bluuebunny/crossref_metadata_2025 huggingface dataset:
Myelin water imaging with clinically feasible scan time: the accelerated BMC‐mcDESPOT (aBMC‐mcDESPOT) method
Authors: Bae Jonghyun , Gong Zhaoyuan , Guo Alex , Laporte John P , Faulkner Mary E , Bouhrara Mustapha
Published: January 2025

Background

In 2016, we introduced the Bayesian Monte Carlo analysis of multicomponent‐driven equilibrium observation of T1 and T2 (BMC‐mcDESPOT) MRI method for myelin water fraction (MWF) mapping, a surrogate of myelin content. While BMC‐mcDESPOT has been extensively applied to study brain aging, dementias, and risk factors influencing myelination, it still requires a lengthy acquisition time (∼17 min) which hampers its integration in clinical studies and trials. In this study, we aim to accelerate the BMC‐mcDESPOT method for whole brain, high‐resolution, MWF mapping within clinically feasible scan time of ∼6 min.

Method

Data Acquisition Reference data (ref‐BMC, no‐acceleration): Two subjects underwent the BMC‐mcDESPOT protocol consisting of SPGR and bSSFP images at 10 different FAs. For assessing reproducibility, three sets of fully‐sampled datasets were collected without any under‐sampling (total acquisition time = 17 min). Compressed sensing acceleration (cs‐BMC): Three sets of accelerated scans were acquired using compressed sensing (CS) with an acceleration factor of ∼2 (9 min) (Figure 1(a)). Image domain acceleration (du‐BMC): The undersampling in the data domain was retrospectively performed on the bSSFP images by selecting only 2 optimal images instead of the full 10 images (12 min) (Figure 1(b)). Combined accelerations (aBMC): The above two acceleration methods were combined and resulted in an acquisition time of ∼6min. (Figure 1(c)) Data Processing We estimated MWF maps from the different undersampling methods described above. The accuracy was evaluated between the MWF from the reference and those from each undersampled dataset. The reproducibility was measured with the coefficient of variations (CoV) across different measurements for each sampling method. We also performed Bland‐Altman analysis to quantify bias and limits of agreement (LOA).

Result

Figure 2 shows a representative slice of MWF estimates from different sampling methods. The different acceleration methods showed similar regional patterns, with relatively low errors (du‐BMC/cs‐BMC/aBMC = 10.8/9.8/13.7%) and CoV (ref‐BMC/du‐BMC/cs‐BMC/aBMC = 9.0/14.3/10.7/15.3%). While a slight bias in the derived MWF values was observed (Fig.3), the LOA for aBMC was under 5% in white matter.

Conclusion

aBMC‐mcDESPOT is a practical MRI method for accurate determination of MWF within 6 min, offering unique opportunities to further integrate myelin imaging in clinical studies and trials.
Comparison of amyloid positron emission tomography, hippocampal volume, and plasma biomarkers in predicting conversion to mild cognitive impairment
Authors: Bilgel Murat , An Yang , Shah Ishaan , Walker Keenan A. , Moghekar Abhay , Ashton Nicholas J. , Kac Przemyslaw Radoslaw , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Thambisetty Madhav , Resnick Susan M.
Published: January 2025

Background

We examined whether brain amyloid PET, hippocampal volume, or plasma biomarkers are better predictors of conversion to mild cognitive impairment (MCI).

Method

In the Baltimore Longitudinal Study of Aging (BLSA), plasma Aß42, Aß40, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) concentrations were measured using Quanterix Simoa Neurology 4‐plex E. Plasma p‐tau181 and p‐tau231 concentrations were measured using in‐house Simoa assays at University of Gothenburg. Brain amyloid was quantified using mean cortical 11C‐Pittsburgh compound B PET distribution volume ratio (mcDVR). Hippocampal volume was calculated from T1‐weighted MRI and adjusted for intracranial volume. Of 178 cognitively unimpaired (CU) participants with both plasma and neuroimaging measurements at baseline, 33 developed MCI during follow up (median time to MCI onset: 3.7 [interquartile range (IQR) 2.1‐6.2] years), 17 of whom had elevated brain PET amyloid at baseline. We used Cox proportional hazards models to examine the association between risk of conversion to MCI (time to conversion right‐censored at death or last visit for CU participants) and baseline plasma or neuroimaging biomarker, adjusting for age, sex, race, and education. To enable comparison of hazard ratios (HRs), we negated plasma Aß42/Aß40 and hippocampal volume, and standardized each biomarker using statistics calculated in a cross‐sectional dataset of CU individuals aged 60‐80 (preferring the visit closest to age 70 per participant) drawn from the larger BLSA. For standardization, we examined subtracting the median and dividing by the IQR and binarization (top tertile vs. not).

