Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

BACKGROUND: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aß) profiles, and to predicted progression to Alzheimer's disease (AD). METHODS: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). FINDINGS: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. INTERPRETATION: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. FUNDING: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau positron emission tomography (PET) in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16), logopenic variant Primary Progressive Aphasia (n = 15), and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent magnetic resonance imaging (MRI), tau PET, and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = .30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = .10), tau in other functional networks (R2 = .11-.26), and the magnitude of cortical atrophy (R2 = .20) and amyloid burden (R2 = .09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients one year later. This simple measure could aid the development of a personalized prognostic, monitoring, and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies.

Aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Evidence exists for aSyn "strains" - conformations with distinct biological properties. However, biomarkers for PD vs. DLB, including potential aSyn strain differences, are lacking. Here, we used two monoclonal antibodies selective for different in vitro aSyn species - termed Strain A and B - to evaluate human brain tissue, cerebrospinal fluid (CSF), and plasma. Surprisingly, levels of Strain A and B aSyn species differed in plasma from individuals with PD vs. DLB in two independent cohorts. Lower plasma aSyn Strain A species also predicted subsequent PD cognitive decline. Strain A and Strain B aSyn species were undetectable in CSF, but plasma aSyn species could template aSyn fibrillization, particularly in PD. Our findings suggest that aSyn strains may impact LBD clinical presentation and originate outside the brain.

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction.

Parkinson's disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum.

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases.

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer's disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).

Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer's disease continuum.

The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer's disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.

Morphometry of medial temporal lobe subregions using high-resolution T2-weighted MRI in ADNI3: Why, how, and what's next?

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.

Detection of sex-specific glutamate changes in subregions of hippocampus in an early-stage Alzheimer's disease mouse model using GluCEST MRI.

INTRODUCTION: Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high-resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex-specific manner. METHODS: Eight-month-old male and female AD mice harboring mutant amyloid precursor protein (APPNL-F/NL-F: n = 36) and wild-type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H-MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. RESULTS: GluCEST measurements revealed significant (p ≤ 0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73 ± 2.12%), hippocampus (11.29 ± 2.41%), and hippocampal fimbriae (19.15 ± 2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22 ± 2.33%; p = 0.01) was also observed in 1H-MRS. DISCUSSIONS: GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. HIGHLIGHTS: Glutamate levels were monitored in different brain regions of early-stage Alzheimer's disease (AD) and wild-type male and female mice using glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex-specific glutamate changes in hippocampal sub-regions using high-resolution GluCEST MRI.

Microstructural changes in the inferior tuberal hypothalamus correlate with daytime sleepiness in Lewy body disease.

Background: Excessive daytime sleepiness (EDS) is a disabling symptom of Lewy body disorders (LBD). The hypothalamus is a key sleep-wake regulator, but its contribution to EDS in LBD remains unclear. Objectives: Use diffusion MRI to evaluate the relationship of hypothalamic microstructure to EDS symptoms in LBD. Methods: We studied 102 patients with clinically-defined LBD (Parkinson's disease, n=93; Parkinson's disease dementia, n=4; and dementia with Lewy bodies, n=5) and Epworth Sleepiness Scale (ESS) within 2 years of MRI. Mean diffusivity (MD) was compared between EDS+ (ESS≥10, n=37) and EDS- (ESS<10, n=65) groups in the whole hypothalamus and three subregions, covarying for age and sex. Secondary analyses tested correlations between subregion MD and continuous ESS, global cognition, and motor scores; and between subregion volume and continuous ESS. Results: MD was increased in EDS+ compared to EDS- only in the inferior tuberal subregion (Cohen's d=0.43, p=0.043, ß=0.117±0.057), with trend level differences in the whole hypothalamus (Cohen's d=0.39, p=0.064, ß=0.070±0.037) and superior tuberal subregion (Cohen's d=0.38, p=0.073, ß=0.063±0.035). No difference was seen in the posterior subregion (Cohen's d=0.1, p=0.628, ß=0.019±0.038). Significant correlations with continuous ESS were seen in MD of whole hypothalamus (r2=0.074, p=0.0057), superior tuberal (r2=0.081, p=0.0038), and inferior tuberal (r2=0.073, p=0.0059) subregions. There was no correlation of hypothalamic MD with global cognition or motor scores, and no correlation of whole/subregional hypothalamic volumes with ESS. Conclusions: Daytime sleepiness associates with increased MD in the inferior tuberal hypothalamus in an LBD cohort. This suggests degeneration within this region could contribute to EDS symptoms.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Ischemic stroke associated with amyloid-related imaging abnormalities in a patient treated with lecanemab.

