bluuebunny/crossref_metadata_2025_split huggingface dataset:
An explainable framework for convolutional neural networks detecting dementia in MRI scans
Authors: Singh Devesh , Grazia Alice , Dyrba Martin , Teipel Stefan 
Published: January 2025

AbstractBackgroundWith a global ageing population, there is an increasing demand for fast and reliable early diagnosis of individuals. Convolutional neural networks (CNNs) have an immense potential in assisting clinicians in diagnosing dementia. Regional atrophy patterns, which are visible in T1‐weighted MRI scans, have been consistently identified by the CNNs with high accuracy. However, there is still hesitation in the clinical adoption of CNNs, due to their black box nature. To alleviate these issues, we propose a framework for explainable CNN models to detect neurodegenerative diseases.MethodUsing data from multiple data cohorts (N>3000) we trained a robust DenseNet CNN model and utilised the feature attribution method ‘layer‐wise relevance propagation’ (LRP) to highlight image features found useful by the model. The FastSurfer tool was utilised to segment individual brain scans into various anatomical region of interests (ROIs). We created an ontology model for encoding the hierarchy of and relationships between neuroanatomical structures. Using linear regression models, controlling for common covariates such as age and sex, we quantified the volumetric deviance from normal levels (w‐scores) for all ROIs. These findings were visualised as summary plots. We integrate two different sources of information about pathology in the MRI scans, 1) the CNN predictive models and 2) the volumetric atrophy analysis. An algorithmic framework was developed to combine both of them and to select the information to be included in textual explanations for the CNN’s predictions (Figure 1).ResultOur framework results in a modular pipeline where each component – attribution method, segmentation tool, ontology model, could be altered due to advancements or changes in the requirements. Our framework implements a plug‐and‐play functionality for interpreting trained CNN models. It assists an easy understanding of CNN’s prediction by generating textual and visual explanations for its findings, with variable degree of detail, focusing on specific ROIs or lobes.ConclusionOur study provides a proof‐of‐concept of utilizing a‐priori neuroanatomical information for explaining CNNs. This prototypical implementation for generating explainable AI is going to be tested by clinicians to evaluate its utility for assisting the diagnosis of dementia. We also plan to extend our framework to other neurodegenerative diseases.
Microstructural assessment of the locus coeruleus‐entorhinal cortex pathway and association with ATN markers in patients with cognitive impairment
Authors: Aiello Marco , Borrelli Pasquale , Cavaliere Carlo , Marizzoni Moira , Ribaldi Federica , Garibotto Valentina , Scheffler Max , Jelescu Ileana O , Jovicich Jorge , Salvatore Marco , Frisoni Giovanni B. , Pievani Michela 
Published: January 2025

AbstractBackgroundThis study investigated microstructural features of the locus coeruleus to entorhinal cortex pathway (LC‐EC) in relation to amyloid (A), tau (T), neurodegeneration (N) markers and cognitive impairment in memory clinic patients.Method124 participants were recruited from the Geneva Memory Clinic (n=30 cognitively unimpaired – CU; n=80 MCI and n=14 dementia ‐ CI) and underwent clinical assessment, 3T MRI scan including diffusion weighted imaging, amyloid PET, and tau PET. Diffusivity indices (fractional anisotropy ‐ FA, mean, axial and radial diffusivities ‐ MD, AxD, RD) were assessed in the LC‐EC pathway using a probabilistic atlas. A, T, N markers were assessed both as continuous and dichotomous measures. Differences in LC‐EC microstructure according to ATN markers and diagnosis were assessed with ANOVA models (FDR correction). Linear regression models were used to test whether LC‐EC pathway microstructure predicted cognitive impairment independently of ATN markers.ResultLower FA (p=0.020) and higher MD, RD and AxD (p<0.005) was observed in participants with tau positivity in the EC (TEC+, Braak stage >1) compared to tau negative subjects (TEC‐). Higher MD, RD and AxD was observed in neurodegeneration positive (N+, medial temporal atrophy) versus negative (N‐) participants (p<0.001), and CI versus CU (p<0.016). There was no difference in LC‐TE microstructure between amyloid positive (A+) and negative (A‐) subjects (p>0.05) nor between tau positive (T+; Braak stage >4) and negative (T‐) subjects (p>0.05). The regression model showed that RD of the LC‐EC tract was associated with clinical diagnosis and mini mental state examination score independently of ATN markers (p<0.05).ConclusionOur results indicate that LC‐EC microstructural measures, specifically RD, are sensitive in detecting CI and provide complementary information over ATN biomarkers. Associations with T suggest that LC‐TE microstructural alterations show regional specificity in the EC.
