Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease

Exploring morphological and microstructural signatures across the Alzheimer's spectrum and risk factors

Neural alterations, including myelin degeneration and inflammation-related iron burden, may accompany early Alzheimer's disease (AD) pathophysiology. This study aims to identify multi-modal signatures associated with MRI-derived atrophy and quantitative MRI (qMRI) measures of myelin and iron in a unique dataset of 158 participants across the AD spectrum, including those without cognitive impairment, at familial risk for AD, with mild cognitive impairment, and with AD dementia. Our results revealed a brain pattern with decreased cortical thickness, indicating increased neuronal death, and compromised hippocampal integrity due to reduced myelin content. This pattern was associated with lifestyle factors such as smoking, high blood pressure, high cholesterol, and anxiety, as well as older age, AD progression, and APOE-ɛ4 carrier status. These findings underscore the value of qMRI metrics as a non-invasive tool, offering sensitivity to lifestyle-related modifiable risk factors and medical history, even in preclinical stages of AD.

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global information and the inability to extract effective features. In this paper, we propose a lightweight classification network Local and Global Graph ConvNeXt. This model has a hybrid architecture of convolutional neural network and Transformers. We build the Global NeXt Block and the Local NeXt Block to extract the local and global features of the structural magnetic resonance imaging (sMRI). These two blocks are optimized by adding global multilayer perceptron and locally grouped attention, respectively. Then, the features are fed into the pixel graph neural network to aggregate the valid pixel features using mask attention. In addition, we decoupled the loss by category to optimize the calculation of the loss. This method was tested on slices of the processed sMRI datasets from ADNI and achieved excellent performance. Our model achieves 95.81% accuracy with fewer parameters and floating point operations per second (FLOPS) than other classical efficient models in the diagnosis of AD.

Integrative MR Imaging Interpretation in Cognitive Impairment with Alzheimer's Disease, Small Vessel Disease, and Glymphatic Function-Related MR Parameters

RATIONALE AND OBJECTIVES

The role of MR imaging in patients with cognitive impairment is to evaluate each component of Alzheimer's disease (AD), small vessel disease (SVD), and glymphatic function. We want to validate the diagnostic performance of the comprehensive interpretation of these parameters to predict the cognitive impairment stage. MATERIALS AND METHODS: This retrospective single-center study included 359 patients with cognitive impairment who had undergone MRI (FLAIR, T2WI, 3D-T1WI, susceptibility-weighted imaging, and diffusion tensor imaging [DTI]) and a neuropsychological screening battery between January 2020 and July 2022. Each AD and SVD-related MR parameter was visually evaluated, and DTI analysis along the perivascular space (ALPS) index was calculated. Volumetry analysis was performed using Neurophet AQUA AI-based software. Using logistic regression analysis, four types of models were developed and compared by adding the components in the following order: (1) clinical factors and AD, (2) SVD, (3) glymphatic function-related MR parameters, and (4) volumetric data. Chi-square automatic interaction detection algorithm was used to develop diagnostic tree analysis (DTA) model to predict late-stage cognitive impairment.

RESULTS

APOE4 status, years of education, medial temporal lobe atrophy score, Fazekas scale score, DTI-ALPS index, and white matter hyperintensity were significant predictors of late-stage cognitive impairment. The performance of the prediction model increased from Model 1 to Model 4 (AUC: 0.880, 0.899, 0.914, and 0.945, respectively). The overall accuracy of the DTA model was 87.47%.

CONCLUSION

Integrative brain MRI assessments in patients with cognitive impairment, AD, SVD, and glymphatic function-related MR parameters, improve the prediction of late-stage cognitive impairment.

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.

Predictors of Retention in the 1Florida Alzheimer's Disease Research Center (ADRC) Over Two Waves

Background: Attrition is a significant methodological concern in longitudinal studies. Sample loss can limit generalizability and compromise internal validity. Methods: Wave one (n = 346) and wave two follow-ups (n = 196) of the 1Florida ADRC clinical core were examined using a 24-month visit window. Results: The sample (59% Hispanic) demonstrated retention rates of 77.2% and 86.2% in waves one and two, respectively. Predictors of lower retention in wave one included older age, amnestic MCI or dementia, and lower cognition and function scores. Completing a baseline MRI and lack of hippocampal atrophy were associated with higher retention in both waves. In wave two, a greater neighborhood disadvantage score was associated with attrition. Discussion: Predictors of retention changed over time, possibly due to the early withdrawal of the most vulnerable in the initial wave. Understanding predictors of retention can facilitate retention strategies, reduce attrition, and increase the validity of findings.

Combined Quantitative amyloid-β PET and Structural MRI Features Improve Alzheimer's Disease Classification in Random Forest Model - A Multicenter Study

RATIONALE AND OBJECTIVES

Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD.

MATERIAL AND METHODS

We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification.

RESULTS

The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others.

CONCLUSION

Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.

Memory consolidation, temporal and parietal atrophy, and metabolism in amyloid-β positive and negative mild cognitive impairment

BACKGROUND

Alzheimer's disease (AD) is classically characterized by alterations in memory consolidation. With the advent of diagnostic biomarkers, some patients clinically diagnosed with AD display biomarkers inconsistent with the diagnosis.

OBJECTIVE

We aimed to explore differences in memory consolidation and neurodegeneration of the temporal and parietal lobes as a function of amyloid-β status in amnestic mild cognitive impairment (aMCI).

METHODS

We examined differences in memory consolidation and neurodegeneration between patients diagnosed with amyloid-β positive aMCI (Aβ+ N = 78), amyloid-β negative aMCI (Aβ- N = 48), and healthy participants (HP; N = 41), within a well-characterized clinical cohort.

RESULTS

Aβ+ exhibited more pronounced consolidation impairments compared to Aβ-, while Aβ- faced more consolidation challenges than HP. Both Aβ+ and Aβ- were similar in hippocampal volume and entorhinal thickness, but Aβ+ had thinner inferior parietal cortex than Aβ-. Using 18F-fluoro-deoxyglucose-positron emission tomography, metabolism in both temporal and parietal regions was lower in Aβ+ relative to Aβ-.

