bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional Connectivity of the Affective Blindsight Pathway in Atypical Alzheimer’s Disease
Authors: Yi Hae Young H , Li Joyce S , Trainer Anne E , Ficek‐Tani Bronte , Constable Todd , Fredericks Carolyn 
Published: January 2025

AbstractBackgroundAffective cognition and emotion processing is impaired in amnestic Alzheimer’s disease (AD), although less is known about atypical (AT) variants such as logopenic variant primary progressive aphasia (lvPPA) and posterior cortical atrophy (PCA). The affective blindsight pathway bypasses V1 via the superior colliculus‐pulvinar route to activate the amygdala in cases of occipital lesioning and may explain maintenance of emotion identification and visual information processing in non‐amnestic AD despite atrophy in visuospatial regions. We sought to characterize functional connectivity from key regions along the affective blindsight pathway in a clinically heterogeneous AD cohort.MethodWe collected structural, resting‐state, and task functional MRI scans where participants viewed affective face and neutral scene images, and neuropsychological data in individuals with clinically heterogeneous AD (8 lvPPA, 9 PCA) and age‐matched healthy controls (24 HC). We assessed connectivity from 4mm radius seeds located along the affective blindsight pathway (superior colliculus, medial pulvinar, and amygdala) using CONN toolbox. Two‐sample t‐tests were used to compute group‐level differences (AT > HC; p < 0.05/0.05), using w‐scores to correct for atrophy.ResultAs expected, AT participants performed significantly worse on visuospatial tasks outside the scanner (WAIS‐III Blocks Design, Benson Complex Figure Copy immediate recall; p < 0.01). During resting state scans, the AT group had relatively lower connectivity to visuospatial regions including the lateral occipital cortex, angular gyrus, and precuneus from the superior colliculus and medial pulvinar seeds. During the scenes/faces task, they displayed relatively higher connectivity to those same visuospatial regions. From the amygdala seed, the AT group had higher connectivity to area V1 during the resting state scan and lower connectivity to frontal lobe regions during scenes/faces.ConclusionWe explored functional connectivity of the affective blindsight pathway in a clinically heterogeneous AD cohort. Connectivity was relatively higher in the AT group from the superior colliculus, medial pulvinar, and amygdala to occipital lobe regions commonly atrophied in PCA and lvPPA, suggesting a function of this pathway may be to compensate for disease‐related atrophy. While preliminary, these findings hint that the blindsight pathway may serve a compensatory function in atypical AD.
A weakly‐supervised, MRI‐guided clustering of cognitive decline: A post‐hoc analysis of the EXPEDITION‐3 clinical trial
Authors: Nallapu Bhargav Teja , Petersen Kellen K. , Demirsoy Idris , Lipton Richard B. , Davatzikos Christos , Rabin Laura , Sajjadi S. Ahmad , Ezzati Ali 
Published: January 2025

AbstractBackgroundAlzheimer’s disease exhibits heterogeneity through varied phenotypic and pathological manifestations. Here, we aimed to investigate the potential of semi‐supervised pattern classification applied to volumetric MRI data in identifying relatively homogeneous subgroups of individuals exhibiting cognitive decline (CD) throughout the study period.MethodWe used data from the placebo arm of trial of Solanezumab for mild dementia due to AD (EXPEDITION‐3 trial). Participants were classified as showing CD if there was any increase in Clinical Dementia Rating (CDR, Sum of Boxes) from baseline to the 80 weeks of follow‐up, and as stable cognition (SC) otherwise. We applied a non‐linear, semi‐supervised clustering algorithm, HYDRA, that captures subtypes within the decliner group accounting for the heterogeneous variations that are also present in the stable group. Two models with different feature‐sets were developed: model I used only baseline volumetric MRI measures, while the model II included both baseline volumetric MRI measures and longitudinal changes observed after 80 weeks.ResultParticipants were 710 individuals with longitudinal MRI and CDR measures (mean age: 72.57[±7.69] years, 60.1% Female). Models I and II each produced two distinct subgroups of CD with notable differences in their baseline MRI volumes, and different rates of change in cognitive outcomes over the course of trial (Table 1). In both the models, one of the subgroups (Slow Decliner) had significantly lower change in scores than the other (Fast Decliner) on the 11‐item version of the Alzheimer’s Disease Assessment Scale–Cognitive subscale (ADAS‐Cog) at the end of Week 80 (Figure 1). In Model‐I, both the decliner subgroups had smaller temporal and parietal lobe volumes at baseline compared to the Stable group (Figure 2). In Model‐II, the Slow Decliner group had higher baseline MRI volumes than the stable group and significantly lower change in ADAS‐Cog compared to that of the Fast Decliner group (Figure 2).ConclusionHeterogeneity in brain atrophy is linked to distinct patterns of cognitive decline. Using longitudinal changes in MRI measures revealed decliner subgroups with greater difference in ADAS‐cog scores compared to those obtained by baseline measures only.
