bluuebunny/crossref_metadata_2025_split huggingface dataset:
Females Derive Greater Neuroprotective Benefits from Social Connectedness than Males
Authors: Manchella Mohit K. , Logan Paige E. , Perry Brea L , Peng Siyun , Risacher Shannon L , Saykin Andrew J. , Apostolova Liana G. 
Published: December 2023

AbstractBackgroundSocial connectedness is associated with better cognitive performance among older adults. Previously, we found that bridging (access to an expansive network of diverse and loosely connected individuals) and bonding (close and supportive relationships to familiar others) associated with preserved gray matter density (GMD). We sought to examine whether sex modulates these relationships in the Social Networks and Alzheimer’s Disease study (SNAD).MethodWe analyzed baseline data of 176 SNAD participants (58 male, 118 female; 117 cognitively‐unimpaired, 59 cognitively‐impaired; average age 68.57+/‐12.26 years). Bridging and bonding metrics were collected via in‐person interviews. We ran analyses in the pooled sample split by sex. Demographics and biomarkers between sexes were compared. Correlations between MRI regions of interest and bridging and bonding in males and females were performed. Voxel‐wise covaried for age, education, and intracranial volume in SPM12 yielded statistical maps for males and females which were corrected for family‐wise errors at p<0.01.ResultTable 1 shows demographic and biomarker comparisons between males and females. In females greater social bridging was significantly associated with (left p = 0.004, right p<0.001), as well as left frontal lobe thickness (p = 0.002), putamen (p = 0.011), and thalamus volumes (p = 0.002). No significant associations were observed for social bridging in males. Greater social bonding was significantly associated with preserved right frontal pole (p = 0.018) and caudal anterior cingulate thickness (p = 0.017) in females and greater right orbitofrontal thickness (p<0.001) in males (Figure 1). Our 3D voxel‐wise regression maps showed that in females greater social bridging was associated with preserved GMD of the frontal, temporal and parietooccipital lobes, while males showed no significant associations (Figure 2). In females greater social bonding was associated with preserved GMD in the lateral temporal and parietal lobes, while in males the associations were localized to the medial frontal and right orbitofrontal lobe (Figure 2).ConclusionWomen compared to men showed stronger effects of social enrichment on GMD and subcortical structures. Social bridging and bonding were associated to different brain regions in males and females. These results suggest that females may have a greater social‐based protection against neurodegeneration compared to their male counterparts.
Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding
Authors: Ma Da , Stocks Jane K , Lockhart Samuel N. , Craft Suzanne , Rosen Howard J. , Gurcan Metin Nafi , Popuri Karteek , Beg Mirza Faisal , Wang Lei 
Published: December 2023

AbstractBackgroundFrontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums.MethodBvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets.ResultVisual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds.ConclusionWe developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.
Longitudinal changes of self‐ and informant‐reported cognitive decline in elderly with regard to clinical progression and amyloid‐status
Authors: Kuhn Elizabeth , Stark Melina , Kleineidam Luca , Wolfsgruber Steffen , Boecker Henning , Bürger Katharina , Düzel Emrah , Fliessbach Klaus , Gaertner Florian , Heneka Michael T. , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Schmid Matthias , Schneider Anja , Spottke Annika , Teipel Stefan , Wiltfang Jens , Jessen Frank , Wagner Michael 
Published: December 2023

AbstractBackgroundSelf‐ and informant‐reports of cognitive decline have been previously associated with a higher risk of cognitive decline in elderly. Yet, few studies assessed their longitudinal changes over time with regard to both incident mild cognitive impairment (iMCI) and Alzheimer’s disease (AD) pathology. This study aims at describing these links to better understand which trajectories are indicative of higher AD‐risk.MethodWe used data from 501 participants followed‐up annually (2.51±1.35 years) in the DELCODE project. All were cognitively unimpaired, and had an amyloid measurement available, at inclusion. Among them, 145 were amyloid‐positive, and 65 progressed to iMCI during the follow‐up period (31 iMCI‐Aß+). The SCD‐Interview was used to measure (i) the number of cognitive domains in which the participants or its relative perceived a decline (i.e., self‐ and informant‐SCD‐I), and (ii) the number of fulfilled SCD‐plus criteria. Linear mixed‐effect models were used to assess SCD trajectories over time, according to four groups defined by amyloid status and clinical progression (Stable‐Aß‐, Stable‐Aß+, iMCI‐Aß‐ and iMCI‐Aß+). Analyses were adjusted for age, sex and education; and their interaction with time.ResultBoth self‐SCD‐I levels and SCD‐plus scores decreased significantly over time, without significant differences between groups (Figure 1A, 1B). Conversely, informant‐SCD‐I levels did not significantly change over time in the whole sample, although slopes significantly differed between groups. Compared to the Stable‐Aß‐ group, there was a greater increase over time of informant‐SCD‐I report only in iMCI‐Aß+ [est 0.16, SE 0.05, p<.001] (Figure 1C). The resulting discrepancy score (Self‐SCD‐I minus Informant‐SCD‐I) tended to decrease over time with significant differences between groups. Compared to the Stable‐Aß‐ group, the discrepancy score showed a faster decrease over time in iMCI‐Aß+ [est ‐0.21, SE 0.056 p<.001] (Figure 1D).ConclusionLongitudinal patterns in subjective reports of cognitive decline might help identify preclinical AD subjects at higher risk of clinical progression. Specifically, an increase of informant‐reported cognitive decline over time may indicate objective cognitive decline in amyloid‐positive subjects. Conversely, the decrease of self‐reported cognitive decline over time, regardless of the group, may reflect generally reduced concerns after inclusion, or result from current limitations in longitudinal SCD assessment.
Associations among plasma, MRI and amyloid PET biomarkers of dementia and the impact of health‐related comorbidities
Authors: Rudolph Marc D. , Sutphen Courtney L. , Register Thomas C. , Whitlow Christopher T. , Sai Kiran K. Solingapuram , Hughes Timothy M. , Bateman James R. , Dage Jeff L. , Russ Kristen A. , Mielke Michelle M. , Craft Suzanne , Lockhart Samuel N. 
Published: December 2023

AbstractBackgroundKnowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.MethodWe examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aß42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aß‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐e4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).ResultPlasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aß42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aß‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aß42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐e4 status exclusively impacted associations with Aß‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aß42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aß‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.ConclusionAmong aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aß‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.
BrainAGE methods: influence of field strength, voxel size, race, and ethnicity
Authors: Dempsey Desarae A , Deardorff Rachael , Saykin Andrew J. , Risacher Shannon L 
Published: December 2023

AbstractBackgroundThe brain‐age algorithm utilizes a regression model of the typical aging trajectory, trained on structural MRIs and chronological ages of a large population of healthy individuals spanning a wide age range (Cole et al. 2017, Neuroimage). MRIs from new populations are then compared to this regression for estimating brain age, a biomarker of brain health. Few studies have investigated stability of the brain‐age model across field strength and the influence of voxel size and race is understudied.MethodWe analyzed stability of the brain‐age model within and across field strength in the ADNI cohort (n = 75: 25 CN/25 MCI/25 AD) using intra‐class correlation coefficients (ICC) and multiple R2 values. Impact of voxel size (0.8mm, 1mm, and ADNI2 sequence 1.1×1.1×1.2mm) was compared in a sample from the Indiana ADRC (n = 48: 19 CN/16 SCD/10 MCI/3 AD). We then calculated brain age gap (BAG; estimated– chronological age) and compared results across self‐reported racial and ethnic groups matched on diagnosis, sex, age, education, and handedness using independent t‐tests. BAG results of African Americans (AAs) from the IADRC (n = 71: 30 CN/19 SCD/15 MCI/7 AD) and ADNI (n = 147: 41 CN/20 EMCI/27 LMCI/15 AD/44 SMC), and Hispanics (n = 76: 24 CN/16 EMCI/11 LMCI/11 AD/14 SMC) and Asians (n = 47: 12 CN/7 EMCI/4 LMCI/9 AD/15 SMC) from ADNI only, were compared to matched non‐Hispanic Whites (NHWs).ResultThe brain‐age method was stable across repeated scans within field strength, but less stable across field strengths (Fig.1). 3T scans consistently estimated individuals to be ∼4.43 years younger than the 1.5T scans. The method was relatively stable within and across voxel sizes (Fig.2). There was a significant difference in BAG between races (NHWs and AAs, and NHWs and Asians), but not between ethnicities (NHWs and Hispanics) (Fig.3).ConclusionDue to the model’s variability across field strength, we propose a linear offset equation for data harmonization (Fig.1): ((3T estimate/0.92)+9.55). We are also investigating ComBat harmonization to eliminate effects of field strength. While other studies show structural differences across race, it will be important to determine whether the self‐reported racial differences in BAG are due to inherent model bias or true biological differences.
Microglial Activation Contributes to Neuropsychiatric Dysfunction in Alzheimer’s Disease
Authors: Aguzzoli Cristiano Schaffer , Ferreira Pamela C.L. , Povala Guilherme , Soares Carolina , Ferrari‐Souza João Pedro , Bellaver Bruna , Zalzale Hussein , Lussier Firoza Z , Rohden Francieli , Abbas Sarah , Lemaire Peter Charles , Leffa Douglas Teixeira , Cabrera Arlec , Therriault Joseph , Benedet Andrea Lessa , Tissot Cécile , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Poltronetti Nina Margherita , Soucy Jean‐Paul , Wang Yi‐Ting , Gauthier Serge , Souza Diogo O. , Ashton Nicholas J. , Schilling Lucas Porcello , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick A. 
Published: December 2023