CONCLUSIONS

These findings suggest pathologies other than AD likely contribute to memory consolidation difficulties in aMCI, and neurodegeneration of the parietal cortex in combination with hypometabolism may contribute to more pronounced consolidation problems in Aβ+.

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Executive function and cortical thickness in biomarker aMCI

INTRODUCTION

Memory deficits are the primary symptom in amnestic Mild Cognitive Impairment (aMCI); however, executive function (EF) deficits are common. The current study examined EF in aMCI based upon amyloid status (A+/A-) and regional atrophy in signature areas of Alzheimer's disease (AD).

METHOD

Participants included 110 individuals with aMCI (A+ = 66; A- = 44) and 33 cognitively healthy participants (HP). EF was assessed using four neuropsychological assessment measures. The cortical thickness of the AD signature areas was calculated using structural MRI data.

RESULTS

A + had greater EF deficits and cortical atrophy relative to A - in the supramarginal gyrus and superior parietal lobule. A - had greater EF deficits relative to HP, but no difference in signature area cortical thickness.

DISCUSSION

The current study found that the degree of EF deficits in aMCI are a function of amyloid status and cortical thinning in the parietal cortex.

MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters

BACKGROUND

Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.

PURPOSE

This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.

METHODS

Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.

RESULTS

With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.

CONCLUSIONS

This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.

Neuron loss in the brain starts in childhood, increases exponentially with age and is halted by GM-CSF treatment in Alzheimer's disease

Aging increases the risk of neurodegeneration, cognitive decline, and Alzheimer's disease (AD). Currently no means exist to measure neuronal cell death during life or to prevent it. Here we show that cross-sectional measures of human plasma proteins released from dying/damaged neurons (ubiquitin C-terminal hydrolase-L1/UCH-L1 and neurofilament light/NfL) become exponentially higher from age 2-85; UCH-L1 rises faster in females. Glial fibrillary acidic protein (GFAP) concentrations, indicating astrogliosis/inflammation, increase exponentially after age 40. Treatment with human granulocyte-macrophage colony-stimulating factor (GM-CSF/sargramostim) halted neuronal cell death, as evidenced by reduced plasma UCH-L1 concentrations, in AD participants to levels equivalent to those of five-year-old healthy controls. The ability of GM-CSF treatment to reduce neuronal apoptosis was confirmed in a rat model of AD. These findings suggest that the exponential increase in neurodegeneration with age, accelerated by neuroinflammation, may underlie the contribution of aging to cognitive decline and AD and can be halted by GM-CSF/sargramostim treatment.

The Role of RIN3 Gene in Alzheimer's Disease Pathogenesis: a Comprehensive Review

Alzheimer's disease (AD) is a globally prevalent form of dementia that impacts diverse populations and is characterized by progressive neurodegeneration and impairments in executive memory. Although the exact mechanisms underlying AD pathogenesis remain unclear, it is commonly accepted that the aggregation of misfolded proteins, such as amyloid plaques and neurofibrillary tau tangles, plays a critical role. Additionally, AD is a multifactorial condition influenced by various genetic factors and can manifest as either early-onset AD (EOAD) or late-onset AD (LOAD), each associated with specific gene variants. One gene of particular interest in both EOAD and LOAD is RIN3, a guanine nucleotide exchange factor. This gene plays a multifaceted role in AD pathogenesis. Firstly, upregulation of RIN3 can result in endosomal enlargement and dysfunction, thereby facilitating the accumulation of beta-amyloid (Aβ) peptides in the brain. Secondly, RIN3 has been shown to impact the PICLAM pathway, affecting transcytosis across the blood-brain barrier. Lastly, RIN3 has implications for immune-mediated responses, notably through its influence on the PTK2B gene. This review aims to provide a concise overview of AD and delve into the role of the RIN3 gene in its pathogenesis.

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

Background

Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images.

Method

MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data.

Results

The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images.

Conclusion

The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

Prediction of Amyloid Positivity in Patients with Subcortical Vascular Cognitive Impairment

Background

Amyloid-β (Aβ) commonly coexists and impacts prognosis in subcortical vascular cognitive impairment (SVCI).

Objective

This study aimed to examine the differences in clinical and neuroimaging variables between Aβ-positive and Aβ-negative SVCI and to propose a prediction model for Aβ positivity in clinically diagnosed SVCI patients.

Methods

A total of 130 patients with SVCI were included in model development, and a separate cohort of 70 SVCI patients was used in external validation. The variables for the prediction model were selected by comparing the characteristics of the Aβ-negative and Aβ-positive SVCI groups. The final model was determined using a stepwise method. The model performance was evaluated using the receiver operating characteristic (ROC) curve and a calibration curve. A nomogram was used for visualization.

Results

Among 130 SVCI patients, 70 (53.8%) were Aβ-positive. The Aβ-positive SVCI group was characterized by older age, tendency to be in the dementia stage, a higher prevalence of APOEɛ4, a lower prevalence of lacune, and more severe medial temporal atrophy (MTA). The final prediction model, which excluded MTA grade following the stepwise method for variable selection, demonstrated good accuracy in distinguishing between Aβ-positive and Aβ-negative SVCI, with an area under the curve (AUC) of 0.80. The external validation demonstrated an AUC of 0.71.

Conclusions

The findings suggest that older age, dementia stage, APOEɛ4 carrier, and absence of lacunes may be predictive of Aβ positivity in clinically diagnosed SVCI patients.

Narcissistic Personality Disorder as Prodromal Feature of Early-Onset, GRN-Positive bvFTD: A Case Report

Background

Behavioral variant frontotemporal dementia (bvFTD) typically involves subtle changes in personality that can delay a timely diagnosis.

Objective

Here, we report the case of a patient diagnosed of GRN-positive bvFTD at the age of 52 presenting with a 7-year history of narcissistic personality disorder, accordingly to DSM-5 criteria.

Methods

The patient was referred to neurological and neuropsychological examination. She underwent 3 Tesla magnetic resonance imaging (MRI) and genetic studies.

Results

The neuropsychological examination revealed profound deficits in all cognitive domains and 3T brain MRI showed marked fronto-temporal atrophy. A mutation in the GRN gene further confirmed the diagnosis.

