Mapping hippocampal and ventricular change in Alzheimer disease

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers

Structural brain changes underlie cognitive changes and interindividual variability in cognition in older age. By using structural MRI data-driven clustering, we aimed to identify subgroups of cognitively unimpaired older adults based on brain change patterns and assess how changes in cortical thickness, surface area, and subcortical volume relate to cognitive change. We tested (1) which brain structural changes predict cognitive change (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease biomarkers, and (3) the degree of overlap between clusters derived from different structural modalities in 1899 cognitively healthy older adults followed up to 16 years. We identified four groups for each brain feature, based on the degree of a main longitudinal component of decline. The minimal overlap between features suggested that each contributed uniquely and independently to structural brain changes in aging. Cognitive change and baseline cognition were associated with cortical area change, whereas higher baseline levels of phosphorylated tau and amyloid-β related to changes in subcortical volume. These results may contribute to a better understanding of different aging trajectories.

Multimodal surface-based transformer model for early diagnosis of Alzheimer's disease

Current deep learning methods for diagnosing Alzheimer's disease (AD) typically rely on analyzing all or parts of high-resolution 3D volumetric features, which demand expensive computational resources and powerful GPUs, particularly when using multimodal data. In contrast, lightweight cortical surface representations offer a more efficient approach for quantifying AD-related changes across different cortical regions, such as alterations in cortical structures, impaired glucose metabolism, and the deposition of pathological biomarkers like amyloid-β and tau. Despite these advantages, few studies have focused on diagnosing AD using multimodal surface-based data. This study pioneers a novel method that leverages multimodal, lightweight cortical surface features extracted from MRI and PET scans, providing an alternative to computationally intensive 3D volumetric features. Our model employs a middle-fusion approach with a cross-attention mechanism to efficiently integrate features from different modalities. Experimental evaluations on the ADNI series dataset, using T1-weighted MRI and [Formula: see text]Fluorodeoxyglucose PET, demonstrate that the proposed model outperforms volume-based methods in both early AD diagnosis accuracy and computational efficiency. The effectiveness of our model is further validated with the combination of T1-weighted MRI, Aβ PET, and Tau PET scans, yielding favorable results. Our findings highlight the potential of surface-based transformer models as a superior alternative to conventional volume-based approaches.

Neuroanatomical and clinical factors predicting future cognitive impairment

Identifying cognitively normal (CN) older adults who will convert to cognitive impairment (CI) due to Alzheimer's disease is crucial for early intervention. Clinical and neuroimaging measures were acquired from 301 CN adults who converted to CI within 15 years of baseline, and 294 who did not. Regional volumes and brain age measures were extracted from T1-weighted magnetic resonance images. Linear discriminant analysis compared non-converters' characteristics against those of short-, mid-, and long-term converters. Conversion was associated with clinical measures such as hearing impairment and self-reported memory decline. Converters' brain volumes were smaller than non-converters' across 48 frontal, temporal, and subcortical structures. Brain age measures of 12 structures were correlated with shorter times to conversion. Conversion prediction accuracy increased from 81.5% to 90.5% as time to conversion decreased. Proximity to CI conversion is foreshadowed by anatomic features of brain aging that enhance the accuracy of predicting conversion.

Remote cognitive tests predict neurodegenerative biomarkers in the Insight 46 cohort

BACKGROUND

Alzheimer's disease-related biomarkers detect pathology years before symptoms emerge, when disease-modifying therapies might be most beneficial. Remote cognitive testing provides a means of assessing early cognitive changes. We explored the relationship between neurodegenerative biomarkers and cognition in cognitively normal individuals.

METHODS

We remotely deployed 13 computerized Cognitron tasks in 255 Insight 46 participants. We generated amyloid load and positivity, white matter hyperintensity volume (WMHV), whole brain and hippocampal volumes at age 73, plus rates of change over 2 years. We examined the relationship between Cognitron, biomarkers, and standard neuropsychological tests.

RESULTS

Slower response time on a delayed recognition task predicted amyloid positivity (odds ratio [OR] = 1.79, confidence interval [CI]: 1.15, 2.95), and WMHV (1.23, CI: 1.00, 1.56). Brain and hippocampal atrophy rates correlated with poorer visuospatial performance (b = -0.42, CI: -0.80, -0.05) and accuracy on immediate recognition (b = -0.01, CI: -0.012, -0.001), respectively. Standard tests correlated with Cognitron composites (rho = 0.50, p < 0.001).

DISCUSSION

Remote computerized testing correlates with standard supervised assessments and holds potential for studying early cognitive changes associated with neurodegeneration.

HIGHLIGHTS

70% of the Online 46 cohort performed a set of remote online cognitive tasks. Response time and accuracy on a memory task predicted amyloid status and load (SUVR). Accuracy on memory and spatial span tasks correlated with longitudinal atrophy rate. The Cognitron tasks correlated with standard supervised cognitive tests. Online cognitive testing can help identify early AD-related memory deficits.

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023

Background

Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

Methods

We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

Results

A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

Conclusion

This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease

Background

Late-onset Alzheimer's disease (LOAD) has been associated with alterations in the morphology of multiple brain structures, and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.

Objective

To determine causal pathways from genetic risk variants of LOAD via brain morphology to LOAD.

