bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differences of ASL‐MRI measured cerebral blood flow among patients with AD, patients with MCI and cognitively umimpaired individuals
Authors: Zan Yunlong , Zhang Miao , Chen Kewei , Li Biao , Huang Qiu 
Published: February 2022

AbstractBackgroundThe purpose of this work is to examine the relative cerebral blood flow (CBF), via arterial spin labeling (ASL) MR imaging, in patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) compared with healthy control (HC) participants.MethodForty‐one patient with AD (21 males, mean age, 65 years ±9, age range, 46‐84 years), 45 patients with MCI (22 males, mean age, 70 years ±9, 42‐84 years) and 19 HCs (7 males, mean age, 67 years ±6, age range, 55‐80years), determined by CDR (dementia: CDR=1/2/3, MCI: CDR= 0.5. HC: CDR=0), underwent 3T MRI scans. Pulsed ASL perfusion images were normalized to the MNI space using SPM12.CBF images were calculated by subtracting the labeled from unlabeled normalized ASL‐MR images using FSL. We then divided CBF images by the mean CBF value in the global brain to obtain the relative CBF (rCBF) images. The statistical analysis among three groups (AD, MCI, and HC) was performed at voxel level with age, sex and education entered as confounding variables.ResultCompared with HC, MCI patients showed rCBF reduction in right inferior occipital gyrus (P<0.005). In patients with AD compared with HC, the rCBF values significantly declined in left Hippocampus, left occipital gyrus, right inferior occipital gyrus, precuneus left thalamus and right inferior temporal region (P<0.005). Compared to the MCI group, the rCBF values in AD group decreased in left middle occipital gyrus, left precuneus and left posterior cingulum (P<0.005), while increased in superior temporal region, anterior cingulum and middle cingulum (P<0.005).ConclusionThe transition from normal cognition to MCI and finally to AD is associated with decreases in rCBF for some brain regions. However, in other regions, the vascular compensatory process may result in rCBF increase during the transition from MCI to AD.
Association between brain reserve proxies and clinical progression in Alzheimer’s disease dementia
Authors: Yoon Hyung‐Jun , Seo Eun Hyun ,   for the Alzheimer’s Disease Neuroimaing Initiative
Published: February 2022

AbstractBackgroundGiven that dementia prevalence is getting higher around the world, it is important to make a prognosis about clinical progression after dementia diagnosis. Brain reserve theory suggests that greater reserve supports for functional adaptation to clinical progression in neurodegeneration. Therefore, we examined if reserve proxies at baseline were associated with clinical progression among Alzheimer’s disease dementia (ADD).MethodWe selected ADD data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Composite FDG‐PET ROIs (angular gyri, posterior cingulate, and inferior temporal gyri; FDG ROIs), structural MRI volumes for hippocampus, entohinal, and intracranial volume (ICV), American National Adult Reading Test (AMNART) error score, functional activities questionnaire (FAQ), mini mental status examination (MMSE), and other clinical and neuropsychological data, and follow‐up data were downloaded. Clinical progression was measured by change score of clinical dementia rating sum of box (CDR SB change) between baseline and the last follow‐up. Cox proportional hazards models were conducted to investigate the associations between reserve proxies and further progression with covariates of age, gender, and MMSE.ResultWe included 120 patients with ADD at baseline. Mean follow‐up month were 11.42 (5.82, range 6∼24month). Based on CDR SB change, 87 were identified as progression group, while 33 were stable group. At baseline, there were no significant group difference in demographic variables, follow‐up month, CDR SB, FDG ROIs, MRI volumes, distribution of APOE ε4 and Aß positive. However, progression group showed lower performance in AMNART and some neuropsychological tests. Among reserve proxies (education, AMNART, FDG ROIs, ICV, FAQ), FDG ROIs (HR=2.209, p=0.002, 95% CI 1.339‐3.643) was significant in the Cox proportional hazard model. This association was observed only in lager ICV group (HR=4.628, p=0.001, 95% CI 1.810‐11.837), whereas no significant association was found in smaller ICV group.ConclusionOur findings indicate that relatively preserved brain metabolism may slow a rate of clinical progression in ADD, particularly in greater ICV group. In addition to ICV, which is widely used proxy for brain reserve, glucose brain metabolism could play a key role in functional compensation in the process of neurodegeneration.
Altered structural connectivity detected with dilated convolutional neural network analysis in the DIAN study and the Wisconsin Registry for Alzheimer’s Prevention
Authors: Zhen Xingjian , Chakraborty Rudrasis , Vogt Nicholas M. , Wang Ruochen , Yang Kao Lee , Adluru Nagesh , Gordon Brian A. , Benzinger Tammie L.S. , McKay Nicole S. , Betthauser Tobey J. , Johnson Sterling C. , Singh Vikas , Bendlin Barbara B. ,   the Dominantly Inherited Alzheimer Network
Published: February 2022