Result

In analyses using IQR normalized scores, mcDVR and plasma Aß42/Aß40 and GFAP had statistically significant HRs (Table 2). In binarized biomarker analyses, mcDVR and plasma GFAP, p‐tau181, and p‐tau231 were statistically significant. Using all plasma biomarkers together yielded the highest concordance: 0.877±0.026 and 0.861±0.026 for the continuous and binarized biomarker analyses, respectively.

Conclusion

Plasma biomarkers provide information beyond demographics regarding the prediction of MCI conversion within 4 years. Risk prediction conveyed by several individual plasma biomarkers exceeded that of brain amyloid PET. Plasma GFAP was consistently identified as contributing to the prediction of MCI conversion, with Aß42/Aß40 and p‐tau exhibiting statistically significant associations in a subset of analyses.
Elevated Blood Biomarkers of Neurodegeneration and Blood Brain Barrier Dysfunction Correlate with Lower MRI Regional Brain Volumes Two Years after COVID‐19
Authors: Frontera Jennifer A. , Ge Yulin , Boutajangout Allal , Ahmed Wajiha , Betensky Rebecca A , Debure Ludovic , Jiang Li , Links Jon , Masurkar Arjun V. , Sun Zhe , Thawani Sujata , Vedvyas Alok , Wisniewski Thomas
Published: January 2025

Background

Cognitive impairment is one of the most frequently reported post‐acute sequelae of COVID‐19, yet the pathophysiology underpinning this symptom remains unknown. We aimed to explore the correlation of blood markers of inflammation, BBB disruption and neurodegeneration with MRI volume measurements in COVID‐19 patients with and without cognitive impairment, and among patients with no prior history of COVID‐19.

Method

We conducted a prospective study of COVID‐19 patients (COV+; laboratory verified SARS‐CoV‐2 infection) and non‐COVID‐19 controls (COV‐; no history of SARS‐CoV‐2 infection and negative SARS‐CoV‐2 nucleocapsid antibody). All patients underwent neuropsychological testing using the Uniform Data Set Version 3 Cognitive Battery. Patients were coded as having cognitive impairment (COG+) based on neuropsychological testing (= 1 abnormal test result), and clinician interview with the patient and informant. 3T MRI (3D T1‐MPRAGE) sequences were utilized for regional volume measurements using MRICloud (Johns Hopkins University) segmentation software. The ratios of regional to total intracranial volume (SPM software) were reported to correct for differences in head size. Fasting plasma biomarkers of neurodegeneration, BBB dysfunction, and inflammation were measured concurrent with MRI testing. Bivariate Spearman correlation coefficients were calculated comparing MRI regional volumetric measures with blood biomarkers among COV+ versus COV‐ subjects with and without cognitive impairment (COG+ versus COG‐). Significance was set at P = 0.001 after Bonferroni correction.

Result

A total of 90 subjects were included (N = 75 COV+ with median 724 days from index SARS‐CoV‐2 infection, N = 15 COV‐; N = 39 COG+, N = 51 COG‐). Among all COV+ subjects (N = 75), neurodegenerative biomarkers (NFL, amyloid‐ß‐40, GFAP, ptau‐181), BBB markers (hepatocyte growth factor) and TNF‐a were significantly negatively correlated with regional MRI volumes, most notably in the limbic and hippocampal regions (Figure 1, bold = P = 0.001); whereas this signature of correlations was not observed in COV‐ subjects (N = 15). Among COV+ subjects with cognitive impairment (COV+COG+, N = 33), significant negative correlations were observed between neurodegenerative biomarkers (NFL, amyloid‐ß‐40, GFAP, ptau‐181) and MRI volumes (particularly frontal, temporal and limbic), whereas COV+COG‐ subjects (N = 42) did not demonstrate this pattern (Figure 2).