Conclusions

The present case documents an unusual onset of bvFTD and highlights the problematic nature of the differential diagnosis between prodromal psychiatric features of the disease and primary psychiatric disorders. Early recognition and diagnosis of bvFTD can lead to appropriate management and support for patients and their families. This case highlights the importance of considering neurodegenerative diseases, such as bvFTD, in the differential diagnosis of psychiatric disorders, especially when exacerbations of behavioral traits manifest in adults.

Protocol for the Tallaght University Hospital Institute for Memory and Cognition-Biobank for Research in Ageing and Neurodegeneration

INTRODUCTION

Alzheimer's disease and other dementias affect >50 million individuals globally and are characterised by broad clinical and biological heterogeneity. Cohort and biobank studies have played a critical role in advancing the understanding of disease pathophysiology and in identifying novel diagnostic and treatment approaches. However, further discovery and validation cohorts are required to clarify the real-world utility of new biomarkers, facilitate research into the development of novel therapies and advance our understanding of the clinical heterogeneity and pathobiology of neurodegenerative diseases.

METHODS AND ANALYSIS

The Tallaght University Hospital Institute for Memory and Cognition Biobank for Research in Ageing and Neurodegeneration (TIMC-BRAiN) will recruit 1000 individuals over 5 years. Participants, who are undergoing diagnostic workup in the TIMC Memory Assessment and Support Service (TIMC-MASS), will opt to donate clinical data and biological samples to a biobank. All participants will complete a detailed clinical, neuropsychological and dementia severity assessment (including Addenbrooke's Cognitive Assessment, Repeatable Battery for Assessment of Neuropsychological Status, Clinical Dementia Rating Scale). Participants undergoing venepuncture/lumbar puncture as part of the clinical workup will be offered the opportunity to donate additional blood (serum/plasma/whole blood) and cerebrospinal fluid samples for longitudinal storage in the TIMC-BRAiN biobank. Participants are followed at 18-month intervals for repeat clinical and cognitive assessments. Anonymised clinical data and biological samples will be stored securely in a central repository and used to facilitate future studies concerned with advancing the diagnosis and treatment of neurodegenerative diseases.

ETHICS AND DISSEMINATION

Ethical approval has been granted by the St. James's Hospital/Tallaght University Hospital Joint Research Ethics Committee (Project ID: 2159), which operates in compliance with the European Communities (Clinical Trials on Medicinal Products for Human Use) Regulations 2004 and ICH Good Clinical Practice Guidelines. Findings using TIMC-BRAiN will be published in a timely and open-access fashion.

A novel cross-layer dual encoding-shared decoding network framework with spatial self-attention mechanism for hippocampus segmentation

Accurate segmentation of the hippocampus from the brain magnetic resonance images (MRIs) is a crucial task in the neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, such as Alzheimer's disease (AD). Automatic segmentation of the hippocampus structures is challenging due to the small volume, complex shape, low contrast and discontinuous boundaries of hippocampus. Although some methods have been developed for the hippocampus segmentation, most of them paid too much attention to the hippocampus shape and volume instead of considering the spatial information. Additionally, the extracted features are independent of each other, ignoring the correlation between the global and local information. In view of this, here we proposed a novel cross-layer dual Encoding-Shared Decoding network framework with Spatial self-Attention mechanism (called ESDSA) for hippocampus segmentation in human brains. Considering that the hippocampus is a relatively small part in MRI, we introduced the spatial self-attention mechanism in ESDSA to capture the spatial information of hippocampus for improving the segmentation accuracy. We also designed a cross-layer dual encoding-shared decoding network to effectively extract the global information of MRIs and the spatial information of hippocampus. The spatial features of hippocampus and the features extracted from the MRIs were combined to realize the hippocampus segmentation. Results on the baseline T1-weighted structural MRI data show that the performance of our ESDSA is superior to other state-of-the-art methods, and the dice similarity coefficient of ESDSA achieves 89.37%. In addition, the dice similarity coefficient of the Spatial Self-Attention mechanism (SSA) strategy and the dual Encoding-Shared Decoding (ESD) strategy is 9.47%, 5.35% higher than that of the baseline U-net, respectively, indicating that the strategies of SSA and ESD can effectively enhance the segmentation accuracy of human brain hippocampus.

Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment

Introduction

Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI).

Methods

Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity.

Results

Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI.

Discussion

Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD.

Highlights

Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.

Procedural learning and retention relative to explicit learning and retention in mild cognitive impairment and Alzheimer's disease using a modification of the trail making test

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) dementia are characterized by pathological changes to the medial temporal lobes, resulting in explicit learning and retention reductions. Studies demonstrate that implicit/procedural memory processes are relatively intact in these populations, supporting different anatomical substrates for differing memory systems. This study examined differences between explicit and procedural learning and retention in individuals with aMCI and AD dementia relative to matched healthy controls. We also examined anatomical substrates using volumetric MRI. Results revealed expected difficulties with explicit learning and retention in individuals with aMCI and AD with relatively preserved procedural memory. Explicit verbal retention was associated with medial temporal cortex volumes. However, procedural retention was not related to medial temporal or basal ganglia volumes. Overall, this study confirms the dissociation between explicit relative to procedural learning and retention in aMCI and AD dementia and supports differing anatomical substrates.

Volume atrophy in medial temporal cortex and verbal memory scores in American Indians: Data from the Strong Heart Study

INTRODUCTION

Distinguishing Alzheimer's disease (AD) patient subgroups may optimize positive clinical outcomes. Cortical atrophy is correlated with memory deficits, but these associations are understudied in American Indians.

METHODS

We collected imaging and cognition data in the Strong Heart Study (SHS), a cohort of 11 tribes across three regions. We processed 1.5T MRI using FreeSurfer and iterative principal component analysis. Linear mixed models estimated volumetric associations with diabetes.

RESULTS

Over mean 7 years follow-up (N = 818 age 65-89 years), overall volume loss was 0.5% per year. Significant losses associated with diabetes were especially strong in the right hemisphere. Annualized hippocampal, parahippocampal, entorhinal atrophy were worse for men, older age, diabetes, hypertension, stroke; and associated with both encoding and retrieval memory losses.

