Brain FDG PET and the diagnosis of dementia. AJR Am J Roentgenol. 2015;204:W76-W85. [PMID: 25539279 DOI: 10.2214/ajr.13.12363]

"Advances in biomarker discovery and diagnostics for alzheimer's disease"

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by intracellular neurofibrillary tangles with tau protein and extracellular β-amyloid plaques. Early and accurate diagnosis is crucial for effective treatment and management.

OBJECTIVE

The purpose of this review is to investigate new technologies that improve diagnostic accuracy while looking at the current diagnostic criteria for AD, such as clinical evaluations, cognitive testing, and biomarker-based techniques.

METHODS

A thorough review of the literature was done in order to assess both conventional and contemporary diagnostic methods. Multimodal strategies integrating clinical, imaging, and biochemical evaluations were emphasised. The promise of current developments in biomarker discovery was also examined, including mass spectrometry and artificial intelligence.

RESULTS

Current diagnostic approaches include cerebrospinal fluid (CSF) biomarkers, imaging tools (MRI, PET), cognitive tests, and new blood-based markers. Integrating these technologies into multimodal diagnostic procedures enhances diagnostic accuracy and distinguishes dementia from other conditions. New technologies that hold promise for improving biomarker identification and diagnostic reliability include mass spectrometry and artificial intelligence.

CONCLUSION

Advancements in AD diagnostics underscore the need for accessible, minimally invasive, and cost-effective techniques to facilitate early detection and intervention. The integration of novel technologies with traditional methods may significantly enhance the accuracy and feasibility of AD diagnosis.

Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database

BACKGROUND

Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative diseases in individual patients. However, VBM is sensitive to the MRI scanner platform and details of the acquisition sequence. To mitigate this limitation, we recently proposed and technically validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database.

OBJECTIVE

Clinical validation of CNN-based VBM.

METHODS

CNN-based VBM was compared with conventional VBM based on a mixed-scanner normative database in 227 consecutive patients (66.0 ± 9.6 years, 53.3% female) with suspected dementing neurodegenerative disease. VBM maps were interpreted visually by two experienced readers, first with respect to the presence of any neurodegenerative disease, then for the differentiation between Alzheimer's disease (AD)-typical and non-AD atrophy patterns. A Likert 6-score was used for both tasks. Simultaneously acquired positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) served as reference standard.

RESULTS

Repeated-measures ANOVA revealed a significant impact of the VBM method on the visual detection of any neurodegenerative disease (p < 0.001). Balanced accuracy/sensitivity/specificity were 80.4/86.3/74.5% for CNN-based VBM versus 75.7/79.5/71.8% for conventional VBM. Differentiation between AD and non-AD typical atrophy patterns did not differ between both VBM methods (p = 0.871).

CONCLUSIONS

CNN-based VBM provides clinically useful accuracy for the detection of neurodegeneration-suspect atrophy with higher sensitivity than conventional VBM with a mixed-scanner normative reference database and without compromising specificity.

Molecular Imaging with PET in the Assessment of Vascular Dementia and Cerebrovascular Disease

Vascular dementia (VaD) is a unique form of cognitive decline caused by impairment of blood flow to the brain. Atherosclerosis is strongly associated with VaD as plaque accumulation can lead to tissue hypoperfusion or stroke. VaD and atherosclerosis are both diagnosed relatively late in their disease courses, prompting the need for novel diagnostic approaches such as PET to visualize subclinical pathophysiologic changes. This review discusses the use of PET in the assessment of VaD and cerebrovascular disease, focusing on the application of [18F] fluorodeoxyglucose to study neurometabolism and [18F] sodium fluoride to quantify arterial calcification.

Using the coefficient of determination to identify injury regions after stroke in pre-clinical FDG-PET images

BACKGROUND

In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature.

MATERIALS AND METHODS

Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.

RESULTS

The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications.

CONCLUSIONS

The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods.

Assessing Mild Traumatic Brain Injury-Associated Blood-Brain Barrier (BBB) Damage and Restoration Using Late-Phase Perfusion Analysis by 3D ASL MRI: Implications for Predicting Progressive Brain Injury in a Focused Review

Mild traumatic brain injury (mTBI) is a common occurrence around the world, associated with a variety of blunt force and torsion injuries affecting all age groups. Most never reach medical attention, and the identification of acute injury and later clearance to return to usual activities is relegated to clinical evaluation-particularly in sports injuries. Advanced structural imaging is rarely performed due to the usual absence of associated acute anatomic/hemorrhagic changes. This review targets physiologic imaging techniques available to identify subtle blood-brain barrier dysfunction and white matter tract shear injury and their association with chronic traumatic encephalopathy. These techniques provide needed objective measures to assure recovery from injury in those patients with persistent cognitive/emotional symptoms and in the face of repetitive mTBI.

Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model

BACKGROUND

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high-field and ultra-high-field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high-resolution MRI improve the quality of PET images?

PURPOSE

This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high-field and ultra-high-field MRI.

METHODS

From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor-corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevin dynamics.

RESULTS

Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state-of-the-art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra-high-field MRI exhibit marked improvements in signal-to-noise characteristics when contrasted with those generated from high-field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer's disease.

CONCLUSIONS

This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTA models.

Blood-brain barrier biomarkers

The blood-brain barrier (BBB) is a dynamic interface that regulates the exchange of molecules and cells between the brain parenchyma and the peripheral blood. The BBB is mainly composed of endothelial cells, astrocytes and pericytes. The integrity of this structure is essential for maintaining brain and spinal cord homeostasis and protection from injury or disease. However, in various neurological disorders, such as traumatic brain injury, Alzheimer's disease, and multiple sclerosis, the BBB can become compromised thus allowing passage of molecules and cells in and out of the central nervous system parenchyma. These agents, however, can serve as biomarkers of BBB permeability and neuronal damage, and provide valuable information for diagnosis, prognosis and treatment. Herein, we provide an overview of the BBB and changes due to aging, and summarize current knowledge on biomarkers of BBB disruption and neurodegeneration, including permeability, cellular, molecular and imaging biomarkers. We also discuss the challenges and opportunities for developing a biomarker toolkit that can reliably assess the BBB in physiologic and pathophysiologic states.

