Neuroimaging in Alzheimer's disease: current role in clinical practice and potential future applications

[A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis]

OBJECTIVE

To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma (HGG) from solitary brain metastasis (SBM).

METHODS

We collected multi-parametric magnetic resonance imaging (MRI) data from 61 patients with HGG and 60 with SBM, and delineated regions of interest (ROI) on T1WI, T2WI, T2-weighted fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) images. The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model. The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity, sensitivity, accuracy, and the area under the ROC curve (AUC) and quantitatively compared with other feature fusion models. Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.

RESULTS

The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871, a sensitivity of 0.817, an accuracy of 0.843, and an AUC of 0.930 for distinguishing HGG from SBM. This feature fusion method exhibited excellent discriminative performance in the visual experiments.

CONCLUSION

The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Multimodal diagnosis model of Alzheimer's disease based on improved Transformer

PURPOSE

Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging.

METHODS

The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD.

RESULTS

The model attained a noteworthy classification accuracy of 98.1% for Alzheimer's disease (AD) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area.

CONCLUSIONS

A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.

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.

The Biochemistry Behind Cognitive Decline: Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent type of dementia. Pathologically, the disease is marked by neurofibrillary tangles (NFT), which are aberrant accumulations of the tau protein that develop inside neurons, and extracellular plaque deposits of the amyloid β peptide (Aβ). These pathological lesions are present in the brain before the beginning of clinical manifestations. However, despite advancements in the comprehension of AD pathophysiology, timely and accurate clinical diagnosis remains challenging. Therefore, developing biomarkers capable of detecting AD during the preclinical phase holds enormous promise for precise diagnosis since detecting the disease early is crucial because it enables interventions when treatments may be more effective. This article intends to provide a comprehensive review of AD biomarkers, discussing their significance, classification, and recent developments in the field.

Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ_(1-40), Aβ_(1-42), t-tau, cleaved-tau (c-tau), p-tau₁₈₁, p-tau₂₃₁, p-tau₃₈₁, and p-tau₄₄₁). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Simulating and estimating agreement in the presence of multiple raters and covariates

Cohen's and Fleiss's kappa are popular estimators for assessing agreement among two and multiple raters, respectively, for a binary response. While additional methods have been developed to account for multiple raters and covariates, they are not always applicable, rarely used, and none simplify to Cohen's kappa. Furthermore, there are no methods to simulate Bernoulli observations under the kappa agreement structure such that the developed methods could be adequately assessed. This manuscript overcomes these shortfalls. First, we developed a model-based estimator for kappa that accommodates multiple raters and covariates through a generalized linear mixed model and encompasses Cohen's kappa as a special case. Second, we created a framework to simulate dependent Bernoulli observations that upholds all 2-tuple pair of rater's kappa agreement structure and includes covariates. We used this framework to assess our method when kappa was nonzero. Simulations showed that Cohen's and Fleiss's kappa estimates were inflated unlike our model-based kappa. We analyzed an Alzheimer's disease neuroimaging study and the classic cervical cancer pathology study. The proposed model-based kappa and advancement in simulation methodology demonstrates that the popular approaches of Cohen's and Fleiss's kappa are poised to yield invalid conclusions while our work overcomes shortfalls, leading to improved inferences.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed

BACKGROUND

Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities.

OBJECTIVE

The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes.

METHODS

DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined.

RESULTS

Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities.

CONCLUSION

Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.

Rapid progress in neuroimaging technologies fuels central nervous system translational medicine

Central nervous system (CNS) drug discovery suffers from high attrition rates; translational neuroscience approaches aiming to reduce these high rates include the use of brain imaging technologies. However, there is a need to better understand what methods are being used and for what diseases and purposes. Our analysis of the literature found that magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) were the neuroimaging techniques used most often in clinical trials for the most prevalent CNS diseases: Alzheimer's disease (AD), Parkinson's disease (PD), depression, and schizophrenia. Moreover, the number of initiated clinical trials using MRI, PET, and SPECT increased over the period 1981-2021. Such insights indicate that the significant increase in the use of neuroimaging studies could decrease the attrition of novel drug candidates in late clinical development.

The Future of Precision Medicine in the Cure of Alzheimer's Disease

This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.

Analysis of neuroprotective medication in patients with neurocognitive disorders: The efficacy and tolerability of highly purified animal tissues extracts

