bluuebunny/crossref_metadata_2025_split huggingface dataset:
Voxel‐wise comparison of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of individuals across the Alzheimer’s disease spectrum – the HEAD study
Authors: Bellaver Bruna , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Amaral Livia , Ferreira Pamela C.L. , Lowe Val J. , soleimani‐meigooni David N. , Oh Hwamee , Tudorascu Dana , Jagust William J. , Klunk William E , Pascual Belen , Gordon Brian A. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali 
Published: January 2025

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aß‐, 15 CU Aß+, 14 MCI Aß‐, 32 MCI Aß+ and 13 AD dementia) with Aß‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aß‐ from CU Aß+ (Fig.1A). The difference between tracers’ AUC was greater in Braaks IV and V (Fig.2A), both regions that are not expected to have tau accumulation in CU Aß+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aß‐ from MCI Aß+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Fig.1B, Fig. 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aß‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Fig.1C, Fig.2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aß‐ from MCI Aß+ individuals (Fig.1D, Fig.2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aß‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aß+ from CU Aß‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.
Multicentre validation of the dementia with Lewy bodies‐related metabolic brain pattern
Authors: Perovnik Matej , Simončič Urban , Kos Hana , Jamšek Jan , Kramberger Milica G , Brendel Matthias , Camacho Valle , Vandenberghe Rik , Garibotto Valentina , Ochoa‐Figueroa Miguel A , Padovani Alessandro , Morbelli Silvia , Ferreira Daniel , Trošt Maja 
Published: January 2025

AbstractBackgroundDementia with Lewy bodies (DLB) is a an a‐synucleinopathy characterized by dementia and a combination of parkinsonism, visual hallucinations, fluctuating cognition or REM sleep behaviour disorder. Specific biomarkers for DLB are lacking. DLB‐related pattern (DLBRP) is a metabolic network imaging biomarker which expression can be quantified on a single patient basis. DLBRP has been identified in few different cohorts but lacks a multicentric validation.MethodsFDG PET scans from 259 DLB patients and 176 normal controls (NC) were obtained from eight centres from European DLB Consortium (EDLB, 180 patients), Alzheimer’s disease Neuroimaging Initiative (ADNI, 135 NC) and local dataset from Ljubljana (LJU, 79 patients and 41 NC). FDG PET scans were pre‐processed and we used topographic profile rating to calculate the DLBRP expression (i.e. subject scores). Subject scores were compared between groups using t tests and ANOVA and correlated with clinical parameters using Pearson’s correlation coefficient.ResultsPatients from different centres differed in MMSE (p < 0.001), proportion of patients with parkinsonism (p < 0.001) and visual hallucinations (p < 0.001), but not in age (p = 0.15) or sex distribution (p = 0.13). DLB patients from all centres had significantly higher DLBRP expression than NCs (all p < 0.001). We could accurately distinguish between DLB and NC (AUC = 0.983) based on DLBRP expression. We observed a negative correlation between DLBRP expression and MMSE (r = –0.30, p < 0.001). The expression of DLBRP differed among centres, post hoc analysis showed that only patients from Barcelona had significantly lower DLBRP subject scores in comparison to other groups but they were also less cognitively impaired. Patients with visual hallucinations (p < 0.001) had significantly higher DLBRP expression than those without hallucinations and patients with abnormal Datscan™ (p = 0.02) had higher DLBRP expression than those with normal Datscan™. Other core features, levodopa or antidementives did not affect the DLBRP expression.ConclusionsWe showed that DLBRP can accurately distinguish between DLB and NC from multiple centres and different scanners. The DLBRP expression correlated with measurements of cognitive impairment. This validation study confirms that the DLBRP is a robust metabolic imaging biomarker of DLB.
Locus coeruleus metabolism predicts the accumulation of Alzheimer’s disease pathology in amyloid‐positive symptomatic individuals
Authors: Koops Elouise A. , Dutta Joyita , Becker Alex , Hanseeuw Bernard J , Sperling Reisa A , Johnson Keith A , Jacobs Heidi I.L. 
