Different patterns of gray matter atrophy in early- and late-onset Alzheimer's disease

Graded Multidimensional Clinical and Radiologic Variation in Patients With Alzheimer Disease and Posterior Cortical Atrophy

BACKGROUND AND OBJECTIVES

Alzheimer disease (AD) spans heterogeneous typical and atypical phenotypes. Posterior cortical atrophy (PCA) is a striking example, characterized by prominent impairment in visual and other posterior functions in contrast to typical, amnestic AD. The primary study objective was to establish how the similarities and differences of cognition and brain volumes within AD and PCA (and by extension other AD variants) can be conceptualized as systematic variations across a transdiagnostic, graded multidimensional space.

METHODS

This was a cross-sectional, single-center, observational, cohort study performed at the National Hospital for Neurology & Neurosurgery, London, United Kingdom. Data were collected from a cohort of patients with PCA and AD, matched for age, disease duration, and Mini-Mental State Examination (MMSE) scores. There were 2 sets of outcome measures: (1) scores on a neuropsychological battery containing 22 tests spanning visuoperceptual and visuospatial processing, episodic memory, language, executive functions, calculation, and visuospatial processing and (2) measures extracted from high-resolution T1-weighted volumetric MRI scans. Principal component analysis was used to extract the transdiagnostic dimensions of phenotypical variation from the detailed neuropsychological data. Voxel-based morphometry was used to examine associations between the PCA-derived clinical phenotypes and the structural measures.

RESULTS

We enrolled 93 participants with PCA (mean: age = 59.9 years, MMSE = 21.2; 59/93 female) and 58 AD participants (mean: age = 57.1 years, MMSE = 19.7; 22/58 female). The principal component analysis for PCA (sample adequacy confirmed: Kaiser-Meyer-Olkin = 0.865) extracted 3 dimensions accounting for 61.0% of variance in patients' performance, reflecting general cognitive impairment, visuoperceptual deficits, and visuospatial impairments. Plotting AD cases into the PCA-derived multidimensional space, and vice versa, revealed graded, overlapping variations between cases along these dimensions, with no evidence for categorical-like patient clustering. Similarly, the relationship between brain volumes and scores on the extracted dimensions was overlapping for PCA and AD cases.

DISCUSSION

These results provide evidence supporting a reconceptualization of clinical and radiologic variation in these heterogenous AD phenotypes as being along shared phenotypic continua spanning PCA and AD, arising from systematic graded variations within a transdiagnostic, multidimensional neurocognitive geometry.

Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease

Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.

Estradiol associations with brain functional connectivity in postmenopausal women

OBJECTIVE

Previous studies have found that estrogens play a role in functional connectivity in the brain; however, little research has been done regarding how estradiol is associated with functional connectivity in postmenopausal women. The purpose of this study was to examine the relationship between estradiol and functional connectivity in postmenopausal women.

METHODS

Structural and blood oxygenation level-dependent resting-state magnetic resonance imaging scans of 88 cognitively healthy postmenopausal individuals were obtained along with blood samples collected the same day as the magnetic resonance imaging to assess hormone levels. We generated connectivity values in CONN toolbox version 20.b, an SPM-based software.

RESULTS

A regression analysis was run using estradiol level and regions of interest (ROI), including the hippocampus, parahippocampus, dorsolateral prefrontal cortex, and precuneus. Estradiol level was found to enhance parahippocampal gyrus anterior division left functional connectivity during ROI-to-ROI regression analysis. Estradiol enhanced functional connectivity between the parahippocampal gyrus anterior division left and the precuneus as well as the parahippocampal gyrus anterior division left and parahippocampal gyrus posterior division right. An exploratory analysis showed that years since the final menstrual period was related to enhanced connectivity between regions within the frontoparietal network.

CONCLUSIONS

These results illustrated the relationship between estradiol level and functional connectivity in postmenopausal women. They have implications for understanding how the functioning of the brain changes for individuals after menopause that may eventually lead to changes in cognition and behavior in older ages.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

BACKGROUND AND OBJECTIVE

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.

METHODS

We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.

RESULTS

We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.

CONCLUSION

We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment

Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.

Cross-species comparative hippocampal transcriptomics in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial pathology, with most cases having a sporadic origin. Recently, knock-in (KI) mouse models, such as the novel humanized amyloid-β (hAβ)-KI, have been developed to better resemble sporadic human AD.

METHODS

Here, we compared hippocampal publicly available transcriptomic profiles of transgenic (5xFAD and APP/PS1) and KI (hAβ-KI) mouse models with early- (EOAD) and late- (LOAD) onset AD patients.

RESULTS

The three mouse models presented more Gene Ontology biological processes terms and enriched signaling pathways in common with LOAD than with EOAD individuals. Experimental validation of consistently dysregulated genes revealed five altered in mice (SLC11A1, S100A6, CD14, CD33, and C1QB) and three in humans (S100A6, SLC11A1, and KCNK). Finally, we identified 17 transcription factors potentially acting as master regulators of AD.

CONCLUSION

Our cross-species analyses revealed that the three mouse models presented a remarkable similarity to LOAD, with the hAβ-KI being the more specific one.

BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.

High burdens of phosphorylated tau protein and distinct precuneus atrophy in sporadic early-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) is a rare devastating subclassification of Alzheimer's disease (AD). EOAD affects individuals <65 years old, and accounts for 5%-10% of all AD cases. Previous studies on EOAD primarily focused on familial forms, whereas research on sporadic EOAD (sEOAD), which represents 85%-90% of EOAD cases, is limited. In this prospective cohort study, participants were recruited between 2018 and 2023 and included patients with sEOAD (n = 110), late-onset AD (LOAD, n = 89), young controls (YC, n = 50), and older controls (OC, n = 25). All AD patients fulfilled the diagnostic criteria based on biomarker evidence. Familial EOAD patients or non-AD dementia patients were excluded. Single molecule array technology was used to measure fluid biomarkers, including cerebrospinal fluid (CSF) and plasma amyloid beta (Aβ) 40, Aβ42, phosphorylated tau (P-tau) 181, total tau (T-tau), serum neurofilament light chain and glial fibrillary acidic protein (GFAP). Patients with sEOAD exhibited more severe executive function impairment and bilateral precuneus atrophy (P < 0.05, family-wise error corrected) than patients with LOAD. Patients with sEOAD showed elevated CSF and plasma P-tau181 levels (154.0 ± 81.2 pg/mL, P = 0.002; and 6.1 ± 2.3 pg/mL, P = 0.046). Moreover, precuneus atrophy was significantly correlated with serum GFAP levels in sEOAD (P < 0.001). Serum GFAP levels (area under the curve (AUC) = 96.0%, cutoff value = 154.3 pg/mL) displayed excellent diagnostic value in distinguishing sEOAD patients from the control group. These preliminary findings highlight the crucial role of tau protein phosphorylation in the pathogenesis and progression of sEOAD.

Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease

Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.

The Early-Onset Alzheimer's Disease Whole-Genome Sequencing Project: Study design and methodology

INTRODUCTION

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations, resulting in a lack of understanding of its molecular etiology.

METHODS

Whole-genome sequencing and harmonization of clinical, neuropathological, and biomarker data of over 5000 EOAD cases of diverse ancestries.

RESULTS

A publicly available genomics resource for EOAD with extensive harmonized phenotypes. Primary analysis will (1) identify novel EOAD risk loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits.

DISCUSSION

This novel resource complements over 50,000 control and late-onset AD samples generated through the Alzheimer's Disease Sequencing Project (ADSP). The harmonized EOAD/ADSP joint call will be available through upcoming ADSP data releases and will allow for additional analyses across the full onset range.

HIGHLIGHTS

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations. This results in a significant lack of understanding of the molecular etiology of this devastating form of the disease. The Early-Onset Alzheimer's Disease Whole-genome Sequencing Project is a collaborative initiative to generate a large-scale genomics resource for early-onset Alzheimer's disease with extensive harmonized phenotype data. Primary analyses are designed to (1) identify novel EOAD risk and protective loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. The harmonized genomic and phenotypic data from this initiative will be available through NIAGADS.

A global view of the genetic basis of Alzheimer disease

The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

Sex difference in evolution of cognitive decline: studies on mouse model and the Dominantly Inherited Alzheimer Network cohort

Women carry a higher burden of Alzheimer's disease (AD) compared to men, which is not accounted entirely by differences in lifespan. To identify the mechanisms underlying this effect, we investigated sex-specific differences in the progression of familial AD in humans and in APPswe/PS1ΔE9 mice. Activity dependent protein translation and associative learning and memory deficits were examined in APPswe/PS1ΔE9 mice and wild-type mice. As a human comparator group, progression of cognitive dysfunction was assessed in mutation carriers and non-carriers from DIAN (Dominantly Inherited Alzheimer Network) cohort. Female APPswe/PS1ΔE9 mice did not show recall deficits after contextual fear conditioning until 8 months of age. Further, activity dependent protein translation and Akt1-mTOR signaling at the synapse were impaired in male but not in female mice until 8 months of age. Ovariectomized APPswe/PS1ΔE9 mice displayed recall deficits at 4 months of age and these were sustained until 8 months of age. Moreover, activity dependent protein translation was also impaired in 4 months old ovariectomized APPswe/PS1ΔE9 mice compared with sham female APPswe/PS1ΔE9 mice. Progression of memory impairment differed between men and women in the DIAN cohort as analyzed using linear mixed effects model, wherein men showed steeper cognitive decline irrespective of the age of entry in the study, while women showed significantly greater performance and slower decline in immediate recall (LOGIMEM) and delayed recall (MEMUNITS) than men. However, when the performance of men and women in several cognitive tasks (such as Wechsler's logical memory) are compared with the estimated year from expected symptom onset (EYO) we found no significant differences between men and women. We conclude that in familial AD patients and mouse models, females are protected, and the onset of disease is delayed as long as estrogen levels are intact.

Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting

Quantitative magnetic resonance (MR) has been used to study cyto- and myelo-architecture of the human brain non-invasively. However, analyzing brain cortex using high-resolution quantitative MR acquisition can be challenging to perform using 3T clinical scanners. MR fingerprinting (MRF) is a highly efficient and clinically feasible quantitative MR technique that simultaneously provides T1 and T2 relaxation maps. Using 3D MRF from 40 healthy subjects (mean age = 25.6 ± 4.3 years) scanned on 3T magnetic resonance imaging, we generated whole-brain gyral-based normative MR relaxation atlases and investigated cortical-region-based T1 and T2 variations. Gender and age dependency of T1 and T2 variations were additionally analyzed. The coefficient of variation of T1 and T2 for each cortical-region was 3.5% and 7.3%, respectively, supporting low variability of MRF measurements across subjects. Significant differences in T1 and T2 were identified among 34 brain regions (P < 0.001), lower in the precentral, postcentral, paracentral lobule, transverse temporal, lateral occipital, and cingulate areas, which contain sensorimotor, auditory, visual, and limbic functions. Significant correlations were identified between age and T1 and T2 values. This study established whole-brain MRF T1 and T2 atlases of healthy subjects using a clinical 3T scanner, which can provide a quantitative and region-specific baseline for future brain studies and pathology detection.

MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI

Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.

Morphological and Structural Network Analysis of Sporadic Alzheimer's Disease Brains Based on the APOE4 Gene

BACKGROUND

Alzheimer's disease (AD) is an increasingly common type of dementia. Apolipoprotein E (APOE) gene is a strong risk factor for AD.

OBJECTIVE

Here, we explored alterations in grey matter structure (GMV) and networks in AD, as well as the effects of the APOEɛ4 allele on neuroimaging regions based on structural magnetic resonance imaging (sMRI).

METHODS

All subjects underwent an sMRI scan. GMV and cortical thickness were calculated using voxel-based morphological analysis, and structural networks were constructed based on graph theory analysis to compare differences between AD and normal controls.

RESULTS

The volumes of grey matter in the bilateral inferior temporal gyrus, right middle temporal gyrus, right inferior parietal lobule, right limbic lobe, right frontal lobe, left anterior cingulate gyrus, and bilateral olfactory cortex of patients with AD were significantly decreased. The cortical thickness in patients with AD was significantly reduced in the left lateral occipital lobe, inferior parietal lobe, orbitofrontal region, precuneus, superior parietal gyrus, right precentral gyrus, middle temporal gyrus, pars opercularis gyrus, insular gyrus, superior marginal gyrus, bilateral fusiform gyrus, and superior frontal gyrus. In terms of local properties, there were significant differences between the AD and control groups in these areas, including the right bank, right temporalis pole, bilateral middle temporal gyrus, right transverse temporal gyrus, left postcentral gyrus, and left parahippocampal gyrus.

CONCLUSION

There were significant differences in the morphological and structural covariate networks between AD patients and healthy controls under APOEɛ4 allele effects.