AbstractBackgroundPrevious studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum.MethodWe assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status.ResultNPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aß and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B).ConclusionOur results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aß and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.
Systematic Comparison of Reproducibility of Diffusion MRI‐Derived Free‐Water Measures Across Two Multi‐Shell Protocols
Authors: Mishra Virendra R , Sreenivasan Karthik , Cordes Dietmar 
Published: December 2023

AbstractBackgroundFree‐water (FW) measures have been widely used to understand and predict the progression of various neurodegenerative disorders1,2. However, the estimation of FW in a brain voxel needs a multi‐shell acquisition to ensure reproducible fit3,4. With the availability of multi‐shell diffusion MRI (dMRI) protocol using simultaneous multislice technique from the Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI‐3), such protocols can now be harmonized across various centers and MRI vendors. However, limited knowledge exists on whether the results obtained for FW measures across HCP protocols are generalizable across ADNI‐3 protocols along with which protocol is more reproducible that will assist us in understanding the variability of FW measure in an individual participant longitudinally. Hence, in this study, we compared the reproducibility of FW across major white matter (WM) tracts across both HCP and ADNI‐3 protocols.MethodA 32‐year‐old healthy male participant was scanned over five weeks, both with Basic (2mm3, b‐value = 1000s/mm2) and Advanced ADNI‐3 protocol and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a clinical 3T Skyra scanner. FW dMRI measures were obtained using the original Matlab implementation (Matlab)5 and DiPY implementation6 of multi‐shell dMRI acquisition (DiPY). WM atlas from Johns Hopkins University (JHU) of 20 major WM tracts pre‐registered to MNI152 was then used as a mask to obtain FW at each WM tract, and the coefficient of variance (CoV) was computed across each protocol. The total acquisition scan time was 75 minutes.ResultFW estimated using Matlab had a mean CoV = 2.78±1.09 across all the protocols with the least CoV for Basic ADNI‐3 (CoV = 1.31±0.54) and the maximum CoV for 2mm3 HCP sequence (CoV = 4.98±1.83) (Fig.1A) while FW estimated using DiPY had a mean CoV = 6.71±2.86 across all the protocols with the least CoV for Advanced ADNI‐3 (CoV = 4.31±1.85) and the maximum CoV for 2mm3 HCP sequence (CoV = 9.66±4.15) (Fig.1A).ConclusionBoth acquisition parameters and processing techniques significantly affect the absolute quantification of FW measures. Furthermore, our data suggest that the pooling of dMRI data across various protocols should be performed with caution.
Multivariate white matter differences links to cognition in individuals with family history of Alzheimer’s disease and APOE4 genetic risk
Authors: Tremblay Stefanie A , Spreng R. Nathan , Alasmar Zaki , Pirhadi Amir , Tardif Christine L , Chakravarty M. Mallar , Villeneuve Sylvia , Leppert Ilana R , Carbonell Felix , Medina Yasser Iturria , Steele Christopher J , Gauthier Claudine J 
Published: December 2023

AbstractBackgroundGrowing evidence suggests Alzheimer’s disease (AD) develops from a complex cascade of events that vary between individuals. Multivariate approaches have the potential to capture the complexity and heterogeneity of pathologies underlying AD in a more holistic manner compared to univariate approaches.MethodThe MRI data of 105 older adults with a family history of AD from the PREVENT‐AD dataset were analysed. Forty six of these individuals had the APOE4‐3 genotype (greater risk of developing AD), while 59 subjects had the APOE3‐3 genotype (normal risk). Diffusion‐weighted imaging and multi‐echo magnetization transfer, proton density and T1‐weighted data were used to compute several WM metrics (Fig1 b‐c). We computed the voxel‐wise Mahalanobis distance (D2) in WM between APOE4‐3 individuals and a reference group (APOE3‐3) (Fig1). D2 is a multivariate measure that combines several MRI metrics, accounting for their covariance, and yielding a score indicative of the degree of abnormality at each WM voxel (Fig2). Associations between WM D2 and cognitive performance were then investigated (Fig3). Independent component analysis (ICA) was performed on spatial dimensions and linear regression analyses were conducted between D2 in ICA components and scores on items of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). We then extracted the contribution of each MRI feature to D2 in the regions where significant associations were found.ResultA significant negative association (R = ‐0.36, p = 0.014) was found between D2 in a component corresponding to the splenium of the corpus callosum (CC) and scores on RBANS‐immediate memory (Fig3), indicating a link between a greater degree of WM abnormality and poorer performance. Proton density (MPM_PD) and longitudinal relaxation rate (MPM_R1), potentially indicative of WM loss, contributed most to D2 in this region.ConclusionOur findings suggest that microstructural alterations in the posterior part of the CC may play a role in memory deficit in older adults who are at greater risk of developing AD. This is in line with other studies that showed a link between CC degeneration and memory impairments in early AD stages.1 1. Qiu et al. (2016). Inter‐hemispheric dysconnectivity in AD and aMCI. Sci.Rep.