ATN incorporating cerebrospinal fluid neurofilament light chain detects frontotemporal lobar degeneration

INTRODUCTION

The ATN framework provides an in vivo diagnosis of Alzheimer's disease (AD) using cerebrospinal fluid (CSF) biomarkers of pathologic amyloid plaques (A), tangles (T), and neurodegeneration (N). ATN is rarely evaluated in pathologically confirmed patients and its poor sensitivity to suspected non-Alzheimer's pathophysiologies (SNAP), including frontotemporal lobar degeneration (FTLD), leads to misdiagnoses. We compared accuracy of ATN (ATN_TAU ) using CSF total tau (t-tau) to a modified strategy (ATN_NfL ) using CSF neurofilament light chain (NfL) in an autopsy cohort.

METHODS

ATN_TAU and ATN_NfL were trained in an independent sample and validated in autopsy-confirmed AD (n = 67) and FTLD (n = 27).

RESULTS

ATN_NfL more accurately identified FTLD as SNAP (sensitivity = 0.93, specificity = 0.94) than ATN_TAU (sensitivity = 0.44, specificity = 0.97), even in cases with co-occurring AD and FTLD. ATN_NfL misclassified fewer AD and FTLD as "Normal" (2%) than ATN_TAU (14%).

DISCUSSION

ATN_NfL is a promising diagnostic strategy that may accurately identify both AD and FTLD, even when pathologies co-occur.

Degeneration of the locus coeruleus is a common feature of tauopathies and distinct from TDP-43 proteinopathies in the frontotemporal lobar degeneration spectrum

Neurodegeneration of the locus coeruleus (LC) in age-related neurodegenerative diseases such as Alzheimer's disease (AD) is well documented. However, detailed studies of LC neurodegeneration in the full spectrum of frontotemporal lobar degeneration (FTLD) proteinopathies comparing tauopathies (FTLD-tau) to TDP-43 proteinopathies (FTLD-TDP) are lacking. Here, we tested the hypothesis that there is greater LC neuropathology and neurodegeneration in FTLD-tau compared to FTLD-TDP. We examined 280 patients including FTLD-tau (n = 94), FTLD-TDP (n = 135), and two reference groups: clinical/pathological AD (n = 32) and healthy controls (HC, n = 19). Adjacent sections of pons tissue containing the LC were immunostained for phosphorylated TDP-43 (1D3-p409/410), hyperphosphorylated tau (PHF-1), and tyrosine hydroxylase (TH) to examine neuromelanin-containing noradrenergic neurons. Blinded to clinical and pathologic diagnoses, we semi-quantitatively scored inclusions of tau and TDP-43 both inside LC neuronal somas and in surrounding neuropil. We also digitally measured the percent area occupied of neuromelanin inside of TH-positive LC neurons and in surrounding neuropil to calculate a ratio of extracellular-to-intracellular neuromelanin as an objective composite measure of neurodegeneration. We found that LC tau burden in FTLD-tau was greater than LC TDP-43 burden in FTLD-TDP (z = - 11.38, p < 0.0001). Digital measures of LC neurodegeneration in FTLD-tau were comparable to AD (z = - 1.84, p > 0.05) but greater than FTLD-TDP (z = - 3.85, p < 0.0001) and HC (z = - 4.12, p < 0.0001). Both tau burden and neurodegeneration were consistently elevated in the LC across pathologic and clinical subgroups of FTLD-tau compared to FTLD-TDP subgroups. Moreover, LC tau burden positively correlated with neurodegeneration in the total FTLD group (rho = 0.24, p = 0.001), while TDP-43 burden did not correlate with LC neurodegeneration in FTLD-TDP (rho = - 0.01, p = 0.90). These findings suggest that patterns of disease propagation across all tauopathies include prominent LC tau and neurodegeneration that are relatively distinct from the minimal degenerative changes to the LC in FTLD-TDP and HC. Antemortem detection of LC neurodegeneration and/or function could potentially improve antemortem differentiation of underlying FTLD tauopathies from clinically similar FTLD-TDP proteinopathies.

Using Medicare claims in identifying Alzheimer's disease and related dementias

INTRODUCTION

This study develops a measure of Alzheimer's disease and related dementias (ADRD) using Medicare claims.

METHODS

Validation resembles the approach of the American Psychological Association, including (1) content validity, (2) construct validity, and (3) predictive validity.

