediction process without MRI intervention. Continuous Dice scores between ICV, GM, WM and CSF predictions from the model in the validation datasets and independently MRI‐derived labels were 0.97, 0.79, 0.81 and 0.72 with a volumetric Pearson correlation coefficient, r of 0.93, 0.84, 0.73 and 0.71, respectively.ConclusionOur pilot study developed a deep learning model that enables the automated segmentation and volumetric measure extraction from cranial CT. With further optimization, training on larger and more diverse datasets, we aim at improving the model to execute predictions comparable to MRI‐based approaches, thus, developing a diagnostic support tool based on routinely acquired CT scans.
Abnormalities of cortical neural synchronization mechanisms in subjects with mild cognitive impairment due to Alzheimer's disease and epileptiform‐like signatures
Authors: Percio Claudio Del , Noce Giuseppe , Bonaventura Carlo Di , Lizio Roberta , Soricelli Andrea , Ferri Raffaele , Nobili Flavio Mariano , Famà Francesco , Palma Eleonora , Cifelli Pierangelo , Marizzoni Moira , Frisoni Giovanni B , Babiloni Claudio 
Published: December 2020

AbstractBackgroundPrevious evidence has shown that Alzheimer’s disease (AD) patients exhibited an increased risk of overt epileptic seizures or subclinical, non‐convulsive, epileptiform‐like electroencephalographic (EEG) signatures (i.e., spike‐sharp wave discharges, giant spikes, etc.) due to temporal and frontal lobe dysfunctions and aberrant cortical neural synchronization. In the present study, cortical sources of resting state eyes‐closed EEG (rsEEG) rhythms were estimated in patients with amnesic mild cognitive impairment due to AD (ADMCI), using normal elderly (Nold) and AD patients with dementia (ADD) as controls. The hypothesis was that rsEEG sources may be more abnormal in ADMCI with than without epileptiform‐like EEG signatures.MethodClinical and rsEEG data in 35 ADD, 50 ADMCI, and 35 Nold subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini Mental State Evaluation (MMSE) score was matched between the ADMCI with and without epileptiform‐like EEG signatures (i.e., spike‐sharp wave discharges, giant spikes, etc.). No subject had received a clinical diagnosis of epilepsy. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources.ResultFrontal and temporal delta source activities were more abnormal in the ADMCI patients with epileptiform‐like EEG signatures (N = 13; 26%; ADMCI‐ELES) and ADD than the control ADMCI patients (N= 37; ADMCI‐noELES; Figure 1). This effect may not depend on sleep onset or epileptiform‐like EEG signatures as “biological artifacts” in the EEG signal, because of the present source analysis was performed from rsEEG epochs free from those signatures.ConclusionThe present findings suggest that in MCI patients, AD neuropathology may derange neurophysiological low‐frequency (i.e. delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance, and such derangement may be more pronounced in frontal and temporal cortical regions in ADMCI patients characterized by epileptiform EEG signatures. Future investigations should cross‐validate the present findings by a longitudinal prospective study testing. We predict that ADMCI patients with those delta source and epileptiform‐like EEG signatures may develop a fast disease progression.
Impact of age, gender, and education on the performance of Addenbrooke’s Cognitive Examination (ACE‐ III) in urban and rural Indians
Authors: Kahali Bratati , Dutt Siddharth , Rao Naren P , Ravindranath Vijayalakshmi ,   SANSCOG and TLSA Investigators
Published: December 2020

AbstractBackgroundAddenbrooke’s Cognitive Examination (ACE‐III) is a well‐validated cognitive screening test, designed to detect Dementia. This work intends to investigate the influence of age, gender, and education in Indian population.MethodSrinivaspura Aging, Neuro Senescence and COGnition (SANSCOG) study and Tata Longitudinal Study on Aging (TLSA) are parallel, prospective, community‐based, cohort studies in India – rural and urban respectively – for comprehensive evaluation of risk and protective factors associated with cognitive changes due to normal ageing, Alzheimer’s disease and other related disorders. 444 and 1344 volunteers were enrolled in TLSA and SANSCOG respectively. ACE‐III was administered on them by trained psychologists. They were classified in four age groups (45– 55, 56–65, 66‐75, and >75 years of age) and six levels for years of education (0‐4, 5‐8, 9‐12, 13‐15, 16‐18, >18). Univariate analysis was performed to study the effect of age, gender, or education, on the ACE‐III total scores. Multivariate analysis was done to study the effects of age, gender, and education on the different cognitive domains of ACE‐III.ResultNo significant differences were observed for ACE‐III among different age groups or gender for individuals enrolled in TLSA, as well as those in SANSCOG. The level of education had a significant effect on ACE‐III as well as all the sub‐domains for SANSCOG participants, but not for TLSA participants. However, age or gender do not show such trends. When the effect of age, gender, and level of education was jointly assessed on these indices, they were found to have significant effects only for visuospatial domain for TLSA participants; and attention and visuospatial domain for SANSCOG participants. Additionally, memory, fluency, language, visuospatial indices of ACE‐III are significantly affected by the age of the participant in SANSCOG.ConclusionThese results show that age, gender and education of participants can have important effects on performance of ACE‐III and its cognitive sub‐domains. Nevertheless, this is an ongoing work, and currently more participants are being assessed in both the studies who would be followed up longitudinally, which can yield precise conclusive results in future.