DISCUSSION

Our findings suggest that diabetes is an important risk factor in American Indians for cortical atrophy and memory loss. Future research should examine opportunities for primary prevention in this underserved population.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

White matter hyperintensities in cholinergic pathways are associated with dementia severity in e4 carriers but not in non-carriers

Background and objectives

Among individuals with Alzheimer's disease (AD), APOE e4 carriers with increased white matter hyperintensities (WMHs) may selectively be at increased risk of cognitive impairment. Given that the cholinergic system plays a crucial role in cognitive impairment, this study aimed to identify how APOE status modulates the associations between dementia severity and white matter hyperintensities in cholinergic pathways.

Methods

From 2018 to 2022, we recruited participants (APOE e4 carriers, n = 49; non-carriers, n = 117) from the memory clinic of Cardinal Tien Hospital, Taipei, Taiwan. Participants underwent brain MRI, neuropsychological testing, and APOE genotyping. In this study, we applied the visual rating scale of the Cholinergic Pathways Hyperintensities Scale (CHIPS) to evaluate WMHs in cholinergic pathways compared with the Fazekas scale. Multiple regression was used to assess the influence of CHIPS score and APOE carrier status on dementia severity based on Clinical Dementia Rating-Sum of Boxes (CDR-SB).

Results

After adjusting for age, education and sex, higher CHIPS scores tended to be associated with higher CDR-SB in APOE e4 carriers but not in the non-carrier group.

Conclusions

Carriers and non-carriers present distinct associations between dementia severity and WMHs in cholinergic pathways. In APOE e4 carriers, increased white matter in cholinergic pathways are associated with greater dementia severity. In non-carriers, WMHs exhibit less predictive roles for clinical dementia severity. WMHs on the cholinergic pathway may have a different impact on APOE e4 carriers vs. non-carriers.

Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging

Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.

Gray Matter Volume as Evidence for Cognitive Reserve in Bilinguals With Mild Cognitive Impairment

BACKGROUND

Compared with monolinguals, bilinguals have a later onset of mild cognitive impairment (MCI) and Alzheimer disease symptoms and greater neuropathology at similar cognitive and clinical levels. The present study follows a previous report showing the faster conversion from MCI to Alzheimer disease for bilingual patients than comparable monolinguals, as predicted by a cognitive reserve (CR).

PURPOSE

Identify whether the increased CR found for bilinguals in the previous study was accompanied by greater gray matter (GM) atrophy than was present for the monolinguals.

METHODS

A novel deep-learning technique based on convolutional neural networks was used to enhance clinical scans into 1 mm MPRAGEs and analyze the GM volume at the time of MCI diagnosis in the earlier study.

PATIENTS

Twenty-four bilingual and 24 monolingual patients were diagnosed with MCI at a hospital memory clinic.

RESULTS

Bilingual patients had more GM loss than monolingual patients in areas related to language processing, attention, decision-making, motor function, and episodic memory retrieval. Bilingualism and age were the strongest predictors of atrophy after other variables such as immigration and education were included in a multivariate model.

DISCUSSION

CR from bilingualism is evident in the initial stages of neurodegeneration after MCI has been diagnosed.

Association Between Hippocampal Volume and African Neuropsychology Memory Tests in Adult Individuals with Probable Alzheimer's Disease in Democratic Republic of Congo

BACKGROUND

Western studies indicate potential associations between hippocampal volume and memory in the trajectory of Alzheimer's disease (AD). However, limited availability of neuroimaging technology and neuropsychological tests appropriate for sub-Saharan African (SSA) countries makes it difficult to establish neuroanatomical associations of hippocampus and memory in this locale.

OBJECTIVE

This study examined hippocampal volumes and memory in healthy control (HC) and probable AD groups in the Democratic Republic of Congo (DRC).

METHODS

Forty-six subjects with probable AD and 29 HC subjects were screened using the Community Instrument for Dementia and the Alzheimer Questionnaire. Participants underwent neuroimaging in Kinshasa, DRC, and memory was evaluated using the African Neuropsychology Battery (ANB). Multiple linear regression was used to determine associations between hippocampal volumes and memory.

RESULTS

Patients with probable AD performed significantly worse than HCs on ANB memory measures, and exhibited greater cerebral atrophy, which was significantly pronounced in the medial temporal lobe region (hippocampus, entorhinal cortex). Both AD and HC subjects exhibited high rates of white matter hyperintensities compared to international base rate prevalence, which was significantly worse for probable AD. Both also exhibited elevated rates of microhemorrhages. Regression analysis demonstrated a significant association between hippocampal volume and ANB memory tests. Hippocampal atrophy discriminated probable AD from the HC group.

CONCLUSIONS

This study establishes the feasibility of conducting neuroimaging research in the SSA, demonstrates many known neuroimaging findings in probable AD patients hold up using culturally appropriate memory tasks, and suggest cardiovascular problems are a greater issue in SSA than in Western countries.

Specificity of Entorhinal Atrophy MRI Scale in Predicting Alzheimer's Disease Conversion

We compared entorhinal cortex atrophy (ERICA) score vs. medial temporal atrophy (MTA) score's ability to predict conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) using magnetic resonance imaging (MRI). We hypothesized that ERICA would show higher specificity. Data from 61 aMCI patients were analyzed. Positive ERICA was associated with AD conversion with a sensitivity of 56% (95% CI: 30-80%) and a specificity of 78% (63-89%) vs. 69% (41-89%) SE and 60% (44-74%) SP for the MTA. Results suggest that ERICA is superior to MTA in predicting conversion from aMCI to AD in a small sample of participants.

Multi-task longitudinal forecasting with missing values on Alzheimer's disease

BACKGROUND AND OBJECTIVE

Machine learning techniques typically used in dementia assessment are not able to learn multiple tasks jointly and deal with time-dependent heterogeneous data containing missing values. In this paper, we reformulate SSHIBA, a recently introduced Bayesian multi-view latent variable model, for jointly learning diagnosis, ventricle volume, and ADAS score in dementia on longitudinal data with missing values.

METHODS

We propose a novel Bayesian Variational inference framework capable of simultaneously imputing missing values and combining information from several views. This way, we can combine different data views from different time-points in a common latent space and learn the relationships between each time-point, using the semi-supervised formulation to fully exploit the temporal structure of the data and handle missing values. In turn, the model can combine all the available information to simultaneously model and predict multiple output variables.