Low-field MRI: Clinical promise and challenges

Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Connectomic neuromodulation for Alzheimer's disease: A systematic review and meta-analysis of invasive and non-invasive techniques

Deep brain stimulation (DBS) and non-invasive neuromodulation are currently being investigated for treating network dysfunction in Alzheimer's Disease (AD). However, due to heterogeneity in techniques and targets, the cognitive outcome and brain network connectivity remain unknown. We performed a systematic review, meta-analysis, and normative functional connectivity to determine the cognitive outcome and brain networks of DBS and non-invasive neuromodulation in AD. PubMed, Embase, and Web of Science were searched using three concepts: dementia, brain connectome, and brain stimulation, with filters for English, human studies, and publication dates 1980-2021. Additional records from clinicaltrials.gov were added. Inclusion criteria were AD study with DBS or non-invasive neuromodulation and a cognitive outcome. Exclusion criteria were less than 3-months follow-up, severe dementia, and focused ultrasound intervention. Bias was assessed using Centre for Evidence-Based Medicine levels of evidence. We performed meta-analysis, with subgroup analysis based on type and age at neuromodulation. To determine the patterns of neuromodulation-induced brain network activation, we performed normative functional connectivity using rsfMRI of 1000 healthy subjects. Six studies, with 242 AD patients, met inclusion criteria. On fixed-effect meta-analysis, non-invasive neuromodulation favored baseline, with effect size -0.40(95% [CI], -0.73, -0.06, p = 0.02), while that of DBS was 0.11(95% [CI] -0.34, 0.56, p = 0.63), in favor of DBS. In patients ≥65 years old, DBS improved cognitive outcome, 0.95(95% [CI] 0.31, 1.58, p = 0.004), whereas in patients <65 years old baseline was favored, -0.17(95% [CI] -0.93, 0.58, p = 0.65). Functional connectivity regions were in the default mode (DMN), salience (SN), central executive (CEN) networks, and Papez circuit. The subgenual cingulate and anterior limb of internal capsule (ALIC) showed connectivity to all targets of neuromodulation. This meta-analysis provides level II evidence of a difference in response of AD patients to DBS, based on age at intervention. Brain stimulation in AD may modulate DMN, SN, CEN, and Papez circuit, with the subgenual cingulate and ALIC as potential targets.

Dynamic network model reveals distinct tau spreading patterns in early- and late-onset Alzheimer disease

BACKGROUND

The clinical features of Alzheimer's disease (AD) vary substantially depending on whether the onset of cognitive deficits is early or late. The amount and distribution patterns of tau pathology are thought to play a key role in the clinical characteristics of AD, which spreads throughout the large-scale brain network. Here, we describe the differences between tau-spreading processes in early- and late-onset symptomatic individuals on the AD spectrum.

METHODS

We divided 74 cognitively unimpaired (CU) and 68 cognitively impaired (CI) patients receiving 18F-flortaucipir positron emission tomography scans into two groups by age and age at onset. Members of each group were arranged in a pseudo-longitudinal order based on baseline tau pathology severity, and potential interregional tau-spreading pathways were defined following the order using longitudinal tau uptake. We detected a multilayer community structure through consecutive tau-spreading networks to identify spatio-temporal changes in the propagation hubs.

RESULTS

In each group, ordered tau-spreading networks revealed the stage-dependent dynamics of tau propagation, supporting distinct tau accumulation patterns. In the young CU/early-onset CI group, tau appears to spread through a combination of three independent communities with partially overlapped territories, whose specific driving regions were the basal temporal regions, left medial and lateral temporal regions, and left parietal regions. For the old CU/late-onset CI group, however, continuation of major communities occurs in line with the appearance of hub regions in the order of bilateral entorhinal cortices, parahippocampal and fusiform gyri, and lateral temporal regions.

CONCLUSION

Longitudinal tau propagation depicts distinct spreading pathways of the early- and late-onset AD spectrum characterized by the specific location and appearance period of several hub regions that dominantly provide tau.

Differential Involvement of the Locus Coeruleus in Early- and Late-Onset Alzheimer's Disease: A Potential Mechanism of Clinical Differences?

Sporadic early-onset Alzheimer's disease (sEOAD) is often associated with atypical clinical features, yet the cause of this heterogeneity remains unclear. This study investigated post-mortem atrophy of the locus coeruleus (LC) in sEOAD and late-onset Alzheimer's disease (LOAD). Levels of LC atrophy, as estimated by pathologist-rating of hypopigmentation, were compared between sEOAD (n = 115) and LOAD (n = 672) participants while controlling for other measures of pathological progression. Subsequent analyses compared low vs. high LC atrophy sEOAD subgroups on neuropsychological test performance. Results show nearly 4 times greater likelihood of higher LC atrophy in sEOAD as compared to LOAD (p < .005). sEOAD participants with greater LC atrophy displayed significantly worse performance on various baseline measures of attentional functioning (p < .05), despite similar global cognition (p = .25). These findings suggest the LC is an important potential driver of clinical and pathological heterogeneity in sEOAD.

Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading

In Alzheimer’s disease (AD), younger symptom onset is associated with accelerated disease progression and tau spreading, yet the mechanisms underlying faster disease manifestation are unknown. To address this, we combined resting-state fMRI and longitudinal tau-PET in two independent samples of controls and biomarker-confirmed AD patients (ADNI/BioFINDER, n = 240/57). Consistent across both samples, we found that younger symptomatic AD patients showed stronger tau-PET in globally connected fronto-parietal hubs, i.e., regions that are critical for maintaining cognition in AD. Stronger tau-PET in hubs predicted faster subsequent tau accumulation, suggesting that tau in globally connected regions facilitates connectivity-mediated tau spreading. Further, stronger tau-PET in hubs mediated the association between younger age and faster tau accumulation in symptomatic AD patients, which predicted faster cognitive decline. These independently validated findings suggest that younger AD symptom onset is associated with stronger tau pathology in brain hubs, and accelerated tau spreading throughout connected brain regions and cognitive decline.

Multiple Cognitive and Behavioral Factors Link Association Between Brain Structure and Functional Impairment of Daily Instrumental Activities in Older Adults

OBJECTIVE

Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs.

METHOD

In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer's type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL.

RESULTS

The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive-behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL.

CONCLUSION

Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer's Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort

BACKGROUND

Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker.

OBJECTIVE

We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients.

METHODS

One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform.

RESULTS

The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed.

CONCLUSION

Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.

Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia

A quantitative analysis of brain volume can assist in the diagnosis of Alzheimer's disease (AD) which is ususally accompanied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 min 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability was high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo is a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.