Hepatopancreatic metabolic disorders and their implications in the development of Alzheimer's disease and vascular dementia

Dementia has been faced with significant public health challenges and economic burdens that urges the need to develop safe and effective interventions. In recent years, an increasing number of studies have focused on the relationship between dementia and liver and pancreatic metabolic disorders that result in diseases such as diabetes, obesity, hypertension and dyslipidemia. Previous reports have shown that there is a plausible correlation between pathologies caused by hepatopancreatic dysfunctions and dementia. Glucose, insulin and IGF-1 metabolized in the liver and pancreas probably have an important influence on the pathophysiology of the most common dementias: Alzheimer's and vascular dementia. This current review highlights recent studies aimed at identifying convergent mechanisms, such as insulin resistance and other diseases, linked to altered hepatic and pancreatic metabolism, which are capable of causing brain changes that ultimately lead to dementia.

Neuroimaging Characteristics of Hearing Loss in the Mayo Clinic Study of Aging

OBJECTIVE

To investigate the association between standard pure tone and speech audiometry with neuroimaging characteristics reflective of aging and dementia in older adults.

STUDY DESIGN

Prospective population-based study.

SETTING

Single tertiary care referral center.

METHODS

Participants from the Mayo Clinic Study of aging 60 years old or older with normal cognition or mild cognitive impairment, baseline neuroimaging, and a behavioral audiogram associated with neuroimaging were eligible for study. Imaging modalities included structural MRI (sMRI) and fluid-attenuated inversion recovery MRI (FLAIR-MRI; N = 605), diffusion tensor imaging MRI (DTI-MRI; N = 444), and fluorodeoxyglucose-positron emission tomography (FDG-PET; N = 413). Multivariable logistic and linear regression models were used to evaluate associations with neuroimaging outcomes.

RESULTS

Mean (SD) pure tone average (PTA) was 33 (15) dB HL and mean (SD) word recognition score (WRS) was 91% (14). There were no significant associations between audiometric performance and cortical thinning assessed by sMRI. Each 10-dB increase in PTA was associated with increased likelihood of abnormal white-matter hyperintensity (WMH) from FLAIR-MRI (odds ratio 1.26, P = .02). From DTI-MRI, participants with <100% WRSs had significantly lower fractional anisotropy in the genu of the corpus callosum (parameter estimate [PE] -0.012, P = .008) compared to those with perfect WRSs. From FDG-PET, each 10% decrease in WRSs was associated with decreased uptake in the anterior cingulate cortex (PE -0.013, P = .001).

CONCLUSION

Poorer audiometric performance was not significantly associated with cortical thinning but was associated with white matter damage relevant to cerebrovascular disease (increased abnormal WMH, decreased corpus callosum diffusion). These neuroimaging results suggest a pathophysiologic link between hearing loss and cerebrovascular disease.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Intermittent Theta Burst Stimulation Attenuates Cognitive Deficits and Alzheimer's Disease-Type Pathologies via ISCA1-Mediated Mitochondrial Modulation in APP/PS1 Mice

Intermittent theta burst stimulation (iTBS), a time-saving and cost-effective repetitive transcranial magnetic stimulation regime, has been shown to improve cognition in patients with Alzheimer's disease (AD). However, the specific mechanism underlying iTBS-induced cognitive enhancement remains unknown. Previous studies suggested that mitochondrial functions are modulated by magnetic stimulation. Here, we showed that iTBS upregulates the expression of iron-sulfur cluster assembly 1 (ISCA1, an essential regulatory factor for mitochondrial respiration) in the brain of APP/PS1 mice. In vivo and in vitro studies revealed that iTBS modulates mitochondrial iron-sulfur cluster assembly to facilitate mitochondrial respiration and function, which is required for ISCA1. Moreover, iTBS rescues cognitive decline and attenuates AD-type pathologies in APP/PS1 mice. The present study uncovers a novel mechanism by which iTBS modulates mitochondrial respiration and function via ISCA1-mediated iron-sulfur cluster assembly to alleviate cognitive impairments and pathologies in AD. We provide the mechanistic target of iTBS that warrants its therapeutic potential for AD patients.

Distinct changes in brain metabolism in patients with dementia and hearing loss

INTRODUCTION

Previous studies have reported that hearing loss (HL) is associated with dementia, although the mechanistic underpinnings remain elusive. This study aimed to evaluate the changes in brain metabolism in patients with HL and different types of dementia.

METHODS

Patients with cognitive impairment (CI) and HL treated at the university-based memory clinic from May 2016 to October 2021 were included. In total, 108 patients with CI and HL prospectively underwent audiometry, neuropsychological test, magnetic resonance imaging, and 18 F-fluorodeoxyglucose positron emission tomography. Twenty-seven individuals without cognitive impairment and hearing loss were enrolled as a control group. Multivariable regression was performed to evaluate brain regions correlated with each pathology type after adjusting for confounding factors.

RESULTS

Multivariable regression analyses revealed that Alzheimer's disease-related CI (ADCI) was associated with hypometabolic changes in the right superior temporal gyrus (STG), right middle temporal gyrus (MTG), and bilateral medial temporal lobe. Lewy body disease-related CI (LBDCI) and vascular CI were associated with hypermetabolic and hypometabolic changes in the ascending auditory pathway, respectively. In the pure ADCI group, the degree of HL was positively associated with abnormal increase of brain metabolism in the right MTG, whereas it was negatively associated with decreased brain metabolism in the right STG in the pure LBDCI group.

CONCLUSION

Each dementia type is associated with distinct changes in brain metabolism in patients with HL.