Conclusion

Two‐years post‐COVID‐19, signature correlations between neurodegenerative, BBB and cytokine blood biomarkers and MRI regional volumes differ between COVID‐19 subjects versus controls, particularly among those with cognitive impairment.
Cortical correlations of CSF Neurogranin, VILIP‐1, Alpha‐Synuclein and NFL in SCD and MCI according to amyloid status
Authors: Guimarães Emanuel R. , de Campos Brunno , Ribeiro Isadora Cristina , Aventurato Ítalo Karmann , da SIlva Marjorie Cristina Rocha , Rizzi Liara , Balthazar Marcio
Published: January 2025

Background

Alzheimer’s disease (AD) pathophysiology is complex and not completely known. Emerging new biomarkers that evaluate synaptic function (VILIP‐1, neurogranin), co‐pathology (alpha‐synuclein), and neurodegeneration (NFL) are potential candidates to be incorporated into the early AD diagnosis. To better understand the relevance of these biomarkers, we evaluated the correlations between their CSF concentrations with whole‐brain grey matter volumes in SCD and MCI, according to their amyloid status (A‐ or A+).

Method

75 participants diagnosed with SCD or MCI were included, 30 A‐ and 45 A+. They all underwent comprehensive neuropsychological assessment, CSF analyses (Roche Elecsys), and volumetric 3T MRI (Philips Achieva). Voxel‐based morphometry was used to quantify brain volumes through CAT12 running on MATLAB 2019b. Partial correlation analysis between CSF biomarkers and cortical volumes was performed with SPSS 22, adjusting for age and sex.

Result

Significant correlations were found in the A‐ but not in the A+ group. NFL and hippocampus (r = 0.40, p = 0.028); VILIP‐1 and superior temporal gyrus (r = ‐0.38, p = 0.035), and superior frontal gyrus (r = ‐0.40, p = 0.025); alpha‐synuclein and hippocampus (r = ‐0.39, p = 0.036), middle frontal gyrus (r = ‐0.375, p = 0.049), and superior temporal gyrus (r = ‐0.478, p = 0.01).

Conclusion

We found different patterns of correlations between brain anatomy and emerging biomarkers, according to amyloid status, in the very early stage of the neurodegenerative process. Interestingly, these correlations were found only in the amyloid‐negative group, which might suggest that different pathological processes involving synaptic function, neurodegeneration, and co‐pathology occur in suspected non‐Alzheimer pathophysiology cases.
Resting‐state activity across cortical hierarchies provides readiness for memorizing in aging humans
Authors: Han Feng , Chen Xi , Varghese Leah , Cassady Kaitlin , Chadwick Trevor , Mason Suzanne , Jagust William J.
Published: January 2025

Background

The neural basis of memory aging remains elusive. The default mode network (DMN) supports memory encoding and retrieval, and its connectivity decreases in aging. Young adults with larger differences in resting‐state functional connectivity (rsFC) between higher‐order DMN and lower‐order sensory/motor network (SMN) have better cognition and memory. We combined resting‐state and task‐based fMRI during an associative memory task to explore how this cross‐hierarchical contrast of rsFC is linked to memory aging.

Method

We included resting‐state prior to (an object‐scene‐pair encoding) task fMRI from 16 young adults (YA; 24.6 ± 3.6 years; 7 females) and 42 cognitively normal older adults (OA; 76.8 ± 6.9 years; 24 females) in the Berkeley Aging Cohort Study. We subtracted the averaged rsFC within lower‐order regions from higher‐order ones, and correlated the cross‐hierarchy contrast of rsFC with age and memory task performance. Brain function contrast between the higher and lower order regions may also manifest as the temporal difference/delay of cortical activation, seen in propagating waves between hierarchical cortices. The propagating number (i.e., frequency) in each direction (“High‐to‐low” or “Low‐to‐high”) was therefore also correlated with memory performance and activation in anterior‐temporal (AT), posterior‐medial (PM), and hippocampal (HPC) memory regions during task‐fMRI.

