bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age‐Related Structural and Vascular Changes in the Choroid Plexus: Insights from the HCP Dataset
Authors: Sun Zhe , Li Chenyang , Wisniewski Thomas , Ge Yulin 
Published: January 2025

AbstractBackgroundThe choroid plexus (ChP) plays a vital role in CSF production and waste clearance. While existing imaging studies have established connections between ChP volume changes and age‐related neurodegenerative diseases, a comprehensive investigation into the microstructural and vascular changes associated with aging remains insufficient. This study aims to explore ChP changes in normal aging using diffusion and perfusion MRI in the HCP‐Aging dataset to enhance our understanding of age‐related microstructural and vascular changes in the ChP.MethodOur analysis included 641 healthy participants (age: 36‐90 years; mean±SD = 60±16 years; 282 males) with the following MRI parameters: (1) T1 MPRAGE for ChP segmentation[1]; (2) Diffusion MRI for quantification of mean diffusivity (MD); (3) PCASL MRI for quantification of cerebral blood flow (CBF) and arterial transit time (ATT). The ChP mask was co‐registered with MD, CBF, and ATT maps to extract corresponding values. Pearson's correlation and one‐way ANOVA were used for age effects and perfusion property comparisons among gray matter (GM), white matter (WM), and ChP, respectively.ResultChP imaging revealed increased volume, increased MD, and decreased CBF across different age groups (Figure 1). Figure 2 demonstrated a positive correlation between ChP volume and age (r = 0.31, p<0.0001), while ChP CBF declined with age (r = ‐0.41, p<0.0001) and MD positively correlated with age (r = 0.56, p<0.0001). Adjusting for age and sex, ChP CBF negatively correlated with volume (r = ‐0.26, p < 0.0001). One‐way ANOVA analyses indicated significant differences in CBF and ATT among ChP, GM, and WM (p<0.0001 for all comparisons), with ChP exihbiting the highest CBF and shortest ATT (Figure 3).ConclusionIn addition to volume changes, we found significant age‐related diffusion and perfusion changes in the ChP in this large dataset. The decline in CBF and the increase in MD with aging suggest vascular degeneration and compensatory dystrophic hyperplasia of surrounding tissues of ChP. These findings in normal aging set a crucial normative reference for future studies on cognitive impairment in abnormal aging processes References: 1. Tadayon, E., et al. J Alzheimers Dis (2020).
Clinical Phenotypes of Alzheimer's Disease: Atrophy Patterns and their Pathological Correlates
Authors: Reijner Niels , Frigerio Irene , Bouwman Maud M.A. , Boon Baayla D.C. , Guizard Nicolas , Jubault Thomas , Hoozemans Jeroen J.M. , Rozemuller Annemieke J.M. , Bouwman Femke H. , Barkhof Frederik , Gordon Elizabeth , van de Berg Wilma D.J. , Jonkman Laura E. 
Published: January 2025

AbstractBackgroundRecent studies highlight distinct patterns of cortical atrophy between amnestic (typical) and non‐amnestic (atypical, with subtypes: behavioural, dysexecutive, logopenic and visuospatial) clinical phenotypes of Alzheimer’s disease (AD). The current study aimed to assess regional MRI patterns of cortical atrophy across AD phenotypes, and their association with amyloid‐beta (Aß), phosphorylated tau (pTau), axonal degeneration (NfL) and microvascular deterioration (COLIV).MethodPostmortem In‐situ 3DT1 3T‐MRI data was collected for 33 AD (17 typical, 16 atypical) and 16 control brain donors. Images were segmented and AAL3 atlas regional volumes were obtained using QyScore®. At subsequent autopsy, eight brain regions were selected, immunostained for Aß (4G8), pTau (AT8), Neurofilament‐light (NFL), and Collagen‐IV (COLIV), and quantified using qupath. Group comparisons and volume‐pathology associations were analyzed using linear models and partial correlations with covariates age, sex, postmortem delay, and intracranial volume.ResultsCompared to controls, AD phenotype groups showed overall lower cortical volume, while only minor volume differences were observed between AD phenotype groups, observed primarily in limbic regions (Figure 1). Across pathological markers, AD phenotype groups showed consistently higher immunoreactivity than controls, while atypical AD showed consistently higher immunoreactivity than typical AD (Figure 2). Moreover, different patterns of pathology could be observed between atypical subtypes (e.g. distinctly higher pTau load in the occipital gyrus of the visuospatial subtype). In typical AD, global volume loss was associated with lower Aß and higher pTau, NFL and COLIV immunoreactivity, while in atypical AD, global volume loss was primarily associated with higher NFL immunoreactivity (Figure 3a). Regionally, AD phenotype differences in atrophy‐pathology association were most pronounced in the (para)hippocampal regions. This distinction was mainly characterized by negative associations for NFL and COLIV, which was only observed in typical AD (Figure 3b‐c).ConclusionAtrophy patterns between AD phenotypes showed only minor differences, potentially attributable to the on average later disease stage of the study cohort. The higher immunoreactivity for pathological markers found in atypical AD might suggest a more severe disease burden. (Para)hippocampal volume decline was associated with axonal and microvascular deterioration in typical AD, but not in the higher pathologically burdened atypical AD group, suggesting a differential susceptibility between AD phenotypes.