Tau pathology increases α‐synuclein spreading from the periphery to the CNS
Authors: Soto‐Faguás Carlos M , Bowman Anna , Woltjer Randall , Unni Vivek K 
Published: January 2025

AbstractBackgroundDementia with Lewy Bodies (DLB) is one of the most common Alzheimer’s Disease (AD)‐related dementias and it is defined by the presence of abnormal cytoplasmic inclusions composed of aggregated α‐synuclein (αsyn) in neuronal soma, known as Lewy bodies (LB). LB often coexists with AD type pathology such as amyloid‐β (Aβ) plaques and neurofibrillary tangles containing hyperphosphorylated tau in several LB dementias, including Parkinson’s Disease Dementia and Lewy Body variant AD. These co‐pathologies likely represent a spectrum of various contributions of shared mechanisms that underlie these diseases. However, the mechanisms of the interaction of these pathological proteins is still poorly understood. Furthermore, there is a lack of mammalian model systems demonstrating robust generation of both Lewy with Aβ and/or tau pathology in the field. Unraveling the mechanistic link between αsyn and AD‐related pathologies might be useful for the treatment of DLB.MethodWe have recently developed a brand‐new transgenic mouse line that expresses A53T αsyn tagged with GFP (A53T Syn‐GFP, AA) that forms LB pathology ∼10x faster than previous models after intracortical/peripheral PFF injection. To investigate the role of tau in αsyn spreading from the periphery to the CNS we crossed the AA mice with the P301S tau transgenic mice (PS19). We have performed monomeric/PPF injection in the gastrocnemius muscle of single and double transgenic mice and carried out a histological analysis of the αsyn and tau pathologies through the spinal cord and the brain.ResultWe found an increase in the number of αsyn+ inclusion induced by PFF injection in the spinal cord and the brain of PS19/AA mice compared to single AA mice. Interestingly, we found sex differences in αsyn pathology being higher in females than males. Furthermore, monomeric αsyn caused reduced but some αsyn inclusions in the spinal cord after muscle injection.ConclusionOur preliminary data shows an increase in αsyn pathology and spreading from the periphery to the brain in the presence of tau pathology suggesting a link between αsyn and tau pathologies that could represent potential treatment target for the treatment of DLB patients.
SMOC1 colocalizes with Alzheimer’s disease neuropathology and delays Aβ aggregation
Authors: Balcomb Kaleah , Johnston Caitlin , Kavanagh Tomas , Halliday Glenda M , Wisniewski Thomas , Sunde Margaret , Drummond Eleanor 
Published: January 2025

AbstractBackgroundSMOC1 has recently emerged as one of the most significant and consistent new biomarkers of early Alzheimer’s disease (AD). SMOC1 is one of the earliest changing proteins in AD, with SMOC1 cerebrospinal fluid levels increasing 29 years before symptom onset in autosomal dominant AD. Despite this clear association with disease, very little is known about the role of SMOC1 in AD or its function in the brain. Therefore, the aim of this study was to study the distribution of SMOC1 in human AD brain tissue and to determine if SMOC1 influenced Aβ aggregation.MethodsThe distribution of SMOC1 in human brain tissue was assessed in 3 brain regions (temporal cortex, hippocampus, frontal cortex) using immunohistochemistry in a cohort of 74 cases encompassing advanced AD, mild cognitive impairment (MCI), preclinical AD and cognitively normal controls. The Aβ‐SMOC1 interaction was assessed in advanced AD human brain tissue (n = 6) using co‐immunoprecipitation and the influence of SMOC1 on Aβ aggregation kinetics was assessed using Thioflavin T assays and electron microscopy.ResultsSMOC1 strongly colocalized with a subpopulation of amyloid plaques in AD (44.0±2.6%), MCI (33.0±5.9%) and preclinical AD (28.5±6.4%). SMOC1 levels in the brain strongly correlated with plaque load, irrespective of disease stage. Confocal imaging confirmed that SMOC1 consistently colocalized with Aβ fibrils within plaques and CAA, suggesting a possible direct interaction. SMOC1 also colocalized with a subpopulation of pTau aggregates in AD (13.2±3.1%), albeit at a much lower intensity than observed for plaques or CAA. A SMOC1‐Aβ interaction was confirmed in human AD brain tissue using co‐immunoprecipitation. Thioflavin T aggregation assays showed that SMOC1 significantly delayed Aβ aggregation in a dose‐dependent manner. Electron microscopy confirmed that the Aβ aggregates generated in the presence of SMOC1 had a different morphology; fibrils were thinner and more twisted and many small, spherical, oligomeric structures were present.ConclusionsSMOC1 colocalizes with amyloid plaques, CAA and tau pathology. SMOC1 interacts with Aβ and delays Aβ aggregation in vitro, making it a key candidate for future drug development studies.