AbstractBackgroundDominantly inherited Alzheimer’s disease (DIAD) and late onset Alzheimer’s disease (LOAD) are characterized by the accumulation of amyloid pathology, and neurodegeneration which heralds the onset of dementia. Loss of structural connectivity prior to development of dementia may be measured using techniques that are sensitive to subtle neurodegeneration such as diffusion MRI (dMRI). We have previously shown the utility of deep learning (using a dilated convolutional neural network (DCNN) model) for analysis of sequential manifold‐valued data. Here, we apply this approach to dMRI data to test for loss of structural connectivity among mutation carriers who will develop DIAD, as well as among individuals who are at risk for LOAD due to amyloid pathology.MethodDataset 1 comprised 170 cognitively unimpaired participants from the Wisconsin Registry for Alzheimer’s Prevention study who underwent [11C]PiB‐PET to determine amyloid status, and dataset 2 comprised 440 participants in the Dominantly Inherited Alzheimer Network (DIAN) study. Demographics are shown in Table 2. Participants underwent diffusion weighted imaging which was processed using MRTrix3 and FSL toolkits. TractSeg was performed on both datasets to generate 50 white matter tracts, and tractometry was performed to generate mean representations of the tracts. Similar to previous work, we trained the dilated CNN model (Figure 1) using DTI values along the tracts for each group. The distance between the parameters of two models is treated as the difference between two groups. We performed permutation testing of 5000 runs on the distance to determine significant group differences within each dataset.ResultThe p‐values for each tract are shown in Table 1. Within the 50 fibers, we identified 14 tracts that differed by amyloid status, and 16 tracts that differed by mutation status. Across the two data sets, 9 tracts, e.g. Arcuate fascicle, and Cingulum, were found to be in common.ConclusionWe demonstrate the ability to use the dilated CNN model to capture alterations along tract fibers among cognitively unimpaired individuals with preclinical amyloid as well as among mutation carriers who will develop DIAD. Longitudinal studies are needed to determine the temporal relationship between the accumulation of amyloid and neurodegeneration in the development of dementia.
In vivo amyloid staging in individuals with subjective cognitive decline in DELCODE Study
Authors: Levin Fedor , Grothe Michel J. , Dyrba Martin , Boecker Henning , Daamen Marcel , Lange Catharina , Buerger Katharina , Heneka Michael T. , Laske Christoph , Peters Oliver , Priller Josef , Ramirez Alfredo , Schneider Anja , Spottke Annika , Wagner Michael , Düzel Emrah , Jessen Frank , Teipel Stefan J. ,   the DELCODE Study Group
Published: February 2022

AbstractBackgroundIn previous research, a data‐driven in vivo staging model of amyloid accumulation was established based on regional frequencies of amyloid positivity in 18F‐florbetapir‐PET scans of cognitively normal older adults. The model was subsequently validated in an independent cohort of participants with subjective memory complaints. In the current study we aim to apply this staging approach in an independent sample of individuals with subjective cognitive decline (SCD) in the multicenter DZNE ‐ Longitudinal Cognitive Impairment and Dementia Study (DELCODE).MethodPET scans with 18F‐florbetaben were obtained in a group of 63 SCD participants (mean age = 71.84y, range: 60.83‐83y; 35% female) from the DELCODE cohort. Participants also underwent MRI acquisition, clinical and neuropsychological assessments. CSF biomarkers and APOE genotypes were assessed for subsets of participants. In the future analysis we will classify participants into amyloid accumulation stages based on regional amyloid positivity as assessed in PET images.ResultAmong participants with available CSF assessments, 38% had abnormal CSF Aβ42/Aβ40 ratio values indicating amyloid positivity. Frequency of APOE4 allele carriers of 39% was suggestive of APOE4 enrichment.ConclusionSCD participants studied within the DELCODE represent a group at an increased risk for preclinical AD. Consistent with that, the current SCD subsample with available 18F‐florbetaben‐PET scans demonstrated heightened rates of abnormal CSF amyloid‐β biomarkers and APOE4 prevalence. Investigation of regional amyloid accumulation in this sample would help assess the validity of the previous in vivo amyloid staging model in preclinical AD, as well as further characterize SCD. In the future analysis we will apply the previously established amyloid staging approach. We will additionally evaluate the hypothesized links between amyloid accumulation stages, cognitive performance, CSF biomarkers and prevalence of APOE genotypes.
Atrophy in bvFTD spans multiple large‐scale networks in prefrontal and temporal cortex
Authors: Eldaief Mark C , Brickhouse Michael , Katsumi Yuta , Carvalho Nicole , Rosen Howard J. , Touroutoglou Alexandra , Dickerson Brad C. 
Published: February 2022