Lower amygdala and hippocampal volume correlates with TDP‐43 load in limbic‐predominant age‐related TDP‐43 encephalopathy (LATE)
Authors: Wesseling Alex J. , Calandri Ismael Luis , Ossenkoppele Rik , Bouwman Maud M.A. , Deshayes Natasja A. C. , van de Berg Wilma D.J. , Rozemuller Annemieke J.M. , Pijnenburg Yolande A.L. , Hoozemans Jeroen J.M. , Jonkman Laura E. 
Published: January 2025

AbstractBackgroundLimbic‐predominant age‐related TDP‐43 encephalopathy (LATE) is often described as occurring in older individuals, either with or without co‐occurring neurodegenerative disease. Because the presence of LATE can only be determined post mortem, little is known about the clinical and neuroimaging features of LATE. The current study aims to assess the correlation between LATE and MRI‐measured amygdala and hippocampal volume in Alzheimer’s disease (AD) and Lewy Body Diseases (LBD), including Parkinson’s disease (PD), PD dementia, and dementia with Lewy BodiesMethodsPost‐mortem in‐situ 3DT1 3T‐MRI data were collected for 51 cases (27 AD and 24 LBD) of which 17 had post‐mortem confirmed LATE neuropathological change (9 AD and 8 LBD), as well as 34 non‐LATE (18 AD and 16 PD) donors (matched on age, sex, and Braak stage). Right hemisphere amygdala and hippocampal volumes were calculated using Freesurfer. Pathological load of TDP‐43, p‐tau and a‐synuclein from the same regions were obtained using IHC and quantified using QuPath. Group differences were assessed with univariate analyses and correlations using linear mixed models, both using age, sex, post‐mortem delay and intracranial volume as covariates.ResultsTwo AD cases with a disease onset below 65 showed LATE pathology. In the combined cohort, brain donors with LATE neuropathological change showed significantly lower amygdala (p=.003) and hippocampal (p=.002) volumes, even when corrected for p‐tau and Lewy body load (Figure 1a). In AD, this difference remained (amygdala p=.001, hippocampus p=.007, corrected for p‐tau), but not in the LBD group. In donors with LATE, TDP‐43 load correlated negatively with MRI measured amygdala and hippocampal volume when corrected for p‐tau and Lewy body load (r= .70; p=.005) (Figure 1b). The same association was found in AD (r=.70; p=.015 corrected for tau load) but not in LBD donors.ConclusionThese results suggest that TDP‐43 inclusions plays a role in hippocampal and amygdala atrophy on MRI, even when correcting for effects of primary pathology. Moreover, a salient detail is that LATE also seems to play a role in the disease presentation of young‐onset dementia patients.
Mapping local and distal effects of different neuropathologies on amygdala volume
Authors: Annettesdotter Amanda , Selezneva Katja , Denning Amanda E , Wuestefeld Anika , Ravikumar Sadhana , Ittyerah Ranjit , Levorse Lisa M , Bedard Madigan , Chung Eunice , Robinson John L. , Schuck Theresa , Lee Eddie B , Lim Sydney A , Trotman Winifred , Bahena Alejandra , Irwin David J , Jimenez Maria del Mar Arroyo , Vela Alicia , Buendia Esther , Martin Maria Mercedes Iniguez de Onzono , Rabal Maria del Pilar Marcos , Munoz Monica , Spotorno Nicola , Hansson Oskar , Insausti Ricardo , Yushkevich Paul A. , Wisse Laura E.M. 