Neuropathology in the LifeAfter90 Study: 2024 update on an Ethnically Diverse Cohort Study of Oldest‐Old
Authors: Dugger Brittany N , Jin Lee‐Way , Patel Viharkumar , Pamatz Madely Martinez , Luu Melanie , Decarli Charles , Gilsanz Paola , Mungas Dan M. , Kawas Claudia H. , Corrada María M. M. , Whitmer Rachel A. 
Published: January 2025

AbstractBackgroundExamining the neuropathology of the oldest‐old has significantly advanced our understanding of the multiple etiologies in very late life. Most studies have included exclusively White decedents with limited ethnoracial diversity. Our goal was to characterize neuropathology in a cohort of ethnically and racially diverse oldest‐old decedents.MethodThe LifeAfter90 study is an ongoing cohort study of Kaiser Permanente Northern California members, aged 90+ with targeted recruitment of individuals across different racial/ethnic groups with no prior diagnosis of dementia in their medical record. Interviews and cognitive assessments occur approximately every 6 months. Brain donation was available to all interested consenting participants. Neuropathology was assessed using National Alzheimer’s Coordinating Center Neuropathology forms and NIA‐AA guidelines diagnoses.ResultAs of January 2024, 340 participants (39%) have enrolled in autopsy (22% Asian, 18% African American, 17% Latino, 9% Multiracial/Other, and 34% White). Of the 340 participants, 91 had died and neuropathological evaluations were completed. The mean age of death was 95 years (range 90‐105), 51 (56%) were female, 12 Asian, 10 Black, 16 Latino, 44 White, and 8 Multiracial/Other. At final clinical exam, 25 participants had dementia (28%), 24 had cognitive impairment no dementia (CIND, 42%), and 41 had normal cognition (45%) (Table 1). Alzheimer disease (AD) and vascular pathologies were the most frequent findings (Figure & Table 2). Nineteen percent of participants did not have AD, one lacked neurofibrillary tangles (NFT). The most severe distributions/densities of NFTs and plaques were infrequent, with high likelihood of AD in only four participants. For vascular pathologies, 54% had moderate/severe white matter rarefaction and 76% had moderate/severe arteriolosclerosis. Thirty‐three percent had Lewy bodies, with six cases having diffuse type. Two cases had hippocampal sclerosis. One case had pathologic evidence of progressive supranuclear palsy and had dementia at last diagnosis.ConclusionThis ethnoracially diverse cohort of over 90 oldest‐old individuals reveal numerous brain pathologies are present with advanced age, with AD and select vascular pathologies being the most common. This confirms previous findings that with increases in cognitive impairment there is increasing pathology severity especially for AD pathologies.