Methods

Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer's Disease Neuroimaging Initiative.

Results

Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala and inferior lateral ventricle mediated the effect of APOE ε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the inferior lateral ventricles, increased the risk of LOAD as well as vice versa.

Conclusions

Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.

Pleiotrophin modulates acute and long-term LPS-induced neuroinflammatory responses and hippocampal neurogenesis

The hippocampus is one of the most vulnerable regions affected in disorders characterized by overt neuroinflammation such as neurodegenerative diseases. Pleiotrophin (PTN) is a neurotrophic factor that modulates acute neuroinflammation in different contexts. PTN is found highly upregulated in the brain in different chronic disorders characterized by neuroinflammation, suggesting an important role in the modulation of sustained neuroinflammation. To test this hypothesis, we studied the acute and long-term effects of a single lipopolysaccharide (LPS; 5 mg/kg) administration in Ptn+/+ and Ptn-/- mice, and in mice with Ptn-overexpression (Ptn-Tg). Endogenous PTN levels proportionally modulate LPS-induced increase in TNF-α plasma levels one hour after treatment. In the dentate gyrus (DG) of the hippocampus, a lower percentage of DCX+ cells were detected in saline-treated Ptn-/- mice compared to Ptn+/+ mice, suggesting a crucial role of PTN in the maintenance of hippocampal neuronal progenitors. The data show that PTN overexpression tends to potentiate acute microglial responses in the DG 16 hours after LPS treatment. Remarkably, a significant increase in the number of neuronal progenitors together with astrogliosis was detected 10 months after a single injection of LPS treatment in wild type mice. However, these LPS-induced long-term effects were prevented in Ptn-/- and Ptn-Tg mice, suggesting that PTN modulates LPS-induced long-term neurogenesis changes and astrocytic response in the hippocampus. The data presented here suggest that endogenous PTN levels are crucial in the regulation of acute LPS-induced systemic and hippocampal microglial responses in young mice. Furthermore, our findings provide evidence of the key role of PTN in the regulation of long-term LPS effects on astrocytic response and neurogenesis in the hippocampus.

OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain

This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1- weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP-T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull-stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP-T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real-world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP-T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP-T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging.

Detection of Alzheimer's Disease Using Hybrid Meta-ROI of MRI Structural Images

BACKGROUND

The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer's disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD.

METHODS

The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification.

RESULTS

An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%.

CONCLUSIONS

Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer's disease.

The intricate interplay between microglia and adult neurogenesis in Alzheimer's disease

Microglia, the resident immune cells of the central nervous system, play a crucial role in regulating adult neurogenesis and contribute significantly to the pathogenesis of Alzheimer's disease (AD). Under physiological conditions, microglia support and modulate neurogenesis through the secretion of neurotrophic factors, phagocytosis of apoptotic cells, and synaptic pruning, thereby promoting the proliferation, differentiation, and survival of neural progenitor cells (NPCs). However, in AD, microglial function becomes dysregulated, leading to chronic neuroinflammation and impaired neurogenesis. This review explores the intricate interplay between microglia and adult neurogenesis in health and AD, synthesizing recent findings to provide a comprehensive overview of the current understanding of microglia-mediated regulation of adult neurogenesis. Furthermore, it highlights the potential of microglia-targeted therapies to modulate neurogenesis and offers insights into potential avenues for developing novel therapeutic interventions.

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

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

Prospective Longitudinal Perfusion in Probable Alzheimer's Disease Correlated with Atrophy in Temporal Lobe

Reduced cerebral blood flow (CBF) in the temporoparietal region and gray matter volumes (GMVs) in the temporal lobe were previously reported in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the temporal relationship between reductions in CBF and GMVs requires further investigation. This study sought to determine if reduced CBF is associated with reduced GMVs, or vice versa. Data came from 148 volunteers of the Cardiovascular Health Study Cognition Study (CHS-CS), including 58 normal controls (NC), 50 MCI, and 40 AD who had perfusion and structural MRIs during 2002-2003 (Time 2). Sixty-three of the 148 volunteers had follow-up perfusion and structural MRIs (Time 3). Forty out of the 63 volunteers received prior structural MRIs during 1997-1999 (Time 1). The relationships between GMVs and subsequent CBF changes, and between CBF and subsequent GMV changes were investigated. At Time 2, we observed smaller GMVs (p<0.05) in the temporal pole region in AD compared to NC and MCI. We also found associations between: (1) temporal pole GMVs at Time 2 and subsequent declines in CBF in this region (p=0.0014) and in the temporoparietal region (p=0.0032); (2) hippocampal GMVs at Time 2 and subsequent declines in CBF in the temporoparietal region (p=0.012); and (3) temporal pole CBF at Time 2 and subsequent changes in GMV in this region (p = 0.011). Therefore, hypoperfusion in the temporal pole may be an early event driving its atrophy. Perfusion declines in the temporoparietal and temporal pole follow atrophy in this temporal pole region.