RESULTS

We found that four items-a Medicare claim recording ADRD 1 year ago, 2 years ago, 3 years ago, and a total stay of 6 months in a nursing home-exhibit a pattern of association consistent with a single underlying ADRD construct, and presence of any two of these four items predict a direct measure of cognitive function and also future claims for ADRD.

DISCUSSION

Our four items are internally consistent with the measurement of a single quantity. The presence of any two items do a better job than a single claim when predicting both a direct measure of cognitive function and future ADRD claims.

Hippocampal subfield pathologic burden in Lewy body diseases vs. Alzheimer's disease

AIMS

Lewy body diseases (LBD) are characterized by alpha-synuclein (SYN) pathology, but comorbid Alzheimer's disease (AD) pathology is common and the relationship between these pathologies in microanatomic hippocampal subfields is understudied. Here we use digital histological methods to test the association between hippocampal SYN pathology and the distribution of tau and amyloid-beta (Aβ) pathology in LBD and contrast with AD subjects. We also correlate pathologic burden with antemortem episodic memory testing.

METHODS

Hippocampal sections from 49 autopsy-confirmed LBD cases, 30 with no/low AD copathology (LBD - AD) and 19 with moderate/severe AD copathology (LBD + AD), and 30 AD patients were stained for SYN, tau, and Aβ. Sections underwent digital histological analysis of subfield pathological burden which was correlated with antemortem memory testing.

RESULTS

LBD - AD and LBD + AD had similar severity and distribution of SYN pathology (P > 0.05), CA2/3 being the most affected subfield (P < 0.02). In LBD, SYN correlated with tau across subfields (R = 0.49, P < 0.001). Tau burden was higher in AD than LBD + AD (P < 0.001), CA1/subiculum and entorhinal cortex (ERC) being most affected regions (P = 0.04 to <0.01). However, tau pathology in LBD - AD was greatest in CA2/3, which was equivalent to LBD + AD. Aβ severity and distribution was similar between LBD + AD and AD. Total hippocampal tau and CA2/3 tau was inversely correlated with memory performance in LBD (R = -0.52, -0.69, P = 0.04, 0.009).

CONCLUSIONS

Our findings suggest that tau burden in hippocampal subfields may map closely with the distribution of SYN pathology in subfield CA2/3 in LBD diverging from traditional AD and contribute to episodic memory dysfunction in LBD.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

INTRODUCTION

Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

METHODS

Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

RESULTS

WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

DISCUSSION

A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Longitudinal atrophy in early Braak regions in preclinical Alzheimer's disease

A major focus of Alzheimer's disease (AD) research has been finding sensitive outcome measures to disease progression in preclinical AD, as intervention studies begin to target this population. We hypothesize that tailored measures of longitudinal change of the medial temporal lobe (MTL) subregions (the sites of earliest cortical tangle pathology) are more sensitive to disease progression in preclinical AD compared to standard cognitive and plasma NfL measures. Longitudinal T1-weighted MRI of 337 participants were included, divided into amyloid-β negative (Aβ-) controls, cerebral spinal fluid p-tau positive (T+) and negative (T-) preclinical AD (Aβ+ controls), and early prodromal AD. Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI using a novel pipeline. Unbiased change rates of subregions were estimated using MRI scans within a 2-year-follow-up period. Experimental results showed that longitudinal atrophy rates of all MTL subregions were significantly higher for T+ preclinical AD and early prodromal AD than controls, but not for T- preclinical AD. Posterior hippocampus and BA35 demonstrated the largest group differences among hippocampus and MTL cortex respectively. None of the cross-sectional MTL measures, longitudinal cognitive measures (PACC, ADAS-Cog) and cross-sectional or longitudinal plasma NfL reached significance in preclinical AD. In conclusion, longitudinal atrophy measurements reflect active neurodegeneration and thus are more directly linked to active disease progression than cross-sectional measurements. Moreover, accelerated atrophy in preclinical AD seems to occur only in the presence of concomitant tau pathology. The proposed longitudinal measurements may serve as efficient outcome measures in clinical trials.