RESULTS

We applied the proposed model to jointly predict diagnosis, ventricle volume, and ADAS score in dementia. The comparison of imputation strategies demonstrated the superior performance of the semi-supervised formulation of the model, improving the best baseline methods. Moreover, the performance in simultaneous prediction of diagnosis, ventricle volume, and ADAS score led to an improved prediction performance over the best baseline method.

CONCLUSIONS

The results demonstrate that the proposed SSHIBA framework can learn an excellent imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.

Alzheimer Hastalığını Hafif Bilişsel Bozukluktan Ayırmak İçin Basit Bir MRI Tabanlı Görsel Kılavuz

Objective: To distinguish between mild cognitive impairment (MCI) and Alzheimer’s disease (AD) by visual assessment of the length of the hippocampus in magnetic resonance imaging (MRI). Method: Consecutive patients diagnosed with MCI and AD were searched on the system retrospectively. MRI was rated for hippocampal atrophy defining with and without loss of hippocampal length. Patients with loss of hippocampal height were classified as having AD by the clinical investigator, and the diagnosis of the patients was checked on the system. Results: A total of 56 memory clinic patients with AD (n=14) and MCI (n=42) were included in the study. AD patients had significantly more hippocampal atrophy than MCI patients (𝜒2=6.222, df=0.13, 𝑝=0.013). Conclusion: There is a complex issue in the differential diagnosis between MCI and AD. A simple glace to the MRI may give a brief opinion to the physician in the clinic routine.

Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity

Neuroimaging‐driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model‐agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder‐specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.

The Parietal Lobe in Alzheimer’s Disease and Blindness

The progressive aging of the population will notably increase the burden of those diseases which leads to a disabling situation, such as Alzheimer’s disease (AD) and ophthalmological diseases that cause a visual impairment (VI). Eye diseases that cause a VI raise neuroplastic processes in the parietal lobe. Meanwhile, the aforementioned lobe suffers a severe decline throughout AD. From this perspective, diving deeper into the particularities of the parietal lobe is of paramount importance. In this article, we discuss the functions of the parietal lobe, review the parietal anatomical and pathophysiological peculiarities in AD, and also describe some of the changes in the parietal region that occur after VI. Although the alterations in the hippocampus and the temporal lobe have been well documented in AD, the alterations of the parietal lobe have been less thoroughly explored. Recent neuroimaging studies have revealed that some metabolic and perfusion impairments along with a reduction of the white and grey matter could take place in the parietal lobe during AD. Conversely, it has been speculated that blinding ocular diseases induce a remodeling of the parietal region which is observable through the improvement of the integration of multimodal stimuli and in the increase of the volume of this cortical region. Based on current findings concerning the parietal lobe in both pathologies, we hypothesize that the increased activity of the parietal lobe in people with VI may diminish the neurodegeneration of this brain region in those who are visually impaired by oculardiseases.

Gray matter microstructural alterations in manganese-exposed welders: a preliminary neuroimaging study

OBJECTIVES

Chronic occupational manganese (Mn) exposure is characterized by motor and cognitive dysfunction. This study aimed to investigate structural abnormalities in Mn-exposed welders compared to healthy controls (HCs).

METHODS

Thirty-five HCs and forty Mn-exposed welders underwent magnetic resonance imaging (MRI) scans in this study. Based on T1-weighted MRI, the voxel-based morphometry (VBM), structural covariance, and receiver operating characteristic (ROC) curve were applied to examine whole-brain structural changes in Mn-exposed welders.

RESULTS

Compared to HCs, Mn-exposed welders had altered gray matter volume (GMV) mainly in the medial prefrontal cortex, lentiform nucleus, hippocampus, and parahippocampus. ROC analysis indicated the potential highest classification power of the hippocampus/parahippocampus. Moreover, distinct structural covariance patterns in the two groups were associated with regions, mainly including the thalamus, insula, amygdala, sensorimotor area, and middle temporal gyrus. No significant relationships were found between the findings and clinical characteristics.

CONCLUSIONS

Our findings showed Mn-exposed welders had changed GMV and structural covariance patterns in some regions, which implicated in motivative response, cognitive control, and emotional regulation. These results might provide preliminary evidence for understanding the pathophysiology of Mn overexposure.

KEY POINTS

• Chronic Mn exposure might be related to abnormal brain structural neural mechanisms. • Mn-exposed welders had morphological changes in brain regions implicated in emotional modulation, cognitive control, and motor-related response. • Altered gray matter volume in the hippocampus/parahippocampus and putamen might serve as potential biomarkers for Mn overexposure.

Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort

To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%, p < 0.001) involving the following etiological subgroups: AD-related MCI (57%, p < 0.01) and non-specific MCI (100%, p < 0.0001); and syndromal dementia group (61%, p < 0.0001) consisting of non-specific dementia subgroup (100%, p < 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (p < 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.

Differential and subtype-specific neuroimaging abnormalities in amnestic and nonamnestic mild cognitive impairment: A systematic review and meta-analysis

While mild cognitive impairment (MCI) has been classified into amnestic MCI (aMCI) and nonamnestic MCI (naMCI), the neuropathological bases of these two subtypes remain elusive. Here, we performed a systematic review and meta-analysis to determine the subtype specificity of neuroimaging abnormalities in MCI and to identify neural features that may differ between aMCI and naMCI. We synthesized 50 studies that used common neuroimaging modalities, including magnetic resonance imaging and positron emission tomography, to compare brain atrophy, white matter abnormalities, cortical thinning, cerebral hypometabolism, amyloid/tau deposition, or other features among aMCI, naMCI, and normal cognition. Compared with normal cognition, aMCI shows diverse neuroimaging abnormalities of large effect sizes. In contrast, naMCI exhibits restricted abnormalities of small effect sizes. Some features, including medial temporal lobe atrophy and white matter abnormalities, are shared by the two MCI subtypes. Overall, brain abnormalities are worse, if not similar, in aMCI than in naMCI. The only neuroimaging abnormality specific to aMCI is increased amyloid burden; no feature specific to naMCI was found. Taken together, our findings have elucidated the neuropathological changes that occur in aMCI and naMCI. Clarifying the neuroimaging profiles of aMCI and naMCI can improve the early identification, differentiation, and intervention of prodromal dementia.

