Magnetic resonance imaging and positron emission tomography in the diagnosis of neurodegenerative dementias

Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases

Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in early diagnosis and management. This narrative review explores the pivotal role of advanced neuroimaging techniques in detecting and managing these diseases at early stages, potentially slowing their progression through timely interventions. Recent advancements in MRI, such as ultra-high-field systems and functional MRI, have enhanced the sensitivity for detecting subtle structural and functional changes. Additionally, the development of novel amyloid-beta tracers and other emerging modalities like optical imaging and transcranial ultrasonography have improved the diagnostic accuracy and capability of existing methods. This review highlights the clinical applications of these technologies in Alzheimer's and Parkinson's diseases, where they have shown improved diagnostic performance, enabling earlier intervention and better prognostic outcomes. Moreover, the integration of artificial intelligence (AI) and longitudinal research is emerging as a promising enhancement to refine early detection strategies further. However, this review also addresses the technical, ethical, and accessibility challenges in the field, advocating for the more extensive use of advanced imaging technologies to overcome these barriers. Finally, we emphasize the need for a holistic approach that incorporates both neurological and psychiatric perspectives, which is crucial for optimizing patient care and outcomes in the management of neurodegenerative diseases.

Circulating Biomarkers for Alzheimer's Disease: Unlocking the Diagnostic Potential in Low- and Middle-Income Countries, Focusing on Africa

BACKGROUND

Alzheimer's disease (AD) is emerging as a significant public health challenge in Africa, with predictions indicating a tripling in incidence by 2050. The diagnosis of AD on the African continent is notably difficult, leading to late detection that severely limits treatment options and significantly impacts the quality of life for patients and their families.

SUMMARY

This review focuses on the potential of high-sensitivity specific blood biomarkers as promising tools for improving AD diagnosis and management globally, particularly in Africa. These advances are particularly pertinent in the continent, where access to medical and technical resources is often limited.

KEY MESSAGES

Identifying precise, sensitive, and specific blood biomarkers could contribute to the biological characterization and management of AD in Africa. Such advances promise to improve patient care and pave the way for new regional opportunities in pharmaceutical research and drug trials on the continent for AD.

Automated Synthesis of [11C]PiB via [11CH3OTf]-as Methylating Agent for PET Imaging of β-Amyloid

AIM

Efficient synthesis of precursor from commercially available starting materials and automated radiosynthesis of [11C]PiB using commercially available dedicated [11C]- Chemistry module from the synthesized precursor.

BACKGROUND

[11C]PiB is a promising radiotracer for PET imaging of β-Amyloid, advancing Alzheimer's disease research. The availability of precursors and protocols for efficient radiolabelling foster the applications of any radiotracer. Efficient synthesis of PiB precursor was performed using anisidine and 4-nitrobenzoyl chloride as starting materials in 5 steps, having addition, substitutions, and cyclization chemical methodologies. This precursor was used for fully automated radiosynthesis of [11C]PiB in a commercially available synthesizer, MPS-100 (SHI, Japan). The synthesized [11C]PiB was purified via solid-phase methodology, and its quality control was performed by the quality and safety criteria required for clinical use.

METHODS

The synthesis of desired precursors and standard authentic compounds started with commercially available materials with 70-80% yields. The standard analytical methods were characterized all synthesized compounds. The fully automated [11C]-chemistry synthesizer (MPS-100) used for radiosynthesis of [11C]PiB with [11C]CH3OTf acts as a methylating agent. For radiolabelling, varied amounts of precursor and time of reaction were explored. The resulting crude product underwent purification through solid-phase cartridges. The synthesized radiotracer was analyzed using analytical tools such as radio TLC, HPLC, pH endo-toxicity, and half-life.

RESULTS

The precursor for radiosynthesis of [11C]PiB was achieved in excellent yield using simple and feasible chemistry. A protocol for radiolabelling of precursor to synthesized [11C]PiB was developed using an automated synthesizer. The crude radiotracer was purified by solid-phase cartridge, with a decay-corrected radiochemical yield of 40±5% and radiochemical purity of more than 97% in approx 20 minutes (EOB). The specific activity was calculated and found in a 110-121 mCi/μmol range.

CONCLUSION

A reliable methodology was developed for preparing precursor followed by fully automated radiolabeling using [11C]MeOTf as a methylating agent to synthesize [11C]PiB. The final HPLC-free purification yielded more than 97% radiochemical purity tracer within one radionuclide half-life. The method was reproducible and efficient for any clinical center.