Multimodal in vivo and postmortem assessments of tau in Lewy body disorders

We compared regional retention of ¹⁸F-flortaucipir between 20 patients with Lewy body disorders (LBD), 12 Alzheimer's disease patients with positive amyloid positron emission tomography (PET) scans (AD+Aβ) and 15 healthy controls with negative amyloid PET scans (HC-Aβ). In LBD subjects, we compared the relationship between ¹⁸F-flortaucipir retention and cerebrospinal fluid (CSF) tau, cognitive performance, and neuropathological tau at autopsy. The LBD cohort was stratified using an Aβ42 cut-off of 192 pg/mL to enrich for groups likely harboring tau pathology (LBD+Aβ = 11, LBD-Aβ = 9). ¹⁸F-flortaucipir retention was higher in LBD+AB than HC-Aβ in five, largely temporal-parietal regions with sparing of medial temporal regions. Higher retention was associated with higher CSF total-tau levels (p = 0.04), poorer domain-specific cognitive performance (p = 0.02-0.04), and greater severity of neuropathological tau in corresponding regions. While ¹⁸F-flortaucipir retention in LBD is intermediate between healthy controls and AD, retention relates to cognitive impairment, CSF total-tau, and neuropathological tau. Future work in larger autopsy-validated cohorts is needed to define LBD-specific tau biomarker profiles.

A quantitative meta-analysis of brain glutamate metabolites in aging

Glutamate (Glu) is a key molecule in cellular metabolism, the most abundant excitatory neurotransmitter in the brain, and the principal neurotransmitter of cortical efferents. Glutamate dysfunction, on the other hand, is common in neurodegenerative disorders, and likely contributes to age-related declines in behavioral and cognitive functioning. Nonetheless, the extant literature measuring age-related changes in brain glutamate in vivo has yet to be comprehensively and quantitatively summarized. This meta-analysis examines proton spectroscopy (¹HMRS) measures of Glu-related brain metabolites across 589 healthy young and older adults. Glu (Cohen's d = -0.82) and Glu+glutamine (Cohen's d = -0.51) concentrations were significantly lower in older compared with younger adults, whereas the concentration of glutamine (d = 0.43) was significantly higher in older individuals. Notably, ¹HMRS methodological choices impacted effect sizes for age-related Glu differences. Glu metabolite change appears to be a robust marker of aging-related neurological change; however, additional studies are needed to elucidate age-related trajectories of glutamatergic alterations and their relationship to cognitive phenotypes.

Neural and behavioral correlates of episodic memory are associated with temporal discounting in older adults

When facing decisions involving trade-offs between smaller, sooner and larger, delayed rewards, people tend to discount the value of future rewards. There are substantial individual differences in this tendency toward temporal discounting, however. One neurocognitive system that may underlie these individual differences is episodic memory, given the overlap in the neural circuitry involved in imagining the future and remembering the past. Here we tested this hypothesis in older adults, including both those that were cognitively normal and those with amnestic mild cognitive impairment (MCI). We found that performance on neuropsychological measures of episodic memory retrieval was associated with temporal discounting, such that people with better memory discounted delayed rewards less. This relationship was specific to episodic memory and temporal discounting, since executive function (another cognitive ability) was unrelated to temporal discounting, and episodic memory was unrelated to risk tolerance (another decision-making preference). We also examined cortical thickness and volume in medial temporal lobe regions critical for episodic memory. Entorhinal cortical thickness was associated with reduced temporal discounting, with episodic memory performance partially mediating this association. The inclusion of MCI participants was critical to revealing these associations between episodic memory and entorhinal cortical thickness and temporal discounting. These effects were larger in the MCI group, reduced after controlling for MCI status, and statistically significant only when including MCI participants in analyses. Overall, these findings suggest that individual differences in temporal discounting are driven by episodic memory function, and that a decline in medial temporal lobe structural integrity may impact temporal discounting.

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

Contribution of mixed pathology to medial temporal lobe atrophy in Alzheimer's disease

INTRODUCTION

It is unclear how different proteinopathies (tau, transactive response DNA-binding protein 43 [TDP-43], amyloid β [Aβ], and α-synuclein) contribute to atrophy within medial temporal lobe (MTL) subregions in Alzheimer's disease (AD).

METHODS

We utilized antemortem structural magnetic resonance imaging (MRI) data to measure MTL substructures and examined the relative contribution of tau, TDP-43, Aβ, and α-synuclein measured in post-mortem tissue from 92 individuals with intermediate to high AD neuropathology. Receiver-operating characteristic (ROC) curves were analyzed for each subregion in order to discriminate TDP-43-negative and TDP-43-positive patients.