PET/CT/MRI in Clinical Trials of Alzheimer's Disease

With the advent of PET imaging in 1976, 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-PET became the preferred method for in vivo investigation of cerebral processes, including regional hypometabolism in Alzheimer's disease. With the emergence of amyloid-PET tracers, [11C]Pittsburgh Compound-B in 2004 and later [18F]florbetapir, [18F]florbetaben, and [18F]flumetamol, amyloid-PET has replaced FDG-PET in Alzheimer's disease anti-amyloid clinical trial treatments to ensure "amyloid positivity" as an entry criterion, and to measure treatment-related decline in cerebral amyloid deposits. MRI has been used to rule out other brain diseases and screen for 'amyloid-related imaging abnormalities' (ARIAs) of two kinds, ARIA-E and ARIA-H, characterized by edema and micro-hemorrhage, respectively, and, to a lesser extent, to measure changes in cerebral volumes. While early immunotherapy trials of Alzheimer's disease showed no clinical effects, newer monoclonal antibody trials reported decreases of 27% to 85% in the cerebral amyloid-PET signal, interpreted by the Food and Drug Administration as amyloid removal expected to result in a reduction in clinical decline. However, due to the lack of diagnostic specificity of amyloid-PET tracers, amyloid positivity cannot prevent the inclusion of non-Alzheimer's patients and even healthy subjects in these clinical trials. Moreover, the "decreasing amyloid accumulation" assessed by amyloid-PET imaging has questionable quantitative value in the presence of treatment-related brain damage (ARIAs). Therefore, future Alzheimer's clinical trials should disregard amyloid-PET imaging and focus instead on assessment of regional brain function by FDG-PET and MRI monitoring of ARIAs and brain volume loss in all trial patients.

Metabolic profiling of Alzheimer's disease: Untargeted metabolomics analysis of plasma samples

Alzheimer's disease (AD) is often not recognized or is diagnosed very late, which significantly reduces the effectiveness of available pharmacological treatments. Metabolomic analyzes have great potential for improving existing knowledge about the pathogenesis and etiology of AD and represent a novel approach towards discovering biomarkers that could be used for diagnosis, prognosis, and therapy monitoring. In this study, we applied the untargeted metabolomic approach to investigate the changes in biochemical pathways related to AD pathology. We used gas chromatography and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS, respectively) to identify metabolites whose levels have changed in subjects with AD diagnosis (N = 40) compared to healthy controls (N = 40) and individuals with mild cognitive impairment (MCI, N = 40). The GC-MS identified significant differences between groups in levels of metabolites belonging to the classes of benzene and substituted derivatives, carboxylic acids and derivatives, fatty acyls, hydroxy acids and derivatives, keto acids and derivatives, and organooxygen compounds. Most of the compounds identified by the LC-MS were various fatty acyls, glycerolipids and glycerophospholipids. All of these compounds were decreased in AD patients and in subjects with MCI compared to healthy controls. The results of the study indicate disturbed metabolism of lipids and amino acids and an imbalance of metabolites involved in energy metabolism in individuals diagnosed with AD, compared to healthy controls and MCI subjects.

MRAβ: A multimodal MRI-derived amyloid-β biomarker for Alzheimer's disease

Florbetapir 18 F (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid-β of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI-derived Amyloid-β (MRAβ) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five-fold cross-validation CollaGAN were applied to generate MRAβ. In the ADNI dataset, we found MRAβ could characterize the subject-level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel-wise two-sample t-tests demonstrated amyloid-β depositions identified by MRAβ in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p < .05, corrected) and were much similar to those by AV45 (r > .92, p < .001). Moreover, a 3D ResNet classifier demonstrated that MRAβ was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRAβ could mimic cortical hyper-AV45 in HCs who later converted to MCI (r = .79, p < .001) and was comparable to AV45 in discriminating them from stable HC (p > .05). In summary, our work illustrates that MRAβ synthesized by multimodal MRI could mimic the cerebral amyloid-β depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular-explainable biomarkers.

The use of neuroimaging techniques in the early and differential diagnosis of dementia

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging

Alzheimer's disease (AD) is a degenerative disorder involving significant memory loss and other cognitive deficits, manifesting as a progression from normal cognitive functioning to mild cognitive impairment to AD. The sooner an accurate diagnosis of probable AD is made, the easier it is to manage symptoms and plan for future therapy. Functional neuroimaging stands to be a useful tool in achieving early diagnosis. Among the many neuroimaging modalities, positron emission tomography (PET) provides direct regional assessment of, among others, brain metabolism, cerebral blood flow, amyloid deposition-all quantities of interest in the characterization of AD. However, there are analytic challenges in identifying early indicators of AD from these high-dimensional imaging data sets, and it is unclear whether early indicators of AD are more likely to emerge in localized patterns of brain activity or in patterns of correlation between distinct brain regions. Early PET-based analyses of AD focused on alterations in metabolic activity at the voxel-level or in anatomically defined regions of interest. Other approaches, including seed-voxel and multivariate techniques, seek to characterize metabolic connectivity by identifying other regions in the brain with similar patterns of activity across subjects. We briefly review various neuroimaging statistical approaches applied to determine changes in metabolic activity or metabolic connectivity associated with AD. We then present an approach that provides a unified statistical framework for addressing both metabolic activity and connectivity. Specifically, we apply a Bayesian spatial hierarchical framework to longitudinal metabolic PET scans from the Alzheimer's Disease Neuroimaging Initiative.

Longitudinal interplay between subclinical atherosclerosis, cardiovascular risk factors, and cerebral glucose metabolism in midlife: results from the PESA prospective cohort study

BACKGROUND

Cardiovascular disease and dementia often coexist at advanced stages. Yet, longitudinal studies examining the interplay between atherosclerosis and its risk factors on brain health in midlife are scarce. We aimed to characterise the longitudinal associations between cerebral glucose metabolism, subclinical atherosclerosis, and cardiovascular risk factors in middle-aged asymptomatic individuals.

METHODS

The Progression of Early Subclinical Atherosclerosis (PESA) study is a Spanish longitudinal observational cohort study of 4184 asymptomatic individuals aged 40-54 years (NCT01410318). Participants with subclinical atherosclerosis underwent longitudinal cerebral [18F]fluorodeoxyglucose ([18F]FDG)-PET, and annual percentage change in [18F]FDG uptake was assessed (primary outcome). Cardiovascular risk was quantified with SCORE2 and subclinical atherosclerosis with three-dimensional vascular ultrasound (exposures). Multivariate regression and linear mixed effects models were used to assess associations between outcomes and exposures. Additionally, blood-based biomarkers of neuropathology were quantified and mediation analyses were performed. Secondary analyses were corrected for multiple comparisons using the false discovery rate (FDR) approach.