Published: January 2025

AbstractBackgroundThe amygdala is a hotspot for neuropathologies; however, it is unclear 1) which neuropathologies lead to amygdala neurodegeneration, 2) what specific amygdala subnuclei are affected, and 3) if the neuropathologies related to amygdala volume are local (inside the amygdala), or distal (in other regions). We investigate the relationships between different neuropathologies (tau, amyloid‐ß [Aß], a‐synuclein [a‐syn], and transactive response DNA‐binding protein 43 [TDP‐43]) and amygdala volumes.MethodWe analyzed postmortem data from 73 individuals with and without neurodegenerative diseases (age: 77±11 [45–101] years; 26 [36%] females; 51 [70%] cognitively impaired). Volumes of the whole amygdala and three amygdala subregions (lateral, B/Ab [basal/accessory basal], Co/Me/Ce [cortical/medial/central] nuclei: for n = 24 individuals) were manually segmented on 0.2×0.2×0.2 mm3 postmortem magnetic resonance images (Figure 1). We used semiquantitative scores (0‐3) of Aß, tau, a‐syn, and TDP‐43 in the amygdala, the dentate gyrus (DG), the Cornu Ammonis 1 and subiculum (CA1/SUB), the entorhinal cortex (ERC), and averaged between the medial temporal lobe (MTL) regions (excluding the amygdala). Partial Spearman correlations were performed to investigate the relationships between neuropathologies and amygdala volumes, adjusting for age, sex, and other pathologies.ResultsHigher ratings of amygdala TDP‐43, MTL TDP‐43, and MTL tau were associated with smaller whole amygdala volume (Figure 2A–B, Figure 3A). However, MTL TDP‐43 was no longer significant when adjusting for amygdala TDP‐43 (Figure 3B). As MTL tau was associated with amygdala volume, we looked into which MTL subregions could be responsible for this association. Here, tau in DG, CA1/SUB, and ERC were all negatively associated with whole amygdala volume (Figure 2B). For exploratory subregional analyses, amygdala a‐syn was initially associated with B/Ab volume (Figure 2C, Figure 3C), but this correlation did not survive FDR‐correction.ConclusionsThese preliminary results indicate that TDP‐43 has a local effect on whole amygdala volume, whereas tau seems to affect amygdala volume distantly through other MTL regions, with the largest effect seen in DG tau, followed by CA1/SUB and ERC. Subregional analyses will be updated once a larger sample is available, as well as further analyses on non‐MTL regions.
Predicting Antipsychotic Drug Doses for BPSD: A Transfer Learning Approach Using Neuroimaging Data
Authors: Yue Ling , Hong Bo , Tao Tianli , Li Xia , Xiao Shifu , Zhang Han 
Published: January 2025

AbstractBackgroundAbout 50‐90% people with dementia would develop behavioral disturbances, namely, behavioral and psychological symptoms of dementia (BPSD). Antipsychotic medications are widely used to control severe BPSD symptoms which suffers serious safety risks. It is challenge for individualized precise prediction of antipsychotic drug doses. Neuroimaging, particularly MRI, reveals brain structure associated with aging, cognitive decline, and psychiatric symptoms, making it a potential tool for predicting the drug doses. This study employs transfer learning to predict drug dose and offer neuroanatomical interpretation of BPSD from the perspective of deep learning.MethodWe employed a two‐step process to train our model (Figure 1). Initially, a large dataset from the Chinese Brain Molecular and Functional Mapping (CBMFM) project (n=646,334 females and 312males, age 18‐82) was used to pretrain the model with a brain age prediction task. Subsequently, the pretrained model was fine‐tuned for drug dose prediction from the Alzheimer's Disease and Related Disorders Center in Shanghai Jiao Tong University (ADRDC) dataset (83 BPSD patients,27 males and 56 females, age 55‐80). Finally, we utilized gradient‐weighted class activation mapping to generate attention maps and conducted statistical analyses on the attention maps to identify critical brain regions for drug dose prediction. To determine the individual usage of different antipsychotic drugs, the concept of defined daily dose (DDD) was used. The DDD, which individualized control the BPSD, was calculated, ranging from 0 to 1.5 mg/day, serving as the label for fine‐tuning.ResultOur pretrained Cas‐ResNet exhibited enhanced performance with fewer training epochs, achieving a competitive Pearson correlation of 0.59 between estimated and real DDD (Figure 2). Through feature interpretability analysis, we identified five significant brain regions crucial for BPSD drug dose prediction, mainly located in the temporal lobe, including the parahippocampal area and the striatum (putamen and caudate)(Figure 3). These findings indicate that the antipsychotic dosage to control the BPSD is linked to brain structural alterations, involving both dementia‐related and emotion‐regulating areas.ConclusionFor the first time, we showed a promising result of using a lightweight deep learning model to predict drug dose prescribed for controlling BPSD. The work promotes the discussion toward appropriate use of antipsychotics in patients with dementia.