Result

Among OAs, cross‐hierarchy contrast of rsFC decreased with aging (Spearman’s ρ = ‐ 0.32, p = 0.040) and in females (p = 2.3 ×10‐3). OAs with lower rsFC contrast had worse memory during retrieval (ρ = 0.36, p = 0.019; Fig. 1). Memory scores were not correlated with mean rsFC within higher‐order regions or lower‐order ones. Propagating number along the “Low‐to‐high” direction decreased in OAs. More propagation was associated with better memory and lower task‐evoked activation in AT, PM, and HPC regions (all ρ strength > 0.35, p < 0.030; Fig. 2&3).

Conclusion

Patterns of resting‐state functional connectivity related to hierarchical cortical organization are associated with aging and memory decline. In particular, slow propagating waves of cortical activation transiting across these hierarchies may serve a preparatory function, readying the brain for memory formation, a process that is affected by aging.
Multi‐atlas fully automated segmentation of the locus coeruleus in in vivo neuromelanin‐sensitive MRI
Authors: de Flores Robin , Blanchard Thaïs , Foyard Emilie , Naveau Mikaël , Delcroix Nicolas , Landeau Brigitte , Yushkevich Paul A. , Chételat Gael
Published: January 2025

Background

Locus coeruleus (LC) imaging using neuromelanin‐sensitive (NM) MRI sequences is a promising biomarker for detecting early Alzheimer’s Disease (AD). Although automatic approaches have been developed to estimate LC integrity by measuring its intensity, these techniques most often rely on a single template built in a standardized space and/or depend on a number of voxels to be accounted that is defined a priori. Thus, these algorithms make it impossible to perform direct volumetric analyses and do not properly account for inter‐individual anatomical variability. To fill this gap, our aim was to develop a new multi‐atlas fully automated segmentation method using the Automatic Segmentation of Hippocampal Subfields (ASHS) software.

Method

We used baseline data from 102 unimpaired older adults (mean age: 73.72 ± 3.5 years; mean education: 13.25 ± 3.1 years; 58 women, 44 men) from the Age‐Well randomized controlled trial for whom high‐resolution NM MRI (T1‐w with magnetization transfer; 0.3×0.3×0.75mm3) and standard T1‐w MRI (1×1×1mm3) were available. The LC were manually segmented in 30 randomly selected participants on NM MRI, and the standard T1‐w MRI, NM MRI and bilateral segmentations were fed into the ASHS training pipeline to generate an atlas. This new atlas was applied to the 72 remaining subjects to segment the LC and we assessed the effects of age, sex and education on both i) LC intensity (normalized by the intensity of the pons) and ii) LC volume (normalized by the total intracranial volume).

Result

Five‐fold cross‐validation experiments revealed high accuracy of the automatic segmentation relative to manual segmentation (Dice coefficient = 0,83 ± 0,04). LC intensity was significantly higher in women than in men (F = 13.61,p<0.001) while no associations with age (ß = ‐0.0002,p = 0.86) or education (ß = 0.002,p = 0.16) were found. In contrast, LC volume was not different between men and women (F = 0.21, p = 0.65) but tended to be negatively associated with age (ß = ‐0.15,p = 0.06) and education (ß = ‐0.19,p = 0.06).

Conclusion

Overall, this new method allows to automatically and accurately segment the LC, and offers the opportunity to measure its integrity both in terms of intensity and volume. This is of importance since these two metrics might provide complementary information about the integrity of the LC.
First GPR39‐imaging radiotracer to track zinc homeostasis in a rodent model of Alzheimer’s disease
Authors: Gollepalli Krishna Kumar , Bhoopal Bhuvanachandra , Damuka Naresh , Krizan Ivan , Miller Mack , Kumar JV Shanmukha , Bashetti Nagaraju , Whitlow Christopher T , Lockhart Samuel N. , Jadiya Pooja , Sai Kiran K. Solingapuram
Published: January 2025

Background

The G protein‐coupled receptor GPR39 is heavily associated with the pathogenesis of neurologic disorders, including Alzheimer’s disease (AD) and related dementia (ADRD). Its dysregulation of zinc 2+ (Zn2+) processes triggers metallic dyshomeostasis, oxidative stress, neuroinflammation, microtubule destabilization, synaptic dysfunction, and tau phosphorylation—all hallmarks of neurodegeneration. Hence, pharmacologic modulation of GPR39 could offer an effective treatment against AD and ADRD. With the right radiotracer, PET could be used to quantify GPR39 in brains in vivo and track distribution, target engagement, and dose occupancy of novel GPR39‐based agonistic drugs to regulate Zn2+ (dys)homeostasis function. In this abstract we report the development and validation of the first PET radiotracer, [11C]TMN‐1324 to image GPR39 in rodent models of AD.