FINDINGS

This longitudinal study included a PESA subcohort of 370 participants (median age at baseline 49·8 years [IQR 46·1-52·2]; 309 [84%] men, 61 [16%] women; median follow-up 4·7 years [IQR 4·2-5·2]). Baseline scans took place between March 6, 2013, and Jan 21, 2015, and follow-up scans between Nov 24, 2017, and Aug 7, 2019. Persistent high risk of cardiovascular disease was associated with an accelerated decline of cortical [18F]FDG uptake compared with low risk (β=-0·008 [95% CI -0·013 to -0·002]; pFDR=0·040), with plasma neurofilament light chain, a marker of neurodegeneration, mediating this association by 20% (β=0·198 [0·008 to 0·740]; pFDR=0·050). Moreover, progression of subclinical carotid atherosclerosis was associated with an additional decline in [18F]FDG uptake in Alzheimer's disease brain regions, not explained by cardiovascular risk (β=-0·269 [95% CI -0·509 to -0·027]; p=0·029).

INTERPRETATION

Middle-aged asymptomatic individuals with persistent high risk of cardiovascular disease and subclinical carotid atherosclerosis already present brain metabolic decline, suggesting that maintenance of cardiovascular health during midlife could contribute to reductions in neurodegenerative disease burden later in life.

FUNDING

Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III, Santander Bank, Pro-CNIC Foundation, BrightFocus Foundation, BBVA Foundation, "la Caixa" Foundation.

Druggability of Targets for Diagnostic Radiopharmaceuticals

Targets play an indispensable and pivotal role in the development of radiopharmaceuticals. However, the initial stages of drug discovery projects are often plagued by frequent failures due to inadequate information on druggability and suboptimal target selection. In this context, we aim to present a comprehensive review of the factors that influence target druggability for diagnostic radiopharmaceuticals. Specifically, we explore the crucial determinants of target specificity, abundance, localization, and positivity rate and their respective implications. Through a detailed analysis of existing protein targets, we elucidate the significance of each factor. By carefully considering and balancing these factors during the selection of targets, more efficacious and targeted radiopharmaceuticals are expected to be designed for the diagnosis of a wide range of diseases in the future.

Brain FDG PET for visualizing the relation between impaired lung function and cognitive decline in lung cancer: a preliminary study

OBJECTIVE

Impaired lung function is associated with an increased risk for cognitive decline. F-18 fluorodeoxyglucose (FDG) PET is a well-known neurodegenerative biomarker for dementia. We investigated the association between lung and brain function using FDG PET in patients with lung cancer.

METHODS

A random sub-sample of 102 patients with lung cancer and without a self-reported history of neuropsychiatric disorders were recruited and underwent both lung function tests and FDG PET scans before treatment. Lung function was analyzed as the percentage predicted value (% pred) of forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1). FDG uptake was measured as standardized uptake values (SUVs) in the frontal, parietal, temporal, and occipital cortices and cognition-related regions. Regional SUV ratios (SUVRs) were calculated by dividing the SUV in each region by the whole-brain SUV and were then evaluated against lung function indices and clinical variables.

RESULTS

After excluding five patients with brain metastases, 97 patients were included in the final analysis (mean age, 67.7 ± 10.3 years). Mean FVC and mean FEV1 were 80.0% ± 15.4% and 77.6% ± 17.8%, respectively. Both FVC and FEV1 were positively correlated with SUVRs in all brain regions after adjusting the data for clinical variables. The degree of decrease in SUVRs related to lung function was not significantly different between cognition-related regions and other regions.

CONCLUSION

Impaired lung function was associated with decreased glucose metabolism in all regions of the brain, indicating that cognitive decline related to decreased glucose metabolism may be due to reduced perfusion.

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.

Imaging blood–brain barrier disruption in neuroinflammation and Alzheimer’s disease

The blood–brain barrier (BBB) is the neurovascular structure that regulates the passage of cells and molecules to and from the central nervous system (CNS). Alzheimer’s disease (AD) is a neurodegenerative disorder that is associated with gradual breakdown of the BBB, permitting entry of plasma-derived neurotoxins, inflammatory cells, and microbial pathogens into the CNS. BBB permeability can be visualized directly in AD patients using imaging technologies including dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging, and recent studies employing these techniques have shown that subtle changes in BBB stability occur prior to deposition of the pathological hallmarks of AD, senile plaques, and neurofibrillary tangles. These studies suggest that BBB disruption may be useful as an early diagnostic marker; however, AD is also accompanied by neuroinflammation, which can complicate these analyses. This review will outline the structural and functional changes to the BBB that occur during AD pathogenesis and highlight current imaging technologies that can detect these subtle changes. Advancing these technologies will improve both the diagnosis and treatment of AD and other neurodegenerative diseases.

Evaluation of advanced, pathophysiologic new targets for imaging of CNS

The inadequate information about the in vivo pathological, physiological, and neurological impairments, as well as the absence of in vivo tools for assessing brain penetrance and the efficiency of newly designed drugs, has hampered the development of new techniques for the treatment for variety of new central nervous system (CNS) diseases. The searching sites such as Science Direct and PubMed were used to find out the numerous distinct tracers across 16 CNS targets including tau, synaptic vesicle glycoprotein, the adenosine 2A receptor, the phosphodiesterase enzyme PDE10A, and the purinoceptor, among others. Among the most encouraging are [¹⁸ F]FIMX for mGluR imaging, [¹¹ C]Martinostat for Histone deacetylase, [¹⁸ F]MNI-444 for adenosine 2A imaging, [¹¹ C]ER176 for translocator protein, and [¹⁸ F]MK-6240 for tau imaging. We also reviewed the findings for each tracer's features and potential for application in CNS pathophysiology and therapeutic evaluation investigations, including target specificity, binding efficacy, and pharmacokinetic factors. This review aims to present a current evaluation of modern positron emission tomography tracers for CNS targets, with a focus on recent advances for targets that have newly emerged for imaging in humans.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival

Background

Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients.

Methods

We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival.

Results

Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%.

Conclusions

These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Develop a diagnostic tool for dementia using machine learning and non-imaging features

Background

Early identification of Alzheimer's disease or mild cognitive impairment can help guide direct prevention and supportive treatments, improve outcomes, and reduce medical costs. Existing advanced diagnostic tools are mostly based on neuroimaging and suffer from certain problems in cost, reliability, repeatability, accessibility, ease of use, and clinical integration. To address these problems, we developed, evaluated, and implemented an early diagnostic tool using machine learning and non-imaging factors.