Intracranial atherosclerosis is linked to lower volume in the posterior portion of the hippocampus
Authors: Chowdhury Gulam Mahfuz , Tazwar Mahir , Evia Arnold M , Kapasi Alifiya , Agrawal Sonal , Bennett David A. , Schneider Julie A. , Arfanakis Konstantinos 
Published: January 2025

AbstractBackgroundIntracranial atherosclerosis is a common age‐related neuropathology that has been linked to cognitive decline and dementia and often mixed with Alzheimer’s and other neuropathologies. But the association of atherosclerosis with brain morphometric abnormalities has not been explored. This work combined Deformation‐based morphometry on ex‐vivo MRI with detailed neuropathological examination in a large number of community‐based older adults to investigate the association.MethodHemispheres from 891 community‐based older adults from four cohort studies of aging: the Rush Memory and Aging Project, Religious Orders Study, Minority Aging Research Study, and African American Clinical Core of the Rush Alzheimer’s Disease Research Center were imaged ex‐vivo on 3T clinical MRI scanners using a multi‐echo spin‐echo sequence with a voxel size = 0.6×0.6×1.5 mm3. All images were non‐linearly registered to an ex vivo brain hemisphere template using ANTs. The logarithm of the Jacobian determinant of the deformation fields was calculated in each voxel and the resulting maps were smoothed using a Gaussian filter with a FWHM = 4mm. All hemispheres underwent detailed neuropathologic examination. The assessed pathologies included atherosclerosis, arteriolosclerosis, cerebral amyloid angiopathy, gross and microscopic infarcts, Alzheimer’s pathology, Lewy bodies, limbic‐predominant age‐related TDP‐43 encephalopathy neuropathological change, and hippocampal sclerosis. Voxel wise linear regression was used to test the association of atherosclerosis with deformations shown in the smoothed LogJ maps, controlling for other age‐related neuropathologies, demographics (age at death, sex, years of education), postmortem interval to fixation, postmortem interval to imaging, and scanner (Fig. 1,2). The FSL PALM tool with 1000 permutations, threshold‐free cluster enhancement, and tail acceleration was used for the statistical analysis. Associations were considered significant at p<0.05 after family‐wise error rate correction for multiple comparisons.ResultVoxel‐wise linear regression showed that intracranial atherosclerosis was significantly associated with lower volume in the posterior body and tail of the hippocampus (p<0.05), independently of the effects of other age‐related neurodegenerative and vascular pathologies (Fig.3). No part of the brain showed significantly higher volume with atherosclerosis.ConclusionThis work demonstrated that intracranial atherosclerosis is associated with lower volume of the posterior body and tail of the hippocampus. This finding is of great interest due to the important role of the hippocampus in cognition.