RESULTS

TDP-43 was strongly associated with anterior MTL regions, whereas tau was relatively more associated with the posterior hippocampus. Among the MTL regions, the anterior hippocampus showed the highest area under the ROC curve (AUC).

DISCUSSION

We found specific contributions of different pathologies on MTL substructure in this population with AD neuropathology. The anterior hippocampus may be a relevant region to detect concomitant TDP-43 pathology in the MTL of patients with AD.

Links between autobiographical memory richness and temporal discounting in older adults

When making choices between smaller, sooner rewards and larger, later ones, people tend to discount future outcomes. Individual differences in temporal discounting in older adults have been associated with episodic memory abilities and entorhinal cortical thickness. The cause of this association between better memory and more future-oriented choice remains unclear, however. One possibility is that people with perceptually richer recollections are more patient because they also imagine the future more vividly. Alternatively, perhaps people whose memories focus more on the meaning of events (i.e., are more "gist-based") show reduced temporal discounting, since imagining the future depends on interactions between semantic and episodic memory. We examined which categories of episodic details - perception-based or gist-based - are associated with temporal discounting in older adults. Older adults whose autobiographical memories were richer in perception-based details showed reduced temporal discounting. Furthermore, in an exploratory neuroanatomical analysis, both discount rates and perception-based details correlated with entorhinal cortical thickness. Retrieving autobiographical memories before choice did not affect temporal discounting, however, suggesting that activating episodic memory circuitry at the time of choice is insufficient to alter discounting in older adults. These findings elucidate the role of episodic memory in decision making, which will inform interventions to nudge intertemporal choices.

Longitudinal progression of grey matter atrophy in non-amnestic Alzheimer's disease

Recent models of Alzheimer's disease progression propose that disease may be transmitted between brain areas either via local diffusion or long-distance transport via white matter fibre pathways. However, it is unclear whether such models are applicable in non-amnestic Alzheimer's disease, which is associated with domain-specific cognitive deficits and relatively spared episodic memory. To date, the anatomical progression of disease in non-amnestic patients remains understudied. We used longitudinal imaging to differentiate earlier atrophy and later disease spread in three non-amnestic variants, including logopenic-variant primary progressive aphasia (n = 25), posterior cortical atrophy (n = 20), and frontal-variant Alzheimer's disease (n = 12), as well as 17 amnestic Alzheimer's disease patients. Patients were compared to 37 matched controls. All patients had autopsy (n = 7) or CSF (n = 67) evidence of Alzheimer's disease pathology. We first assessed atrophy in suspected sites of disease origin, adjusting for age, sex, and severity of cognitive impairment; we then performed exploratory whole-brain analysis to investigate longitudinal disease spread both within and outside these regions. Additionally, we asked whether each phenotype exhibited more rapid change in its associated disease foci than other phenotypes. Finally, we investigated whether atrophy was related to structural brain connectivity. Each non-amnestic phenotype displayed unique patterns of initial atrophy and subsequent neocortical change that correlated with cognitive decline. Longitudinal atrophy included areas both proximal to and distant from sites of initial atrophy, suggesting heterogeneous mechanisms of disease spread. Moreover, regional rates of neocortical change differed by phenotype. Logopenic-variant patients exhibited greater initial atrophy and more rapid longitudinal change in left lateral temporal areas than other groups. Frontal-variant patients had pronounced atrophy in left insula and middle frontal gyrus, combined with more rapid atrophy of left insula than other non-amnestic patients. In the medial temporal lobes, non-amnestic patients had less atrophy at their initial scan than amnestic patients, but longitudinal rate of change did not differ between patient groups. Medial temporal sparing in non-amnestic Alzheimer's disease may thus be due in part to later onset of medial temporal degeneration than in amnestic patients rather than different rates of atrophy over time. Finally, the magnitude of longitudinal atrophy was predicted by structural connectivity, measured in terms of node degree; this result provides indirect support for the role of long-distance fibre pathways in the spread of neurodegenerative disease. 10.1093/brain/awz091_video1 awz091media1 6041544065001.

Estimating regional cerebral blood flow using resting-state functional MRI via machine learning

BACKGROUND

Perfusion MRI is an important modality in many brain imaging protocols, since it probes cerebrovascular changes in aging and many diseases; however, it may not be always available.