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

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

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping

Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several prevalent explainability methods such as gradient-weighted class activation mapping (Grad-CAM) to an fMRI-based 3D-VGG16 network for AD diagnosis to improve the model's explainability. The aim is to explore the specific Region of Interest (ROI) of brain the model primarily focuses on when making predictions, as well as whether there are differences in these ROIs between AD and normal controls (NCs). First, we utilized multiple resting-state functional activity maps including ALFF, fALFF, ReHo, and VMHC to reduce the complexity of fMRI data, which differed from many studies that utilized raw fMRI data. Compared to methods utilizing raw fMRI data, this manual feature extraction approach may potentially alleviate the model's burden. Subsequently, 3D-VGG16 were employed for AD classification, where the final fully connected layers were replaced with a Global Average Pooling (GAP) layer, aimed at mitigating overfitting while preserving spatial information within the feature maps. The model achieved a maximum of 96.4% accuracy on the test set. Finally, several 3D CAM methods were employed to interpret the models. In the explainability results of the models with relatively high accuracy, the highlighted ROIs were primarily located in the precuneus and the hippocampus for AD subjects, while the models focused on the entire brain for NC. This supports current research on ROIs involved in AD. We believe that explaining deep learning models would not only provide support for existing research on brain disorders, but also offer important referential recommendations for the study of currently unknown etiologies.

Longitudinal assessment of ventricular volume trajectories in early-stage schizophrenia: evidence of both enlargement and shrinkage

BACKGROUND

Lateral ventricular enlargement represents a canonical morphometric finding in chronic patients with schizophrenia; however, longitudinal studies elucidating complex dynamic trajectories of ventricular volume change during critical early disease stages are sparse.

METHODS

We measured lateral ventricular volumes in 113 first-episode schizophrenia patients (FES) at baseline visit (11.7 months after illness onset, SD = 12.3) and 128 age- and sex-matched healthy controls (HC) using 3T MRI. MRI was then repeated in both FES and HC one year later.

RESULTS

Compared to controls, ventricular enlargement was identified in 18.6% of patients with FES (14.1% annual ventricular volume (VV) increase; 95%CI: 5.4; 33.1). The ventricular expansion correlated with the severity of PANSS-negative symptoms at one-year follow-up (p = 0.0078). Nevertheless, 16.8% of FES showed an opposite pattern of statistically significant ventricular shrinkage during ≈ one-year follow-up (-9.5% annual VV decrease; 95%CI: -23.7; -2.4). There were no differences in sex, illness duration, age of onset, duration of untreated psychosis, body mass index, the incidence of Schneiderian symptoms, or cumulative antipsychotic dose among the patient groups exhibiting ventricular enlargement, shrinkage, or no change in VV.

CONCLUSION

Both enlargement and ventricular shrinkage are equally present in the early stages of schizophrenia. The newly discovered early reduction of VV in a subgroup of patients emphasizes the need for further research to understand its mechanisms.

Sleep Behaviors and the Shape of Subcortical Brain Structures in Children with Overweight/Obesity: A Cross-Sectional Study

OBJECTIVES

To examine the relationship between sleep and subcortical brain structures using a shape analysis approach.

METHODS

A total of 98 children with overweight/obesity (10.0 ± 1.1 y, 59 boys) were included in the cross-sectional analyses. Sleep behaviors (i.e., wake time, sleep onset time, total time in bed, total sleep time, sleep efficiency, and wakening after sleep onset) were estimated with wrist-worn accelerometers. The shape of the subcortical brain structures was acquired by magnetic resonance imaging. A partial correlation permutation approach was used to examine the relationship between sleep behaviors and brain shapes.

RESULTS

Among all the sleep variables studied, only total time in bed was significantly related to pallidum and putamen structure, such that those children who spent more time in bed had greater expansions in the right and left pallidum (211-751 voxels, all p's <0.04) and right putamen (1783 voxels, p = 0.03).

CONCLUSIONS

These findings suggest that more time in bed was related to expansions on two subcortical brain regions in children with overweight/obesity.

Aging pattern of the brainstem based on volumetric measurement and optimized surface shape analysis

The brainstem, a small and crucial structure, is connected to the cerebrum, spinal cord, and cerebellum, playing a vital role in regulating autonomic functions, transmitting motor and sensory information, and modulating cognitive processes, emotions, and consciousness. While previous research has indicated that changes in brainstem anatomy can serve as a biomarker for aging and neurodegenerative diseases, the structural changes that occur in the brainstem during normal aging remain unclear. This study aimed to examine the age- and sex-related differences in the global and local structural measures of the brainstem in 187 healthy adults (ranging in age from 18 to 70 years) using structural magnetic resonance imaging. The findings showed a significant negative age effect on the volume of the two major components of the brainstem: the medulla oblongata and midbrain. The shape analysis revealed that atrophy primarily occurs in specific structures, such as the pyramid, cerebral peduncle, superior and inferior colliculi. Surface area and shape analysis showed a trend of flattening in the aging brainstem. There were no significant differences between the sexes or sex-by-age interactions in brainstem structural measures. These findings provide a systematic description of age associations with brainstem structures in healthy adults and may provide a reference for future research on brain aging and neurodegenerative diseases.

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers

Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-β (Aβ42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and Aβ42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and Aβ42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.