Effect of Probiotic Bifidobacterium breve in Improving Cognitive Function and Preventing Brain Atrophy in Older Patients with Suspected Mild Cognitive Impairment: Results of a 24-Week Randomized, Double-Blind, Placebo-Controlled Trial

Background:

Probiotics have been reported to ameliorate cognitive impairment.

Objective:

We investigated the effect of the probiotic strain Bifidobacterium breve MCC1274 (A1) in enhancing cognition and preventing brain atrophy of older patients with mild cognitive impairment (MCI).

Methods:

In this RCT, 130 patients aged from 65 to 88 years old with suspected MCI received once daily either probiotic (B. breve MCC1274, 2×10¹⁰ CFU) or placebo for 24 weeks. Cognitive functions were assessed by ADAS-Jcog and MMSE tests. Participants underwent MRI to determine brain atrophy changes using Voxel-based Specific Regional Analysis System for Alzheimer’s disease (VSRAD). Fecal samples were collected for the analysis of gut microbiota composition.

Results:

Analysis was performed on 115 participants as the full analysis set (probiotic 55, placebo 60). ADAS-Jcog subscale “orientation” was significantly improved compared to placebo at 24 weeks. MMSE subscales “orientation in time” and “writing” were significantly improved compared to placebo in the lower baseline MMSE (< 25) subgroup at 24 weeks. VSRAD scores worsened in the placebo group; probiotic supplementation tended to suppress the progression, in particular among those subjects with progressed brain atrophy (VOI Z-score ≥1.0). There were no marked changes in the overall composition of the gut microbiota by the probiotic supplementation.

Conclusion:

Improvement of cognitive function was observed on some subscales scores only likely due to the lower sensitiveness of these tests for MCI subjects. Probiotics consumption for 24 weeks suppressed brain atrophy progression, suggesting that B. breve MCC1274 helps prevent cognitive impairment of MCI subjects.

Loss of corneal nerves and brain volume in mild cognitive impairment and dementia

Introduction

This study compared the capability of corneal confocal microscopy (CCM) with magnetic resonance imaging (MRI) brain volumetry for the diagnosis of mild cognitive impairment (MCI) and dementia.

Methods

In this cross-sectional study, participants with no cognitive impairment (NCI), MCI, and dementia underwent assessment of Montreal Cognitive Assessment (MoCA), MRI brain volumetry, and CCM.

Results

Two hundred eight participants with NCI (n = 42), MCI (n = 98), and dementia (n = 68) of comparable age and gender were studied. For MCI, the area under the curve (AUC) of CCM (76% to 81%), was higher than brain volumetry (52% to 70%). For dementia, the AUC of CCM (77% to 85%), was comparable to brain volumetry (69% to 93%). Corneal nerve fiber density, length, branch density, whole brain, hippocampus, cortical gray matter, thalamus, amygdala, and ventricle volumes were associated with cognitive impairment after adjustment for confounders (All P's < .01).

Discussion

The diagnostic capability of CCM compared to brain volumetry is higher for identifying MCI and comparable for dementia, and abnormalities in both modalities are associated with cognitive impairment.

Brain Imaging Changes and Related Risk Factors of Cognitive Impairment in Patients With Heart Failure

Background/Aims

To explore the imaging changes and related risk factors of heart failure (HF) patients with cognitive impairment (CI).

Methods

A literature search was systematically carried out in PubMed, Web of Science, Embase, and Cochrane Library. In this systematic review, important relevant information was extracted according to the inclusion and exclusion criteria. The methodological quality was assessed by three scales according to the different study types.

Results

Finally, 66 studies were included, involving 33,579 patients. In the imaging changes, the severity of medial temporal lobe atrophy (MTA) and the decrease of gray Matter (GM) volume were closely related to the cognitive decline. The reduction of cerebral blood flow (CBF) may be correlated with CI. However, the change of white matter (WM) volume was possibly independent of CI in HF patients. Specific risk factors were analyzed, and the data indicated that the increased levels of B-type natriuretic peptide (BNP)/N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the comorbidities of HF, including atrial fibrillation (AF), diabetes mellitus (DM) and anemia were definitely correlated with CI in patients with HF, respectively. Certain studies had also obtained independent correlation results. Body mass index (BMI), depression and sleep disorder exhibited a tendency to be associated with CI. Low ejection fraction (EF) value (<30%) was inclined to be associated with the decline in cognitive function. However, no significant differences were noted between heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) in cognitive scores.

Conclusion

BNP/NT-proBNP and the comorbidities of HF including AF, DM and anemia were inextricably correlated with CI in patients with HF, respectively. These parameters were independent factors. The severity of MTA, GM volume, BMI index, depression, sleep disorder, and low EF value (<30%) have a disposition to associated with CI. The reduction in the CBF volume may be related to CI, whereas the WM volume may not be associated with CI in HF patients. The present systematic review provides an important basis for the prevention and treatment of CI following HF.

Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer's disease

Characterizing relationships between gray matter (GM) and white matter (WM) in early Alzheimer's disease (AD) would improve understanding of how and when AD impacts the brain. However, modeling these relationships across brain regions and longitudinally remains a challenge. Thus, we propose extending joint independent component analysis (jICA) into spatiotemporal modeling of regional cortical thickness and WM bundle volumes leveraging multimodal MRI. We jointly characterize these GM and WM features in a normal aging (n=316) and an age- and sex-matched preclinical AD cohort (n=81) at each of two imaging sessions spaced three years apart, training on the normal aging population in cross-validation and interrogating the preclinical AD cohort. We find this joint model identifies reproducible, longitudinal changes in GM and WM between the two imaging sessions and that these changes are associated with preclinical AD and are plausible considering the literature. We compare this joint model to two focused models: (1) GM features at the first session and WM at the second and (2) vice versa. The joint model identifies components that correlate poorly with those from the focused models, suggesting the different models resolve different patterns. We find the strength of association with preclinical AD is improved in the GM to WM model, which supports the hypothesis that medial temporal and frontal thinning precedes volume loss in the uncinate fasciculus and inferior anterior-posterior association fibers. These results suggest that jICA effectively generates spatiotemporal hypotheses about GM and WM in preclinical AD, especially when specific intermodality relationships are considered a priori.

