bluuebunny/crossref_metadata_2025_split huggingface dataset:
White Matter, Aging, and Cognition: New Insights Using Advanced MR Relaxometry and Symmetric Data‐driven Fusion
Authors: Gong Zhaoyuan , Bilgel Murat , Faulkner Mary E , Bae Jonghyun , Laporte John P , Guo Alex , Bouhrara Mustapha 
Published: January 2025

AbstractBackgroundCognitive decline during normative aging significantly impacts the quality of life, while the rate varies among individuals. MRI studies have highlighted the correlation between cognitive functions and brain macrostructure. However, cerebral microstructural alterations, especially in white matter, may precede macrostructural changes, driving early cognitive decline. Using advanced MR relaxometry, we measured R1 and R2 relaxation rates and Myelin Water Fraction (MWF) to elucidate the relationship between microstructural differences and cognitive decline in aging. Additionally, to leverage the shared information across all MRI metrics, we used advanced data fusion techniques to derive new features in identifying significant relationships with cognition that cannot be seen with a single MRI metric.Method121 cognitively unimpaired participants from the BLSA and GESTALT studies underwent cognitive testing. R1, R2, and MWF were calculated using BMC‐mcDESPOT. Multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) was used to estimate five new white matter integrity features. Linear mixed‐effects models assessed associations between white matter integrity and longitudinal cognitive changes, accounting for age, sex, education, and race.ResultLower R1, R2, and MWF values, indicating worse microstructural integrity and low myelin content, were all significantly associated with steeper decline in executive function and processing speed across all regions‐of‐interest (ROIs). Moreover, data fusion yielded five new white matter integrity features (Figure 1). Lower loading coefficients of component 4 were significantly associated with steeper decline in executive function, processing speed and attention. Interestingly, lower loading coefficients of component 2 were significantly associated with steeper decline in verbal fluency and memory. These associations with attention and memory were not observed using any of the single MRI metrics, demonstrating the power and sensitivity of data fusion.ConclusionThis study establishes a link between white matter microstructural integrity and the rate of cognitive decline in normative aging, suggesting that MRI metrics and their fusion features are not only reflective of microstructural changes but are also predictive of cognitive trajectory. These findings highlight the potential of MR relaxometry in early intervention and contribute to the growing body of evidence supporting the importance of white matter health in cognitive aging.
Age‐related variations in diffuse axonal injury following mild traumatic brain injury
Authors: Criado‐Marrero Marangelie , Ravi Sakthivel , Bhaskar Ekta , Febo Marcelo , Abisambra Jose F. F 
Published: January 2025

AbstractBackgroundRepetitive mild traumatic brain injury (rmTBI) represents a substantial health challenge, urging a more thorough investigation into its early effects and possible interventions. The collective consequences of rmTBI encompass various neurobiological and neuropsychological impairments, increasing susceptibility to diseases like Alzheimer’s and related dementias. Employing the Closed‐Head Impact Model of Engineered Rotational Acceleration (CHIMERA) approach for TBI induction, our prior study revealed connectivity alterations within 53% of regions in young and aged wild‐type mice five days post‐injury.MethodGiven rmTBI’s potential for causing diffuse axonal injury, the current study utilized advanced neuroimaging techniques—Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI)—to detect white matter damage in 36 regions of the brain.ResultMajor age‐dependent disruptions were observed in the corticospinal tract, hippocampal commissure, anterior cingulate, and optic tract. Surprisingly, mean diffusivity (MD), a reflection of overall water diffusion, showed unique age‐related effects. Three main regions were affected by injury in young mice: the entorhinal cortex, anterior cingulate, and optic tract. Regardless of injuries, aged animals exhibited an inverse correlation between fractional anisotropy (FA) and radial diffusivity (RD) in the secondary motor area, corticospinal tract, and hippocampal commissure.ConclusionOur discoveries are significant for revealing age‐specific changes and understanding the intricate connection between age and injury, shaping brain tissue integrity, particularly in the context of aging research.
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.
Plasma and MRI biomarkers predict post‐mortem tau pathology
Authors: Hu Mingzhao , Moloney Christina M. , Przybelski Scott A. , Algeciras‐Schimnich Alicia , Fought Angela J. , Rothberg Darren M. , Nguyen Aivi T. , Reichard Ross R. , Dickson Dennis W. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Mielke Michelle M. , Graff‐Radford Jonathan , Murray Melissa E. , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total ß‐amyloid Aß42 relative to Aß40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.
Hypothalamic involvement and biomarker candidacy in Alzheimer’s disease in Down syndrome
Authors: Brown Stephanie S G , Zaman Shahid , O'Brien John T 
Published: January 2025