Association Between Acoustic Features and Brain Volumes: the Framingham Heart Study

Introduction

Although brain magnetic resonance imaging (MRI) is a valuable tool for investigating structural changes in the brain associated with neurodegeneration, the development of non-invasive and cost-effective alternative methods for detecting early cognitive impairment is crucial. The human voice has been increasingly used as an indicator for effectively detecting cognitive disorders, but it remains unclear whether acoustic features are associated with structural neuroimaging.

Methods

This study aims to investigate the association between acoustic features and brain volume and compare the predictive power of each for mild cognitive impairment (MCI) in a large community-based population. The study included participants from the Framingham Heart Study (FHS) who had at least one voice recording and an MRI scan. Sixty-five acoustic features were extracted with the OpenSMILE software (v2.1.3) from each voice recording. Nine MRI measures were derived according to the FHS MRI protocol. We examined the associations between acoustic features and MRI measures using linear regression models adjusted for age, sex, and education. Acoustic composite scores were generated by combining acoustic features significantly associated with MRI measures. The MCI prediction ability of acoustic composite scores and MRI measures were compared by building random forest models and calculating the mean area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation.

Results

The study included 4,293 participants (age 57 ± 13 years, 53.9% women). During 9.3±3.7 years follow-up, 106 participants were diagnosed with MCI. Seven MRI measures were significantly associated with more than 20 acoustic features after adjusting for multiple testing. The acoustic composite scores can improve the AUC for MCI prediction to 0.794, compared to 0.759 achieved by MRI measures.

Discussion

We found multiple acoustic features were associated with MRI measures, suggesting the potential for using acoustic features as easily accessible digital biomarkers for the early diagnosis of MCI.

Hippocampal atrophy is associated with hearing loss in cognitively normal adults

Objectives

A growing body of evidence suggests that age-related hearing loss (HL) is associated with morphological changes of the cerebral cortex, but the results have been drawn from a small amount of data in most studies. The aim of this study is to investigate the correlation between HL and gray matter volume (GMV) in a large number of subjects, strictly controlling for an extensive set of possible biases.

Methods

Medical records of 576 subjects who underwent pure tone audiometry, brain magnetic resonance imaging (MRI), and the Korean Mini-Mental State Exam (K-MMSE) were reviewed. Among them, subjects with normal cognitive function and free of central nervous system disorders or coronary artery disease were included. Outliers were excluded after a sample homogeneity check. In the end, 405 subjects were enrolled. Pure tone hearing thresholds were determined at 0.5, 1, 2, and 4 kHz in the better ear. Enrolled subjects were divided into 3 groups according to pure tone average: normal hearing (NH), mild HL (MHL), and moderate-to-severe HL (MSHL) groups. Using voxel-based morphometry, we evaluated GMV changes that may be associated with HL. Sex, age, total intracranial volume, type of MRI scanner, education level, K-MMSE score, smoking status, and presence of hypertension, diabetes mellitus and dyslipidemia were used as covariates.

Results

A statistically significant negative correlation between the hearing thresholds and GMV of the hippocampus was elucidated. Additionally, in group comparisons, the left hippocampal GMV of the MSHL group was significantly smaller than that of the NH and MHL groups.

Conclusion

Based on the negative correlation between hearing thresholds and hippocampal GMV in cognitively normal old adults, the current study indicates that peripheral deafferentation could be a potential contributing factor to hippocampal atrophy.

Early-onset Alzheimer's disease with depression as the first symptom: a case report with literature review

Background

Alzheimer's disease is a common neurodegenerative disease, and patients with early-onset Alzheimer's disease (onset age < 65 years) often have atypical symptoms, which are easily misdiagnosed and missed. Multimodality neuroimaging has become an important diagnostic and follow-up method for AD with its non-invasive and quantitative advantages.

Case presentation

We report a case of a 59-year-old female with a diagnosis of depression at the age of 50 after a 46-year-old onset and a 9-year follow-up observation, who developed cognitive dysfunction manifested by memory loss and disorientation at the age of 53, and eventually developed dementia. Combined with neuropsychological scales (MMSE and MOCA scores decreased year by year and finally reached the dementia criteria) and the application of multimodal imaging. MRI showed that the hippocampus atrophied year by year and the cerebral cortex was extensively atrophied. 18F-FDG PET image showed hypometabolism in right parietal lobes, bilateral frontal lobes, bilateral joint parieto-temporal areas, and bilateral posterior cingulate glucose metabolism. The 18F-AV45 PET image showed the diagnosis of early-onset Alzheimer's disease was confirmed by the presence of Aβ deposits in the cerebral cortex.