Dissecting the clinical heterogeneity of early-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) is a rare but particularly devastating form of AD. Though notable for its high degree of clinical heterogeneity, EOAD is defined by the same neuropathological hallmarks underlying the more common, late-onset form of AD. In this review, we describe the various clinical syndromes associated with EOAD, including the typical amnestic phenotype as well as atypical variants affecting visuospatial, language, executive, behavioral, and motor functions. We go on to highlight advances in fluid biomarker research and describe how molecular, structural, and functional neuroimaging can be used not only to improve EOAD diagnostic acumen but also enhance our understanding of fundamental pathobiological changes occurring years (and even decades) before the onset of symptoms. In addition, we discuss genetic variation underlying EOAD, including pathogenic variants responsible for the well-known mendelian forms of EOAD as well as variants that may increase risk for the much more common forms of EOAD that are either considered to be sporadic or lack a clear autosomal-dominant inheritance pattern. Intriguingly, specific pathogenic variants in PRNP and MAPT-genes which are more commonly associated with other neurodegenerative diseases-may provide unexpectedly important insights into the formation of AD tau pathology. Genetic analysis of the atypical clinical syndromes associated with EOAD will continue to be challenging given their rarity, but integration of fluid biomarker data, multimodal imaging, and various 'omics techniques and their application to the study of large, multicenter cohorts will enable future discoveries of fundamental mechanisms underlying the development of EOAD and its varied clinical presentations.

Automatized FACEmemory® scoring is related to Alzheimer's disease phenotype and biomarkers in early-onset mild cognitive impairment: the BIOFACE cohort

Background

FACEmemory® is the first computerized, self-administered verbal episodic memory test with voice recognition. It can be conducted under minimal supervision and contains an automatic scoring system to avoid administrator errors. Moreover, it is suitable for discriminating between cognitively healthy and amnestic mild cognitive impairment (MCI) individuals, and it is associated with Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers. This study aimed to determine whether FACEmemory scoring is related to performance on classical memory tests and to AD biomarkers of brain magnetic resonance imaging (MRI) and CSF in patients with early-onset MCI (EOMCI).

Methods

Ninety-four patients with EOMCI from the BIOFACE study completed FACEmemory, classical memory tests (the Spanish version of the Word Free and Cued Selective Reminding Test -FCSRT-, the Word List from the Wechsler Memory Scale, third edition, and the Spanish version of the Rey–Osterrieth Complex Figure Test), and a brain MRI. Eighty-two individuals also underwent a lumbar puncture.

Results

FACEmemory scoring was moderately correlated with FCSRT scoring. With regard to neuroimaging MRI results, worse execution on FACEmemory was associated with lower cortical volume in the right prefrontal and inferior parietal areas, along with the left temporal and associative occipital areas. Moreover, the total FACEmemory score correlated with CSF AD biomarkers (Aβ1-42/Aβ1-40 ratio, p181-tau, and Aβ1-42/p181-tau ratio). When performance on FACEmemory was compared among the ATN classification groups, significant differences between the AD group and normal and SNAP groups were found.

Conclusions

FACEmemory is a promising tool for detecting memory deficits sensitive to early-onset AD, but it also allows the detection of memory-impaired cases due to other etiologies. Our findings suggest that FACEmemory scoring can detect the AD endophenotype and that it is also associated with AD-related changes in MRI and CSF in patients with EOMCI. The computerized FACEmemory tool might be an opportunity to facilitate early detection of MCI in younger people than 65, who have a growing interest in new technologies.

Early Alzheimer's disease-like reductions in gray matter and cognitive function with aging in nonhuman primates

Introduction

Age-related neuropathology associated with sporadic Alzheimer's disease (AD) often develops well before the onset of symptoms. Given AD's long preclinical period, translational models are needed to identify early signatures of pathological decline.

Methods

Using structural magnetic resonance imaging and cognitive assessments, we examined the relationships among age, cognitive performance, and neuroanatomy in 48 vervet monkeys (Chlorocebus aethiops sabaeus) ranging from young adults to very old.

Results

We found negative associations of age with cortical gray matter volume (P = .003) and the temporal-parietal cortical thickness meta-region of interest (P = .001). Additionally, cortical gray matter volumes predicted working memory at approximately 1-year follow-up (correct trials at the 20s delay [P = .008]; correct responses after longer delays [P = .004]).

Discussion

Cortical gray matter diminishes with age in vervets in regions relevant to AD, which may increase risk of cognitive impairment. This study lays the groundwork for future investigations to test therapeutics to delay or slow pathological decline.

Multiple-region grey matter atrophy as a predictor for the development of dementia in a community: the Hisayama Study

OBJECTIVE

To assess the association of regional grey matter atrophy with dementia risk in a general older Japanese population.

METHODS

We followed 1158 dementia-free Japanese residents aged ≥65 years for 5.0 years. Regional grey matter volume (GMV) at baseline was estimated by applying voxel-based morphometry methods. The GMV-to-total brain volume ratio (GMV/TBV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. We assessed whether the predictive ability of a model based on known dementia risk factors could be improved by adding the total number of regions with grey matter atrophy among dementia-related brain regions, where the cut-off value for grey matter atrophy in each region was determined by receiver operating characteristic curves.

RESULTS

During the follow-up, 113 participants developed all-cause dementia, including 83 with Alzheimer's disease (AD). Lower GMV/TBV of the medial temporal lobe, insula, hippocampus and amygdala were significantly/marginally associated with higher risk of all-cause dementia and AD (all p for trend ≤0.08). The risks of all-cause dementia and AD increased significantly with increasing total number of brain regions exhibiting grey matter atrophy (both p for trend <0.01). Adding the total number of regions with grey matter atrophy into a model consisting of known risk factors significantly improved the predictive ability for AD (Harrell's c-statistics: 0.765-0.802; p=0.02).

CONCLUSIONS

Our findings suggest that the total number of regions with grey matter atrophy among the medial temporal lobe, insula, hippocampus and amygdala is a significant predictor for developing dementia, especially AD, in the general older population.

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD.

Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models.

Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set.

Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.

Differential associations between neocortical tau pathology and blood flow with cognitive deficits in early-onset vs late-onset Alzheimer's disease

Purpose

Early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD) differ in neuropathological burden and type of cognitive deficits. Assessing tau pathology and relative cerebral blood flow (rCBF) measured with [¹⁸F]flortaucipir PET in relation to cognition may help explain these differences between EOAD and LOAD.