"Neurocognitive disorders are extremely invalidating psychiatric disorders with chronic courses and significant negative impacts over all areas of cognitive functioning and behavioral activity. Although extensive research on these progressive neurodegenerative disorders has been conducted, pathogenetic treatments with long-term significant benefits are yet controversial. From a clinical perspective, there is an acute need to find therapeutic strategies that could delay cognitive impairment in patients diagnosed with Alzheimer’s disease (AD), vascular dementia (VaD), Lewy body dementia (LBD), etc. Also, slowing the transition from mild cognitive impairment (MCI) to clinically significant AD is another important clinical aspect, with a major impact on the patient’s daily functioning, quality of life, and caregivers’ burden. Acetylcholinesterase inhibitors (AChEI) are still the first line of treatment in AD patients, and they are also administered in the case of VaD or Parkinson’s dementia. Various nootropics have been studied in this population, as add-on agents. Highly purified animal tissue extracts (HPATE) are administered in patients with neurocognitive disorders due to their neurotrophic properties, but many questions remain unanswered regarding their pharmacodynamic characteristics. These extracts may be added to AChEI to enhance their pro-cognitive effect, but evidence to support the superior efficacity of this association versus AChEI monotherapy is mainly derived from low-to-medium quality clinical trials. In conclusion, HPATE may be a useful add-on to first-line pro-cognitive agents in AD and VaD, but larger trials with better methodology are needed. In particular cases, however, HPATE may be of significant interest for patients with mild-to-moderate AD, based on results from clinical practice."

The unique effect of TDP-43 on hippocampal subfield morphometry and cognition

•We explored postmortem TDP-43 burden and antemortem hippocampal surface deformation. •TDP-43 was uniquely associated with inward deformation in the hippocampus. •Deformation patterns account for co-existing disease showing TDP-43′s unique effect. •Deformation was significantly correlated with cognition scores.

Convolution Neural Networks for the Automatic Segmentation of 18F-FDG PET Brain as an Aid to Alzheimer’s Disease Diagnosis

Our work aims to exploit deep learning (DL) models to automatically segment diagnostic regions involved in Alzheimer’s disease (AD) in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) volumetric scans in order to provide a more objective diagnosis of this disease and to reduce the variability induced by manual segmentation. The dataset used in this study consists of 102 volumes (40 controls, 39 with established Alzheimer’s disease (AD), and 23 with established mild cognitive impairment (MCI)). The ground truth was generated by an expert user who identified six regions in original scans, including temporal lobes, parietal lobes, and frontal lobes. The implemented architectures are the U-Net3D and V-Net networks, which were appropriately adapted to our data to optimize performance. All trained segmentation networks were tested on 22 subjects using the Dice similarity coefficient (DSC) and other similarity indices, namely the overlapping area coefficient (AOC) and the extra area coefficient (EAC), to evaluate automatic segmentation. The results of each labeled brain region demonstrate an improvement of 50%, with DSC from about 0.50 for V-Net-based networks to about 0.77 for U-Net3D-based networks. The best performance was achieved by using U-Net3D, with DSC on average equal to 0.76 for frontal lobes, 0.75 for parietal lobes, and 0.76 for temporal lobes. U-Net3D is very promising and is able to segment each region and each class of subjects without being influenced by the presence of hypometabolic regions.

Neuroimaging of Mouse Models of Alzheimer’s Disease

Magnetic resonance imaging (MRI) and positron emission tomography (PET) have made great strides in the diagnosis and our understanding of Alzheimer’s Disease (AD). Despite the knowledge gained from human studies, mouse models have and continue to play an important role in deciphering the cellular and molecular evolution of AD. MRI and PET are now being increasingly used to investigate neuroimaging features in mouse models and provide the basis for rapid translation to the clinical setting. Here, we provide an overview of the human MRI and PET imaging landscape as a prelude to an in-depth review of preclinical imaging in mice. A broad range of mouse models recapitulate certain aspects of the human AD, but no single model simulates the human disease spectrum. We focused on the two of the most popular mouse models, the 3xTg-AD and the 5xFAD models, and we summarized all known published MRI and PET imaging data, including contrasting findings. The goal of this review is to provide the reader with broad framework to guide future studies in existing and future mouse models of AD. We also highlight aspects of MRI and PET imaging that could be improved to increase rigor and reproducibility in future imaging studies.

MultiSDGAN: translation of OCT images to superresolved segmentation labels using multi-discriminators in multi-stages

Optical coherence tomography (OCT) has been identified as a non-invasive and inexpensive imaging modality to discover potential biomarkers for Alzheimer's diagnosis and progress determination. Current hypotheses presume the thickness of the retinal layers, which are analyzable within OCT scans, as an effective biomarker for the presence of Alzheimer's. As a logical first step, this work concentrates on the accurate segmentation of retinal layers to isolate the layers for further analysis. This paper proposes a generative adversarial network (GAN) that concurrently learns to increase the image resolution for higher clarity and then segment the retinal layers. We propose a multi-stage \& multi-discriminatory generative adversarial network (MultiSDGAN) specifically for superresolution and segmentation of OCT scans of the retinal layer. The resulting generator is adversarially trained against multiple discriminator networks at multiple stages. We aim to avoid early saturation of generator model training leading to poor segmentation accuracies and enhance the process of OCT domain translation by satisfying all the discriminators in multiple scales. We also investigated incorporating the Dice loss and Structured Similarity Index Measure (SSIM) as additional loss functions to specifically target and improve our proposed GAN architecture's segmentation and superresolution performance, respectively. The ablation study results conducted on our data set suggest that the proposed MultiSDGAN with ten-fold cross-validation (10-CV) provides a reduced equal error rate with 44.24% and 34.09% relative improvements, respectively (p-values of the improvement level tests<.01). Furthermore, our experimental results also demonstrate that the addition of the new terms to the loss function improves the segmentation results significantly by relative improvements of 31.33% (p-value<.01).

Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer's Disease Diagnosis

The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.

Photobiomodulation Therapy for Dementia: A Systematic Review of Pre-Clinical and Clinical Studies

BACKGROUND

Photobiomodulation (PBM) involves the use of red and/or near-infrared light from lasers or LEDs to improve a wide range of medical disorders. Transcranial PBM, sometimes accompanied by intranasal PBM, has been tested to improve many brain disorders, including dementia.

OBJECTIVE

To conduct a systematic review according to PRISMA guidelines of pre-clinical and clinical studies reporting the use of PBM, which were considered relevant to dementia.

METHODS

Literature was searched between 1967 and 2020 using a range of keywords relevant to PBM and dementia. The light source and wavelength(s), output power, irradiance, irradiation time, fluence or total energy (dose), operation mode (continuous or pulsed) irradiation, approach and site, number of treatment sessions, as well as study outcome(s) were extracted.

RESULTS

Out of 10,473 initial articles, 36 studies met the inclusion criteria. Nine articles reported in vitro studies, 17 articles reported studies in animal models of dementia, and 10 studies were conducted in dementia patients. All of the included studies reported positive results. The clinical studies were limited by the small number of patients, lack of placebo controls in some instances, and only a few used objective neuroimaging methods.

CONCLUSION

The preliminary evidence of clinical benefit, the lack of any adverse effects, and the remarkable ease of use, suggest larger clinical trials should be conducted as soon as possible.

Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities

The last years have evinced a remarkable growth in neuroimaging studies around the world. All these studies have contributed to a better understanding of the cerebral outcomes of dementia, even in the earliest phases. In low- and middle-income countries, studies involving structural and functional neuroimaging are challenging due to low investments and heterogeneous populations. Outstanding the importance of diagnosing mild cognitive impairment and dementia, the purpose of this paper is to offer an overview of neuroimaging dementia research in Brazil. The review includes a brief scientometric analysis of quantitative information about the development of this field over the past 10 years. Besides, discusses some peculiarities and challenges that have limited neuroimaging dementia research in this big and heterogeneous country of Latin America. We systematically reviewed existing neuroimaging literature with Brazilian authors that presented outcomes related to a dementia syndrome, published from 2010 to 2020. Briefly, the main neuroimaging methods used were morphometrics, followed by fMRI, and DTI. The major diseases analyzed were Alzheimer's disease, mild cognitive impairment, and vascular dementia, respectively. Moreover, research activity in Brazil has been restricted almost entirely to a few centers in the Southeast region, and funding could be the main driver for publications. There was relative stability concerning the number of publications per year, the citation impact has historically been below the world average, and the author's gender inequalities are not relevant in this specific field. Neuroimaging research in Brazil is far from being developed and widespread across the country. Fortunately, increasingly collaborations with foreign partnerships contribute to the impact of Brazil's domestic research. Although the challenges, neuroimaging researches performed in the native population regarding regional peculiarities and adversities are of pivotal importance.

Discriminative Feature Network Based on a Hierarchical Attention Mechanism for Semantic Hippocampus Segmentation

The morphological analysis of hippocampus is vital to various neurological studies including brain disorders and brain anatomy. To assist doctors in analyzing the shape and volume of the hippocampus, an accurate and automatic hippocampus segmentation method is highly demanded in the clinical practice. Given that fully convolutional networks (FCNs) have made significant contributions in biomedical image segmentation applications, we propose a notably discriminative feature network based on a hierarchical attention mechanism in hippocampal segmentation. First, considering the problem that the hippocampus is a rather small part in MR images, we design a context-aware high-level feature extraction module (CHFEM) to extract high-level features of scale invariance in the encoder stage. Further, we introduce a hierarchical attention mechanism into our segmentation framework. The mechanism is divided into three parts: a low-level feature spatial attention module (LFSAM) is developed to learn the spatial relationship between different pixels on each channel in the low-level stage of the encoder, a high-level feature channel attention module (HFCAM) is to model the semantic information relationship on different channel images in the high-level stage of the encoder, and a cross-connected attention module (CCAM) is designed in the decoder part to further suppress the noisy boundaries of hippocampus and simultaneously utilize the attentional low-level features from the encoder to better guide the high-level hippocampus edge segmentation in the decoder phase. The proposed approach achieves outstanding performance on the ADNI dataset and the Decathlon dataset compared with other semantic segmentation models and existing hippocampal segmentation approaches. Source code is available at https://github.com/LannyShi/Hippocampal-segmentation.