Aberrant resting‐state functional connectivity of the default‐mode network relates to cognitive decline in the earliest Alzheimer’s continuum
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

AbstractBackgroundAmyloid‐ß (Aß) pathology affects resting state functional connectivity (RSFC), even in cognitively unimpaired (CU) individuals. However, the impact of such an aberrant RSFC on cognitive decline is yet to be determined. Moreover, most prior research focused on fibrillary Aß deposition to predict RSFC, while early Aß dysmetabolism as reflected by cerebrospinal fluid (CSF) concentrations has received less attention. We assessed RSFC as a function of both CSF Aß and p‐tau in CU individuals, and further analyzed the impact of biomarker‐dependent RSFC on the longitudinal cognitive performance.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). RSFC was computed amongst 246 brain regions of the Brainnetome atlas using the CONN toolbox, selecting a cluster threshold of p<0.005. The effects of CSF biomarkers on RSFC were adjusted by age, sex, years of education and APOE‐e4 status.ResultOf the entire sample, 38.4% had positive CSF Aß42/40 markers. Low CSF Aß42/40 ratios were associated to a higher RSFC between visual areas and anterior as well as posterior subdivisions of the default‐mode network (DMN) (Fig. 1). These results survived a family‐wise error rate p‐value<0.005. High levels of CSF p‐tau were related to a higher RSFC between inferior temporal areas and the anterior DMN, as well as a reduced RSFC between visual and the somatomotor network. The Aß‐related higher RSFC significantly predicted longitudinal cognitive decline in PACC, episodic memory (EM) and executive control (EC), in models adjusted by CSF biomarkers (Fig. 2), and further modulated the association between CSF Aß42/40 and PACC longitudinal decline (Fig. 3)ConclusionIn CU individuals, CSF Aß and p‐tau affect RSFC in networks relevant for cognitive performance. Low CSF Aß42/40 was related to hyperconnectivity between the DMN and the visual system. Lack of DMN segregation as a function of CSF Aß42/40 may represent a driving mechanism of cognitive decline in the earliest Alzheimer’s continuum.
The relationship between sex‐steroid hormones and brain volume variation in aging
Authors: Magalhães Thamires Naela Cardoso , Jackson Trevor Bryan , Gonzales Ivan Herrejon , Bernard Jessica A 
Published: January 2025

AbstractBackgroundOlder females, particularly susceptible to Alzheimer's disease (AD), may be affected by hormonal fluctuation during life. We aim to investigate the relationship between changes in brain volume and sex steroid hormones over time. We hypothesize that levels of sex hormones (17ß‐estradiol, progesterone, and testosterone) relate to changes in brain volume, especially in the hippocampus (HPC) and cerebellum (CB). The growing relevance of the CB in cognitive functions supports the exploration of potential associations in AD.MethodSubjects underwent structural magnetic resonance imaging (MRI) in conjunction with a series of cognitive and motor tasks. Roughly 12 months later, a subgroup of 91 participants—comprising 27 individuals in early middle age (average age 41±4.7 years; 13 females), 32 in late middle age (average age 58±4 years; 19 females), and 32 older adults (average age 72±6 years; 16 females)—underwent follow‐up MRI and task assessments (such as MoCA, digit, and symbol span). Saliva samples were collected during both visits for the quantification of sex hormones.ResultsAnalyzing longitudinal data with regression models that included all subjects, we found that baseline T and P levels were predictive of increased volumes in the bilateral cerebellar cortex and left HPC, respectively. In essence, individuals with higher levels of these basal hormones exhibited greater volumes over time. However, upon dividing the sample by sex, the result about P levels remained consistent in the female group. This suggests that, in females, higher baseline P levels continued to predict a greater volume in the left HPC (r = 0.351, p = 0.009; R2= 0.57, p=0.001). Upon closer examination of the groups independently, we identified positive correlations between baseline P levels and changes in volume (delta) of the left hippocampus (r = 0.441, p = 0.03) specifically in the middle‐aged group.ConclusionBoth T and P levels show a protective effect against age‐related structural changes. Ongoing analyses will explore this phenomenon further, with a focus on observing hormonal stages within the sample for clearer understanding.