Methods

Seventy-nine amyloid-positive individuals with a clinical diagnosis of AD (EOAD: n = 35, age-at-PET = 59 ± 5, MMSE = 23 ± 4; LOAD: n = 44, age-at-PET = 71 ± 5, MMSE = 23 ± 4) underwent a 130-min dynamic [¹⁸F]flortaucipir PET scan and extensive neuropsychological assessment. We extracted binding potentials (BP_ND) and R₁ (proxy of rCBF) from parametric images using receptor parametric mapping, in medial and lateral temporal, parietal, occipital, and frontal regions-of-interest and used nine neuropsychological tests covering memory, attention, language, and executive functioning. We first examined differences between EOAD and LOAD in BP_ND or R₁ using ANOVA (region-of-interest analysis) and voxel-wise contrasts. Next, we performed linear regression models to test for potential interaction effects between age-at-onset and BP_ND/R₁ on cognition.

Results

Both region-of-interest and voxel-wise contrasts showed higher [¹⁸F]flortaucipir BP_ND values across all neocortical regions in EOAD. By contrast, LOAD patients had lower R₁ values (indicative of more reduced rCBF) in medial temporal regions. For both tau and flow in lateral temporal, and occipitoparietal regions, associations with cognitive impairment were stronger in EOAD than in LOAD (EOAD BP_ND − 0.76 ≤ stβ ≤  − 0.48 vs LOAD − 0.18 ≤ stβ ≤  − 0.02; EOAD R₁ 0.37 ≤ stβ ≤ 0.84 vs LOAD − 0.25 ≤ stβ ≤ 0.16).

Conclusions

Compared to LOAD, the degree of lateral temporal and occipitoparietal tau pathology and relative cerebral blood-flow is more strongly associated with cognition in EOAD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05669-6.

The Longitudinal Early-onset Alzheimer's Disease Study (LEADS): Framework and methodology

Patients with early-onset Alzheimer's disease (EOAD) are commonly excluded from large-scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial-ready network. LEADS will follow 400 amyloid beta (Aβ)-positive EOAD, 200 Aβ-negative EOnonAD that meet National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age-matched controls. Participants will undergo clinical and cognitive assessments, magnetic resonance imaging (MRI), [18 F]Florbetaben and [18 F]Flortaucipir positron emission tomography (PET), lumbar puncture, and blood draw for DNA, RNA, plasma, serum and peripheral blood mononuclear cells, and post-mortem assessment. To develop more effective AD treatments, scientists need to understand the genetic, biological, and clinical processes involved in EOAD. LEADS will develop a public resource that will enable future planning and implementation of EOAD clinical trials.

A heterozygous de novo PSEN1 mutation in a patient with early-onset parkinsonism

BACKGROUND

Mutations in presenilin 1 (PSEN1) are the most common known genetic cause of early-onset Alzheimer's disease. Patients with PSEN1 mutations exhibit broad phenotypes. Here, we report clinical, neuroimaging and genetic findings in a patient with a de novo mutation in PSEN1 (c.697A > G, p.M233V) presenting with early-onset parkinsonism as the initial and primary symptom.

METHODS

We recruited a family with one affected patient with early-onset parkinsonism. The patient underwent comprehensive neurological examination and imaging evaluation. Whole genome sequencing was performed for the proband.

RESULTS

The patient presented with parkinsonism and mild cognitive impairment. He had a good response to levodopa. Brain MRI evaluation showed atrophy of the bilateral frontotemporal lobe and hippocampus. ¹⁸F-fluorodeoxyglucose-positron emission tomography (PET) and ¹¹C-2β-carbomethoxy-3β-(4-fluorophenyl) tropane-PET showed decreased metabolism and dopamine transporter distribution in the bilateral putamen and caudate nucleus. ¹¹C-Pittsburgh compound B -PET showed β-amyloid protein deposition. Genetic analysis identified a heterozygous de novo variant in PSEN1 (c.697A > G, p.M233V).

CONCLUSIONS

Screening for PSEN1 variations should be considered in patients with levodopa-responsive early-onset parkinsonism.

A Comparison of Behavioral and Psychological Symptoms of Dementia (BPSD) and BPSD Sub-Syndromes in Early-Onset and Late-Onset Alzheimer's Disease

BACKGROUND

Behavioral and psychological symptoms of dementia (BPSD) have a large impact on the quality of life of patients with Alzheimer's disease (AD). Few studies have compared BPSD between early-onset (EOAD) and late-onset (LOAD) patients, finding conflicting results.

OBJECTIVE

The aims of this study were to: 1) characterize the presence, overall prevalence, and time of occurrence of BPSD in EOAD versus LOAD; 2) estimate the prevalence over time and severity of each BPSD in EOAD versus LOAD in three stages: pre-T0 (before the onset of the disease), T0 (from onset to 5 years), and T1 (from 5 years onwards); 3) track the manifestation of BPSD sub-syndromes (i.e., hyperactivity, psychosis, affective, and apathy) in EOAD versus LOAD at T0 and T1.

METHODS

The sample includes 1,538 LOAD and 387 EOAD diagnosed from 1996 to 2018. Comprehensive assessment batteries, including the Neuropsychiatric Inventory (NPI), were administered at the first medical assessment and at different follow-up period.

RESULTS

The overall prevalence for the most of BPSD was significantly higher in EOAD compared to LOAD whereas most BPSD appeared significantly later in EOAD patients. Between the two groups, from pre-T0 to T1 we recorded a different pattern of BPSD prevalence over time as well as for BPSD sub-syndromes at T0 and T1. Results on severity of BPSD did not show significant differences.

CONCLUSION

EOAD and LOAD represent two different forms of a single entity not only from a neuropathological, cognitive, and functional level but also from a psychiatric point of view.

Evidence for a pervasive autobiographical memory impairment in Logopenic Progressive Aphasia

Although characterized primarily as a language disorder, mounting evidence indicates episodic amnesia in Logopenic Progressive Aphasia (LPA). Whether such memory disturbances extend to information encoded pre-disease onset remains unclear. To address this question, we examined autobiographical memory in 10 LPA patients, contrasted with 18 typical amnestic Alzheimer's disease and 16 healthy Control participants. A validated assessment, the Autobiographical Interview, was employed to explore autobiographical memory performance across the lifespan under free and probed recall conditions. Relative to Controls, LPA patients showed global impairments across all time periods for free recall, scoring at the same level as disease-matched cases of Alzheimer's disease. Importantly, these retrieval deficits persisted in LPA, even when structured probing was provided, and could not be explained by overall level of language disruption or amount of information generated during autobiographical narration. Autobiographical memory impairments in LPA related to gray matter intensity decrease in predominantly posterior parietal brain regions implicated in memory retrieval. Together, our results suggest that episodic memory disturbances may be an under-appreciated clinical feature of LPA.