The prevalence of mild cognitive impairment in Gulf War veterans: a follow-up study

Introduction

Gulf War Illness (GWI), also called Chronic Multisymptom Illness (CMI), is a multi-faceted condition that plagues an estimated 250,000 Gulf War (GW) veterans. Symptoms of GWI/CMI include fatigue, pain, and cognitive dysfunction. We previously reported that 12% of a convenience sample of middle aged (median age 52 years) GW veterans met criteria for mild cognitive impairment (MCI), a clinical syndrome most prevalent in older adults (e.g., ≥70 years). The current study sought to replicate and extend this finding.

Methods

We used the actuarial neuropsychological criteria and the Montreal Cognitive Assessment (MoCA) to assess the cognitive status of 952 GW veterans. We also examined regional brain volumes in a subset of GW veterans (n = 368) who had three Tesla magnetic resonance images (MRIs).

Results

We replicated our previous finding of a greater than 10% rate of MCI in four additional cohorts of GW veterans. In the combined sample of 952 GW veterans (median age 51 years at time of cognitive testing), 17% met criteria for MCI. Veterans classified as MCI were more likely to have CMI, history of depression, and prolonged (≥31 days) deployment-related exposures to smoke from oil well fires and chemical nerve agents compared to veterans with unimpaired and intermediate cognitive status. We also replicated our previous finding of hippocampal atrophy in veterans with MCI, and found significant group differences in lateral ventricle volumes.

Discussion

Because MCI increases the risk for late-life dementia and impacts quality of life, it may be prudent to counsel GW veterans with cognitive dysfunction, CMI, history of depression, and high levels of exposures to deployment-related toxicants to adopt lifestyle habits that have been associated with lowering dementia risk. With the Food and Drug Administration's recent approval of and the VA's decision to cover the cost for anti-amyloid β (Aβ) therapies, a logical next step for this research is to determine if GW veterans with MCI have elevated Aβ in their brains.

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques

Background

Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques.

Objective

To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio.

Methods

We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score.

Results

Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors.

CONCLUSIONS

Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Behavioral and Neuronal Characterizations, across Ages, of the TgSwDI Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder that currently affects as many as 50 million people worldwide. It is neurochemically characterized by an aggregation of β-amyloid plaques and tau neurofibrillary tangles that result in neuronal dysfunction, cognitive decline, and a progressive loss of brain function. TgSwDI is a well-studied transgenic mouse model of AD, but no longitudinal studies have been performed to characterize cognitive deficits or β-amyloid plaque accumulation for use as a baseline reference in future research. Thus, we use behavioral tests (T-Maze, Novel Object Recognition (NOR), Novel Object Location (NOL)) to study long-term and working memory, and immunostaining to study β-amyloid plaque deposits, as well as brain size, in hippocampal, cerebellum, and cortical slices in TgSwDI and wild-type (WT) mice at 3, 5, 8, and 12 months old. The behavioral results show that TgSwDI mice exhibit deficits in their long-term spatial memory starting at 8 months old and in long-term recognition memory at all ages, but no deficits in their working memory. Immunohistochemistry showed an exponential increase in β-amyloid plaque in the hippocampus and cortex of TgSwDI mice over time, whereas there was no significant accumulation of plaque in WT mice at any age. Staining showed a smaller hippocampus and cerebellum starting at 8 months old for the TgSwDI compared to WT mice. Our data show how TgSwDI mice differ from WT mice in their baseline levels of cognitive function and β-amyloid plaque load throughout their lives.

Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.

The association between retina thinning and hippocampal atrophy in Alzheimer's disease and mild cognitive impairment: a meta-analysis and systematic review

Introduction

The retina is the "window" of the central nervous system. Previous studies discovered that retinal thickness degenerates through the pathological process of the Alzheimer's disease (AD) continuum. Hippocampal atrophy is one of the typical clinical features and diagnostic criteria of AD. Former studies have described retinal thinning in normal aging subjects and AD patients, yet the association between retinal thickness and hippocampal atrophy in AD is unclear. The optical coherence tomography (OCT) technique has access the non-invasive to retinal images and magnetic resonance imaging can outline the volume of the hippocampus. Thus, we aim to quantify the correlation between these two parameters to identify whether the retina can be a new biomarker for early AD detection.

Methods

We systematically searched the PubMed, Embase, and Web of Science databases from inception to May 2023 for studies investigating the correlation between retinal thickness and hippocampal volume. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the study quality. Pooled correlation coefficient r values were combined after Fisher's Z transformation. Moderator effects were detected through subgroup analysis and the meta-regression method.

Results

Of the 1,596 citations initially identified, we excluded 1,062 studies after screening the titles and abstract (animal models, n = 99; irrelevant literature, n = 963). Twelve studies met the inclusion criteria, among which three studies were excluded due to unextractable data. Nine studies were eligible for this meta-analysis. A positive moderate correlation between the retinal thickness was discovered in all participants of with AD, mild cognitive impairment (MCI), and normal controls (NC) (r = 0.3469, 95% CI: 0.2490-0.4377, I2 = 5.0%), which was significantly higher than that of the AD group (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%) (p < 0.05). Among different layers, the peripapillary retinal nerve fiber layer (pRNFL) indicated a moderate positive correlation with hippocampal volume (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%). The retinal pigmented epithelium (RPE) was also positively correlated [r = 0.1421, 95% CI:(-0.0447-0.3192), I2 = 84.1%]. The retinal layers and participants were the main overall heterogeneity sources. Correlation in the bilateral hemisphere did not show a significant difference.