AbstractBackgroundConverging evidence suggests that neurodegenerative disorders preferentially affect specific large‐scale neural networks 1. This is thought to explain common phenotypic presentations of these conditions. For example, atrophy patterns in Alzheimer’s disease target episodic memory circuits2. Examination of network‐specific neurodegeneration could lend insight into how these conditions spread within vulnerable cortical systems 3. Behavioral variant Frontotemporal Dementia (bvFTD) is most commonly linked to degeneration in the salience network, with clinical presentations of bvFTD explained as deficits in salience processing 4,5. However, phenotypic presentations of bvFTD are diverse6 and this syndrome is caused by varied proteinopathies and gene mutations7. As such, we hypothesized that bvFTD patients would exhibit atrophy patterns that cross canonical network boundaries within prefrontal and temporal cortex.MethodWe estimated cortical thickness using surface‐based analyses described in our previous studies2 in two independent sporadic bvFTD samples: a Massachusetts General Hospital sample (n=30) and a sample derived from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD) (n=60; excluding cases contributed by MGH). Cortical thickness in these samples were then compared to that measured in two groups of age and gender matched controls (n=71 and n=133, respectively). In order to estimate individual network atrophy in bvFTD, we then compared the resultant cortical thickness maps to the topography of seven large‐scale cortical networks derived from the Yeo et al. 2011 parcellation8.ResultWe found highly similar patterns of cortical atrophy across the two independent samples. In both samples, cortical atrophy in bvFTD was not confined to the salience network, but also spanned rostral portions of the default‐mode network (including the hippocampal formation), the limbic (semantic appraisal) network, and rostral nodes of the frontoparietal control network. Moreover, regions exhibiting the most prominent atrophy included anterior temporal regions, not only the salience network.ConclusionThese findings argue against the hypothesis that bvFTD preferentially affects the salience network and instead underscores the distributed impact this syndrome has across prefrontal and anterior temporal cortical circuits. This heterogeneity could reflect the diverse neuropathological etiologies giving rise to the cognitive‐behavioral syndrome of bvFTD. Future efforts should be directed toward examining selectively‐vulnerable circuits in bvFTD syndromes associated with specific molecular pathologies.
Current status and quantitative results of the AMYPAD prognostic and natural history study
Authors: Gispert Juan Domingo , Alves Isadora Lopes , Gray Katherine R. , Buckley Christopher , Collij Lyduine E. , Heeman Fiona , Salvadó Gemma , García David Vállez , Connelly Peter , Boutoleau‐Bretonnière Claire , Pasquier Florence , Dumurgier Julien , Gabelle Audrey , Dubois Bruno , Payoux Pierre , Grau‐Rivera Oriol , Martinez‐Lage Pablo , Boada Mercè , Marquié Marta , Vandenberghe Rik , Hanseeuw Bernard J. , Kivipelto Miia , Schöll Michael , Scheltens Philip , Frisoni Giovanni B. , Ritchie Craig W. , Vellas Bruno , Stephens Andrew W. , Ford Lisa , Molinuevo Jose , Visser Pieter Jelle , Farrar Gill , Barkhof Frederik ,   AMYPAD Consortium
Published: February 2022