DNAzyme-Powered Three-Dimensional DNA Walker Nanoprobe for Detection Amyloid β-Peptide Oligomer in Living Cells and in Vivo

Amyloid β-peptide oligomer (AβO) is widely acknowledged as the promising biomarker for the diagnosis of Alzheimer's disease (AD). In this work, we designed a three-dimensional (3D) DNA walker nanoprobe for AβO detection and real-time imaging in living cells and in vivo. The presence of AβO triggered the DNAzyme walking strand to cleave the fluorophore (TAMRA)-labeled substrate strand modified on the gold nanoparticle (AuNP) surface and release TAMRA-labeled DNA fragment, resulting in the recovery of fluorescent signal. The entire process was autonomous and continuous, without external fuel strands or protease, and finally produced plenty of TAMRA fluorescence, achieving signal amplification effect. The nanoprobe enabled the quantitative detection of AβO in vitro, and the limit of detection was 22.3 pM. Given the good biocompatibility of 3D DNA walker nanoprobe, we extended this enzyme-free signal amplification method to real-time imaging of AβO. Under the microscope, nanoprobe accurately located and visualized the distribution of AβO in living cells. Moreover, in vivo imaging results showed that our nanoprobe could be used to effectively distinguish the AD mice from the wild-type mice. This nanoprobe with the advantages of great sensitivity, high specificity, and convenience, provides an outstanding prospect for AD's early diagnosis development.

Retinal nerve fiber layer thickness is associated with hippocampus and lingual gyrus volumes in nondemented older adults

OBJECTIVES

Abnormal retina structures, such as thinner retinal nerve fiber layer (RNFL), have been frequently reported in patients with Alzheimer's disease (AD). However, the association between RNFL and brain structures in cognitively normal adults remains unknown. We therefore set out to conduct a cross-sectional investigation to determine whether RNFL thickness is associated with brain structure volumes in nondemented older adults.

METHODS

We measured RNFL thickness by optical coherence tomography and brain structure volumes by 3 T magnetic resonance imaging. Cognitive function was assessed using the Chinese version of Repeatable Battery for the Assessment of Neurological Status. Pearson correlation was initially employed to screen for the potential associations among RNFL thickness, brain structure volumes and cognitive function. And then, multivariable linear regression models were conducted to further examine such associations adjusting for possible confounding factors, including age, sex, years of education and the estimated total intracranial volume (eTIV).

RESULTS

113 participants (≥ 65 years old) were screened and 80 of them (mean age: 68 ± 5.3 years; 48% male) were included in the final analysis. RNFL thickness in temporal quadrant was associated with medial temporal lobes volumes [unadjusted: r = 0.155, P = 0.175; adjusted: β = 0.205 (0.014, 0.383), P = 0.035], and especially associated with the hippocampus volume [unadjusted: r = 0.213, P = 0.062; adjusted: β = 0.251 (0.060, 0.435), P = 0.011] after adjusted for age, sex, years of education and eTIV. Moreover, it showed that RNFL thickness in inferior quadrant [unadjusted: r = 0.221, P = 0.052; adjusted: β = 0.226 (0.010. 0.446), P = 0.041] was significantly associated with occipital lobes volumes after the adjustment of age, sex, years of education and eTIV, and selectively associated with the substructure of lingual gyrus volume [unadjusted: r = 0.223, P = 0.050; adjusted: β = 0.278 (0.058, 0.487), P = 0.014]. In addition, average RNFL thickness was associated with the cognitive domain of visuospatial/constructional [unadjusted: r = 0.114, P = 0.322; adjusted: β = 0.216 (0.006, 0.426), P = 0.044] after the adjustment in these nondemented older adults.

CONCLUSIONS

Quadrant-specific associations exist between RNFL thickness and brain regions vulnerable to aging or neurodegeneration in older adults with normal cognition. These findings would promote further investigations into using RNFL as a noninvasive and less expensive biomarker of neurocognitive aging and AD-related neurodegeneration.

[18F]-florbetaben PET/CT Imaging in the Alzheimer's Disease Mouse Model APPswe/PS1dE9

BACKGROUND

Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer's disease (AD). In preclinical research, [¹⁸F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [¹⁸F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques.

METHODS

Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [¹⁸F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [¹⁸F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed.

RESULTS

Visual inspection and SUVs revealed an increased cerebral uptake of [¹⁸F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis.

CONCLUSION

The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [¹⁸F]-florbetaben in the APPswe/PS1dE9 mouse model.

O-GlcNAcylation as a Therapeutic Target for Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia and the number of elderly patients suffering from AD has been steadily increasing. Despite worldwide efforts to cope with this disease, little progress has been achieved with regard to identification of effective therapeutics. Thus, active research focusing on identification of new therapeutic targets of AD is ongoing. Among the new targets, post-translational modifications which modify the properties of mature proteins have gained attention. O-GlcNAcylation, a type of PTM that attaches O-linked β-N-acetylglucosamine (O-GlcNAc) to a protein, is being sought as a new target to treat AD pathologies. O-GlcNAcylation has been known to modify the two important components of AD pathological hallmarks, amyloid precursor protein, and tau protein. In addition, elevating O-GlcNAcylation levels in AD animal models has been shown to be effective in alleviating AD-associated pathology. Although studies investigating the precise mechanism of reversal of AD pathologies by targeting O-GlcNAcylation are not yet complete, it is clearly important to examine O-GlcNAcylation regulation as a target of AD therapeutics. This review highlights the mechanisms of O-GlcNAcylation and its role as a potential therapeutic target under physiological and pathological AD conditions.

Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer's Disease

BACKGROUND

The only conclusive way to diagnose Alzheimer's is to carry out brain autopsy of the patient's brain tissue and ascertain whether the subject had Alzheimer's or any other form of dementia. However, due to the non-feasibility of such methods, to diagnose and conclude the conditions, medical practitioners use tests that examine a patient's mental ability.

OBJECTIVE

Accurate diagnosis at an early stage is the need of the hour for initiation of therapy. The cause for most Alzheimer's cases still remains unknown except where genetic distinctions have been observed. Thus, a standard drug regimen ensues in every Alzheimer's patient, irrespective of the cause, which may not always be beneficial in halting or reversing the disease progression. To provide a better life to such patients by suppressing existing symptoms, early diagnosis, curative therapy, site-specific delivery of drugs, and application of hyphenated methods like artificial intelligence need to be brought into the main field of Alzheimer's therapeutics.

METHODS

In this review, we have compiled existing hypotheses to explain the cause of the disease, and highlighted gene therapy, immunotherapy, peptidomimetics, metal chelators, probiotics and quantum dots as advancements in the existing strategies to manage Alzheimer's.

CONCLUSION

Biomarkers, brain-imaging, and theranostics, along with artificial intelligence, are understood to be the future of the management of Alzheimer's.

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis

Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer's detection. However, it requires a defined basis function considered a significant problem. In this work, the concept of empirical mode decomposition is introduced as an alternative to process Alzheimer signals. The performance of empirical mode decomposition heavily relies on a parameter called threshold. In our previous works, we found that the existing thresholding techniques were not able to highlight relevant information. The use of Tsallis entropy as a thresholder is evaluated through the combination of empirical mode decomposition and neural networks. Thanks to the extraction of better features that boost the classification accuracy, the proposed approach outperforms the state-of-the-art in terms of peak signal to noise ratio and root mean square error. Hence, our methodology is more likely to succeed than methods based on other landmarks such as Bayes, Normal and Visu shrink. We finally report an accuracy rate of 80%, while the aforementioned techniques only yield performances of 65%, 60% and 40%, respectively.

Exposure to Environmental and Occupational Particulate Air Pollution as a Potential Contributor to Neurodegeneration and Diabetes: A Systematic Review of Epidemiological Research

It has been hypothesised that environmental air pollution, especially airborne particles, is a risk factor for type 2 diabetes mellitus (T2DM) and neurodegenerative conditions. However, epidemiological evidence is inconsistent and has not been previously evaluated as part of a systematic review. Our objectives were to carry out a systematic review of the epidemiological evidence on the association between long-term exposure to ambient air pollution and T2DM and neurodegenerative diseases in adults and to identify if workplace exposures to particles are associated with an increased risk of T2DM and neurodegenerative diseases. Assessment of the quality of the evidence was carried out using the GRADE system, which considers the quality of the studies, consistency, directness, effect size, and publication bias. Available evidence indicates a consistent positive association between ambient air pollution and both T2DM and neurodegeneration risk, such as dementia and a general decline in cognition. However, corresponding evidence for workplace exposures are lacking. Further research is required to identify the link and mechanisms associated with particulate exposure and disease pathogenesis and to investigate the risks in occupational populations. Additional steps are needed to reduce air pollution levels and possibly also in the workplace environment to decrease the incidence of T2DM and cognitive decline.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

BACKGROUND

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).

METHODS

We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients.

RESULTS

Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei.

CONCLUSION

The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals

Objective:

To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer’s disease (AD).

Method:

Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation.

Results:

The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities.

Conclusion:

In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.

Ultra-sensitive detection of protein biomarkers for diagnosis of Alzheimer's disease

Beta amyloid peptide, tau, and phosphorylated tau are well recognized as promising biomarkers for the diagnosis of Alzheimer's disease (AD). In this work, we developed a direct, versatile, and ultrasensitive multiplex assay for the quantification of trace amounts of these protein biomarkers for AD in different types of biological fluids including cerebrospinal fluid, serum, saliva, and urine. The detection assay is based on the immunoreaction between the target proteins and their corresponding pair of antibodies followed by fluorescence labelling with a newly developed indolium-based turn-on fluorophore, namely SIM. SIM was tailor-made as a reporter to provide a high signal-to-noise ratio for the detection assay. An exceptionally low limit of detection down to the femto-molar level was achieved in this assay with minute consumption of the sample. This versatile detection assay is capable of reliably quantifying not only the target proteins simultaneously from a CSF sample in an hour but also trace amounts of protein biomarkers in saliva and urine. This assay has a high potential to serve as a practical tool for the diagnosis of AD.

Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference

OBJECTIVES

In recent years, large-scale longitudinal neuroimaging studies have improved our understanding of healthy ageing and pathologies including Alzheimer's disease (AD). A particular focus of these studies is group differences and identification of participants at risk of deteriorating to a worse diagnosis. For this, statistical analysis using linear mixed-effects (LME) models are used to account for correlated observations from individuals measured over time. A Bayesian framework for LME models in AD is introduced in this paper to provide additional insight often not found in current LME volumetric analyses.

SETTING AND PARTICIPANTS

Longitudinal neuroimaging case study of ageing was analysed in this research on 260 participants diagnosed as either healthy controls (HC), mild cognitive impaired (MCI) or AD. Bayesian LME models for the ventricle and hippocampus regions were used to: (1) estimate how the volumes of these regions change over time by diagnosis, (2) identify high-risk non-AD individuals with AD like degeneration and (3) determine probabilistic trajectories of diagnosis groups over age.

RESULTS

We observed (1) large differences in the average rate of change of volume for the ventricle and hippocampus regions between diagnosis groups, (2) high-risk individuals who had progressed from HC to MCI and displayed similar rates of deterioration as AD counterparts, and (3) critical time points which indicate where deterioration of regions begins to diverge between the diagnosis groups.

CONCLUSIONS

To the best of our knowledge, this is the first application of Bayesian LME models to neuroimaging data which provides inference on a population and individual level in the AD field. The application of a Bayesian LME framework allows for additional information to be extracted from longitudinal studies. This provides health professionals with valuable information of neurodegeneration stages, and a potential to provide a better understanding of disease pathology.

Younger Onset Dementia

This literature review focused on the experience, care, and service requirements of people with younger onset dementia. Systematic searches of 10 relevant bibliographic databases and a rigorous examination of the literature from nonacademic sources were undertaken. Searches identified 304 articles assessed for relevance and level of evidence, of which 74% were academic literature. The review identified the need for (1) more timely and accurate diagnosis and increased support immediately following diagnosis; (2) more individually tailored services addressing life cycle issues; (3) examination of the service needs of those living alone; (4) more systematic evaluation of services and programs; (5) further examination of service utilization, costs of illness, and cost effectiveness; and (6) current Australian clinical surveys to estimate prevalence, incidence, and survival rates. Although previous research has identified important service issues, there is a need for further studies with stronger research designs and consideration of the control of potentially confounding factors.

PRECISION MEDICINE - The Golden Gate for Detection, Treatment and Prevention of Alzheimer's Disease

During this decade, breakthrough conceptual shifts have commenced to emerge in the field of Alzheimer's disease (AD) recognizing risk factors and the non-linear dynamic continuum of complex pathophysiologies amongst a wide dimensional spectrum of multi-factorial brain proteinopathies/neurodegenerative diseases. As is the case in most fields of medicine, substantial advancements in detecting, treating and preventing AD will likely evolve from the generation and implementation of a systematic precision medicine strategy. This approach will likely be based on the success found from more advanced research fields, such as oncology. Precision medicine will require integration and transfertilization across fragmented specialities of medicine and direct reintegration of Neuroscience, Neurology and Psychiatry into a continuum of medical sciences away from the silo approach. Precision medicine is biomarker-guided medicine on systems-levels that takes into account methodological advancements and discoveries of the comprehensive pathophysiological profiles of complex multi-factorial neurodegenerative diseases, such as late-onset sporadic AD. This will allow identifying and characterizing the disease processes at the asymptomatic preclinical stage, where pathophysiological and topographical abnormalities precede overt clinical symptoms by many years to decades. In this respect, the uncharted territory of the AD preclinical stage has become a major research challenge as the field postulates that early biomarker guided customized interventions may offer the best chance of therapeutic success. Clarification and practical operationalization is needed for comprehensive dissection and classification of interacting and converging disease mechanisms, description of genomic and epigenetic drivers, natural history trajectories through space and time, surrogate biomarkers and indicators of risk and progression, as well as considerations about the regulatory, ethical, political and societal consequences of early detection at asymptomatic stages. In this scenario, the integrated roles of genome sequencing, investigations of comprehensive fluid-based biomarkers and multimodal neuroimaging will be of key importance for the identification of distinct molecular mechanisms and signaling pathways in subsets of asymptomatic people at greatest risk for progression to clinical milestones due to those specific pathways. The precision medicine strategy facilitates a paradigm shift in Neuroscience and AD research and development away from the classical "one-size-fits-all" approach in drug discovery towards biomarker guided "molecularly" tailored therapy for truly effective treatment and prevention options. After the long and winding decade of failed therapy trials progress towards the holistic systems-based strategy of precision medicine may finally turn into the new age of scientific and medical success curbing the global AD epidemic.