AbstractBackgroundPoor sleep is emerging as an important and modifiable risk factor in the development of dementia. The hypothalamus is the only neuroanatomical site of orexin‐producing neurones in the brain and modulates sleep and wakefulness behaviour. Due its small size and lack of defined contrast in conventional neuroimaging acquisitions, relatively little evidence exists as to the role of the hypothalamus in humans in neurodegeneration and sleep quality, and whether it may have mechanistic importance and biomarker candidacy.MethodsA total of 353 participants with DS and 37 sibling controls underwent structural brain scanning at 3T as part of the ABC‐DS international consortium research project. Hypothalami were segmented in the T1‐weighted images (Fig. 1) and assessed volumetrically as proportional to intracranial volume (ICV) and grey matter volume.ResultsWhole hypothalamic volumes, when normalised to ICV, are significantly reduced in people with DS who have mild cognitive impairment (MCI) and dementia compared to those who are healthy (p < 0.05) (Fig. 2). Additionally, smaller hypothalamic volumes normalised to ICV were significantly associated with poorer cognitive performance (p < 0.05). When normalised to grey matter volume, which controls for the atrophic processes of dementia on a whole brain level, we find that healthy people with DS disproportionally smaller hypothalamic nuclei developmentally. Specifically, hypothalamic volume in the anterior‐inferior sub‐nucleus is significantly smaller in people with DS with an AD diagnosis (p < 0.05) when normalised to grey matter volume, and significantly associated with age (p < 0.05), indicating an accelerated or preferential atrophy in this location. Larger hypothalamic volumes were associated with being overweight or obese.ConclusionsThese findings suggest that the whole hypothalamic structure is sensitive to AD‐related atrophic processes (Fig. 3), which are not present in sibling controls, and that hypothalamus volume is closely associated with cognitive performance in DS. The finding of an anterior‐inferior nucleus‐specific atrophy in AD which is disproportionate to whole brain grey matter loss is supportive of this region having an accelerated decline which may be linked to sleep as a risk factor or sleep‐related dementia symptomatology, as it is known to contain the suprachiasmatic nucleus.
Quantification of tau pathology in the basal forebrain: Associations with age, amyloid, brain structure and cognition
Authors: Chadwick Trevor , Ward Tyler J. , Baker Suzanne L. , Jagust William J. , Landau Susan M. , Harrison Theresa M. 
Published: January 2025

AbstractBackgroundTau pathology accumulates early in the basal forebrain (BF) in Alzheimer’s disease (AD). The feasibility of measuring in vivo BF tau is unclear given PET resolution and possible partial volume effects of off‐target signal (OTS) which varies by tracer.MethodWe compared measurements of tau in cognitively unimpaired older adults with either an FTP or MK6240 scan: 93 FTP scans from the Berkeley Aging Cohort Study (BACS), 424 FTP scans from ADNI (N = 517 FTP scans; 72.5±7.1 years, 61% F) and 888 MK6240 scans (68.4±5.2 years, 62% F) from the U.S. POINTER imaging study were processed identically to derive SUVRs in native space FreeSurfer regions and template space BF. Using linear regression we explored associations between tau‐PET ROIs across the brain focusing on the temporal lobe (entorhinal cortex [ERC], temporal MetaROI), BF (Ch1‐3, Ch4) and common regions of OTS (e.g., pallidum, thalamus). We examined relationships between tau in these ROIs and age, Aß, hippocampal volume, BF volume, and cognition.ResultThe MK6240 cohort was significantly younger, less educated, more racially diverse, and had higher hippocampal and BF Ch1‐3 volume (Table. 1). For both tracers, BF and temporal lobe ROI SUVRs were positively correlated with Aß, while OTS ROI SUVRs were not related to Aß (Fig.1). In the BF, the effect size (Cohen’s d) of t‐tests comparing tau SUVR by Aß status was higher using MK6240 compared to FTP. BF and ERC SUVR positively correlated with age and negatively correlated with hippocampal volume and episodic memory (Fig.2). BF Ch4 SUVR negatively correlated with BF Ch4 volume in both cohorts.ConclusionEvidence that BF tau, along with temporal lobe tau, correlates with Aß uptake suggests that AD‐relevant tau pathology can be measured in vivo in the BF. In addition, BF tau and temporal tau showed similar relationships to other key variables including age, brain structure and cognition with both tracers. MK6240 slightly better differentiated Aß positive and negative subjects in BF SUVR measurements compared to FTP. Future research will investigate if BF tau is a critical early marker of AD progression.
DeepSUVR: Using temporal constraints to improve SUVR and Centiloid quantification
Authors: Bourgeat Pierrick , Fripp Jurgen , Gillman Ashley G , Cox Timothy , Goyal Manu S. , Tosun Duygu , LaMontagne Pamela , Benzinger Tammie L.S. , Weiner Michael S. W. , Villemagne Victor L , Masters Colin L , Rowe Christopher C. , Dore Vincent 
Published: January 2025