Conclusion

The correlation between RNFL thickness and hippocampal volume is more predominant in both NC and AD groups than other layers. Whole retinal thickness is positively correlated to hippocampal volume not only in AD continuum, especially in MCI, but also in NC.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, CRD42022328088.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

OBJECTIVE

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD.

METHODS

We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy.

RESULTS

We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set.

CONCLUSION

The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the "social brain", are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants ( N = 24 $$ N=24 $$ ). In IS-RSA, we proposed a novel pipeline, that is, computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, we found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.

Differential involvement of hippocampal subfields in the relationship between Alzheimer's pathology and memory interference in older adults

Introduction

We tested whether Alzheimer's disease (AD) pathology predicts memory deficits in non-demented older adults through its effects on medial temporal lobe (MTL) subregional volume.

Methods

Thirty-two, non-demented older adults with cerebrospinal fluid (CSF) (amyloid-beta [Aβ]42/Aβ40, phosphorylated tau [p-tau]181, total tau [t-tau]), positron emission tomography (PET; 18F-florbetapir), high-resolution structural magnetic resonance imaging (MRI), and neuropsychological assessment were analyzed. We examined relationships between biomarkers and a highly granular measure of memory consolidation, retroactive interference (RI).

Results

Biomarkers of AD pathology were related to RI. Dentate gyrus (DG) and CA3 volume were uniquely associated with RI, whereas CA1 and BA35 volume were related to both RI and overall memory recall. AD pathology was associated with reduced BA35, CA1, and subiculum volume. DG volume and Aβ were independently associated with RI, whereas CA1 volume mediated the relationship between AD pathology and RI.

Discussion

Integrity of distinct hippocampal subfields demonstrate differential relationships with pathology and memory function, indicating specificity in vulnerability and contribution to different memory processes.

Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging

OBJECTIVES

This article aimed to carry out a narrative literature review of early diagnostic markers of Alzheimer's disease (AD) based on both micro and macro levels of pathology, indicating the shortcomings of current biomarkers and proposing a novel biomarker of structural integrity that associates the hippocampus and adjacent ventricle together. This could help to reduce the influence of individual variety and improve the accuracy and validity of structural biomarker.

METHODS

This review was based on presenting comprehensive background of early diagnostic markers of AD. We have compiled those markers into micro level and macro level, and discussed the advantages and disadvantages of them. Eventually the ratio of gray matter volume to ventricle volume was put forward.

RESULTS

The costly methodologies and related high patient burden of "micro" biomarkers (cerebrospinal fluid biomarkers) hinder the implementation in routine clinical examination. In terms of "macro" biomarkers- hippocampal volume (HV), there is a large variation of it among population, which undermines its validity Considering the gray matter atrophies while the adjacent ventricular volume enlarges, we assume the hippocampal to ventricle ratio (HVR) is a more reliable marker than HV alone the emerging evidence showed hippocampal to ventricle ratio predicts memory functions better than HV alone in elderly sample.

CONCLUSIONS

The ratio between gray matter structures and adjacent ventricular volumes counts as a promising superior diagnostic marker of early neurodegeneration.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

BACKGROUND

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development).

OBJECTIVE

To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements.

METHODS

With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction.

RESULTS

We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics.

CONCLUSION

The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

BACKGROUND

Alzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans.

OBJECTIVE

To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.

METHODS

Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry.

RESULTS

We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort.

CONCLUSION

Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

Sulfatide Deficiency, an Early Alzheimer's Lipidomic Signature, Causes Brain Ventricular Enlargement in the Absence of Classical Neuropathological Hallmarks

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive memory loss and a decline in activities of daily life. Ventricular enlargement has been associated with worse performance on global cognitive tests and AD. Our previous studies demonstrated that brain sulfatides, myelin-enriched lipids, are dramatically reduced in subjects at the earliest clinically recognizable AD stages via an apolipoprotein E (APOE)-dependent and isoform-specific process. Herein, we provided pre-clinical evidence that sulfatide deficiency is causally associated with brain ventricular enlargement. Specifically, taking advantage of genetic mouse models of global and adult-onset sulfatide deficiency, we demonstrated that sulfatide losses cause ventricular enlargement without significantly affecting hippocampal or whole brain volumes using histological and magnetic resonance imaging approaches. Mild decreases in sulfatide content and mild increases in ventricular areas were also observed in human APOE4 compared to APOE2 knock-in mice. Finally, we provided Western blot and immunofluorescence evidence that aquaporin-4, the most prevalent aquaporin channel in the central nervous system (CNS) that provides fast water transportation and regulates cerebrospinal fluid in the ventricles, is significantly increased under sulfatide-deficient conditions, while other major brain aquaporins (e.g., aquaporin-1) are not altered. In short, we unraveled a novel and causal association between sulfatide deficiency and ventricular enlargement. Finally, we propose putative mechanisms by which sulfatide deficiency may induce ventricular enlargement.

Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data

Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse. In this work, we consider the problem of analyzing time-to-event data in the presence of functional predictors. We develop a conditional generalized Kaplan–Meier (KM) estimator that incorporates functional predictors using kernel weights and rigorously establishes its asymptotic properties. In addition, we propose to select the optimal bandwidth based on a time-dependent Brier score. We then carry out extensive numerical studies to examine the finite sample performance of the proposed functional KM estimator and bandwidth selector. We also illustrated the practical usage of our proposed method by using a data set from Alzheimer’s Disease Neuroimaging Initiative data.

G-RMOS: GPU-accelerated Riemannian Metric Optimization on Surfaces

Surface mapping is used in various brain imaging studies, such as for mapping gray matter atrophy patterns in Alzheimer's disease. Riemannian metrics on surface (RMOS) is a state-of-the-art surface mapping algorithm that optimizes Riemannian metrics to establish one-to-one correspondences between surfaces in the Laplace-Beltrami embedding space. However, owing to the complex calculation with accurate one-to-one correspondences, RMOS registration takes a long time. In this study, we propose G-RMOS, a graphics processing unit (GPU)-accelerated RMOS registration pipeline that uses three GPU kernel design strategies: 1. using GPU computing capability with a batch scheme; 2. using the cache in the GPU block to minimize memory latency in register and shared memory; and 3. maximizing the effective number of instructions per GPU cycle using instruction level parallelism. Using the experimental results, we compare the acceleration speed of the G-RMOS framework with that of RMOS using hippocampus and cortical surfaces, and show that G-RMOS achieves a significant speedup in surface mapping. We also compare the memory requirements for cortical surface mapping and show that G-RMOS uses less memory than RMOS.

A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes

Environmental cues influence the highly dynamic morphology of microglia. Strategies to characterize these changes usually involve user-selected morphometric features, which preclude the identification of a spectrum of context-dependent morphological phenotypes. Here we develop MorphOMICs, a topological data analysis approach, which enables semiautomatic mapping of microglial morphology into an atlas of cue-dependent phenotypes and overcomes feature-selection biases and biological variability. We extract spatially heterogeneous and sexually dimorphic morphological phenotypes for seven adult mouse brain regions. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration mouse models, with females showing a faster morphological shift in affected brain regions. Remarkably, microglia morphologies reflect an adaptation upon repeated exposure to ketamine anesthesia and do not recover to control morphologies. Finally, we demonstrate that both long primary processes and short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs opens a new perspective to characterize microglial morphology.

Individuals with cannabis use are associated with widespread morphological alterations in the subregions of the amygdala, hippocampus, and pallidum

BACKGROUND

Cannabis is the most frequently used illicit drug worldwide. Although multiple structural MRI studies of individuals with cannabis use (CB) have been undertaken, the reports of the volume alterations in the amygdala, hippocampus, and pallidum are not consistent. This study aims to detect subregion-level morphological alterations, analyze the correlation areas with cannabis usage characteristics, and gain new insights into the neuro mechanisms of CB.

METHODS

By leveraging the novel surface-based subcortical morphometry method, 20 CB and 22 age- and sex-matched healthy controls (HC) were included to explore their volumetric and morphological differences in the three subcortical structures. Afterward, the correlation analysis between surface morphological eigenvalues and cannabis usage characteristics was performed.

RESULTS

Compared with volumetric measures, the surface-based subcortical morphometry method detected more significant global morphological deformations in the left amygdala, right hippocampus, and right pallidum (overall-p < 0.05, corrected). More obvious morphological alterations (atrophy or expansion) were observed in specific subregions (vertex-based p-value<0.05, uncorrected) of the three subcortical structures. Both positive and negative subregional correlation areas were reported by the correlation analysis.

CONCLUSIONS

The current study illuminated new pathophysiologic mechanisms in the amygdala, hippocampus, and pallidum at the subregion level, which may inform the subsequent smaller-scale CB research.

LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain's subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging data sets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we found that the Alzheimer's Disease (AD) can significantly speed the shape change of ventricle and hippocampus from 60 to 75 years old compared with normal aging.

Neuronal density in the brain cortex and hippocampus in Clsnt2-KO mouse strain modeling autistic spectrum disorder

Autistic spectrum disorders (ASD) represent conditions starting in childhood, which are characterized by diff iculties with social interaction and communication, as well as non-typical and stereotyping models of behavior. The mechanisms and the origin of these disorders are not yet understood and thus far there is a lack of prophylactic measures for these disorders. The current study aims to estimate neuronal density in the prefrontal cortex and four hippocampal subf ields, i. e. СA1, СA2, СA3, and DG in Clstn2-KO mice as a genetic model of ASD. In addition, the level of neurogenesis was measured in the DG area of the hippocampus. This mouse strain was obtained by a knockout of the calsinthenin-2 gene (Clsnt2) in C57BL/6J mice; the latter (wild type) was used as controls. To estimate neuronal density, serial sections were prepared on a cryotome for the above-mentioned brain structures with the subsequent immunohistochemical labeling and confocal microscopy; the neuronal marker (anti-NeuN) was used as the primary antibody. In addition, neurogenesis was estimated in the DG region of the hippocampus; for this purpose, a primary antibody against doublecortin (anti-DCX) was used. In all cases Goat anti-rabbit IgG was used as the secondary antibody. The density of neurons in the CA1 region of the hippocampus was lower in Clstn2-KO mice of both sexes as compared with controls. Moreover, in males of both strains, neuronal density in this region was lower as compared to females. Besides, the differences between males and females were revealed in two other hippocampal regions. In the CA2 region, a lower density of neurons was observed in males of both strains, and in the CA3 region, a lower density of neurons was also observed in males as compared to females but only in C57BL/6J mice. No difference between the studied groups was revealed in neurogenesis, nor was it in neuronal density in the prefrontal cortex or DG hippocampal region. Our new f indings indicate that calsyntenin-2 regulates neuronal hippocampal density in subf ield-specif ic manner, suggesting that the CA1 neuronal subpopulation may represent a cellular target for early-life preventive therapy of ASD.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia

A consequence of our progressively ageing global population is the increasing prevalence of worldwide age-related cognitive decline and dementia. In the absence of effective therapeutic interventions, identifying risk factors associated with cognitive decline becomes increasingly vital. Novel perspectives suggest that a dynamic bidirectional communication system between the gut, its microbiome, and the central nervous system, commonly referred to as the microbiota-gut-brain axis, may be a contributing factor for cognitive health and disease. However, the exact mechanisms remain undefined. Microbial-derived metabolites produced in the gut can cross the intestinal epithelial barrier, enter systemic circulation and trigger physiological responses both directly and indirectly affecting the central nervous system and its functions. Dysregulation of this system (i.e., dysbiosis) can modulate cytotoxic metabolite production, promote neuroinflammation and negatively impact cognition. In this review, we explore critical connections between microbial-derived metabolites (secondary bile acids, trimethylamine-N-oxide (TMAO), tryptophan derivatives and others) and their influence upon cognitive function and neurodegenerative disorders, with a particular interest in their less-explored role as risk factors of cognitive decline.

Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimers Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The Value of Shunt Surgery or Prophylactic Antiepileptic Therapy or Both in the Development of Dementia at Early Stages in Patients With Ventricular Dilatation

The primary purpose of the current study was to determine the value of the shunt surgery and/or prophylactic antiepileptic therapy, in patients after mild traumatic brain injury (mTBI) with ventricular dilatation (VD) and incipient cognitive impairment, in the prevention of cognitive deterioration and probably in the development of dementia.

Based on the following criteria: a) mTBI b) VD detected in CT scan during admission, and c) the presence of one of the following: i) dizziness, ii) headache, and iii) seizures, admitted to the Emergency Department between January 2010 and January 2020, we enrolled 127 of 947 eligible subjects.

The subjects were divided into five groups: Group A (control group): only VD illustration in CT scan, Group B: incipient dementia, who had a more insidious onset presenting with cognitive dysfunctions at indefinite ages, Group C: shunt system (SH)/antiepileptic drugs (AEDs) presenting with cognitive dysfunction and urinary incontinence or gait disturbances or both, that were treated as idiopathic normal-pressure hydrocephalus (iNPH) with the surgical placement of an SH and AED therapy (standard AED phenytoin (1000 mg loading dose followed by 300 mg) daily), and Group D: AED, presenting with cognitive dysfunctions at indefinite ages and one or two episodes of seizures in the past, treated with AED from the very first moment of initiation with a standard AED phenytoin (1000 mg loading dose followed by 300 mg) daily.

Overall, improvement in daily activities was achieved in 14.1% (18 of 127 patients), recording a significantly higher performance in group D (5.5%) rather than in groups A (1.5%), B (3.1%), and C (3.9%), (p < 0.05). We concluded that changes in VD (ΔVD) were associated with improvement in mRS (ΔmRS ≥ 1) - daily activities and mental status. ΔVD was also independently associated with reduced daily activities during the long-term follow-up. Interestingly, therapeutic shunting and AED in patients with a history of epilepsies may have a positive impact on the development of mental status impairment. This is a novel observation that has to be confirmed by more extensive multicenter studies in the future.

The impact of Alzheimer's disease susceptibility loci on lateral ventricular surface morphology in older adults

The enlargement of ventricular volume is a general trend in the elderly, especially in patients with Alzheimer's disease (AD). Multiple susceptibility loci have been reported to have an increased risk for AD and the morphology of brain structures are affected by the variations in the risk loci. Therefore, we hypothesized that genes contributed significantly to the ventricular surface, and the changes of ventricular surface were associated with the impairment of cognitive functions. After the quality controls (QC) and genotyping, a lateral ventricular segmentation method was employed to obtain the surface features of lateral ventricle. We evaluated the influence of 18 selected AD susceptibility loci on both volume and surface morphology across 410 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI). Correlations were conducted between radial distance (RD) and Montreal Cognitive Assessment (MoCA) subscales. Only the C allele at the rs744373 loci in BIN1 gene significantly accelerated the atrophy of lateral ventricle, including the anterior horn, body, and temporal horn of left lateral ventricle. No significant effect on lateral ventricle was found at other loci. Our results revealed that most regions of the bilateral ventricular surface were significantly negatively correlated with cognitive scores, particularly in delayed recall. Besides, small areas of surface were negatively correlated with language, orientation, and visuospatial scores. Together, our results indicated that the genetic variation affected the localized areas of lateral ventricular surface, and supported that lateral ventricle was an important brain structure associated with cognition in the elderly.