Widespread neurite degeneration in mild cognitive impairment and Alzheimer’s disease revealed using advanced diffusion MRI
Authors: Laporte John P , Gong Zhaoyuan , Guo Alex , Bae Jonghyun , Faulkner Mary E , Bouhrara Mustapha 
Published: January 2025

AbstractBackgroundNeurite degeneration is increasingly suspected to represent a causal feature of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Therefore, sensitive and specific imaging biomarkers of neuronal degeneration are needed to elucidate the mechanisms underlying cognitive impairment in MCI and AD. However, the recently developed Neurite Orientation Dispersion and Density Imaging (NODDI) MRI technique, used to measure the neurite density index (NDI), has some limitations. To address these, we have introduced the constrained NODDI (C‐NODDI) analysis providing more accurate determination of NDI (PMC10435293). Here, we investigated neurite degeneration in MCI and AD using C‐NODDI and compared the results to those derived using NODDI.MethodsWe obtained Multi‐shell diffusion MR images from the ADNI study (Table 1) and calculated NDI using NODDI or C‐NODDI. We conducted a voxelwise two‐sample t‐test to compare NDI of control vs. AD, control vs. MCI, and MCI vs. AD groups. Further, we assessed the regional associations between NDI and overall cognition as measured using the Mini‐Mental State Examination (MMSE) score (dependent variable), controlling for age and sex. All continuous variables were Z‐scored.ResultsThe AD group exhibited significantly lower NDI values as compared to control, with C‐NODDI exhibiting greater sensitivity in capturing regional NDI variations as compared to NODDI (Figure.1). Further, unlike NODDI, C‐NODDI was capable of detecting subtitle differences in NDI between control and MCI and between MCI and AD (Figure.1). Interestingly, NODDI exhibited higher NDI values in MCI or AD as compared to control in several brain regions (Figure.1), a physiologically implausible result that C‐NODDI corrected. Additionally, the associations between MMSE and NDI derived using C‐NODDI were significant in several white matter regions, with higher NDI values corresponding to expected higher MMSE scores (Figure.2). NODDI has failed to capture this anticipated association.ConclusionsThis study provides evidence of widespread neuronal degeneration in MCI and AD. Importantly, our results highlight the superior sensitivity of C‐NODDI analysis in detecting these differences in neurite density as compared to NODDI, making it a potent MRI method for clinical investigations and trials studying neuronal degeneration in aging and age‐related diseases.