Published: January 2025

AbstractBackgroundHigher cerebrospinal fluid noradrenergic metabolic turnover has been associated with higher levels of Alzheimer’s disease pathology in cognitively impaired individuals. It remains unclear whether there is a specific anatomic vulnerability to metabolic alterations within the locus coeruleus (LC) and whether this hypermetabolism relates to steeper rates of pathology accumulation. Here, we overcome a spatial limitation of Positron Emission Tomography (PET) imaging in small nuclei with a dedicated Magnetic Resonance Imaging (MRI)‐guided framework to recover PET resolution. This method allowed us to investigate the [18F]Fluorodeoxyglucose (FDG)‐PET signal, reflecting energetic metabolic demand of the LC and its association with serial beta‐amyloid and tau‐PET.MethodsWe included 78 amyloid‐positive, cognitively impaired participants from the ADNI‐3 cohort (Table 1). The LC was identified using a dilated Keren template registered to each T1 (Fig 1A). The LC mask was subdivided into a rostral, middle, and caudal part to investigate region‐specific associations (Fig 1B). We used an MR joint‐entropy‐penalty algorithm to quantify PET signal via deconvolution based on spatially‐variant blur kernels. The deconvolution step was stabilized using an anatomical‐based joint‐entropy prior (Fig 1C). The FDG‐PET signal was referenced to the pons, excluding the LC (Fig 1D). Amyloid positivity was determined as a centiloid score = 21, reflecting moderate neuritic plaques, on florbetaben and florbetapir scans. Linear mixed effects models investigated relationships between LC‐FDG, global beta‐amyloid and meta‐temporal tau burden (flortaucipir) over time. Age and sex were included as covariates.ResultsHigher LC FDG‐PET signal was associated with higher meta‐temporal tau‐PET at baseline (T = 2.87, p = 0.00463) and accumulation over time (T = 2.257, p = 0.0273; Fig 2A&C). Higher LC FDG‐PET signal was associated with higher global amyloid at baseline (T = 2.066, p = 0.041) and faster amyloid accumulation over time in the dementia group (T = ‐2.74, p = 0.00904; Fig 2B&D), following a rostro‐caudal gradient.ConclusionIn a cohort with cognitive impairment, we observed steeper accumulation of Alzheimer’s disease pathology in individuals with higher LC FDG‐PET signal, with a specific amyloid‐related vulnerability for the rostral parts of the LC. Future steps will be to include cognitively unimpaired individuals to investigate the relationship between LC FDG‐PET signal and pathology accumulation in early stages of the disease process.
Impact of TDP‐43 co‐pathology on limbic cortical microstructure in Alzheimer's disease: evidence from an autopsy‐confirmed cohort with in vivo diffusion MRI
Authors: Torso Mario , Ridgway Gerard R , Hardwidge Jamie , Hardingham Ian , Chance Steven A ,   National Alzheimer’s Coordinating Center,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: January 2025

AbstractBackgroundTDP‐43 (TAR DNA‐binding protein 43) is one of the most frequently observed co‐pathologies in Alzheimer's disease (AD). Recognizing the diversity of pathological features in individuals with AD, including the presence of TDP‐43, may lead to more personalized and effective treatment approaches. We investigate ante‐mortem cortical microstructural changes in MRI with subsequent autopsy confirmation of Alzheimer’s disease neuropathological changes (ADNC) with and without TDP‐43 comorbidity.MethodParticipants with ADNC and ante‐mortem MRI were obtained from the National Alzheimer's Coordinating Center (NACC; n=46) and the Alzheimer's Disease Neuroimaging Initiative (ADNI; n=8). Participants were grouped based on TDP‐43 presence (36 AD “pure” and 18 AD with TDP‐43). Thal phase, CERAD neuritic plaque assessment, Braak NFT, and combined ABC score, were used as AD neuropathological hallmarks. Cases with TDP‐43 = Stage 2 were considered AD with TDP‐43. Structural and diffusion MRI were used to calculate cortical diffusivity measures in five functional macroregions: Primary Sensory, Unimodal, Limbic/Proisocortex, Heteromodal and Primary Motor (Ffytche & Wible 2014, PMID:24936089; Mesulam 1998, PMID:9648540). Cortical mean diffusivity and three minicolumn‐inspired cortical diffusivity measures were calculated: the angle between the radial minicolumnar direction and the principal diffusion direction (AngleR); the principal diffusion component parallel with the minicolumns (ParlPD), and the diffusion components perpendicular to the minicolumns (PerpPD+) (Torso et al. 2022, PMID:36281682). Groups were characterized using clinical, demographic, and neuropathological data (see table). Differences in diffusion metrics were tested adjusting for interval between MRI scan date and autopsy date, scanner manufacturer, diffusion b‐value, age and sex, with FDR correction (pFDR<0.05).ResultThe Limbic/Proisocortex macroregion exhibited significantly higher AngleR (F1,36= 7.978; pFDR=0.04) in AD with TDP‐43 (AngleR mean 1.135) compared to the AD pure group (1.055).ConclusionDiffusion MRI can detect differences in cortical microstructure of AD with TDP‐43 co‐pathology, suggesting more extensive cortical damage in AD with TDP‐43 comorbidity that involves Limbic cortical areas (cingulate, entorhinal, parahippocampal, and temporal pole). Consistent with previous findings regarding early cortical changes associated with amyloid deposition and neuroinflammation (Torso et al. 2022, PMID:36281682 Torso et al. 2323PMID:37794477), the diffusion‐derived metric AngleR appears particularly sensitive to subtle effects, such as the impact of TDP‐43 in addition to ADNC.