AbstractBackgroundUnderstanding the role of amyloid imaging in the earliest stages of Alzheimer’s Disease (AD) becomes increasingly relevant for secondary prevention. In this context, the AMYPAD Prognostic and Natural History Study (PNHS) is an open‐label, prospective, multi‐centre cohort study (http://amypad.eu/) to determine the value of quantitative amyloid PET imaging in determining AD dementia risk. The study recruits from several European cohorts and aims at enrolling 2000 non‐demented individuals with a particular focus on those with emerging amyloid pathology.MethodAs of January 25th, 2020, AMYPAD PNHS had 17 actively recruiting sites across Europe, and 782 non‐demented participants (>95% cognitively unimpaired) had consented to participate in the study. Of those, 448 are from EPAD LCS, 129 from EMIF‐AD Twin 60++, 111 from FACEHBI, 70 from ALFA+, 17 from FPACK, and 7 from the UCL‐2010‐412 cohort. Of the consented subjects, 334 already had an amyloid PET scan before this study. Within AMYPAD PNHS, 588 subjects have hitherto been scanned with either [18F]flutemetamol or [18F]florbetaben. Of note, 53.6% of the total number of scans have been acquired with a dual‐time window dynamic protocol. The primary outcome measure is the Centiloid value (CL) using the whole cerebellum as reference region, to enable quantitative comparability between the two tracers in the study. Also, images have been visually assessed by local raters. Of the 588 acquired scans, 296 have been quantified with IXICO’s LEAP pipeline and passed Quality Control, thus rendering valid CL values. Subjects were categorized as amyloid‐negative (CL≤12); as in a “gray‐zone” (12<CL≤50) or amyloid‐positive (CL>50) and a Gaussian Mixture Model was used to assess the population’s Centiloid distribution.ResultBased on visual assessment, 28% of the fully 296 quantified scans were visually rated as positive and 72% as negative. Quantitatively, 50% of them were categorized as negative, 33% in the gray‐zone, and 17% were positive (Figure 1). Centiloid values ranged from ‐30.3 to 154.2 (Mean±SD: 22.5±32.0 CL) (Figure 2).ConclusionPreliminary quantitative results indicate that PNHS is being successful in recruiting a cohort mostly composed of cognitively unimpaired individuals who are enriched for early amyloid abnormalities, which is the intended target population.
Microglial activation and tau propagate jointly across Braak stages
Authors: Pascoal Tharick A. , Benedet Andréa Lessa , Ashton Nicholas J. , Kang Min Su , Therriault Joseph , Chamoun Mira , Lussier Firoza Z. , Tissot Cécile , Ottoy Julie , Mathotaarachchi Sulanta , Stevenson Jenna , Massarweh Gassan , Schöll Michael , Soucy Jean‐Paul , Blennow Kaj , Zetterberg Henrik , Gauthier Serge , Rosa‐Neto Pedro 
Published: February 2022

AbstractBackgroundCompelling experimental evidence suggests that microglial activation is involved in the spread of tau neurofibrillary tangles over the neocortex in Alzheimer’s disease (AD). Here, we tested the hypothesis that the spatial propagation of microglial activation and tau accumulation colocalize in a Braak‐like pattern in the human brain.MethodWe studied 130 subjects (86 cognitively unimpaired and 44 cognitively impairment elderly) with microglia activation [11C]PBR28 PET, Amyloid [18F]NAV4694 PET, cognitive tests, magnetic resonance imaging, and cerebrospinal fluid (CSF) biomarkers. Participants also had baseline and follow‐up tau [18F]MK6240 PET scans. We used linear regressions and partial correlations to estimate the association between topographical distributions of microglial activation and tau accumulation.Result(A)Microglial activation (MA) connectivity analysis between regions comprising Braak histopathological stages (Braak I‐VI) across our population demonstrated that MA regions correlated hierarchically with each other, where a Braak‐like stage region only correlates with the previous and subsequent stage. (B)Tau tangles connectivity analysis between regions that comprise Braak stages (Braak I‐VI) across our population demonstrated that tau regions correlated hierarchically with each other, where a Braak stage only correlates with the previous/subsequent stage (C)The scatterplot shows that the elements of the MA‐MA matrix and tau‐tau matrix were highly positively correlated with each other, reinforcing the notion of a joint topographic propagation of MA and tau tangles through Braak stages. (D) Rates of tau propagation over 1‐year hierarchically correlated in Braak stages. (E)Significant Pearson correlation between longitudinal tau propagation and baseline tau network. (F)Significant Pearson correlation between longitudinal tau propagation and baseline microglia network. (G)Partial residuals plots show the results of regression between network of tau propagation over 1‐year and both baseline topographic circuits, MA‐MA and tau‐tau networks. The model suggests that the pathways of longitudinal tau propagation depended on underlying microglia, rather than tau circuits. (H)The figure shows thatmicroglial activation levels in the transentorhinal cortex predicted longitudinal tau spread over the neocortex.ConclusionOur results support a model in which an interaction between amyloid load and activated microglia set the pace for tau spread across Braak stages, while the simultaneous cerebral presence of these pathologies potentiates the development of dementia.
The heterogeneous brain: Mapping individualised patterns of atrophy in Alzheimer’s disease using spatial normative models
Authors: Verdi Serena , Kia Seyed Mostafa , Marquand Andre F. , Schott Jonathan M. , Cole James H. ,   Alzheimer’s Disease Neuroimaging Initiative
Published: February 2022