Isomerized Aβ in the brains can distinguish the status of amyloidosis in the Alzheimer’s Disease continuum
Authors: Mukherjee Soumya , Dubois Celine , Perez Keyla , Birchall Ian , Hinton Fairlie , McLean Catriona , Masters Colin L , Roberts Blaine Russell 
Published: January 2025

AbstractBackgroundThe extracellular amyloid plaques, one of the pathological hallmarks of Alzheimers Disease (AD), are frequently also observed in the cortex of cognitively unimpaired subjects or as co‐pathology in other neurodegenerative diseases. Progressive deposition of fibrillar amyloid‐β (Aβ) as amyloid plaques for two decades prior disease onset leads to extensive isomerization of Aβ N‐terminus. Quantifying the extent of isomerized Aβ can be provide insight into the different stages of amyloidosis in the brain.MethodsWe measured Aβ isoforms using mass spectrometry. We analysed postmortem brains (frontal cortex) from patient with AD (n = 59), Parkinson disease (n = 57) and age matched control brains (61). We also measured Asp residue isomerization at Aβ N‐terminus.ResultsWe observed a significant increase in isomerization of Aβ N‐terminus in AD compared to age‐matched control brains in the soluble and insoluble pool.1‐3 While AD brains contain significant amount of Asp‐1 and Asp‐7 isomerized Aβ1‐15, cognitively unimpaired patients with amyloid deposits (CU‐AP) also have minor isomerized species. We further demonstrate that ratio of isomerized N‐terminus Aβ (Aβ1‐15) species in the brain soluble pool differentiates amyloid deposits in AD brain compared to CU‐AP individuals and patients with co‐morbidities.ConclusionsIsomerized N‐terminus Aβ (Aβ1‐15) is a common feature of parenchymal amyloid deposits in the brain. Our results suggest Aβ deposits associated with early AD neuropathologic changes are distinct to the mature plaques found in AD, a consequence of prolonged deposition in the later. Our results have implications for early therapeutic targeting of these modified species as well potential for better biofluids biomarker development to track drug efficacy.References1. Mukherjee S, Perez KA, Lago LC, et al. Quantification of N‐terminal amyloid‐β isoforms reveals isomers are the most abundant form of the amyloid‐β peptide in sporadic Alzheimer’s disease. Brain Commun. 2021;3(2). https://doi.org/10.1093/braincomms/fcab0282. Mukherjee S, Perez KA, Dubois C, et al. Citrullination of Amyloid‐β Peptides in Alzheimer’s Disease. ACS Chem Neurosci. 2021;12(19):3719‐3732. https://doi.org/10.1021/acschemneuro.1c004743. Mukherjee S, Fjeldsted JC, Masters CL, Roberts BR. Enhanced ion mobility resolution of Abeta isomers from human brain using high‐resolution demultiplexing software. Anal Bioanal Chem. Published online April 15, 2022. https://doi.org/10.1007/s00216‐022‐04055‐x
Performance of the Amsterdam IADL scale in Assessing Functional Decline among Older People in Primary Health Care
Authors: Fawad Ayesha , Nilsson Maria H , Sikkes Sietske A.M , Postema Merel C. , Stomrud Erik , Tideman Pontus , Hansson Oskar , Palmqvist Sebastian 
Published: January 2025

AbstractBackgroundThe Amsterdam Instrumental ADL Questionnaire (A‐IADL‐Q) has shown promising performance in detecting subtle impairment in IADL independency. Importantly, it has not been validated in older people in a primary care setting, i.e., where the initial work‐up and triaging of cognitive impairment occurs. The main aim was to examine the accuracy of the A‐IADL‐Q for estimating the cognitive stage in a primary care sample, both in the whole population and within the Alzheimer’s disease (AD) continuum. Secondary aims were to examine associations with the clinical dementia rating (CDR) scale and global cognition (MMSE), including the effects of non‐cognitive co‐morbidities and medications.MethodThis cross‐sectional study includes informants of 362 participants from the BioFINDER‐Primary care study. They completed the A‐IADL‐Q (higher scores = “better”) either on an iPad or at home on a computer. Cognitive stage was assessed by a dementia expert (blinded to A‐IADL‐Q scores). Amyloid status was determined using FDA‐approved CSF or PET measures. The main outcome was cognitive stage according to the NIA‐AA scale. Secondary outcomes were CDR‐sum of the boxes (CDR‐SOB) and MMSE. Receiver Operating Curves (ROC) analyses were used to calculate the area under the curves (AUCs).ResultThe mean age of participants (44.9% female) was 76.1 (SD 7.2) years. Out of which, 83 (23%) had subjective cognitive decline (SCD), 169 (47%) mild cognitive impairment (MCI), and 110 (30%) dementia. The AUC (95% CI) for differentiating between SCD versus MCI/dementia was 0.89 (0.85‐0.92), and it was 0.89 (0.85‐0.93) for SCD/MCI versus dementia. In amyloid positive participants (n = 303), AUCs were similar. Better A‐IADL‐Q scores were associated with worse results on CDR‐SOB (r = ‐0.73, p<0.001) and better global cognition (MMSE, r = 0.361, p<0.001). The effects of different co‐morbidities on the A‐IADL score, adjusted for cognitive stage, will be presented at AAIC.ConclusionOur findings suggest that the A‐IADL‐Q accurately estimates the cognitive stage among cognitively impaired older people (with high prevalence of comorbidities) in a primary care setting. This can aid primary care physicians in the diagnosis of cognitive impairment, clinical management/support, and as part of the work‐up for identifying eligible participants for anti‐amyloid treatment.