Investigating the Effect of Tau Deposition and Apoe on Hippocampal Morphometry in Alzheimer’s Disease: A Federated Chow Test Model

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. Tau tangle is the specific protein pathological hallmark of AD and plays a crucial role in leading to dementia-related structural deformations observed in magnetic resonance imaging (MRI) scans. The volume loss of hippocampus is mainly related to the development of AD. Besides, apolipoprotein E (APOE) also has significant effects on the risk of developing AD. However, few studies focus on integrating genotypes, MRI, and tau deposition to infer multimodal relationships. In this paper, we proposed a federated chow test model to study the synergistic effects of APOE and tau on hippocampal morphometry. Our experimental results demonstrate our model can detect the difference of tau deposition and hippocampal atrophy among the cohorts with different genotypes and subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal tau in the homozygote cohort. Our model will provide novel insight into the neural mechanisms about the individual impact of APOE and tau deposition on brain imaging.

Ventricular features as reliable differentiators between bvFTD and other dementias

•

Our results showed a consistent pattern of ventricle enlargement in the bvFTD patients, particularly in the anterior parts of the frontal and temporal horns of the lateral ventricles.

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The estimation of the proposed ventricular anteroposterior ratio (APR) resulted in statistically significant difference compared to all other groups.

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Our study proposes an easy to obtain and generalizable ventricle-based feature (APR) from T1-weighted structural MRI (routinely acquired and available in the clinic) that can be used not only to differentiate bvFTD from normal subjects, but also from other FTD variants (SV and PNFA), MCI, and AD patients.

•

We have made our ventricle feature estimation and bvFTD diagnosis tool (VentRa) publicly available, allowing application of our model in other studies.

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If validated in a prospective study, VentRa has the potential to aid bvFTD diagnosis, particularly in settings where access to specialized FTD care is limited.

Introduction

Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases.

Methods

Using 678 subjects −69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer’s Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts.

Results

Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left–right lateral ventricle ratios), and 76% accuracy using only the single APR feature.

Discussion

Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

FKBP51 modulates hippocampal size and function in post-translational regulation of Parkin

FK506-binding protein 51 (encoded by Fkpb51, also known as Fkbp5) has been associated with stress-related mental illness. To investigate its function, we studied the morphological consequences of Fkbp51 deletion. Artificial Intelligence-assisted morphological analysis revealed that male Fkbp51 knock-out (KO) mice possess more elongated dentate gyrus (DG) but shorter hippocampal height in coronal sections when compared to WT. Primary cultured Fkbp51 KO hippocampal neurons were shown to exhibit larger dendritic outgrowth than wild-type (WT) controls and pharmacological manipulation experiments suggest that this may occur through the regulation of microtubule-associated protein. Both in vitro primary culture and in vivo labeling support a role for FKBP51 in the regulation of microtubule-associated protein expression. Furthermore, Fkbp51 KO hippocampi exhibited decreases in βIII-tubulin, MAP2, and Tau protein levels, but a greater than 2.5-fold increase in Parkin protein. Overexpression and knock-down FKBP51 demonstrated that FKBP51 negatively regulates Parkin in a dose-dependent and ubiquitin-mediated manner. These results indicate a potential novel post-translational regulatory mechanism of Parkin by FKBP51 and the significance of their interaction on disease onset. KO has more flattened hippocampus using AI-assisted measurement Both pyramidal cell layer (PCL) of CA and granular cell layer (GCL) of DG distinguishable as two layers: deep cell layer and superficial layer. Distinct MAP2 expression between deep and superficial layer between KO and WT, Higher Parkin expression in KO brain Mechanism of FKBP51 inhibition resulting in Parkin, MAP2, Tau, and Tubulin expression differences between KO and WT mice, and resulting neurite outgrowth differences.

Analysis of Hippocampus Evolution Patterns and Prediction of Conversion in Mild Cognitive Impairment Using Multivariate Morphometry Statistics

Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer’s disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease. Objective: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features. Methods: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs. Results: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction. Conclusion: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.

Co-sparse Non-negative Matrix Factorization

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property. Nowadays, especially in the neuroimaging field, it is common to have at least thousands of voxels while the sample size is only hundreds. The non-negative matrix factorization encounters both computational and theoretical challenge with such high-dimensional data, i.e., there is no guarantee for a sparse and part-based representation of data. To this end, we introduce a co-sparse non-negative matrix factorization method to high-dimensional data by simultaneously imposing sparsity in both two decomposed matrices. Instead of adding some sparsity induced penalty such as l ₁ norm, the proposed method directly controls the number of non-zero elements, which can avoid the bias issues and thus yield more accurate results. We developed an alternative primal-dual active set algorithm to derive the co-sparse estimator in a computationally efficient way. The simulation studies showed that our method achieved better performance than the state-of-art methods in detecting the basis matrix and recovering signals, especially under the high-dimensional scenario. In empirical experiments with two neuroimaging data, the proposed method successfully detected difference between Alzheimer's patients and normal person in several brain regions, which suggests that our method may be a valuable toolbox for neuroimaging studies.

Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

BACKGROUND

A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes.

OBJECTIVE

We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61).

METHODS

Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs.

RESULTS

The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC.

CONCLUSION

The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.