Conclusion

Early-onset Alzheimer's disease, which starts with depression, often has atypical symptoms and is prone to misdiagnosis. The combination of neuropsychological scales and neuroimaging examinations are good screening tools that can better assist in the early diagnosis of Alzheimer's disease. Graphical Abstract.

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.

Quantitative Brain Positron Emission Tomography in Female 5XFAD Alzheimer Mice: Pathological Features and Sex-Specific Alterations

Successful back-translating clinical biomarkers and molecular imaging methods of Alzheimer's disease (AD), including positron emission tomography (PET), are very valuable for the evaluation of new therapeutic strategies and increase the quality of preclinical studies. ¹⁸F-Fluorodeoxyglucose (FDG)–PET and ¹⁸F-Florbetaben–PET are clinically established biomarkers capturing two key pathological features of AD. However, the suitability of ¹⁸F-FDG– and amyloid–PET in the widely used 5XFAD mouse model of AD is still unclear. Furthermore, only data on male 5XFAD mice have been published so far, whereas studies in female mice and possible sex differences in ¹⁸F-FDG and ¹⁸F-Florbetaben uptake are missing. The aim of this study was to evaluate the suitability of ¹⁸F-FDG– and ¹⁸F-Florbetaben–PET in 7-month-old female 5XFAD and to assess possible sex differences between male and female 5XFAD mice. We could demonstrate that female 5XFAD mice showed a significant reduction in brain glucose metabolism and increased cerebral amyloid deposition compared with wild type animals, in accordance with the pathology seen in AD patients. Furthermore, we showed for the first time that the hypometabolism in 5XFAD mice is gender-dependent and more pronounced in female mice. Therefore, these results support the feasibility of small animal PET imaging with ¹⁸F-FDG- and ¹⁸F-Florbetaben in 5XFAD mice in both, male and female animals. Moreover, our findings highlight the need to account for sex differences in studies working with 5XFAD mice.

The Clinical Spectrum of Young Onset Dementia Points to Its Stochastic Origins

BACKGROUND

Dementia is a major global health problem and the search for improved therapies is ongoing. The study of young onset dementia (YOD)-with onset prior to 65 years-represents a challenge owing to the variety of clinical presentations, pathology, and gene mutations. The advantage of the investigation of YOD is the lack of comorbidities that complicate the clinical picture in older adults. Here we explore the origins of YOD.

OBJECTIVE

To define the clinical diversity of YOD in terms of its demography, range of presentations, neurological examination findings, comorbidities, medical history, cognitive findings, imaging abnormalities both structural and functional, electroencephagraphic (EEG) data, neuropathology, and genetics.

METHODS

A prospective 20-year study of 240 community-based patients referred to specialty neurology clinics established to elucidate the nature of YOD.

RESULTS

Alzheimer's disease (AD; n = 139) and behavioral variant frontotemporal (bvFTD; n = 58) were the most common causes with a mean age of onset of 56.5 years for AD (±1 SD 5.45) and 57.1 years for bvFTD (±1 SD 5.66). Neuropathology showed a variety of diagnoses from multiple sclerosis, Lewy body disease, FTD-MND, TDP-43 proteinopathy, adult-onset leukoencephalopathy with axonal steroids and pigmented glia, corticobasal degeneration, unexplained small vessel disease, and autoimmune T-cell encephalitis. Non-amnestic forms of AD and alternative forms of FTD were discovered. Mutations were only found in 11 subjects (11/240 = 4.6%). APOE genotyping was not divergent between the two populations.

CONCLUSION

There are multiple kinds of YOD, and most are sporadic. These observations point to their stochastic origins.

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.

Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries.

OBJECTIVE

To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc.

METHODS

The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics.

RESULTS

Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis.

CONCLUSION

Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.