Hippocampo-Horn Percentage and Parietal Atrophy Score for Easy Visual Assessment of Brain Atrophy on Magnetic Resonance Imaging in Early- and Late-Onset Alzheimer's Disease

BACKGROUND

Magnetic resonance imaging (MRI) visual scales of brain atrophy are important for differential diagnosis of dementias in routine clinical practice. Atrophy patterns in early- and late-onset Alzheimer's disease (AD) can be different according to some studies.

OBJECTIVE

Our goal was to assess brain atrophy patterns in early- and late-onset AD using our recently developed simple MRI visual scales and evaluate their reliability.

METHODS

We used Hippocampo-horn percentage (Hip-hop) and Parietal Atrophy Score (PAS) to compare mediotemporal and parietal atrophy on brain MRI among 4 groups: 26 patients with early-onset AD, 21 younger cognitively normal persons, 32 patients with late-onset AD, and 36 older cognitively normal persons. Two raters scored all brain MRI to assess reliability of the Hip-hop and PAS. Brain MRIs were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

RESULTS

The patients with early-onset AD had significantly more pronounced mediotemporal and also parietal atrophy bilaterally compared to the controls (both p < 0.01). The patients with late-onset AD had significantly more pronounced only mediotemporal atrophy bilaterally compared to the controls (p < 0.000001), but parietal lobes were the same. Intra-rater and inter-rater reliability of both visual scales Hip-hop and PAS were almost perfect in all cases (weighted-kappa value ranged from 0.90 to 0.99).

CONCLUSION

While mediotemporal atrophy detected using Hip-hop is universal across the whole AD age spectrum, parietal atrophy detected using PAS is worth rating only in early-onset AD. Hip-hop and PAS are very reliable MRI visual scales.

Posterior Cingulate Cortex Hypometabolism in Non-Amnestic Variants of Alzheimer's Disease

BACKGROUND

Hypometabolism of the posterior cingulate cortex (PCC) is an important diagnostic feature of late-onset, amnestic Alzheimer's disease (AD) measured with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). However, it is unclear whether PCC hypometabolism has diagnostic value in young-onset, non-amnestic variants of AD, which exhibit less pathology in the hippocampus and default mode network.

OBJECTIVE

Evaluate the prevalence and diagnostic value of PCC hypometabolism in non-amnestic variants of AD.

METHODS

We retrospectively identified 60 patients with young-onset, atypical dementia who have undergone a detailed clinical evaluation, FDG-PET, and an amyloid biomarker (amyloid-PET or cerebrospinal fluid analysis). We quantitatively analyzed regional hypometabolism in 70 regions of interest (ROI) using the MIMneuro® software.

RESULTS

Based on a cut-off of z-score < -1.5 for significant PCC hypometabolism, the prevalence of PCC hypometabolism in non-amnestic variants of AD was 65% compared to 28% in clinical variants of frontotemporal dementia (FTD). The ROI with the maximal hypometabolism was the dominant middle temporal gyrus in the language variant of AD (mean z score -2.28), middle occipital gyrus in PCA (-3.24), middle temporal gyrus in frontal AD (-2.70), and angular gyrus in corticobasal syndrome due to AD (-2.31). The PCC was not among the 10 most discriminant regions between non-amnestic variants of AD versus clinical variants of FTD.

CONCLUSION

We conclude that PCC hypometabolism is not a discriminant feature to distinguish non-amnestic variants of AD from clinical variants of FTD-and should be interpreted with caution in patients with young-onset, non-amnestic dementia.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker.

METHODS

Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (D_s), fast diffusion coefficient (D_f), perfusion fraction (fp) and D_f*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis.

RESULTS

Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the D_f of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B.

CONCLUSION

IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.

Novel insights into the relationship between cerebellum and dementia: A narrative review as a toolkit for clinicians

The role of the cerebellum in neurodegenerative disorders that target cognitive functions has been a subject of increasing interest over the past years. However, a review focused on making clinicians more aware of the role of the cerebellum in dementia is still missing. This narrative review explores the possible factors explaining the involvement of the cerebellum in different kinds of dementia by providing more insights on how this structure can be relevant in clinical practice. It emerged that, despite overlapping in specific areas, structural cerebellar alterations in dementia show a certain degree of disease-specificity. Furthermore, the relevance of cerebellar changes in dementia is corroborated by correlations observed between their topography and cognitive symptomatology, as well as by its previously ignored involvement of the cerebellum in early stages of dementia. Despite needing further investigations, these findings could become a useful diagnostic aid for clinicians that should not be overlooked, in particular for those individuals who do not show distinct and manifest brain or neuropsychological alterations, but that still make clinicians suspect the presence of a neurocognitive disease.

Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease

There is evidence of longitudinal atrophy in posterior brain areas in early-onset Alzheimer's disease (EOAD; aged < 65 years), but no studies have been conducted in an EOAD cohort with fluid biomarkers characterization. We used 3T-MRI and Freesurfer 6.0 to investigate cortical and subcortical gray matter loss at two years in 12 EOAD patients (A + T + N + ) compared to 19 controls (A-T-N-) from the Hospital Clínic Barcelona cohort. We explored group differences in atrophy patterns and we correlated atrophy and baseline CSF-biomarkers levels in EOAD. We replicated the correlation analyses in 14 EOAD (A + T + N + ) and 55 late-onset AD (LOAD; aged ≥ 75 years; A + T + N + ) participants from the Alzheimer's disease Neuroimaging Initiative. We found that EOAD longitudinal atrophy spread with a posterior-to-anterior gradient and beyond hippocampus/amygdala. In EOAD, higher initial CSF NfL levels correlated with higher ventricular volumes at baseline. On the other hand, higher initial CSF Aβ42 levels (within pathological range) predicted higher rates of cortical loss in EOAD. In EOAD and LOAD subjects, higher CSF t-tau values at baseline predicted higher rates of subcortical atrophy. CSF p-tau did not show any significant correlation. In conclusion, posterior cortices, hippocampus and amygdala capture EOAD atrophy from early stages. CSF Aβ42 might predict cortical thinning and t-tau/NfL subcortical atrophy.

Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls

OBJECTIVES

The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC.

METHODS

Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted.

RESULTS

Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881.

CONCLUSIONS

EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully.

SIGNIFICANCE

This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.