INTRODUCTION: Anti-amyloid antibody therapies such as lecanemab are increasingly being used to treat Alzheimer's disease (AD). These therapies are associated with a high rate of amyloid-related imaging abnormalities (ARIA). METHODS: We review the case history of a patient who developed ARIA associated with lecanemab treatment. RESULTS: In addition to microhemorrhages and cerebral edema that are recognized features of ARIA, the patient developed several ischemic strokes. The patient also experienced frequent electrographic seizures without overt clinical seizures. The patient demonstrated clinical and radiographic improvement after steroid treatment. DISCUSSION: Our case suggests that ischemic strokes may be a feature of ARIA and highlights the importance of having a high clinical suspicion for seizures in ARIA. As anti-amyloid therapies are likely going to be increasingly used to treat AD, it is important to appreciate the spectrum of clinical and radiographic findings that can result as side effects from this class of therapies. HIGHLIGHTS: We report a patient who developed severe amyloid-related imaging abnormalities (ARIA) after treatment with lecanemab. Our report suggests that ischemic strokes may be a novel imaging feature of ARIA. Our report highlights the need for high clinical suspicion for seizures in ARIA.

Epigenetic and genetic risk of Alzheimer disease from autopsied brains in two ethnic groups.

Genetic variants and epigenetic features both contribute to the risk of Alzheimer's disease (AD). We studied the AD association of CpG-related single nucleotide polymorphisms (CGS), which act as a hub of both the genetic and epigenetic effects, in Caribbean Hispanics (CH) and generalized the findings to Non-Hispanic Whites (NHW). First, we conducted a genome-wide, sliding-window-based association with AD, in 7,155 CH and 1,283 NHW participants. Next, using data from the dorsolateral prefrontal cortex in 179 CH brains, we tested the cis- and trans-effects of AD-associated CGS on brain DNA methylation to mRNA expression. For the genes with significant cis- and trans-effects, we investigated their enriched pathways. We identified six genetic loci in CH with CGS dosage associated with AD at genome-wide significance levels: ADAM20 (Score = 55.19, P = 4.06 × 10-8), the intergenic region between VRTN and SYNDIG1L (Score = - 37.67, P = 2.25 × 10-9), SPG7 (16q24.3) (Score = 40.51, P = 2.23 × 10-8), PVRL2 (Score = 125.86, P = 1.64 × 10-9), TOMM40 (Score = - 18.58, P = 4.61 × 10-8), and APOE (Score = 75.12, P = 7.26 × 10-26). CGSes in PVRL2 and APOE were also significant in NHW. Except for ADAM20, CGSes in the other five loci were associated with CH brain methylation levels (mQTLs) and CGSes in SPG7, PVRL2, and APOE were also mQTLs in NHW. Except for SYNDIG1L (P = 0.08), brain methylation levels in the other five loci affected downstream mRNA expression in CH (P < 0.05), and methylation at VRTN and TOMM40 were also associated with mRNA expression in NHW. Gene expression in these six loci were also regulated by CpG sites in genes that were enriched in the neuron projection and glutamatergic synapse pathways (FDR < 0.05). DNA methylation at all six loci and mRNA expression of SYNDIG1 and TOMM40 were significantly associated with Braak Stage in CH. In summary, we identified six CpG-related genetic loci associated with AD in CH, harboring both genetic and epigenetic risks. However, their downstream effects on mRNA expression maybe ethnic specific and different from NHW.

Cytoarchitectonic gradients of laminar degeneration in behavioural variant frontotemporal dementia.

Behavioral variant frontotemporal dementia (bvFTD) is a clinical syndrome primarily caused by either tau (bvFTD-tau) or TDP-43 (bvFTD-TDP) proteinopathies. We previously found lower cortical layers and dorsolateral regions accumulate greater tau than TDP-43 pathology; however, patterns of laminar neurodegeneration across diverse cytoarchitecture in bvFTD is understudied. We hypothesized that bvFTD-tau and bvFTD-TDP have distinct laminar distributions of pyramidal neurodegeneration along cortical gradients, a topologic order of cytoarchitectonic subregions based on increasing pyramidal density and laminar differentiation. Here, we tested this hypothesis in a frontal cortical gradient consisting of five cytoarchitectonic types (i.e., periallocortex, agranular mesocortex, dysgranular mesocortex, eulaminate-I isocortex, eulaminate-II isocortex) spanning anterior cingulate, paracingulate, orbitofrontal, and mid-frontal gyri in bvFTD-tau (n=27), bvFTD-TDP (n=47), and healthy controls (HC; n=32). We immunostained all tissue for total neurons (NeuN; neuronal-nuclear protein) and pyramidal neurons (SMI32; non-phosphorylated neurofilament) and digitally quantified NeuN-immunoreactivity (ir) and SMI32-ir in supragranular II-III, infragranular V-VI, and all I-VI layers in each cytoarchitectonic type. We used linear mixed-effects models adjusted for demographic and biologic variables to compare SMI32-ir between groups and examine relationships with the cortical gradient, long-range pathways, and clinical symptoms. We found regional and laminar distributions of SMI32-ir expected for HC, validating our measures within the cortical gradient framework. While SMI32-ir loss was relatively uniform along the cortical gradient in bvFTD-TDP, SMI32-ir progressively decreased along the cortical gradient of bvFTD-tau and included greater SMI32-ir loss in supragranular eulaminate-II isocortex in bvFTD-tau versus bvFTD-TDP (p=0.039). Using a ratio of SMI32-ir to model known long-range connectivity between infragranular mesocortex and supragranular isocortex, we found a larger laminar ratio in bvFTD-tau versus bvFTD-TDP (p=0.019), suggesting select long-projecting pathways may contribute to isocortical-predominant degeneration in bvFTD-tau. In cytoarchitectonic types with the highest NeuN-ir, we found lower SMI32-ir in bvFTD-tau versus bvFTD-TDP (p=0.047), suggesting pyramidal neurodegeneration may occur earlier in bvFTD-tau. Lastly, we found that reduced SMI32-ir related to behavioral severity and frontal-mediated letter fluency, not temporal-mediated confrontation naming, demonstrating the clinical relevance and specificity of frontal pyramidal neurodegeneration to bvFTD-related symptoms. Our data suggest loss of neurofilament-rich pyramidal neurons is a clinically relevant feature of bvFTD that selectively worsens along a frontal cortical gradient in bvFTD-tau, not bvFTD-TDP. Therefore, tau-mediated degeneration may preferentially involve pyramidal-rich layers that connect more distant cytoarchitectonic types. Moreover, the hierarchical arrangement of cytoarchitecture along cortical gradients may be an important neuroanatomical framework for identifying which types of cells and pathways are differentially involved between proteinopathies.