Methods and results

A total of 654 participants aged 65 or older from the Nursing Home in Hangzhou, China were identified. Information collected from these patients includes dementia status and 70 demographic, cognitive, socioeconomic, and clinical features. Logistic regression, support vector machine (SVM), neural network, random forest, extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and best subset models were trained, tuned, and internally validated using a novel double cross validation algorithm and multiple evaluation metrics. The trained models were also compared and externally validated using a separate dataset with 1,100 participants from four communities in Zhejiang Province, China. The model with the best performance was then identified and implemented online with a friendly user interface. For the nursing dataset, the top three models are the neural network (AUROC = 0.9435), XGBoost (AUROC = 0.9398), and SVM with the polynomial kernel (AUROC = 0.9213). With the community dataset, the best three models are the random forest (AUROC = 0.9259), SVM with linear kernel (AUROC = 0.9282), and SVM with polynomial kernel (AUROC = 0.9213). The F1 scores and area under the precision-recall curve showed that the SVMs, neural network, and random forest were robust on the unbalanced community dataset. Overall the SVM with the polynomial kernel was found to be the best model. The LASSO and best subset models identified 17 features most relevant to dementia prediction, mostly from cognitive test results and socioeconomic characteristics.

Conclusion

Our non-imaging-based diagnostic tool can effectively predict dementia outcomes. The tool can be conveniently incorporated into clinical practice. Its online implementation allows zero barriers to its use, which enhances the disease's diagnosis, improves the quality of care, and reduces costs.

Dysfunctional Glucose Metabolism in Alzheimer’s Disease Onset and Potential Pharmacological Interventions

Alzheimer’s disease (AD) is the most common age-related dementia. The alteration in metabolic characteristics determines the prognosis. Patients at risk show reduced glucose uptake in the brain. Additionally, type 2 diabetes mellitus increases the risk of AD with increasing age. Therefore, changes in glucose uptake in the cerebral cortex may predict the histopathological diagnosis of AD. The shifts in glucose uptake and metabolism, insulin resistance, oxidative stress, and abnormal autophagy advance the pathogenesis of AD syndrome. Here, we summarize the role of altered glucose metabolism in type 2 diabetes for AD prognosis. Additionally, we discuss diagnosis and potential pharmacological interventions for glucose metabolism defects in AD to encourage the development of novel therapeutic methods.

Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis

The Alzheimer′s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge is the most comprehensive challenge to date with regard to the number of subjects, considered features, and challenge participants. The initial objective of TADPOLE was the identification of the most predictive data, features, and methods for the progression of subjects at risk of developing Alzheimer′s. The challenge was successful in recognizing tree-based ensemble methods such as gradient boosting and random forest as the best methods for the prognosis of the clinical status in Alzheimer’s disease (AD). However, the challenge outcome was limited to which combination of data processing and methods exhibits the best accuracy; hence, it is difficult to determine the contribution of the methods to the accuracy. The quantification of feature importance was globally approached by all the challenge participant methods. In addition, TADPOLE provided general answers that focused on improving performance while ignoring important issues such as interpretability. The purpose of this study is to intensively explore the models of the top three TADPOLE Challenge methods in a common framework for fair comparison. In addition, for these models, the most meaningful features for the prognosis of the clinical status of AD are studied and the contribution of each feature to the accuracy of the methods is quantified. We provide plausible explanations as to why the methods achieve such accuracy, and we investigate whether the methods use information coherent with clinical knowledge. Finally, we approach these issues through the analysis of SHapley Additive exPlanations (SHAP) values, a technique that has recently attracted increasing attention in the field of explainable artificial intelligence (XAI).

Deep learning-based brain age prediction in normal aging and dementia

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.

Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method

BACKGROUND

With the development of noninvasive imaging technology, collecting different imaging measurements of the same brain has become more and more easy. These multimodal imaging data carry complementary information of the same brain, with both specific and shared information being intertwined. Within these multimodal data, it is essential to discriminate the specific information from the shared information since it is of benefit to comprehensively characterize brain diseases. While most existing methods are unqualified, in this paper, we propose a parameter decomposition based sparse multi-view canonical correlation analysis (PDSMCCA) method. PDSMCCA could identify both modality-shared and -specific information of multimodal data, leading to an in-depth understanding of complex pathology of brain disease.

RESULTS

Compared with the SMCCA method, our method obtains higher correlation coefficients and better canonical weights on both synthetic data and real neuroimaging data. This indicates that, coupled with modality-shared and -specific feature selection, PDSMCCA improves the multi-view association identification and shows meaningful feature selection capability with desirable interpretation.

CONCLUSIONS

The novel PDSMCCA confirms that the parameter decomposition is a suitable strategy to identify both modality-shared and -specific imaging features. The multimodal association and the diverse information of multimodal imaging data enable us to better understand the brain disease such as Alzheimer's disease.

BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer's disease diagnosis

BACKGROUND AND OBJECTIVE

Multi-modal medical images, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), have been widely used for the diagnosis of brain disorder diseases like Alzheimer's disease (AD) since they can provide various information. PET scans can detect cellular changes in organs and tissues earlier than MRI. Unlike MRI, PET data is difficult to acquire due to cost, radiation, or other limitations. Moreover, PET data is missing for many subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To solve this problem, a 3D end-to-end generative adversarial network (named BPGAN) is proposed to synthesize brain PET from MRI scans, which can be used as a potential data completion scheme for multi-modal medical image research.

METHODS

We propose BPGAN, which learns an end-to-end mapping function to transform the input MRI scans to their underlying PET scans. First, we design a 3D multiple convolution U-Net (MCU) generator architecture to improve the visual quality of synthetic results while preserving the diverse brain structures of different subjects. By further employing a 3D gradient profile (GP) loss and structural similarity index measure (SSIM) loss, the synthetic PET scans have higher-similarity to the ground truth. In this study, we explore alternative data partitioning ways to study their impact on the performance of the proposed method in different medical scenarios.

RESULTS

We conduct experiments on a publicly available ADNI database. The proposed BPGAN is evaluated by mean absolute error (MAE), peak-signal-to-noise-ratio (PSNR) and SSIM, superior to other compared models in these quantitative evaluation metrics. Qualitative evaluations also validate the effectiveness of our approach. Additionally, combined with MRI and our synthetic PET scans, the accuracies of multi-class AD diagnosis on dataset-A and dataset-B are 85.00% and 56.47%, which have been improved by about 1% and 1%, respectively, compared to the stand-alone MRI.