Contrast of Early Phase Amyloid PET
Authors: Kameyama Masashi , Sakata Muneyuki , Ishibashi Kenji , Tago Tetsuro , Kamitaka Yuto , Toyohara Jun , Ishii Kenji 
Published: January 2025

AbstractBackgroundThe method of obtaining pseudo‐cerebral blood flow images from the initial minutes of amyloid PET scans has been proposed. This technique proves to be highly valuable as it not only indicates the presence of amyloid accumulation but also reveals sites of neurodegeneration. However, the obtained early images represent the K1 image rather than cerebral blood flow. Consequently, the contrast in early images from amyloid PET tracers is thought to be compromised when compared to cerebral blood flow images.MethodTo enhance the contrast in early images obtained from amyloid PET tracers and thereby improve diagnostic accuracy, we developed a method for contrast recovery and established a methodology for determining the parameter involved in achieving this contrast recovery, drawing insights from a thorough literature review. Additionally, we conducted a comparative analysis involving PiB’s early images and FDG, investigating their respective contributions.ResultOur study demonstrated a strong correlation between FDG and early images of PiB, with relatively inconspicuous underestimations in high‐blood‐flow regions.ConclusionWhile we theoretically established a method to recover the contrast in early images, our findings suggest that amyloid PET tracers may exhibit a higher permeability‐surface area product than anticipated. This observation raises the possibility that, without correction, these tracers might inherently provide sufficient contrast.
Cortical Myelination, Cognition, and Cardiovascular Fitness: New Insights using Advanced Magnetic Resonance Relaxometry
Authors: Guo Alex , Faulkner Mary E , Gong Zhaoyuan , Bae Jonghyun , Laporte John P , Bouhrara Mustapha 
Published: January 2025

AbstractBackgroundAlterations in grey matter (GM) are known to contribute to cognitive decline in aging and dementia. Recent evidence suggests that individuals with higher cardiorespiratory fitness (CRF) may have a lower risk of GM atrophy and subsequent cognitive decline. However, the role of cortical myelination in cognitive decline and its association with CRF remains elusive. We used two microstructure MRI measures: MWF, which is a direct measure of myelin content, and R1, which is sensitive to lipids, the main constituents of myelin, to first, investigate the relationship between myelination and longitudinal changes in cognition, and second, determine whether higher CRF is associated with higher myelination in cortical regions.Method123 participants (55.1±20.4 years) underwent our BMC‐mcDESPOT MRI protocol for MWF and R1 mapping with mean values calculated in 6 GM regions (Table 1). Cognitive performance scores were obtained retrospectively at the time of MRI and before. VO2max, the gold standard measure of CRF, was also measured using our established treadmill protocol. The association between each MRI measure and changes in cognition was evaluated using a linear mixed‐effects model. The association between VO2max and each MRI measure was assessed using a multiple linear regression model. Relevant covariates were included in both models (Fig1. caption & Fig.2 caption).ResultLower myelin content in various GM regions, indicated by lower MWF and R1 values, is associated with a steeper decline in all cognitive domains (Fig. 1). These associations were prominent with processing speed and executive function (Table 1A). Moreover, higher VO2max was associated with higher myelin content in several GM regions (Table 1B). An interaction between age and VO2max revealed a steeper positive slope in the older age group, suggesting that the beneficial effect of VO2max on MWF is more pronounced at older ages; and a negative slope in the lower VO2max group, indicating that lower VO2max levels are associated with more rapid demyelination with advanced ages (Fig.2, Table 1B).ConclusionThese original findings highlight the importance of cortical myelination in cognitive functioning, and suggest that improving CRF could represent a non‐pharmaceutical intervention toward better myelination and offset subsequent cognitive decline.
Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients
Authors: Soleimani Laili , Cheng Fu‐Yuan , Sano Mary , Kia Arash 
Published: January 2025

AbstractBackgroundDementia poses a significant global crisis, yet 60% of cases go undetected, particularly among specific sub‐populations. Timely diagnosis is crucial for implementing early intervention strategies. Challenges of current screening tools (e.g., Montreal Cognitive Assessment (MOCA) and Mini‐mental Status Exam (MMSE)) include their rule‐based nature, time‐consuming administration and potential bias against individuals with lower education or different socioeconomic backgrounds.MethodIn this study, we created a patient cohort within Mount Sinai Health System, identifying individuals at high‐risk for cognitive impairment using MMSE scores 23 and below. Extracting clinical notes from inpatient and outpatient settings, we applied specific sampling logic to select the last five notes for each patient before their MMSE. Utilizing word‐embedding techniques, we vectorized sentences and input these vectors into a deep neural network for Named Entity Recognition to extract clinical concepts (e.g diagnosis, signs and symptoms, medications). A psychiatrist reviewed and selected 337 features based on their frequency in the cohort and clinical relevance. Using these features, we created a map to generate feature vectors, and following a train‐test split, 70% of the data was used to train a classifier using the random forest algorithm. The remaining 30% of the cohort was employed for validation purposes.ResultOut of 4,366 patients, with documented MMSE scores from 2011 to 2023, 73% had one MMSE, and 29% scored below 24. The developed model demonstrated an AUC ROC of 0.93. At a threshold of 0.5, it showed a sensitivity of 0.86 and specificity of 0.85 in the validation set. On the test set, the AUC ROC was 0.68, with a sensitivity of 0.62 and specificity of 0.63 at a threshold of 0.5.ConclusionThe development and validation of this digital tool exemplify translational research that leverages Real‐World Data (RWD) from EMR. The tool aims to assist clinicians in efficient dementia detection across different stages of the disease. Our next phase involves optimizing the feature engineering, fine‐tune the model, and deploying it in the production environment. This will facilitate automated screening of geriatric patients during clinical encounters at Mount Sinai Hospital, allowing us to measure the tool's prospective performance.
Dynamic Changes in Quantitative MRI‐based Biomarkers and Its Association with Pathology of Alzheimer’s Disease – A Longitudinal Study
Authors: Fan Xiang , Cai Yuan , Liu Wanting , Zhao Lei , Luo Yishan , Shi Lin , Mok Vincent Chung Tong 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) resemblance atrophy index (AD‐RAI) is a novel MRI‐based machine‐learning derived biomarker for AD that is valid in the detection of early AD. We aimed to investigate the association between the serial change of AD‐RAI and cognitive progression overtime among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects with and without A+T+ (i.e., Aß+ and Tau+) and to compare it with other conventional MRI biomarkers (i.e., hippocampal volume (HV), hippocampal fraction (HF), brain parenchymal volume (BPV) and fraction (BPF)), which have been used in the past as surrogate markers for AD.MethodWe selected participants from the ADNI databases and included those aged 55 to 90 years, diagnosed with CU and MCI at baseline with high‐quality volumetric MRI data and CSF at baseline, and at least once matched T1W scans and CSF during follow‐up. We define A+T+ based on the threshold value of Aß1‐42 and ptau in CSF. A total of 168 participants were recruited with serial MRI scans and corresponding CSF in total. We assessed longitudinal changes of the MRI biomarkers (i.e., AD‐RAI, HV, HF, BPV, BPF) in correlation with changes in time and conversion status with and without A+T+. We used a linear mixed‐effects model (LMM) in SPSS 26.0 to explore the serial changes of the imaging biomarkers and interaction effects with and without A+T+ over time, and further explore the association between dynamic CSF Aß1‐42 and dynamic changes in the imaging biomarkers’ interaction effects over time.ResultAD‐RAI with A+T+ increased significantly faster than non‐A+T+ over time (P < 0.05), while HV and HF with A+T+ decreased significantly faster than non‐A+T+ over time (P < 0.05, respectively), which performed better than BPV/BPF. Serial AD‐RAI was significantly associated with changes in CSF Aß1‐42 over time, which outperforms other imaging biomarkers.ConclusionSerial AD‐RAI outperforms other conventional biomarkers in correlating with AD pathologies. It may be used as a surrogate marker for monitoring disease progression or treatment response in AD.