Cognitive reserve is associated with the recruitment of compensatory brain networks in individuals at risk for Alzheimer’s disease
Authors: Lizarraga Aldana , Kassinopoulos Michalis , González‐de‐Echávarri José María , Huguet Jordi , Sánchez‐Benavides Gonzalo , Brugulat‐Serrat Anna , Suarez‐Calvet Marc , Milà‐Alomà Marta , Blennow Kaj , Zetterberg Henrik , Kollmorgen Gwendlyn , Quijano‐Rubio Clara , Molinuevo Jose Luis , Gispert Juan Domingo , Cacciaglia Raffaele 
Published: January 2025

AbstractBackgroundCognitive Reserve (CR) refers to the brain’s ability to maintain optimal cognitive function despite damage or pathology. The neural implementation of CR is a major research focus, and resting‐state functional connectivity (RSFC) has emerged as a promising imaging correlate of CR. We assessed RSFC as a function of two different proxy measures of CR and further assessed the impact of these brain networks on longitudinal cognitive performance in a sample of cognitively unimpaired (CU) individuals at risk of Alzheimer’s disease (AD).MethodAnalyses were conducted in 328 CU individuals from the ALFA cohort (mean age = 60.8, SD = 4.74) with available CSF Aß, p‐tau, resting‐state fMRI and longitudinal cognitive assessment (average follow‐up time = 3.35 years, SD = 0.53). CSF Aß42 and Aß40 were assessed with the exploratory NeuroToolKit, while p‐tau181 was measured with the Elecsys® Phospho‐Tau (181P) CSF immunoassay (both Roche Diagnostics International Ltd). We examined the impact of years of education (YOE) and global score of the Cognitive Reserve Questionnaire (CRQ) on the RSFC amongst 246 brain regions of the Brainnetome atlas using the CONN toolbox, selecting a cluster threshold of p<0.005, and adjusting or the effects of age, sex, and APOE status.ResultOf the entire sample, 38.4% had positive CSF Aß42/40 markers. YOE was related to an increased RSFC between regions of the salience network and the anterior default‐mode network (DMN). Increased negative RSFC was found as a function of YOE between primary visual areas and regions of the executive control as well as dorsal attention (Fig. 1). In models adjusted by CSF biomarkers, the increased RSFC between the anterior DMN and salience regions predicted better PACC score over time and further modulated the association between CSF Aß42/40 and longitudinal PACC (Fig. 2). CRQ score was associated with a decreased RSFC between the posterior DMN and inferior temporal areas. These patterns of reduced RSFC significantly mediated the impact of CSF Aß42/40 on longitudinal memory performance (Fig.3).ConclusionRSFC may provide insights into the mechanisms relating CR and cognitive resilience in preclinical AD. Further, the expression of RSFC patterns may serve as an outcome in intervention studies aiming to boost CR.
Reproducibility Challenges in Brain PET Data Analysis
Authors: Naseri Maryam , Carmichael Owen T. , Pillai Sreekrishna R. 
Published: January 2025

AbstractBackgroundWhile a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, less attention has been paid to reproducibility of brain positron emission tomography (PET) research.MethodsWe examined the current landscape of factors that contribute to reproducible neuroimaging data analysis, comparing brain MRI to brain PET. The factors included scientific standards, analytic plan pre‐registration, data and code sharing, containerized workflows, and standardized processing pipelines. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we conducted one case study in which the brain PET processing pipeline developed for one flagship study (Alzheimer’s Disease Neuroimaging Initiative, ADNI) for brain 18F‐florbetapir and 18F‐fluorodeoxyglucose (FDG) analysis was reproduced in our laboratory.ResultsCompared to fMRI, PET research encounters substantial challenges in adopting essential reproducibility practices. While 85% of fMRI studies adhere to the standardized reporting practices, PET is in the early stages of establishing reporting practices, lacking a standardized checklist. Pre‐registration is prevalent for fMRI (57.6%) but notably absent in PET, with no pre‐registered PET studies found on platforms such as the Center for Open Science. The number of shared datasets is higher for fMRI compared to PET, with 92 fMRI datasets and only 9 PET datasets on repositories like OpenNeuro. Moreover, there are very few data repositories for newer PET radiotracers. Containerized software availability also favor fMRI, with platforms like (Brain Imaging Data Structure, BIDS) apps hosting 66% dedicated fMRI tools, while no PET‐specific software is available. Standardized processing pipelines follow the same trend, with fMRI boasting optimized workflows for specific protocols, while PET has few dedicated pipelines and lacks validation studies for existing options. Furthermore, while our case study demonstrated excellent correlation results for both the 18F‐FDG and 18F‐florbetapir methods (Figure 2), the replication of the ADNI PET pipeline posed substantial challenges and implementation delays. These obstacles and delays in execution were due to incomplete documentation and ambiguities in application details.ConclusionPET neuroimaging lags behind its MRI‐related counterparts in achieving robust reproducibility.