Association between brain metabolism and anxiety in long COVID in an underrepresented cohort
Authors: de Souza Débora Guerini , Silveira Maiele Dornelles , Borelli Wyllians Vendramini , Senger Joana Emilia , Machado Luiza Santos , Ferrari‐Souza João Pedro , de Bastiani Marco Antônio , Povala Guilherme , da Silva Ana Paula Bornes , de Mello Guilherme Bastos , da Ros João Pedro Uglione , Jotz Arthur Viana , Kadan Matheus Fakhri , Radaelli Graciane , Faria Daniele de Paula de Paula , Coutinho Artur Martins , Lourenco Mychael V , Pascoal Tharick A. , Rosa‐Neto Pedro , Matushita Cristina Sebastião , Soder Ricardo Benardi , Schumacher‐Schuh Artur Francisco , Souza Diogo O. , da Costa Jaderson Costa , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundLong COVID is an under‐characterized disorder that affects a wide range of individuals after COVID‐19 resolution. Long COVID individuals report persistent neurological manifestations, such as anxiety. Understanding its effects in the brain might help uncover the actual burden imposed by the pandemic sequelae and either define or discard long COVID as a risk factor for neurodegenerative diseases. Here, we aim to identify whether there is an association between brain metabolism and anxiety in an underrepresented population.MethodCommunity‐dwelling individuals, above 50 years old, from Porto Alegre, Brazil, were divided into long COVID (n = 39) and control groups (n = 10) were evaluated with a battery of neuropsychological testing, including the GAD‐7 scale of anxiety. Then, they underwent a brain [18F]FDG‐PET scan (images normalized by the pons). We conducted a voxel‐wise linear regression testing the association between [18F]FDG metabolism and GAD‐7, and corrected for education, sex, and age. The analysis was corrected for multiple comparisons using the cluster‐wise random field theory method (significant t←3.34 and t>3.34, p<0.001, df = 35).ResultWe found that GAD‐7 score presented a widespread negative association with [18F]FDG metabolism in multiple gray and white matter regions (Figure 1). Specifically, hippocampus (tmax = ‐3.34, p = 0.002), amygdala (tmax = ‐3.82, p = 0.0005), cerebellum (tmax = ‐4.28, p = 0.0001), and lateral occipitotemporal gyrus (tmax = ‐5.26, p = 0.0001) had the most relevant associated clusters in gray matter, while temporal lobe (tmax = ‐3.9, p = 0.0004) and frontal lobe (tmax = ‐4.33, p = 0.0001) presented the most relevant associated clusters in white matter.ConclusionAnxiety symptoms are a highly self‐reported symptom in long COVID. Here we show that anxiety is widely associated with reduced brain glucose metabolism in crucial areas for the limbic system and cognition, such as the hippocampus and amygdala. The peculiar associations between anxiety and FDG metabolism in white matter may suggest inflammatory responses triggered by long COVID. These data provide new insights into the mechanisms underlying long COVID symptoms in the brain.