The role of optical coherence tomography in Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most common cause of dementia and its incidence is increasing worldwide along with population aging. Previous clinical and histologic studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of AD patients.

Main body

Optical coherence tomography (OCT) is a non-invasive technology that acquires cross-sectional images of retinal structures allowing neural fundus integrity assessment. Several previous studies demonstrated that both peripapillary retinal nerve fiber layer and macular thickness measurements assessed by OCT were able to detect neuronal loss in AD. Moreover, recent advances in OCT technology, have allowed substantial enhancement in ultrastructural evaluation of the macula, enabling the assessment not only of full-thickness retinal measurements but also of inner retinal layers, which seems to be a promising approach, mainly regarding the assessment of retinal ganglion cell layer impairment in AD patients. Furthermore, retinal neuronal loss seems to correlate with cognitive impairment in AD, reinforcing the promising role of OCT in the clinical evaluation of these patients.

Conclusion

The purpose of this article is to review the main findings on OCT in AD patients, to discuss the role of this important diagnostic tool in these patients and how OCT technology may be useful in understanding morphological retinal changes in AD.

Insight into the Molecular Imaging of Alzheimer's Disease

Alzheimer's disease is a complex neurodegenerative disease affecting millions of individuals worldwide. Earlier it was diagnosed only via clinical assessments and confirmed by postmortem brain histopathology. The development of validated biomarkers for Alzheimer's disease has given impetus to improve diagnostics and accelerate the development of new therapies. Functional imaging like positron emission tomography (PET), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), and proton magnetic resonance spectroscopy provides a means of detecting and characterising the regional changes in brain blood flow, metabolism, and receptor binding sites that are associated with Alzheimer's disease. Multimodal neuroimaging techniques have indicated changes in brain structure and metabolic activity, and an array of neurochemical variations that are associated with neurodegenerative diseases. Radiotracer-based PET and SPECT potentially provide sensitive, accurate methods for the early detection of disease. This paper presents a review of neuroimaging modalities like PET, SPECT, and selected imaging biomarkers/tracers used for the early diagnosis of AD. Neuroimaging with such biomarkers and tracers could achieve a much higher diagnostic accuracy for AD and related disorders in the future.

A review of β-amyloid neuroimaging in Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia worldwide. As advancing age is the greatest risk factor for developing AD, the number of those afflicted is expected to increase markedly with the aging of the world's population. The inability to definitively diagnose AD until autopsy remains an impediment to establishing effective targeted treatments. Neuroimaging has enabled in vivo visualization of pathological changes in the brain associated with the disease, providing a greater understanding of its pathophysiological development and progression. However, neuroimaging biomarkers do not yet offer clear advantages over current clinical diagnostic criteria for them to be accepted into routine clinical use. Nonetheless, current insights from neuroimaging combined with the elucidation of biochemical and molecular processes in AD are informing the ongoing development of new imaging techniques and their application. Much of this research has been greatly assisted by the availability of transgenic mouse models of AD. In this review we summarize the main efforts of neuroimaging in AD in humans and in mouse models, with a specific focus on β-amyloid, and discuss the potential of new applications and novel approaches.

The link between cardiovascular risk, Alzheimer's disease, and mild cognitive impairment: support from recent functional neuroimaging studies

OBJECTIVE

To review functional neuroimaging studies about the relationship between cardiovascular risk factors (CVRFs), Alzheimer's disease (AD), and mild cognitive impairment (MCI).

METHODS

We performed a comprehensive literature search to identify articles in the neuroimaging field addressing CVRF in AD and MCI. We included studies that used positron emission tomography (PET), single photon emission computerized tomography (SPECT), or functional magnetic resonance imaging (fMRI).

RESULTS

CVRFs have been considered risk factors for cognitive decline, MCI, and AD. Patterns of AD-like changes in brain function have been found in association with several CVRFs (both regarding individual risk factors and also composite CVRF measures). In vivo assessment of AD-related pathology with amyloid imaging techniques provided further evidence linking CVRFs and AD, but there is still limited information resulting from this new technology.

CONCLUSION

There is a large body of evidence from functional neuroimaging studies supporting the hypothesis that CVRFs may play a causal role in the pathophysiology of AD. A major limitation of most studies is their cross-sectional design; future longitudinal studies using multiple imaging modalities are expected to better document changes in CVRF-related brain function patterns and provide a clearer picture of the complex relationship between aging, CVRFs, and AD.

White matter abnormalities associated with Alzheimer's disease and mild cognitive impairment: a critical review of MRI studies

In this article, the authors aim to present a critical review of recent MRI studies addressing white matter (WM) abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI), by searching PubMed and reviewing MRI studies evaluating subjects with AD or MCI using WM volumetric methods, diffusion tensor imaging and assessment of WM hyperintensities. Studies have found that, compared with healthy controls, AD and MCI samples display WM volumetric reductions and diffusion tensor imaging findings suggestive of reduced WM integrity. These changes affect complex networks relevant to episodic memory and other cognitive processes, including fiber connections that directly link medial temporal structures and the corpus callosum. Abnormalities in cortico-cortical and cortico-subcortical WM interconnections are associated with an increased risk of progression from MCI to dementia. It can be concluded that WM abnormalities are detectable in early stages of AD and MCI. Degeneration of WM networks causes disconnection among neural cells and the degree of such changes is related to cognitive decline.