Method

GPR39 agonist TM‐N1324, was selected as the lead compound for its brain penetration and high GPR39 affinity—acts with and without Zn2+ to both human and rodent GPR39s. To validate [11C]TM‐N1324 imaging potential, we performed 0‐60 min dynamic PET imaging and post‐PET biodistribution with [11C]TM‐N1324 in (a) normal C57BL/J mice (4 mo, n = 4) and (b) male Aß‐overexpressing APP/PS1 transgenic (TG) mice and age‐matched wild‐type (WT) mice (12 mo, n = 4/group). Brain autoradiography investigated regional uptake in the same cohort. TM‐N1324 blocking studies (n = 4) confirmed in vivo GPR39 specificity of the radioligand

Result

Radiochemistry was fully optimized for high radiochemical purity (>98%) and molar activity (>48635 MBq/µmol) decay corrected to end‐of‐synthesis. PET imaging of rodents showed brain penetration and ∼65±4% blocking with TM‐N1324 pretreatment. It demonstrated ∼22±3% lower brain uptake in APP/PS1 TG mice compared to WT littermates (Fig 1). Autoradiography showed significant uptake in cortical regions (high GPR39‐expressing regions). Biodistribution showed ∼33±1% lower brain uptake in TG mice than WTs.

Conclusion

Our pilot study here with the first GPR39‐imaging PET radiotracer, [11C]TM‐N1324 demonstrated brain penetration, differential uptake in mouse model of AD, and regional specificity. By validating our highly specific GPR39 PET radiotracer for studying Zn2+‐based dyshomeostasis, we will elucidate GPR39 PET imaging strategies to monitor target responses to potential novel therapeutics.
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

Background

Cognitive 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.

Method

121 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.

Result

Lower 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.

Conclusion

This 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

Background

Repetitive 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.

Method

Given 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.

Result

Major 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.

Conclusion

Our 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

Background

Affective 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.

Method

We 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.

Result

As 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.

Conclusion

We 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

Background

Imaging 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.

Methods

Using 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.

Results

The 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.

Conclusions

The 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

Background

Poor 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.

Methods

A 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.

Results

Whole 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.

Conclusions

These 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

Background

Tau 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.

Method

We 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.

Result

The 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.

Conclusion

Evidence 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

Background

PET 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.

Method

3098 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.

Result

DeepSUVR 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).

Conclusion

We 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

Background

Neighborhood 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.

Method

This 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.

Result

ADI 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).

Conclusion

Our 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

Background

States 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.

Method

We 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).

Result

MetS‐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.

Conclusion

We 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.
TAU EXACERBATES THE DEVELOPMENT OF DISTINCT NEUROPSYCHIATRIC SYMPTOMS IN LATE BRAAK STAGE – THE HEAD STUDY
Authors: Aguzzoli Cristiano Schaffer , Povala Guilherme , Ferreira Pamela C.L. , Lussier Firoza Z , Bauer‐Negrini Guilherme , Amaral Livia , Soares Carolina , Ferrari‐Souza João Pedro , Zalzale Hussein , Bellaver Bruna , Rohden Francieli , Abbas Sarah , Leffa Douglas Teixeira , Schilling Lucas Porcello , Zimmer Eduardo R. , Tudorascu Dana , Pascual Belen , Gordon Brian A. , Lowe Val J. , Oh Hwamee , Soleimani‐Meigooni David N. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali
Published: January 2025

Background

Recent studies showed that neuroinflammation plays a key role in triggering specific neuropsychiatric symptoms (NPS), such as irritability and agitation, in individuals with Alzheimer’s disease (AD). While prior studies showed an association between tau pathology and all NPS domains, the extent to which tau influences each specific NPS domain remains unclear. Here, we aim to investigate the association of tau and NPS domains in the AD continuum. We hypothesize that tau plays a comparatively greater effect on the emergence of psychotic symptoms compared to other NPS domains.