CONCLUSIONS

The experimental results of quantitative measures, qualitative displays, and classification evaluation demonstrate that the synthetic PET images by BPGAN are reasonable and high-quality, which provide complementary information to improve the performance of AD diagnosis. This work provides a valuable reference for multi-modal medical image analysis.

Gamma-induction in frontotemporal dementia (GIFTeD) randomized placebo-controlled trial: Rationale, noninvasive brain stimulation protocol, and study design

INTRODUCTION

Frontotemporal dementia (FTD) is a neurodegenerative disorder for which there is no effective pharmacological treatment. Recently, interneuron activity responsible for fast oscillatory brain activity has been found to be impaired in a mouse model of FTD with consequent cognitive and behavioral alterations. In this study, we aim to investigate the safety, tolerability, and efficacy of a novel promising therapeutic intervention for FTD based on 40 Hz transcranial alternating current stimulation (tACS), a form of non-invasive brain stimulation thought to engage neural activity in a frequency-specific manner and thus suited to restore altered brain oscillatory patterns.

METHODS

This is a multi-site, randomized, double-blind, placebo-controlled trial on 50 patients with a diagnosis of behavioral variant FTD (bvFTD). Participants will be randomized to undergo either 30 days of 1-hour daily tACS or Sham (placebo) tACS. The outcomes will be assessed at baseline, right after the intervention and at a 3- to 6-months follow-up. The primary outcome measures are represented by the safety and feasibility of tACS administration, which will be assessed considering the nature, frequency, and severity of adverse events as well as attrition rate, respectively. To assess secondary outcomes, participants will undergo extensive neuropsychological and behavioral assessments and fluorodeoxyglucose (FDG)-positron emission tomography (PET) scans to evaluate changes in brain metabolism, functional and structural magnetic resonance imaging (MRI), resting and evoked electroencephalography, as well as blood biomarkers to measure changes in neurodegenerative and neuroinflammatory markers.

RESULTS

The trial started in October 2020 and will end in October 2023. Study protocols have been approved by the local institutional review board (IRB) at each data-collection site.

DISCUSSION

This study will evaluate the safety and tolerability of 40 Hz tACS in bvFTD patients and its efficacy on gamma oscillatory activity, cognitive function, and brain glucose hypometabolism.

The changes in the 18F FDG metabolism in the muscles by the use of cuboid support insoles

Healthy men aged 55,39, 23.45 years were administered 18F-fluorodeoxyglucose (¹⁸F-FDG) after fasting for over 5 h; then, a 30-min self-paced walking (6-min walk and 2-min rest + 6-min walk and 2-min rest + 6-min walk and 2-min rest + 6-min walk) session was performed. While walking, the same athletic shoes were used, same with walking supports, flat insoles, and cuboid support insoles (BMZ Inc., Tokyo, Japan). The walking test was performed with eye open. The examination was performed over 30 days apart. ¹⁸F-FDG accumulation within the gastrocnemius muscle was higher, the walking speed was improved. These results suggest that the use of cuboid support insoles may improve the cadence of the lower leg muscles.

Advanced imaging tools for evaluating cardiac morphological and functional impairment in hypertensive disease

Arterial hypertension represents a systemic burden, and it is responsible of various morphological, functional and tissue modifications affecting the heart and the cardiovascular system. Advanced imaging techniques, such as speckle tracking and three-dimensional echocardiography, cardiac magnetic resonance, computed tomography and PET-computed tomography, are able to identify cardiovascular injury at different stages of arterial hypertension, from subclinical alterations and overt organ damage to possible complications related to pressure overload, thus giving a precious contribution for guiding timely and appropriate management and therapy, in order to improve diagnostic accuracy and prevent disease progression. The present review focuses on the peculiarity of different advanced imaging tools to provide information about different and multiple morphological and functional aspects involved in hypertensive cardiovascular injury. This evaluation emphasizes the usefulness of the emerging multiimaging approach for a comprehensive overview of arterial hypertension induced cardiovascular damage.

Lack of association between cortical amyloid deposition and glucose metabolism in early stage Alzheimer´s disease patients

Background

Beta amyloid (Aβ) causes synaptic dysfunction leading to neuronal death. It is still controversial if the magnitude of Aβ deposition correlates with the degree of cognitive impairment. Diagnostic imaging may lead to a better understanding the role of Aβ in development of cognitive deficits. The aim of the present study was to investigate if Aβ deposition in the corresponding brain region of early stage Alzheimer´s disease (AD) patients, directly correlates to neuronal dysfunction and cognitive impairment indicated by reduced glucose metabolism.

Patients and methods

In 30 patients with a clinical phenotype of AD and amyloid positive brain imaging, 2-[18F] fluoro-2-deoxy-d-glucose (FDG) PET/CT was performed. We extracted the average [18F] flutemetamol (Vizamyl) uptake for each of the 16 regions of interest in both hemispheres and computed the standardized uptake value ratio (SUVR) by dividing the Vimazyl intensities by the mean signal of positive and negative control regions. Data were analysed using the R environment for statistical computing and graphics.

Results

Any negative correlation between Aβ deposition and glucose metabolism in 32 dementia related and corresponding brain regions in AD patients was not found. None of the correlation coefficient values were statistically significant different from zero based on two-sided p- value.

Conclusions

Regional Aβ deposition did not correlate negatively with local glucose metabolism in early stage AD patients. Our findings support the role of Aβ as a valid biomarker, but does not permit to conclude that Aβ is a direct cause for an aberrant brain glucose metabolism and neuronal dysfunction.

Alzheimer's Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases

Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research.

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach

PURPOSE

Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is required. We developed a deep-learning (DL)-based signature using positron emission tomography (PET) to objectively evaluate cognitive decline in patients with stroke.

METHODS

We built a DL model that differentiated Alzheimer's disease (AD) from normal controls (NC) using brain fluorodeoxyglucose (FDG) PET from the Alzheimer's Disease Neuroimaging Initiative database. The model was directly transferred to a prospectively enrolled cohort of patients with stroke to differentiate patients with dementia from those without dementia. The accuracy of the model was evaluated by the area under the curve values of receiver operating characteristic curves (AUC-ROC). We visualized the distribution of DL-based features and brain regions that the model weighted for classification. Correlations between cognitive signature from the DL model and clinical variables were evaluated, and survival analysis for post-stroke dementia was performed in patients with stroke.