NEW METHOD

We introduce a new method that seeks to estimate regional perfusion properties using spectral information of resting-state functional MRI (rsfMRI) via machine learning. We used pairs of rsfMRI and arterial spin labeling (ASL) images from the same individuals with normal cognition and mild cognitive impairment (MCI), and built support vector machine models aiming to estimate regional cerebral blood flow (CBF) from the rsfMRI signal alone.

RESULTS

This method demonstrated higher associations between the estimated CBF and actual CBF (ASL-CBF) at the total lobar gray matter (r = 0.40; FDR-p = 1.9e-03), parietal lobe (r = 0.46, FDR-p = 8e-04), and occipital lobe (r = 0.35; FDR-p = 0.01) using rsfMRI signals of frequencies [0.01-0.15] Hertz compared to frequencies [0.01-0.10] Hertz and [0.01-0.20] Hertz. We further observed significant associations between the estimated CBF and actual CBF in 24 regions of interest (p < 0.05), with the highest association observed in the superior parietal lobule (r = 0.50, FDR-p = 0.002). Moreover, the estimated CBF at superior parietal lobule showed significant correlation with the mini-mental state exam (MMSE) score (r = 0.27; FDR-p = 0.04) and decreased in MCI with lower MMSE score compared to NC group (FDR-p = 0.04).

COMPARISON WITH EXISTING METHODS

Consistent with previous findings, this new method also suggests that rsfMRI signals contain perfusion information.

CONCLUSION

The proposed framework can obtain estimates of regional perfusion from rsfMRI, which can serve as surrogate perfusion measures in the absence of ASL.

Arterial spin labeling versus 18F-FDG-PET to identify mild cognitive impairment

•

¹⁸F-FDG-PET is a neurodegeneration biomarker, but is costly and requires a radioactive tracer.

•

Arterial spin labeled perfusion MRI (ASL-MRI) provides similar information noninvasively.

•

We compared ASL-MRI and FDG-PET in mild cognitive impairment (MCI) and older controls.

•

At a group level, patterns and strength of ASL hypoperfusion and FDG-PET hypometabolism in MCI were comparable.

•

ASL can provide a reasonable alternative for FDG-PET in clinical research.

Neurodegenerative biomarkers support diagnosis and measurement of disease progression in the Alzheimer's disease (AD) continuum. ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography (¹⁸F-FDG-PET), which measures glucose metabolism, is one of the most commonly used biomarkers of neurodegeneration, but is expensive and requires exposure to ionizing radiation. Arterial Spin Labeled (ASL) perfusion Magnetic Resonance Imaging (MRI) provides non invasive quantification of cerebral blood flow (CBF), which is believed to be tightly coupled to glucose metabolism. Here we aimed to compare the performances of ASL derived CBF and ¹⁸F-FDG-PET derived standardized uptake value ratio (SUVR) in discriminating patients with mild cognitive impairment (MCI) from older Controls. 2D pseudo continuous ASL and ¹⁸F-FDG-PET data with adequate scan quality from 50 MCI study participants (age=73.0 ± 7.0 years, 16 female) and 35 older controls (age=70.2 ± 6.9 years, 20 female), acquired in close temporal proximity, usually on the same day, were considered for this study. We assessed Control-patient group differences both at voxel level and within a priori regions of interest (ROIs). We also compared their area under receiver operating characteristic curves (AUC) with mean CBF or SUVR in a priori selected posterior cingulate cortex (PCC). CBF and ¹⁸F-FDG-PET showed abnormalities in similar areas, particularly in medial temporoparietal regions, consistent with the typically observed pattern of prodromal AD. The hypoperfusion pattern with relative CBF (obtained by normalizing voxel CBF values with mean CBF in putamen) was more localized than with absolute CBF. Pearson's correlation coefficients between the T-scores corresponding to the group-differences obtained with ¹⁸F-FDG-PET SUVR and absolute and relative ASL CBF were 0.46 and 0.43 (p<0.001), respectively. ROI analyses were also consistent, with the strongest differences observed in PCC (p<0.01). ¹⁸F-FDG-PET SUVR, absolute and relative CBF in the PCC ROI demonstrated moderate and similar discriminatory power in predicting MCI status with AUC of 0.71 ± 0.12, 0.77 ± 0.12 and 0.74 ± 0.13, respectively. In conclusion, ASL CBF may be a reasonable, less expensive and safer substitute for ¹⁸F-FDG-PET in clinical research.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Characterization of hippocampal subfields using ex vivo MRI and histology data: Lessons for in vivo segmentation