Baseline levels and longitudinal changes in plasma Aß42/40 among Black and white individuals.

Blood-based biomarkers of Alzheimer disease (AD) may facilitate testing of historically under-represented groups. The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to compare AD biomarkers in participants who identify their race as either Black or white. Plasma samples from 324 Black and 1,547 white participants underwent analysis with C2N Diagnostics' PrecivityAD test for Aß42 and Aß40. Compared to white individuals, Black individuals had higher average plasma Aß42/40 levels at baseline, consistent with a lower average level of amyloid pathology. Interestingly, this difference resulted from lower average levels of plasma Aß40 in Black participants. Despite the differences, Black and white individuals had similar longitudinal rates of change in Aß42/40, consistent with a similar rate of amyloid accumulation. Our results agree with multiple recent studies demonstrating a lower prevalence of amyloid pathology in Black individuals, and additionally suggest that amyloid accumulates consistently across both groups.

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality.

Background: Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. Method: MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. Results: The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. Conclusion: The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease.

Our current understanding of the spread and neurodegenerative effects of tau neurofibrillary tangles (NFTs) within the medial temporal lobe (MTL) during the early stages of Alzheimer's Disease (AD) is limited by the presence of confounding non-AD pathologies and the two-dimensional (2-D) nature of conventional histology studies. Here, we combine ex vivo MRI and serial histological imaging from 25 human MTL specimens to present a detailed, 3-D characterization of quantitative NFT burden measures in the space of a high-resolution, ex vivo atlas with cytoarchitecturally-defined subregion labels, that can be used to inform future in vivo neuroimaging studies. Average maps show a clear anterior to poster gradient in NFT distribution and a precise, spatial pattern with highest levels of NFTs found not just within the transentorhinal region but also the cornu ammonis (CA1) subfield. Additionally, we identify granular MTL regions where measures of neurodegeneration are likely to be linked to NFTs specifically, and thus potentially more sensitive as early AD biomarkers.

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.

α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies.

Spread and aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). While evidence exists for multiple aSyn protein conformations, often termed "strains" for their distinct biological properties, it is unclear whether PD and DLB result from aSyn strain differences, and biomarkers that differentiate PD and DLB are lacking. Moreover, while pathological forms of aSyn have been detected outside the brain ( e.g., in skin, gut, blood), the functional significance of these peripheral aSyn species is unclear. Here, we developed assays using monoclonal antibodies selective for two different aSyn species generated in vitro - termed Strain A and Strain B - and used them to evaluate human brain tissue, cerebrospinal fluid (CSF), and plasma, through immunohistochemistry, enzyme-linked immunoassay, and immunoblotting. Surprisingly, we found that plasma aSyn species detected by these antibodies differentiated individuals with PD vs. DLB in a discovery cohort (UPenn, n=235, AUC 0.83) and a multi-site replication cohort (Parkinson's Disease Biomarker Program, or PDBP, n=200, AUC 0.72). aSyn plasma species detected by the Strain A antibody also predicted rate of cognitive decline in PD. We found no evidence for aSyn strains in CSF, and ability to template aSyn fibrillization differed for species isolated from plasma vs. brain, and in PD vs. DLB. Taken together, our findings suggest that aSyn conformational differences may impact clinical presentation and cortical spread of pathological aSyn. Moreover, the enrichment of these aSyn strains in plasma implicates a non-central nervous system source.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau (18F-Flortaucipir) PET, and amyloid (either 18F-Florbetaben or 11C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.