RESULTS

The classification of AD vs. NC subjects was performed with AUC-ROC of 0.94 (95% confidence interval [CI], 0.89-0.98). The transferred model discriminated stroke patients with dementia (AUC-ROC = 0.75). The score of cognitive decline signature using FDG PET was positively correlated with age, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio and negatively correlated with body mass index in patients with stroke. We found that the cognitive decline score was an independent risk factor for dementia following stroke (hazard ratio, 10.90; 95% CI, 3.59-33.09; P < 0.0001) after adjustment for other key variables.

CONCLUSION

The DL-based cognitive signature using FDG PET was successfully transferred to an independent stroke cohort. It is suggested that DL-based cognitive evaluation using FDG PET could be utilized as an objective biomarker for cognitive dysfunction in patients with cerebrovascular diseases.

The use of positron emission tomography/magnetic resonance imaging in dementia: A literature review

OBJECTIVES

Positron emission tomography-magnetic resonance imaging (PET/MRI) is an emerging hybrid imaging system in clinical nuclear medicine. Research demonstrates a comparative utility to current unimodal and hybrid methods, including PET-computed tomography (PET/CT), in several medical subspecialities such as neuroimaging. The aim of this review is to critically evaluate the literature from 2016 to 2021 using PET/MRI for the investigation of patients with mild cognitive impairment or dementia, and discuss the evidence base for widening its application into clinical practice.

METHODS

A comprehensive literature search using the PubMed database was conducted to retrieve studies using PET/MRI in relation to the topics of mild cognitive impairment, dementia, or Alzheimer's disease between January 2016 and January 2021. This search strategy enabled studies on all dementia types to be included in the analysis. Studies were required to have a minimum of 10 human subjects and incorporate simultaneous PET/MRI.

RESULTS

A total of 116 papers were retrieved, with 39 papers included in the final selection. These were broadly categorised into reviews (12), technical/methodological papers (11) and new data studies (16). For the current review, discussion focused on findings from the new data studies.

CONCLUSIONS

PET/MRI offers additional insight into the underlying anatomical, metabolic and functional changes associated with dementia when compared with unimodal methods and PET/CT, particularly relating to brain regions including the hippocampus and default mode network. Furthermore, the improved diagnostic utility of PET/MRI, as reported by radiologists, offers improved classification of dementia patients, with important implications for clinical management.

Optimizing Use of Neuroimaging Tools in Evaluation of Prodromal Alzheimer's Disease and Related Disorders

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by preclinical, pre-dementia, and dementia phases. Progression of the disease leads to cognitive decline and is associated with loss of functional independence, personality changes, and behavioral disturbances. Current guidelines for AD diagnosis include the use of neuroimaging tools as biomarkers for identifying and monitoring pathological changes. Various imaging modalities, namely magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and PET with amyloid-beta tracers are available to facilitate early accurate diagnoses. Enhancing diagnosis in the early stages of the disease can allow for timely interventions that can delay progression of the disease. This paper will discuss the characteristic findings associated with each of the imaging tools for patients with AD, with a focus on FDG-PET due to its established accuracy in assisting with the differential diagnosis of dementia and discussion of other methods including MRI. Diagnostically-relevant features to aid clinicians in making a differential diagnosis will also be pointed out and multimodal imaging will be reviewed. We also discuss the role of quantification software in interpretation of brain imaging. Lastly, to guide evaluation of patients presenting with cognitive deficits, an algorithm for optimal integration of these imaging tools will be shared. Molecular imaging modalities used in dementia evaluations hold promise toward identifying AD-related pathology before symptoms are fully in evidence. The work describes state of the art functional and molecular imaging methods for AD. It will also overview a clinically applicable quantitative method for reproducible assessments of such scans in the early identification of AD.

A Bayesian group lasso classification for ADNI volumetrics data

The primary objective of this paper is to develop a statistically valid classification procedure for analyzing brain image volumetrics data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in elderly subjects with cognitive impairments. The Bayesian group lasso method thereby proposed for logistic regression efficiently selects an optimal model with the use of a spike and slab type prior. This method selects groups of attributes of a brain subregion encouraged by the group lasso penalty. We conduct simulation studies for high- and low-dimensional scenarios where our method is always able to select the true parameters that are truly predictive among a large number of parameters. The method is then applied on dichotomous response ADNI data which selects predictive atrophied brain regions and classifies Alzheimer's disease patients from healthy controls. Our analysis is able to give an accuracy rate of 80% for classifying Alzheimer's disease. The suggested method selects 29 brain subregions. The medical literature indicates that all these regions are associated with Alzheimer's patients. The Bayesian method of model selection further helps selecting only the subregions that are statistically significant, thus obtaining an optimal model.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups

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Cognitive-subgroups can be identified among individuals with AD dementia.

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Subgroup-specific patterns and longitudinal trajectories of hypometabolism observed.

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Regional hypometabolism matched respective cognitive profiles of subgroups.

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Cognitive-classification yields biologically distinct subgroups.

Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [¹⁸F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Glucose metabolic crosstalk and regulation in brain function and diseases

Brain glucose metabolism, including glycolysis, the pentose phosphate pathway, and glycogen turnover, produces ATP for energetic support and provides the precursors for the synthesis of biological macromolecules. Although glucose metabolism in neurons and astrocytes has been extensively studied, the glucose metabolism of microglia and oligodendrocytes, and their interactions with neurons and astrocytes, remain critical to understand brain function. Brain regions with heterogeneous cell composition and cell-type-specific profiles of glucose metabolism suggest that metabolic networks within the brain are complex. Signal transduction proteins including those in the Wnt, GSK-3β, PI3K-AKT, and AMPK pathways are involved in regulating these networks. Additionally, glycolytic enzymes and metabolites, such as hexokinase 2, acetyl-CoA, and enolase 2, are implicated in the modulation of cellular function, microglial activation, glycation, and acetylation of biomolecules. Given these extensive networks, glucose metabolism dysfunction in the whole brain or specific cell types is strongly associated with neurologic pathology including ischemic brain injury and neurodegenerative disorders. This review characterizes the glucose metabolism networks of the brain based on molecular signaling and cellular and regional interactions, and elucidates glucose metabolism-based mechanisms of neurological diseases and therapeutic approaches that may ameliorate metabolic abnormalities in those diseases.