Relationship Between General Cognition, Visual Assessed Cortical Atrophy, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: A Cross-Sectional Study from a Chinese PUMCH Cohort

BACKGROUND

Cerebrospinal fluid (CSF) biomarkers are widely accepted as manifestations of Alzheimer's disease (AD) pathogenesis and incorporated into biological definition of AD. However, the correlations between CSF and other biomarkers such as neuroimaging and neuropsychiatric evaluation are complicated and inconsistent.

OBJECTIVE

We aimed to better interpreting CSF biomarkers results accompanying with other indexes in improving accurate diagnosis of AD.

METHODS

112 AD patients and 30 cognitive normal controls were selected. Commercial accessible ELISA kits were introduced for measurement of CSF t-tau, p-tau181, Aβ1-42, and NfL based on standard protocol. MRI examinations were performed using a 3-T MRI scanner and visual rating scales including medial temporal atrophy score and Koedam's scale were used to evaluate medial temporal atrophy and posterior region atrophy.

RESULTS

CSF biomarkers' profile including decreased concentration of Aβ1-42, increased concentration of t-tau, p-tau181, t-tau/Aβ 1-42, and NfL were diagnostic between AD and control. CSF biomarkers profile was not influenced by the APOE genotype. Increased concentration of t-tau and NfL, as well as ratio of t-tau/Aβ 1-42 were related to decrease of Mini-Mental State Examination (MMSE) score while concentration of Aβ1-42 not. Visual assessed cortical atrophy was related to MMSE score, but most of the CSF biomarkers were not related to atrophy, except that increased concentration of p-tau181 was significantly associated with atrophy of posterior cortical region.

CONCLUSION

Our results supported CSF biomarkers were helpful in diagnosis of AD. However, CSF biomarkers were cross-sectional reflection of pathogenesis, which did not correlate well with clinical progression. CSF biomarkers should be interpreted in combination with MRI and cognitive evaluation in clinical use.

Age at onset reveals different functional connectivity abnormalities in prodromal Alzheimer's disease

Age at symptom onset (AAO) underlies different Alzheimer's disease (AD) clinical variants: late-onset AD (LOAD) is characterized by memory deficits, while early-onset AD (EOAD) presents predominantly with non-memory symptoms. The involvement of different neural networks may explain these distinct clinical phenotypes. In this study, we tested the hypothesis of an early and selective involvement of neural networks based on AAO in AD. Twenty memory clinic patients with prodromal AD (i.e., mild cognitive impairment with an AD-like cerebrospinal fluid profile) and 30 healthy controls underwent a cognitive evaluation and a resting state functional MRI exam. Independent component analysis was performed to assess functional connectivity (FC) in the following networks: default mode, frontoparietal, limbic, visual, and sensorimotor. Patients were stratified into late-onset (pLOAD) and early-onset (pEOAD) prodromal AD according to the AAO and controls were stratified into younger and older groups accordingly. Decreased FC within the default mode and the limbic networks was observed in pLOAD, while pEOAD showed lower FC in the frontoparietal and visual networks. The sensorimotor network did not show differences between groups. A significant association was found between memory and limbic network FC in pLOAD, and between executive functions and frontoparietal network FC in pEOAD, although the latter association did not survive multiple comparison correction. Our findings indicate that aberrant connectivity in memory networks is associated with pLOAD, while networks underlying executive and visuo-spatial functions are affected in pEOAD. These findings are in line with the hypothesis that the pathophysiological mechanisms underlying EOAD and LOAD are distinct.

Differential vulnerability of the cerebellum in healthy ageing and Alzheimer's disease

Recent findings challenge the prior notion that the cerebellum remains unaffected by Alzheimer's disease (AD). Yet, it is unclear whether AD exacerbates age-related cerebellar grey matter decline or engages distinct structural and functional territories. We performed a meta-analysis of cerebellar grey matter loss in normal ageing and AD. We mapped voxels with structural decline onto established brain networks, functional parcellations, and along gradients that govern the functional organisation of the cerebellum. Importantly, these gradients track continuous changes in cerebellar specialisation providing a more nuanced measure of the functional profile of regions vulnerable to ageing and AD. Gradient 1 progresses from motor to cognitive territories; Gradient 2 isolates attentional processing; Gradient 3 captures lateralisation differences in cognitive functions. We identified bilateral and right-lateralised posterior cerebellar atrophy in ageing and AD, respectively. Age- and AD-related structural decline only showed partial spatial overlap in right lobule VI/Crus I. Despite the seemingly distinct patterns of AD- and age-related atrophy, the functional profiles of these regions were similar. Both participate in the same macroscale networks (default mode, frontoparietal, attention), support executive functions and language processing, and did not exhibit a difference in relative positions along Gradients 1 or 2. However, Gradient 3 values were significantly different in ageing vs. AD, suggesting that the roles of left and right atrophied cerebellar regions exhibit subtle functional differences despite their membership in similar macroscale networks. These findings provide an unprecedented characterisation of structural and functional differences and similarities in cerebellar grey matter loss between normal ageing and AD.

Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data

For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with excessive regularization. On magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer’s disease, GraphMM produces greater yield than conventional large-scale testing procedures.

Different Patterns of Gray Matter Volume Reduction in Early-onset and Late-onset Alzheimer Disease

BACKGROUND

Individuals with early-onset Alzheimer disease (EOAD) differ from those with late-onset Alzheimer disease (LOAD) not only in genetics and age at onset but also in their clinical symptoms.

OBJECTIVE

To differentiate the neuropathological and neurocognitive features of EOAD and LOAD by comparing the pattern of regional gray matter volume (GMV) reduction and its symptomatic correlates.

METHOD

Three-dimensional T1-weighted MRIs and Mini-Mental State Examination (MMSE) scores were obtained from 12 individuals with EOAD, 65 with LOAD, and 49 healthy controls (HC). Regional GMV reduction between the three groups was assessed using voxel-based morphometry. Multiple regression analyses were conducted with MMSE total score as an independent variable.

RESULTS

Compared to the HC, both AD groups showed a significant GMV reduction in the bilateral hippocampus and the left temporoparietal junction; in addition, the LOAD group showed one in the bilateral anterior temporal lobes. Multiple regression analyses revealed a positive correlation between MMSE total score and GMV in the left anterior temporal lobe in both AD groups; that is, lower scores were associated with reduced GMV. Interestingly, a positive correlation in hippocampal GMV was revealed only in the LOAD group.

CONCLUSION

MMSE total score is associated with the anterior temporal lobe volume in individuals with AD. Hippocampal volume and its relationship with MMSE total score are associated with LOAD pathophysiology but not EOAD pathophysiology. The hippocampal volume reduction and low MMSE scores are hallmarks of LOAD but are less specific to EOAD, which may cause a delay in diagnosis.