The role of amyloid β in the pathogenesis of Alzheimer's disease

The amyloid-β peptide (Aβ) is widely considered to be the major toxic agent in the pathogenesis of Alzheimer's disease, a condition which afflicts approximately 36 million people worldwide. Despite a plethora of studies stretching back over two decades, identifying the toxic Aβ species has proved difficult. Debate has centred on the Aβ fibril and oligomer. Despite support from numerous experimental models, important questions linger regarding the role of the Aβ oligomer in particular. It is likely a huge array of oligomers, rather than a single species, which cause toxicity. Reappraisal of the role of the Aβ fibril points towards a dynamic relationship with the Aβ oligomer within an integrated system, as supported by evidence from microglia. However, some continue to doubt the pathological role of amyloid β, instead proposing a protective role. If the field is to progress, all Aβ oligomers should be characterised, the nomenclature revised and a consistent experimental protocol defined. For this to occur, collaboration will be required between major research groups and innovative analytical tools developed. Such action must surely be taken if amyloid-based therapeutic endeavour is to progress.

The history of cerebral PET scanning: from physiology to cutting-edge technology

OBJECTIVE

To review the discoveries underpinning the introduction of cerebral PET scanning and highlight its modern applications.

BACKGROUND

Important discoveries in neurophysiology, brain metabolism, and radiotracer development in the post-World War II period provided the necessary infrastructure for the first cerebral PET scan.

METHODS

A complete review of the literature was undertaken to search for primary and secondary sources on the history of PET imaging. Searches were performed in PubMed, Google Scholar, and select individual journal Web sites. Written autobiographies were obtained through the Society for Neuroscience Web site at www.sfn.org. A reference book on the history of radiology, Naked to the Bone, was reviewed to corroborate facts and to locate references. The references listed in all the articles and books obtained were reviewed.

RESULTS

The neurophysiologic sciences required to build cerebral PET imaging date back to 1878. The last 60 years have produced an evolution of technological advancements in brain metabolism and radiotracer development. These advancements facilitated the development of modern cerebral PET imaging. Several key scientists were involved in critical discoveries and among them were Angelo Mosso, Charles Roy, Charles Sherrington, John Fulton, Seymour Kety, Louis Sokoloff, David E. Kuhl, Gordon L. Brownell, Michael Ter-Pogossian, Michael Phelps, and Edward Hoffman.

CONCLUSIONS

Neurophysiology, metabolism, and radiotracer development in the postwar era synergized the development of the technology necessary for cerebral PET scanning. Continued use of PET in clinical trials and current developments in PET-CT/MRI hybrids has led to advancement in diagnosis, management, and treatment of neurologic disorders.

Resting-state functional connectivity in normal brain aging

The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rs-fMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field.

A review of neuroimaging biomarkers of Alzheimer's disease

Neuroimaging biomarkers have potential role in the early diagnosis as well as periodic follow-up of neurodegenerative diseases such as Alzheimer's disease (AD). Structural imaging biomarkers can be used to predict those who are at risk or in preclinical stages of AD. It could possibly be useful even in predicting the conversion of Mild Cognitive Impairment (MCI) an early stage of AD to AD. In addition there has been a lot of progress in molecular imaging in AD. This article presents a review of recent progress in selected imaging biomarkers for early diagnosis, classification, and progression, of AD. A comprehensive integrative strategy initiated early in the cognitive decline is perhaps the most effective method of controlling progression to Alzheimer's disease.

Will posttranslational modifications of brain proteins provide novel serological markers for dementias?

Drug development for dementias is significantly hampered by the lack of easily accessible biomarkers. Fluid biomarkers of dementias provide indications of disease stage, but have little prognostic value, cannot detect early pathological changes, and can only be measured in CSF (cerebrospinal fluid) which significantly limits their applicability. In contrast, imaging based biomarkers can provide indications of probability of disease progression, yet are limited in applicability due to cost, radiation and radio-tracers. These aspects highlight the need for other approaches to the development of biomarkers of dementia, which should focus on not only providing information about pathological changes, but also on being measured easily and reproducibly. For other diseases, focus on development of assays monitoring highly specific protease-generated cleavage fragments of proteins has provided assays, which in serum or plasma have the ability to predict early pathological changes. Proteolytic processing of brain proteins, such as tau, APP, and α-synuclein, is a key pathological event in dementias. Here, we speculate that aiming biomarker development for dementias at detecting small brain protein degradation fragments of generated by brain-derived proteases specifically in blood samples could lead to the development of novel markers of disease progression, stage and importantly of treatment efficacy.