Neuroimaging in Vascular Cognitive Impairment and Dementia: A Systematic Review

Cerebrovascular diseases are well established causes of cognitive impairment. Different etiologic entities, such as vascular dementia (VaD), vascular cognitive impairment, subcortical (ischemic) VaD, and vascular cognitive disorder, are included in the umbrella definition of vascular cognitive impairment and dementia (VCID). Because of the variability of VCID clinical presentation, there is no agreement on criteria defining the neuropathological threshold of this disorder. In fact, VCID is characterized by cerebral hemodynamic alteration which ranges from decreased cerebral blood flow to small vessels disease and involves a multifactorial process that leads to demyelination and gliosis, including blood-brain barrier disruption, hypoxia, and hypoperfusion, oxidative stress, neuroinflammation and alteration on neurovascular unit coupling, cerebral microbleeds, or superficial siderosis. Numerous criteria for the definition of VaD have been described: the National Institute of Neurological Disorders and Stroke Association Internationale pour Recherche'-et-l'Enseignement en Neurosciences criteria, the State of California Alzheimer's Disease Diagnostic and Treatment Centers criteria, DSM-V criteria, the Diagnostic Criteria for Vascular Cognitive Disorders (a VASCOG Statement), and Vascular Impairment of Cognition Classification Consensus Study. Neuroimaging is fundamental for definition and diagnosis of VCID and should be used to assess the extent, location, and type of vascular lesions. MRI is the most sensible technique, especially if used according to standardized protocols, even if CT plays an important role in several conditions. Functional neuroimaging, in particular functional MRI and PET, may facilitate differential diagnosis among different forms of dementia. This systematic review aims to explore the state of the art and future perspective of non-invasive diagnostics of VCID.

Visual versus semiquantitative analysis of 18F- fluorodeoxyglucose-positron emission tomography brain images in patients with dementia

Various studies have reported to the superiority of semiquantitative (SQ) analysis over visual analysis in detecting metabolic changes in the brain. In this study, we aimed to determine the limitations of SQ analysis programs and the current status of ¹⁸F- fluorodeoxyglucose (FDG)-positron emission tomography (PET) scan in dementia. ¹⁸F- FDG-PET/computed tomography (CT) brain images of 39 patients with a history of dementia were analyzed both visually and semiquantitatively. Using the visually markedly abnormal ¹⁸F- FDG-PET images as standard, we wanted to test the accuracy of two commercially available SQ analysis programs. SQ analysis results were classified as matching, partially matching and nonmatching with visually markedly abnormal studies. On visual analysis, ¹⁸F- FDG-PET showed marked regional hypometabolism in 19 patients, mild abnormalities in 8 and was normal in 12 patients. SQ analysis-1 results matched with visual analysis in 8 patients (42.1%) and partially matched in 11. SQ analysis-2 findings matched with visual analysis in 11 patients (57.8%) and partially matched in 7 and did not match in 1. Marked regional hypometabolism was either on the left side of the brain or was more significant on the left than the right in 63% of patients. Preservation of metabolism in sensorimotor cortex was seen in various dementia subtypes. Reviewing images in color scale and maximum intensity projection (MIP) image was helpful in demonstrating and displaying regional abnormalities, respectively. SQ analysis provides less accurate results as compared to visual analysis by experts. Due to suboptimal image registration and selection of brain areas, SQ analysis provides inaccurate results, particularly in small areas and areas in close proximity. Image registration and selection of areas with SQ programs should be checked carefully before reporting the results.

Global brain glucose uptake on 18F-FDG-PET/CT is influenced by chronic cardiovascular risk

PURPOSE

The goal of this study was to assess global cerebral glucose uptake in subjects with known cardiovascular risk factors by employing a quantitative 18F-fluorodeoxyglucose-PET/computed tomography (FDG-PET/CT) technique. We hypothesized that at-risk subjects would demonstrate decreased global brain glucose uptake compared to healthy controls.

METHODS

We compared 35 healthy male controls and 14 male subjects at increased risk for cardiovascular disease (CVD) as assessed by the systematic coronary risk evaluation (SCORE) tool. All subjects were grouped into two age-matched cohorts: younger (<50 years) and older (≥50 years). The global standardized uptake value mean (Avg SUVmean) was measured by mapping regions of interest of the entire brain across the supratentorial structures and cerebellum. Wilcoxon's rank-sum test was used to assess the differences in Avg SUVmean between controls and at-risk subjects.

RESULTS

Younger subjects demonstrated higher brain Avg SUVmean than older subjects. In addition, in both age strata, the 10-year risk for fatal CVD according to the SCORE tool was significantly greater in the at-risk groups than in healthy controls (younger: P = 0.0304; older: P = 0.0436). In the younger cohort, at-risk subjects demonstrated significantly lower brain Avg SUVmean than healthy controls (P = 0.0355). In the older cohort, at-risk subjects similarly had lower Avg SUVmean than controls (P = 0.0343).

CONCLUSIONS

Global brain glucose uptake appears to be influenced by chronic cardiovascular risk factors. Therefore, FDG-PET/CT may play a role in determining the importance of CVD on brain function and has potential for monitoring the efficacy of various therapeutic interventions.

Alzheimer's Dementia: The Emerging Role of Positron Emission Tomography

Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 50% to 80% of all cases of dementia. The diagnosis of probable AD is based on clinical criteria and overlapping clinical features pose a challenge to accurate diagnosis. However, neuroimaging has been included as a biomarker in various published criteria for the diagnosis of probable AD, in the absence of a confirmatory diagnostic test during life. Advances in neuroimaging techniques and their inclusion in diagnostic and research criteria for the diagnosis of AD includes the use of positron emission tomography (PET) imaging as a biomarker in various therapeutic and prognostic studies in AD. The development and application of a range of PET tracers will allow more detailed assessment of people with AD and will improve diagnostic specificity and targeted therapy of AD. The aim of this review is to summarize current evidence on PET imaging using the non-specific tracer [18F]fluorodeoxyglucose and specific tracers that target amyloid and tau pathology in people with AD.