AbstractBackgroundAlzheimer’s Disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. Neuroimaging biomarkers have considerable utility in AD research, however, common statistical designs do not capture neuroanatomical heterogeneity, generally assuming the effects of AD on the brain will be the same in different patients. Spatial normative modelling is an emerging technique that can reveal individual patterns of neuroanatomy by quantifying deviations from normative ranges (Verdi et al., 2021). On multi‐site Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied a hierarchical Bayesian regression (HBR) spatial normative model (Kia et al., 2020). We compared patterns of cortical thickness heterogeneity in AD patients, people with Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) people.MethodStructural imaging data provided estimates of cortical thickness across 148 (ROIs), generated using FreeSurfer on n=563 T1‐weighted MRI scans acquired across 38 sites. We then applied the HBR spatial normative model, using a separate healthy reference dataset of n=33,073 to index population variability, predicting cortical thickness at each ROI using age and sex. Next, we applied transfer learning, to recalibrate the normative model to the CN participants from ADNI. This generated cortical thickness z‐scores across ROIs for each participant (Fig. 1). Z‐scores < ‐1.96 were identified as outliers.ResultLinear regression revealed group differences of outliers summed across 148 ROIs: AD had significantly more ROI outliers than MCI and CN participants (β=10.99,95%CI=[5.55,16.42], p=8.28×10‐5) (Fig. 2). ANOVAs at each ROI demonstrated that this difference was within temporal regions (Fig. 3), e.g., parahippocampal gyrus (F(2,23.33),CI=[ 0.29, 0.57],FDR p=3.60×10‐8). Calculating the percentage of outliers in each ROI within groups revealed that outlying ROIs in AD patients only partially overlap. The parahippocampal gyrus had the highest number of AD patients with outliers (n=27 out of n=59 (45.7%)); more than half of the AD group appeared normal in this region (Fig. 4).ConclusionOur novel quantitative estimate of spatial heterogeneity of cortical thickness in AD patients suggests that the impact of AD on the brain is not consistent between patients. Individualised patient neuroanatomical maps have the potential to be a marker of disease, and could be used to track an individual’s disease progression or treatment response.
Mild behavioral impairment is associated with progression to Alzheimer's disease: A clinicopathological study
Authors: Ruthirakuhan Myuri , Ismail Zahinoor , Herrmann Nathan , Gallagher Damien , Lanctôt Krista L. 
Published: February 2022

AbstractIntroductionMild behavioral impairment (MBI) is characterized by later‐life emergence of neuropsychiatric symptoms. Investigating its relationship with progression to Alzheimer's disease (AD) would provide insight on its importance as a predictor of AD.MethodsCognitively normal participants (N = 11,372) from the National Alzheimer's Coordinating Center were stratified by MBI status, using the Neuropsychiatric Inventory‐Questionnaire. We investigated whether MBI and its domains were predictors of progression to clinically‐diagnosed AD. MBI as a predictor of progression to neuropathology‐confirmed AD was also investigated in those with neuropathological data.ResultsSix percent (N = 671) of participants progressed to AD. MBI (N = 2765) was a significant predictor of progression to clinically‐diagnosed (hazard ratio [HR] = 1.75) and neuropathology‐confirmed AD (HR = 1.59). MBI domains were also associated with clinically‐diagnosed AD, with psychosis having the greatest effect (HR = 6.49).DiscussionThese findings support the biological underpinnings of MBI, emphasizing the importance of later life behavioral changes in dementia detection and prognostication.