AbstractBackgroundPET quantification using the Standardised Uptake Value Ratio (SUVR) relies on the availability of a robust reference region. Intrinsic noise, spill in, and specific binding in the reference region can impact the reliability of the resulting SUVR. We evaluate a novel deep learning method trained on longitudinal data that penalises unexpected temporal changes and learns a SUVR correction factor that compensates for any noise or bias in the reference region, resulting in an improved quantification.Method3098 participants with 2+ visits in AIBL/ADNI/OASIS (8826 scans) had their Amyloid PET images spatially normalised and quantified using the Centiloid SPM pipeline. A deep learning network (DeepSUVR) was trained to predict a SUVR correction factor (CF) for each spatially normalised image. For each iteration, the prediction was run on 2 random timepoints from the same participant, resulting in 2 Adjusted Centiloids computed using SUVR*CF and each tracer’s standard Centiloid transform. A loss function was defined to penalise unexpected temporal changes: Centiloid decreasing over time, Centiloid deviating from the curve defining the change in Centiloid/Year given the mean Centiloid. The loss also included a penalty for over‐correction (Slope of the linear regression between original and adjusted Centiloid should be 1). The model was trained using a subset of ADNI/AIBL (2037 participants) and evaluated in terms of longitudinal consistency on the remaining ADNI/AIBL participants and on OASIS data. The correlations between head‐to‐head tracers in the OASIS and GAAIN calibration datasets were also computed.ResultDeepSUVR increased the correlation between each pair of 11C‐PIB/18F‐Tracers in the GAAIN calibration dataset (Fig1) and between pairs of 11C‐PIB/18F‐Florbetapir in OASIS (from R2 = 0.86 to R2 = 0.92) (Fig2). Using DeepSUVR, the correlation between Centiloid/Year vs mean Centiloid increased from ρ = 0.39 to ρ = 0.48 in AIBL/ADNI and ρ = 0.47 to ρ = 0.51 in OASIS (Fig3).ConclusionWe have proposed a novel deep learning technique to correct SUVR estimation based on noise in longitudinal trends. The longitudinal constraint led to higher agreement between tracers in the head‐to‐head GAAIN/OASIS datasets and higher longitudinal consistency. Importantly, while the model was trained using pairs of images, the prediction is run on single images. Future work will evaluate this approach on tau tracers.
Neighborhood disadvantage is associated with altered cortical organization
Authors: Safai Apoorva , Johnson Sterling C. , Jonaitis Erin M. , Langhough Rebecca E. , Powell W. Ryan , Buckingham William R. , Bendlin Barbara B. , Kind Amy J.H. , Tiwari Pallavi 
Published: January 2025

AbstractBackgroundNeighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity networks (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs can capture intricate changes in cortical patterns related to neighborhood disadvantage and cognitive function, potentially explaining risk for later life cognitive decline among individuals who live in disadvantaged contexts.MethodThis study included cognitively unimpaired participants from the Wisconsin Alzheimer’s Disease Research Center and Wisconsin Registry for Alzheimer’s Prevention. and their neighborhood disadvantage was indexed by the area deprivation index (ADI), a metric representing income, education, employment, and housing quality at the census block group geographic level(n=526, age=62.96±8.377, gender(M:F)=181:345, ADI(L:H)=452,74). We compared MSNs by ADI state rank scores in the two highest (9,10) and lowest (1,2) ADI deciles based on previous studies reporting strongest health effects among individuals living at the highest levels of disadvantage. Cognitive performance was indexed by six tests evaluating memory, executive functioning, and the pre‐clinical Alzheimer’s cognitive composite(PACC). T1‐weighted MRI scans of all participants underwent standard preprocessing steps in CAT12 toolbox MSN was constructed for each participant (Figure‐1). We used linear regression to examine ADI associations with cognitive scores and four local and four global MSN features. Mediating effect of MSN features on the relationship between ADI and cognitive performance was statistically assessed using a non‐parametric bootstrapping (10,000) approach. Model covariates included age, gender, education (years), and total intracranial volume.ResultADI rank showed negative association with category fluency(CF)(ß=‐0.176, p=0.039), WAIS‐DigitSymbol(ß=‐0.442, p=0.011), Wechler’s Memory scale(WMS‐LM)(ß=‐0.218, p=0.001) and PACC scores(ß=‐0.056, p=0.001), indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal brain regions (Figure‐2a, b) differed by ADI group. Centrality of left fusiform and bankssts showed a mediating effect between association of neighborhood disadvantage and performance on the WMS and PACC scores respectively (Figure‐2c).ConclusionOur findings suggest differences in local cortical organization by neighborhood disadvantage, which also mediated the relationship between ADI and cognitive performance, providing a possible network‐based mechanism, explaining risk for cognitive decline and dementia associated with disadvantaged neighborhoods. Future work will examine the dosage and timing of neighborhood disadvantage on structural organization of the brain.
Metabolic syndrome severity score associates with structural brain features and functional connectivity of Default Mode Network subsystems in the HABS‐HD study
Authors: Green Zachary D. , O'Bryant Sid E. , Johnson Leigh A. , Burns Jeffrey M , Morris Jill K 
Published: January 2025

AbstractBackgroundStates of altered metabolic health such as metabolic syndrome (MetS), obesity, and insulin resistance increase Alzheimer’s Disease (AD) risk. Individuals with these conditions, like those with AD, have demonstrated changes in the structural and functional features of the Default Mode Network (DMN). Here, we characterize associations between systemic metabolic dysfunction, brain structure (Cortical Thickness and Hippocampal Volume) and functional connectivity of DMN subnetworks.MethodWe leveraged data from the Health and Aging Brain Study: Health Disparities (HABS‐HD) dataset, an ongoing longitudinal study that characterizes AD biomarkers across diverse populations. Specifically, we used data from the initial visits of the Hispanic and Non‐Hispanic White (NHW) cohorts. Individuals were categorized as cognitively healthy (n = 1192; age = 66.1 years, 65% female) or cognitively impaired (n = 307; age = 67.4 years, 50% female). We used a continuous measure of metabolic risk (Mets‐Z) that is more sensitive and ethnicity‐specific than categorical definitions of MetS. Mets‐Z a weighted composite of systolic blood pressure, triglycerides, HDL cholesterol, glucose, and waist circumference. Structural measures were acquired using FreeSurfer. Functional connectivity was measured via resting‐state fMRI, and pre‐processed using FSL. We utilized both summary metrics (ROI‐based connectivity estimations) and voxel‐wise approaches (FSL’s randomise).ResultMetS‐Z differed between diagnostic groups (t = ‐2.387, p = .017) and between Hispanics and NHWs (t = ‐11.864, p<.001). Meta‐ROI cortical thickness was negatively associated with both cognitive status (ß = ‐.152, p < .001) and MetS‐Z (ß = ‐.085, p < .001). Hippocampal volume was found to have a negative relationship with MetS‐Z (left, ß = ‐.073, p < .001; right, ß = ‐.072, p < .001). Functional connectivity, specifically of the posterior DMN, was significantly associated with MetS‐Z. There was a high degree of overlap between the clusters of voxels in which MetS‐Z and pDMN connectivity associated, and the clusters demonstrating decreased connectivity to due to age and cognitive status.ConclusionWe present evidence of altered DMN connectivity due to a higher load of metabolic risk in an ethnoracially diverse cohort of older adults, with and without cognitive impairment. Future studies should leverage the longitudinal HABS‐HD dataset to examine if higher metabolic dysfunction predicts differences in the trajectory of DMN connectivity.