Hippocampal subfield segmentation on in vivo MRI is of great interest for cognition, aging, and disease research. Extant subfield segmentation protocols have been based on neuroanatomical references, but these references often give limited information on anatomical variability. Moreover, there is generally a mismatch between the orientation of the histological sections and the often anisotropic coronal sections on in vivo MRI. To address these issues, we provide a detailed description of hippocampal anatomy using a postmortem dataset containing nine specimens of subjects with and without dementia, which underwent a 9.4 T MRI and histological processing. Postmortem MRI matched the typical orientation of in vivo images and segmentations were generated in MRI space, based on the registered annotated histological sections. We focus on the following topics: the order of appearance of subfields, the location of subfields relative to macroanatomical features, the location of subfields in the uncus and tail and the composition of the dark band, a hypointense layer visible in T2-weighted MRI. Our main findings are that: (a) there is a consistent order of appearance of subfields in the hippocampal head, (b) the composition of subfields is not consistent in the anterior uncus, but more consistent in the posterior uncus, (c) the dark band consists only of the CA-stratum lacunosum moleculare, not the strata moleculare of the dentate gyrus, (d) the subiculum/CA1 border is located at the middle of the width of the hippocampus in the body in coronal plane, but moves in a medial direction from anterior to posterior, and (e) the variable location and composition of subfields in the hippocampal tail can be brought back to a body-like appearance when reslicing the MRI scan following the curvature of the tail. Our findings and this publicly available dataset will hopefully improve anatomical accuracy of future hippocampal subfield segmentation protocols.

Use of Flutemetamol F 18-Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment

Importance

Patients with amnestic mild cognitive impairment (aMCI) may progress to clinical Alzheimer disease (AD), remain stable, or revert to normal. Earlier progression to AD among patients who were β-amyloid positive vs those who were β-amyloid negative has been previously observed. Current research now accepts that a combination of biomarkers could provide greater refinement in the assessment of risk for clinical progression.

Objective

To evaluate the ability of flutemetamol F 18 and other biomarkers to assess the risk of progression from aMCI to probable AD.

Design, Setting, and Participants

In this multicenter cohort study, from November 11, 2009, to January 16, 2014, patients with aMCI underwent positron emission tomography (PET) at baseline followed by local clinical assessments every 6 months for up to 3 years. Patients with aMCI (365 screened; 232 were eligible) were recruited from 28 clinical centers in Europe and the United States. Physicians remained strictly blinded to the results of PET, and the standard of truth was an independent clinical adjudication committee that confirmed or refuted local assessments. Flutemetamol F 18-labeled PET scans were read centrally as either negative or positive by 5 blinded readers with no knowledge of clinical status. Statistical analysis was conducted from February 19, 2014, to January 26, 2018.

Interventions

Flutemetamol F 18-labeled PET at baseline followed by up to 6 clinical visits every 6 months, as well as magnetic resonance imaging and multiple cognitive measures.

Main Outcomes and Measures

Time from PET to probable AD or last follow-up was plotted as a Kaplan-Meier survival curve; PET scan results, age, hippocampal volume, and aMCI stage were entered into Cox proportional hazards logistic regression analyses to identify variables associated with progression to probable AD.

Results

Of 232 patients with aMCI (118 women and 114 men; mean [SD] age, 71.1 [8.6] years), 98 (42.2%) had positive results detected on PET scan. By 36 months, the rates of progression to probable AD were 36.2% overall (81 of 224 patients), 53.6% (52 of 97) for patients with positive results detected on PET scan, and 22.8% (29 of 127) for patients with negative results detected on PET scan. Hazard ratios for association with progression were 2.51 (95% CI, 1.57-3.99; P < .001) for a positive β-amyloid scan alone (primary outcome measure), 5.60 (95% CI, 3.14-9.98; P < .001) with additional low hippocampal volume, and 8.45 (95% CI, 4.40-16.24; P < .001) when poorer cognitive status was added to the model.

Conclusions and Relevance

A combination of positive results of flutemetamol F 18-labeled PET, low hippocampal volume, and cognitive status corresponded with a high probability of risk of progression from aMCI to probable AD within 36 months.