Biological aging is associated with atypical neurodegenerative patterns in Alzheimer's disease
Authors: Sreepada Lasya P , Brown Christopher , Das Sandhitsu R. , Yushkevich Paul A. , Wolk David A , McMillan Corey T 
Published: January 2025

AbstractBackgroundAtypical presentations of Alzheimer’s disease (AD), which demonstrate more cortical involvement relative to medial temporal lobe (MTL), are generally associated with younger age of onset. Age, defined chronologically, is a primary driver of AD pathology and neurodegeneration. However, some young onset cases are typical, amnestic presentations and some older onset cases present more cortical atrophy. We hypothesize that a biological definition of age, defined using epigenetic clock measures, may be even more tightly linked to atypicality than chronological age.MethodSubjects from ADNI with blood DNA methylation (DNAm) samples and T1 MRI (N=646, 55.7% female, 75.5 +/‐ 7.4 years, Table 1) were selected. Participants were defined as “young” (< 65 years) or “old” (> 80 years) amyloid‐negative controls or amyloid‐positive MCI/AD. DNAm was assayed on Illumina EPIC arrays. Epigenetic age was computed by applying the Shireby cortical epigenetic clock. We then defined epigenetic age acceleration (EAA) by regressing epigenetic age against chronological age and extracting the residual. Subjects were classified as appearing “biologically younger” (EAA < 1 SD below the mean), “neutral” (EAA within 1 SD), or “biologically older” (EAA > 1 SD above the mean). Regional cortical thickness measures were derived from MRI using FreeSurfer 5.1 and harmonized for scanner differences using longitudinal ComBat. We computed composite bilateral thickness z‐scores, adjusted for age and sex relative to controls, in the cortical AD signature anatomical ROIs (Figure 1) and the MTL (entorhinal and parahippocampal cortices). Finally, we defined an AD “mismatch” score as the signed difference of the composite cortical and MTL thickness z‐scores.ResultCortical thickness z‐scores were lower in MCI/AD relative to controls. Lower mismatch scores, reflecting greater cortical neurodegeneration relative to MTL, were observed in young‐onset relative to older‐onset MCI/AD. Similarly, mismatch scores were significantly lower in biologically younger cases relative to biologically older cases (t=2.5589; p = 0.01; Figure 2).ConclusionOur results support the notion that biological age, as measured by epigenetic modifiers, modulates the degree of atypicality in AD patients, akin to chronological age. These findings suggest that biological aspects of aging may influence the topography of AD‐related pathology and spread.
Detection of presymptomatic cerebral amyloid angiopathy, CAA, using diffusion MRI analysis of cortical grey matter in hereditary mutation carriers
Authors: Chance Steven A , Hardingham Ian , Valotti Michele , van Rooden Sanneke , van der Grond Jeroen 
Published: January 2025

AbstractBackgroundHCHWA‐D mutation carriers experience highly heritable early onset of cerebral amyloid angiopathy (CAA). Diffusion MRI appears to be sensitive to CAA and detects punctate cortical lesions in CAA cases post‐mortem. DTI of white matter tracts was found to be sensitive to alterations in symptomatic HCHWA‐D carriers but did not show alterations in presymptomatic carriers (Schouten et al. https://doi.org/10.1161/JAHA.118.011288.). The diffusion signal from cortical grey matter microstructure may yet detect presymptomatic cases.MethodCDM (cortical disarray measurement) quantifies DTI parameters related to the vertical minicolumnar microstructure of cortical grey matter that is disrupted in neurodegeneration. AngleR is the angle between the principal diffusion direction and the cortical minicolumn axis. PerpPD+ and ParlPD are the components parallel and perendicular to the minicolumn axis. Participants from an existing dataset (Schouten et al.), included 12 symptomatic and 11 asymptomatic HCHWA‐D mutation carriers and 24 non‐carrier controls (based on: genotype, clinical status, and age). Diffusion MRI had been acquired at a single time point from a Philips Achieva 3T scanner at Leiden University Medical Center. Statistical GLM used multivariate ANOVA, classifying patients by Mutation status (carrier vs non‐carrier) and Clinical status (non‐carrier control, asymptomatic carrier, symptomatic carrier), covarying for age.ResultFor the whole brain cortical average PerpPD+ was significantly increased in mutation carriers (p<0.05) driven by a significantly increased PerpPD+ in symptomatic carriers compared to both asymptomatic groups (controls and carriers) (p<0.05). At the regional level some measures in prespecified regions of interest were significantly altered in symptomatic carriers and, notably, significantly different in mutation carriers (p<0.05), both symptomatic and asymptomatic groups, compared to controls.ConclusionDiffusion MRI analysis of cerebral cortex detects microstructural differences related to clinical symptoms of CAA in HCHWA‐D mutation carriers and particular measures of the microstructure organisation differentiate the CAA group, including presymptomatic, from controls.