The Assessment of Visuospatial Skills and Verbal Fluency in the Diagnosis of Alzheimer's Disease

BACKGROUND

In the diagnosis of Alzheimer's disease (AD), examining memory is predominant. Our aim was to analyze the potential role of various cognitive domains in the cognitive evaluation of AD.

METHODS

In total, 110 individuals with clinically defined AD and 45 healthy control participants underwent neuropsychological evaluation including Addenbrooke's Cognitive Examination (ACE). Patients with AD were selected in three groups based on disease duration in years (Group 1: ≤2 years, n = 36; Group 2: 2-4 years, n = 44; Group 3: ≥4 years, n = 30). Covariance-weighted intergroup comparison was performed on the global cognitive score and subscores of cognitive domains. Spearman's rho was applied to study the correlation between cognitive subscores and disease duration. The Wilcoxon signed-rank test was used for within-group analysis among ACE cognitive subscores.

RESULTS

Significant difference was found between ACE total scores among groups (χ2 = 119.1; p < 0.001) with a high negative correlation (p < 0.001; r = -0.643). With a longer disease duration, all the subscores of ACE significantly decreased (p-values < 0.001). The visuospatial score showed the strongest negative correlation with disease duration with a linear trajectory in decline (r = -0.85). In the early phase of cognitive decline, verbal fluency was the most impaired cognitive subdomain (normalized value = 0.64), and it was significantly reduced compared to all other subdomains (p-values < 0.05).

CONCLUSION

We found that the impairment of verbal fluency is the most characteristic feature of early cognitive decline; therefore, it might have crucial importance in the early detection of AD. Based on our results, the visuospatial assessment might be an ideal marker to monitor the progression of cognitive decline in AD.

Assessing and disclosing test results for ‘mild cognitive impairment’: the perspective of old age psychiatrists in Scotland

Background

Mild cognitive impairment (MCI) is a condition that exists between normal healthy ageing and dementia with an uncertain aetiology and prognosis. This uncertainty creates a complex dynamic between the clinicians’ conception of MCI, what is communicated to the individual about their condition, and how the individual responds to the information conveyed to them. The aim of this study was to explore clinicians’ views around the assessment and communication of MCI in memory clinics.

Method

As part of a larger longitudinal study looking at patients’ adjustment to MCI disclosure, we interviewed Old Age Psychiatrists at the five participating sites across Scotland. The study obtained ethics approvals and the interviews (carried out between Nov 2020–Jan 2021) followed a semi-structured schedule focusing on [1] how likely clinicians are to use the term MCI with patients; [2] what tests clinicians rely on and how much utility they see in them; and [3] how clinicians communicate risk of progression to dementia. The interviews were voice recorded and were analysed using reflective thematic analysis.

Results

Initial results show that most clinicians interviewed (Total N = 19) considered MCI to have significant limitations as a diagnostic term. Nevertheless, most clinicians reported using the term MCI (n = 15/19). Clinical history was commonly described as the primary aid in the diagnostic process and also to rule out functional impairment (which was sometimes corroborated by Occupational Therapy assessment). All clinicians reported using the Addenbrooke’s Cognitive Examination-III as a primary assessment tool. Neuroimaging was frequently found to have minimal usefulness due to the neuroradiological reports being non-specific.

Conclusion

Our study revealed a mixture of approaches to assessing and disclosing test results for MCI. Some clinicians consider the condition as a separate entity among neurodegenerative disorders whereas others find the term unhelpful due to its uncertain prognosis. Clinicians report a lack of specific and sensitive assessment methods for identifying the aetiology of MCI in clinical practice. Our study demonstrates a broad range of views and therefore variability in MCI risk disclosure in memory assessment services which may impact the management of individuals with MCI.

Imaging biomarkers for Alzheimer's disease and glaucoma: Current and future practices

Glaucoma is a leading cause of blindness worldwide. Although intraocular pressure is the main risk factor for glaucoma, several intraocular pressure independent factors have been associated with the risk of developing the disease and its progression. The diagnosis of glaucoma relies on clinical features of the optic nerve, visual field test, and optical coherence tomography. However, the multidisciplinary aspect of the disease suggests that other biomarkers may be useful for the diagnosis, thus underling the importance of novel imaging techniques supporting clinicians. This review analyzes the common pathogenic mechanisms between glaucoma and Alzheimer's disease and the possible novel approaches for diagnosis and follow up.

Impaired Recognition of Facial and Vocal Emotions in Mild Cognitive Impairment

OBJECTIVE

The ability to recognize others' emotions is a central aspect of socioemotional functioning. Emotion recognition impairments are well documented in Alzheimer's disease and other dementias, but it is less understood whether they are also present in mild cognitive impairment (MCI). Results on facial emotion recognition are mixed, and crucially, it remains unclear whether the potential impairments are specific to faces or extend across sensory modalities.

METHOD

In the current study, 32 MCI patients and 33 cognitively intact controls completed a comprehensive neuropsychological assessment and two forced-choice emotion recognition tasks, including visual and auditory stimuli. The emotion recognition tasks required participants to categorize emotions in facial expressions and in nonverbal vocalizations (e.g., laughter, crying) expressing neutrality, anger, disgust, fear, happiness, pleasure, surprise, or sadness.

RESULTS

MCI patients performed worse than controls for both facial expressions and vocalizations. The effect was large, similar across tasks and individual emotions, and it was not explained by sensory losses or affective symptomatology. Emotion recognition impairments were more pronounced among patients with lower global cognitive performance, but they did not correlate with the ability to perform activities of daily living.

CONCLUSIONS

These findings indicate that MCI is associated with emotion recognition difficulties and that such difficulties extend beyond vision, plausibly reflecting a failure at supramodal levels of emotional processing. This highlights the importance of considering emotion recognition abilities as part of standard neuropsychological testing in MCI, and as a target of interventions aimed at improving social cognition in these patients.

Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls

Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).

Utility of Plasma Neurofilament Light in the 1Florida Alzheimer's Disease Research Center (ADRC)

Background:

Plasma NfL (pNfL) levels are elevated in many neurological disorders. However, the utility of pNfL in a clinical setting has not been established.