Method

We assessed 56 individuals (21 cognitively unimpaired (CU), 23 MCI, and 12 AD dementia) from the HEAD study who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q) and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694 or [11C]PiB), and tau tangles ([18F]MK6240) at the same visit. We selected individuals with an NPI‐Q total score =1. Tau SUVR values were tailored with a mask from Braak stages I‐VI, using the inferior cerebellar gray matter as reference region. Leave‐one‐out voxel wise and linear regression tested the association between each NPI‐Q domain and biomarkers accounting for age, sex, cognitive status, and study site.

Result

CI individuals had significantly higher NPI‐Q score and Braak VI PET SUVR than CU individuals (Table 1). NPI‐Q score was significantly associated with tau‐PET in the periRolandic and supplementary motor cortex (Figures 1A). Linear regression showed that NPI‐Q associates with tau‐PET in the Braak stage VI (Figure 1B). Leave‐one‐out regression analysis revealed that delusions, motor disturbances, and anxiety contributed most to the association between tau‐PET and NPS (Figure 2A, C). These domains presented a higher magnitude of association compared to each other NPI‐Q domain (Figure 2B). Notably, irritability, agitation, and disinhibition exerted a negative effect to the association, emphasizing that tau may not play a role in the development of these symptoms (Figure 2A, D)

Conclusion

Our study supports previous evidence suggesting that irritability and agitation may not be triggered by tau, but rather by other pathological process such as neuroinflammation. These findings provide additional rationale for the therapeutics aiming to mitigate irritability and agitation in AD patients.
Cortical tau aggregation drives axonal degeneration in white matter fiber tracts in Alzheimer's disease
Authors: Pescoller Julia , Dewenter Anna , Steward Anna , Dehsarvi Amir , Wagner Fabian , Gross Mattes , Zhu Zeyu , Ewers Michael , Roemer Sebastian Niclas , Brendel Matthias , Franzmeier Nicolai
Published: January 2025

Background

In Alzheimer’s disease (AD), cortical tau aggregation is a strong predictor of cortical brain atrophy as shown by MRI and PET studies, particularly driving the degeneration of neuronal somata in the grey matter. However, tau’s physiological role is to stabilize microtubules within axons in the brain’s white matter (WM) pathways. Therefore, tau’s white‐to‐grey‐matter translocation and aggregation in neurofibrillary tangles close to neuronal somata may induce WM degeneration through destabilization of axonal microtubule integrity. To address this, we determined whether cortical tau predicted faster atrophy of connected WM tracts in AD.

Method

We included from ADNI cohort 37 amyloid‐PET negative (Aß‐) cognitively normal (CN) participants and 88 amyloid‐PET positive (Aß+) participants across the AD‐spectrum (i.e. CN/MCI/Dementia = 50/28/10), with baseline amyloid‐PET, longitudinal tau‐PET and longitudinal structural MRI data. For replication, we included baseline amyloid‐PET, tau‐PET and MRI data of 321 CN‐Aß+ subjects from the A4 cohort. T1‐weighed MRIs were segmented into grey and white matter and non‐linearly normalized to MNI space using CAT12. The cortical Brainnetome Atlas and a diffusion imaging‐based tractography atlas were applied tau‐PET and MRI data to i) determine cortical tau‐PET accumulation rates within Brainnetome ROIs, and ii) assess WM volume changes within fiber tracts connected to each cortical ROI. Statistical regression‐models were adjusted for age, sex, WM hyperintensity volume, global amyloid, intracranial volume, and APOE4‐status.

Result

In ADNI, higher baseline temporo‐parietal tau‐PET predicted faster volume reductions in connected WM tracts (Figure 1A), especially pronounced in Aß+ subjects (Figure 1B). Similarly, faster tau accumulation was strongly linked to widespread WM volume reductions in connected fiber tracts (Figure 2A; Figure 2B), particularly exacerbated in Aß+ APOE4 carriers (Figure 3A) compared to non‐carriers (Figure 3B). These results suggest that tau accumulation and WM degeneration are parallel processes in AD, modulated by APOE4. In the A4 validation sample of preclinical AD patients, we detected congruent associations between inferio‐temporal tau‐PET increase and reduced WM volume in connected fiber tracts (Figure 4).

Conclusion

Cortical tau aggregation is associated with progressive WM atrophy in connected fiber tracts throughout the brain, suggesting that tau accumulation triggers axonal degeneration, which may induce neuronal disconnection and dysfunction and thereby contributing to AD progression.