Abnormalities of Cortical Neural Synchronization Mechanisms in Patients With Dementia Due to Parkinson’s and Sintomatic Huntington’s Diseases
Authors: Noce Giuseppe , Percio Claudio Del , Lizio Roberta , Lopez Susanna , Jakhar Dharmendra , Güntekin Bahar , Yener Görsev , Taylor John‐Paul , De Tommaso Marina , Delussi Marianna , Babiloni Claudio 
Published: January 2025

AbstractBackgroundParkinson’s disease and Huntington’s disease are both neurodegenerative conditions involving the basal ganglia area of the brain. Both conditions can cause symptoms that affect movement. Cognitive decline or dementia can also occur in both. Resting state EEG (rsEEG) rhythms reflect neurophysiological mechanisms and operational functions related to the fluctuation of brain arousal and quiet vigilance in humans. The hypothesis was that rsEEG sources may be more abnormal in Huntington’s disease patients in symptomatic stage (S‐HD) than patients with dementia due to Parkinson’s disease.MethodClinical and rsEEG datasets in 16 PDD, 18 S‐HD, and 25 matched cognitively unimpaired (Nold) participants ‐ matched as demography, education, and gender ‐ were taken from an international archive. The eLORETA freeware was used to estimate cortical rsEEG sources at delta, theta, alpha1, alpha2, alpha3, beta1, beta2, and gamma frequency bands.ResultResults showed lower amplitude of the posterior alpha activities and higher amplitude of widespread low frequencies bands (i.e., delta and theta) in the PDD and S‐HD groups than in the Healthy group. As compared to the PDD group, the S‐HD showed greater reductions in the rsEEG alpha 2 rhythms in the frontal and temporal regions (see Figure 1).ConclusionThese results suggest that cortical sources of rsEEG rhythms might reflect different abnormalities of the core neurophysiological mechanisms underlying brain arousal in quiet wakefulness and low vigilance in PDD, and S‐HD patients. The mentioned rsEEG markers might be clinically useful in the disease staging, monitoring over time, and drug discovery.
Identifying transition points in AD biomarkers using machine learning: A supplement to the reliable worsening method
Authors: Gebre Robel K , Jack Clifford R. , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundBiomarker cutpoints have received significant importance in AD research. Reliable worsening method based on serial biomarker data has been proposed (Jack et al., 2017 Alz & Dem). Advanced machine learning models (ML) and explainable AI provide an opportunity to enhance existing techniques. We aimed to estimate biomarker cutpoints on cross‐sectional data using ML by identifying important transitions in the biomarker values.MethodWe first trained an ML model with the biomarker of interest as an input including other available biomarkers, demographics, and diagnosis, and predicted global cognition z‐score. We then obtained SHAP values (Shapley Additive exPlanations), fit a curve between the SHAP values and biomarker values, and calculated the first derivatives (slope) of the curve. Then, a CUSUM (Cumulative Sum) analysis was used to identify SHAP change points and cross‐referenced to values where the slopes were steepest near the zero‐crossing location of the curve. A zero SHAP is an important indicator of a feature’s progressive importance for model prediction because a feature’s influence on prediction significantly shifts when its SHAP values go from positive to negative or vice versa. However, relying on zero‐crossing alone may be insufficient when the relationship is non‐linear. Consequently, we calculated the second derivatives to identify inflection points (peaks and troughs) along the curve. Lastly, we compiled all the potential cutpoints and performed post‐processing as shown in Fig. 1.ResultThe algorithm identified cutpoints at 1.44 SUVR for all and at 1.42 SUVR for CU participants (Fig. 2). At age 70, both cutpoints estimated the percentage of abnormals at less than 40% (Fig. 3).ConclusionTPE amyloid‐PET SUVR cutpoints for CUs equal the reported RW values (1.42 SUVR). A strength of the TPE is its application on cross‐sectional data making it versatile for various biomarkers across different cohorts.