Psoas Muscle Sarcopenia Predicts Brain Volume Loss on MRI in 7,149 Individuals
Authors: Raji Cyrus A. , Meysami Somayeh , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul 
Published: January 2025

AbstractBackgroundSarcopenia has been linked to brain atrophy and there is lack of information on specific muscle groups that may contribute to this link. The psoas muscles are sensitive to sarcopenia and thus may sensitively relate to brain aging and Alzheimer disease risk.MethodThis study utilized 7,149 healthy individuals across four sites (Mean age 53.06 ± 12.95 years, age range 18‐97 years; 48% women; 52% men; 38% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences included coronal T1 for segmentation of psoas muscle volumes. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 segmented 96 brain regions. Partial correlation analysis was done on psoas muscle volumes and brain regions controlling for sex and total intracranial volume to determine if lower psoas muscle volumes correlated to brain atrophy. Multiple comparisons were accounted for using the Bonferroni Method.ResultLower psoas muscle volumes demonstrated a statistically partial correlation to lower gray matter (rp = ‐0.164, p = 2.66×10‐43) and white matter volumes (rp = ‐0.171, p = 4.39×10‐47). The frontal lobe exhibited a negative correlation (rp = ‐0.172, p = 1.30×10‐47), identical to the temporal lobe (rp = ‐0.172, p = 1.21×10‐47). Negative correlations were also noted in the parietal lobe (rp = ‐0.119, p = 8.72×10‐23), and occipital lobe (rp = ‐0.139; p = 2.89×10‐31). The hippocampus also showed a statistically significant negative correlation (rp = ‐0.145, p = 7.65×10‐34), as did the posterior cingulate (rp = ‐0.162, p = 5.20×10‐42) and the precuneus (rp = ‐0.101, p = 1.34×10‐16). The cerebral ventricles showed a non‐significant negative correlation (rp = ‐0.025, p = 0.345).ConclusionWe demonstrate a significant correlation between lower psoas muscle volume and brain volume loss, including AD risk regions such as the hippocampus and precuneus. These MRI findings underscore the importance of muscle health in relation to brain integrity. The volume of the psoas, which is involved in basic leg flexion activities such as walking, could be a crucial indicator of brain health and Alzheimer risk.
Large‐scale Normative Modeling of Brain Microstructure over the Human Lifespan
Authors: Villalon‐Reina Julio E , Zhu Alyssa H , Benavidez Sebastian M , Moreau Clara , Feng Yixue , Laltoo Emily , Chattopadhyay Tamoghna , Nourollahimoghadam Elnaz , Thomopoulos Sophia I , Nabulsi Leila , Lawrence Katherine E , Weiner Michael W , Jack Clifford R. , Nir Talia M , Jahanshad Neda , Thompson Paul M. 
Published: January 2025

AbstractBackgroundNormative models (NM) of brain metrics based on large, diverse populations offer novel strategies to detect individual brain abnormalities. To create an age‐dependent statistical model of brain microstructure over the human lifespan, we built the largest multi‐site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 54,591 subjects. We used state‐of‐the‐art tools to adjust for site‐dependent effects.MethodWe used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves (ages 3‐100 years) by merging data from different sections of the lifespan. We analyzed 19 international public datasets with the ENIGMA‐DTI protocol. Mean fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AxD, RD) were extracted for 21 bilateral ROIs from the JHU‐WM atlas and the whole WM skeleton. Regressions were run with each metric per ROI as a function of age and sex, and the DTI protocol as the batch effect (37 protocols). Z‐scores were derived for 81 patients with Alzheimer’s disease (AD; mean age: 77y±8.4, 46M/35F) and 225 MCI participants (mean age: 75.1y±8.1, 127M/98F) and extreme deviations (Z>|2|) were quantified for each condition. ROI‐wise areas under the ROC curve were calculated to determine the classification accuracy of the Z‐scores.ResultThe average FA for the overall WM skeleton reached its peak value at 27 years, with minima for MD, RD and AxD at 47, 42 and 54 years, respectively. We found extreme FA deviations (Z>2) for MCI (AUC=0.67) and AD (AUC=0.66) in the genu of the corpus callosum, extreme RD deviations (Z<2) in the tapetum (AUC=0.73) and Extreme AxD deviations (Z>2) for MCI in the superior longitudinal fasciculus (AUC=0.65). See Figures 1 & 2.ConclusionNM of brain microstructure with HBR yielded lifespan trajectories for widely‐used DTI metrics. By pooling large scale multi‐site dMRI to create a statistical reference, single subject deviations are detectable across the lifespan for AD and MCI. These NM will be publicly available to the research community.