White matter hyperintensities and patterns of atrophy in early onset Alzheimer's disease with causative gene mutations

OBJECTIVE

White matter hyperintensities could be found in many degenerative dementias including Alzheimer's disease (AD). Pathogenesis of white matter hyperintensities in AD is complicated. We aim to identify the features of white matter hyperintensities and the atrophy pattern in early onset Alzheimer's disease with causative gene mutations.

METHODS

7 AD dementia patients with causative mutations were included and the clinical history, neuropsychology, neuroimaging,APOE genotype and whole-genome sequencing (WGS) were analyzed. Axial T1-weighted images and Fluid attenuated inversion recovery (FLAIR) were analyzed with visual rating scale to examine cortical atrophy and white matter hyperintensities.

RESULTS

5 female and 2 male patients with 4PSEN1, 2PSEN2 and 1APP mutation were included. The average age of onset was 46.7y/o (44-52) and the duration of disease was 28.6 months (8-60). Clinical phenotype included memory loss (100 %), visual/spatial disorder (100 %), executive dysfunction (100 %), calculation disorder (85.7 %), disorientation (85.7 %), language problem (57.1 %), personality change (28.6 %) and movement disorder (14.3 %). The grading of posterior cortex atrophy was higher than medial temporal lobe atrophy. Periventricular hyperintensities surrounding occipital and frontal horn of ventricle and sub-ventricular bands were most common, while small foci of lesions were also detected in deep white matter, sub-cortical and juxta-cortical area. Mutations carriers in the APP gene or PSEN1 gene postcodon 200 had more severe white matter hyperintensities than other mutations.

CONCLUSION

White matter hyperintensities were found in early onset AD with causative mutations. The severity was related to genotypes and spatial distributions. Axon degeneration following neuronal loss and ischemic injury might be the pathogenesis of white matter damage. Severer atrophy in the posterior cortex than medial temporal lobe can present in early onset AD.

Gray Matter Deterioration Pattern During Alzheimer's Disease Progression: A Regions-of-Interest Based Surface Morphometry Study

Accurate detection of the regions of Alzheimer's disease (AD) lesions is critical for early intervention to effectively slow down the progression of the disease. Although gray matter volumetric abnormalities are commonly detected in patients with mild cognition impairment (MCI) and patients with AD, the gray matter surface-based deterioration pattern associated with the progression of the disease from MCI to AD stages is largely unknown. To identify group differences in gray matter surface morphometry, including cortical thickness, the gyrification index (GI), and the sulcus depth, 80 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were split into healthy controls (HCs; N = 20), early MCIs (EMCI; N = 20), late MCIs (LMCI; N = 20), and ADs (N = 20). Regions-of-interest (ROI)-based surface morphometry was subsequently studied and compared across the four stage groups to characterize the gray matter deterioration during AD progression. Co-alteration patterns (Spearman's correlation coefficient) across the whole brain were also examined. Results showed that patients with MCI and AD exhibited a significant reduction in cortical thickness (p < 0.001) mainly in the cingulate region (four subregions) and in the temporal (thirteen subregions), parietal (five subregions), and frontal (six subregions) lobes compared to HCs. The sulcus depth of the eight temporal, four frontal, four occipital, and eight parietal subregions were also significantly affected (p < 0.001) by the progression of AD. The GI was shown to be insensitive to AD progression (only three subregions were detected with a significant difference, p < 0.001). Moreover, Spearman's correlation analysis confirmed that the co-alteration pattern of the cortical thickness and sulcus depth indices is predominant during AD progression. The findings highlight the relevance between gray matter surface morphometry and the stages of AD, laying the foundation for in vivo tracking of AD progression. The co-alteration pattern of surface-based morphometry would improve the researchers' knowledge of the underlying pathologic mechanisms in AD.

Early-Onset Alzheimer's Disease: What Is Missing in Research?

PURPOSE OF REVIEW

Early-onset Alzheimer's disease (EOAD), defined as Alzheimer's disease (AD) occurring before age 65, is significantly less well studied than the late-onset form (LOAD) despite EOAD often presenting with a more aggressive disease progression. The aim of this review is to summarize the current understanding of the etiology of EOAD, their translation into clinical practice, and to suggest steps to be taken to move our understanding forward.

RECENT FINDINGS

EOAD cases make up 5-10% of AD cases but only 10-15% of these cases show known mutations in the APP, PSEN1, and PSEN2, which are linked to EOAD. New data suggests that these unexplained cases following a non-Mendelian pattern of inheritance is potentially caused by a mix of common and newly discovered rare variants. However, only a fraction of this genetic variation has been identified to date leaving the molecular mechanisms underlying this type of AD and their association with clinical, biomarker, and neuropathological changes unclear. While great advancements have been made in characterizing EOAD, much work is needed to disentangle the molecular mechanisms underlying this type of AD and to identify putative targets for more precise disease screening, diagnosis, prevention, and treatment.

Quantitative endophenotypes as an alternative approach to understanding genetic risk in neurodegenerative diseases

Endophenotypes, as measurable intermediate features of human diseases, reflect underlying molecular mechanisms. The use of quantitative endophenotypes in genetic studies has improved our understanding of pathophysiological changes associated with diseases. The main advantage of the quantitative endophenotypes approach to study human diseases over a classic case-control study design is the inferred biological context that can enable the development of effective disease-modifying treatments. Here, we summarize recent progress on biomarkers for neurodegenerative diseases, including cerebrospinal fluid and blood-based, neuroimaging, neuropathological, and clinical studies. This review focuses on how endophenotypic studies have successfully linked genetic modifiers to disease risk, disease onset, or progression rate and provided biological context to genes identified in genome-wide association studies. Finally, we review critical methodological considerations for implementing this approach and future directions.

Luteinizing hormone and the aging brain

Fluctuations in luteinizing hormone (LH) release contribute to the development and maintenance of the reproductive system and become dysregulated during aging. Of note, increasing evidence supports extra-gonadal roles for LH within the CNS, particularly as it relates to cognition and plasticity in aging and age-related degenerative diseases such as Alzheimer's disease (AD). However, despite increasing evidence that supports a link between this hormone and CNS function, the mechanisms underlying LH action within the brain and how they influence cognition and plasticity during the lifespan is poorly understood and, in fact, often in conflict. This chapter aims to provide an up-to-date review of the literature addressing the role of LH signaling in the context of CNS aging and disease and put forward a unifying hypothesis that may explain currently conflicting theories regarding the role of LHCGR signaling in CNS function and dysfunction in aging and disease.