Objective:

In a cohort of diverse older participants, we examined: 1) the association of pNfL to age, sex, Hispanic ethnicity, diagnosis, and structural and amyloid imaging biomarkers; and 2) its association to baseline and longitudinal cognitive and functional performance.

Methods:

309 subjects were classified at baseline as cognitively normal (CN) or with cognitive impairment. Most subjects had structural MRI and amyloid PET scans. The most frequent etiological diagnosis was Alzheimer’s disease (AD), but other neurological and neuropsychiatric disorders were also represented. We assessed the relationship of pNfL to cognitive and functional status, primary etiology, imaging biomarkers, and to cognitive and functional decline.

Results:

pNfL increased with age, degree of hippocampal atrophy, and amyloid load, and was higher in females among CN subjects, but was not associated with Hispanic ethnicity. Compared to CN subjects, pNfL was elevated among those with AD or FTLD, but not those with neuropsychiatric or other disorders. Hippocampal atrophy, amyloid positivity and higher pNfL levels each added unique variance in predicting greater functional impairment on the CDR-SB at baseline. Higher baseline pNfL levels also predicted greater cognitive and functional decline after accounting for hippocampal atrophy and memory scores at baseline.

Conclusion:

pNfL may have a complementary and supportive role to brain imaging and cognitive testing in a memory disorder evaluation, although its diagnostic sensitivity and specificity as a stand-alone measure is modest. In the absence of expensive neuroimaging tests, pNfL could be used for differentiating neurodegenerative disease from neuropsychiatric disorders.

Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer's disease: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic performance and reliability of the medial temporal lobe atrophy (MTA) scale in patients with Alzheimer's disease.

METHODS

A systematic literature search of MEDLINE and EMBASE databases was performed to select studies that evaluated the diagnostic performance or reliability of MTA scale, published up to January 21, 2021. Pooled estimates of sensitivity and specificity were calculated using a bivariate random-effects model. Pooled correlation coefficients for intra- and interobserver agreements were calculated using the random-effects model based on Fisher's Z transformation of correlations. Meta-regression was performed to explain the study heterogeneity. Subgroup analysis was performed to compare the diagnostic performance of the MTA scale and hippocampal volumetry.

RESULTS

Twenty-one original articles were included. The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% (95% CI, 68-79%) and 88% (95% CI, 83-91%), respectively. The area under the curve of the MTA scale was 0.88 (95% CI, 0.84-0.90). Meta-regression demonstrated that the difference in the method of rating the MTA scale was significantly associated with study heterogeneity (p = 0.04). No significant difference was observed in five studies regarding the diagnostic performance between MTA scale and hippocampal volumetry (p = 0.40). The pooled correlation coefficients for intra- and interobserver agreements were 0.85 (95% CI, 0.69-0.93) and 0.83 (95% CI, 0.66-0.92), respectively.

CONCLUSIONS

Our meta-analysis demonstrated a good diagnostic performance and reliability of the MTA scale in Alzheimer's disease.

KEY POINTS

• The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% and 88%, respectively. • There was no significant difference in the diagnostic performance between MTA scale and hippocampal volumetry. • The reliability of MTA scale was excellent based on the pooled correlation coefficient for intra- and interobserver agreements.

Corneal Nerve and Brain Imaging in Mild Cognitive Impairment and Dementia

Background:

Visual rating of medial temporal lobe atrophy (MTA) is an accepted structural neuroimaging marker of Alzheimer’s disease. Corneal confocal microscopy (CCM) is a non-invasive ophthalmic technique that detects neuronal loss in peripheral and central neurodegenerative disorders.

Objective:

To determine the diagnostic accuracy of CCM for mild cognitive impairment (MCI) and dementia compared to medial temporal lobe atrophy (MTA) rating on MRI.

Methods:

Subjects aged 60–85 with no cognitive impairment (NCI), MCI, and dementia based on the ICD-10 criteria were recruited. Subjects underwent cognitive screening, CCM, and MTA rating on MRI.

Results:

182 subjects with NCI (n = 36), MCI (n = 80), and dementia (n = 66), including AD (n = 19, 28.8%), VaD (n = 13, 19.7%), and mixed AD (n = 34, 51.5%) were studied. CCM showed a progressive reduction in corneal nerve fiber density (CNFD, fibers/mm²) (32.0±7.5 versus 24.5±9.6 and 20.8±9.3, p < 0.0001), branch density (CNBD, branches/mm²) (90.9±46.5 versus 59.3±35.7 and 53.9±38.7, p < 0.0001), and fiber length (CNFL, mm/mm²) (22.9±6.1 versus 17.2±6.5 and 15.8±7.4, p < 0.0001) in subjects with MCI and dementia compared to NCI. The area under the ROC curve (95% CI) for the diagnostic accuracy of CNFD, CNBD, CNFL compared to MTA-right and MTA-left for MCI was 78% (67–90%), 82% (72–92%), 86% (77–95%) versus 53% (36–69%) and 40% (25–55%), respectively, and for dementia it was 85% (76–94%), 84% (75–93%), 85% (76–94%) versus 86% (76–96%) and 82% (72–92%), respectively.

Conclusion:

The diagnostic accuracy of CCM, a non-invasive ophthalmic biomarker of neurodegeneration, was high and comparable with MTA rating for dementia but was superior to MTA rating for MCI.

Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum

The structural covariance network (SCN) has provided a perspective on the large‐scale brain organization impairment in the Alzheimer's Disease (AD) continuum. However, the successive structural impairment across brain regions, which may underlie the disrupted SCN in the AD continuum, is not well understood. In the current study, we enrolled 446 subjects with AD, mild cognitive impairment (MCI) or normal aging (NA) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The SCN as well as a casual SCN (CaSCN) based on Granger causality analysis were applied to the T1‐weighted structural magnetic resonance images of the subjects. Compared with that of the NAs, the SCN was disrupted in the MCI and AD subjects, with the hippocampus and left middle temporal lobe being the most impaired nodes, which is in line with previous studies. In contrast, according to the 194 subjects with records on CSF amyloid and Tau, the CaSCN revealed that during AD progression, the CaSCN was enhanced. Specifically, the hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions. Taken together, these findings provide a comprehensive view of brain atrophy in the AD continuum and the relationships among the brain atrophy in different regions, which may provide novel insight into the progression of AD.