Detection of Active Caspase-3 in Mouse Models of Stroke and Alzheimer's Disease with a Novel Dual Positron Emission Tomography/Fluorescent Tracer [68Ga]Ga-TC3-OGDOTA

Apoptosis is a feature of stroke and Alzheimer's disease (AD), yet there is no accepted method to detect or follow apoptosis in the brain in vivo. We developed a bifunctional tracer [68Ga]Ga-TC3-OGDOTA containing a cell-penetrating peptide separated from fluorescent Oregon Green and 68Ga-bound labels by the caspase-3 recognition peptide DEVD. We hypothesized that this design would allow [68Ga]Ga-TC3-OGDOTA to accumulate in apoptotic cells. In vitro, Ga-TC3-OGDOTA labeled apoptotic neurons following exposure to camptothecin, oxygen-glucose deprivation, and β-amyloid oligomers. In vivo, PET showed accumulation of [68Ga]Ga-TC3-OGDOTA in the brain of mouse models of stroke or AD. Optical clearing revealed colocalization of [68Ga]Ga-TC3-OGDOTA and cleaved caspase-3 in brain cells. In stroke, [68Ga]Ga-TC3-OGDOTA accumulated in neurons in the penumbra area, whereas in AD mice [68Ga]Ga-TC3-OGDOTA was found in single cells in the forebrain and diffusely around amyloid plaques. In summary, this bifunctional tracer is selectively associated with apoptotic cells in vitro and in vivo in brain disease models and represents a novel tool for apoptosis detection that can be used in neurodegenerative diseases.

From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome

The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.

Chemical exchange saturation transfer MRI shows low cerebral 2-deoxy-D-glucose uptake in a model of Alzheimer's Disease

Glucose is the central nervous system's only energy source. Imaging techniques capable to detect pathological alterations of the brain metabolism are useful in different diagnostic processes. Such techniques are also beneficial for assessing the evaluation efficacy of therapies in pre-clinical and clinical stages of diseases. Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a possible alternative to positron emission tomography (PET) imaging that has been widely explored in cancer research in humans and animal models. We propose that pathological alterations in brain 2-deoxy-D-glucose (2DG) uptake, typical of neurodegenerative diseases, can be detected with CEST MRI. Transgenic mice overexpressing a mutated form of amyloid precusrsor protein (APP23), a model of Alzheimer's disease, analyzed with CEST MRI showed a clear reduction of 2DG uptake in different brain regions. This was reminiscent of the cerebral condition observed in Alzheimer's patients. The results indicate the feasibility of CEST for analyzing the brain metabolic state, with better image resolution than PET in experimental models.

Alzheimer-like brain metabolic and structural features in cholesterol-fed rabbit detected by magnetic resonance imaging

BACKGROUND

Hypercholesterolemia is known to increase the risk of AD in later life, the purpose of this study is to illustrate brain metabolic and structural changes in a cholesterol-fed rabbit model of Alzheimer's Disease (AD) by using clinical 3 T Magnetic Resonance Imaging (MRI).

METHODS

The Institutional Animal Care and Use Committee of Zhejiang Chinese Medical University approved the study. Totally 16 Japanese White Rabbits (JWR) were randomly divided into 2 groups including normal control group fed with routine diet (group NC) and high cholesterol diet group (group CD) fed a 2% cholesterol diet with 0.24 ppm copper in the drinking water for 12 weeks. Magnetic resonance spectroscopy (MRS) and structural image of rabbit brain were performed by using a 3 Tesla (T) MRI scanner with an 8 channel Rabbit coil. The chemical metabolites were identified by LC Model including N-acetylaspartate (NAA), creatine (Cr), glutamate (Glu), glutamine (Gln), Glycerophosphatidylcholine (GPC), phosphorylcholine (PCH), and myoinositol (MI). The relative concentrations (/Cr) were analyzed. Additionally, Amyloid-β (Aβ) accumulation in the brain was measured postmortem. For comparisons of MR and Aβ data between groups, two-tailed t-tests were performed.

RESULTS

The ratio of NAA/Cr (0.76 ± 0.10) and Glu/Cr (0.90 ± 0.14) in group CD were lower than those in the group NC (0.87 ± 0.06, 1.13 ± 0.22, respectively, P <  0.05). Compared to the group NC (2.88 ± 0.09 cm3, 0.63 ± 0.08 cm3, respectively), the cortical and hippocampal volumes (2.60 ± 0.14 cm3 and 0.47 ± 0.07 cm3, respectively) of rabbits brain decreased in the group CD while the third and lateral ventricular volumes enlarged (44.56 ± 6.01 mm3 vs 31.40 ± 6.14 mm3, 261.40 ± 30.98 mm3 vs 153.81 ± 30.08 mm3, P <  0.05). These metabolic and structural changes were additionally accompanied by the significant increase of Aβ1-42 in the cortex and hippocampus (163.60 ± 16.26 pg/mg and 215.20 ± 69.86 pg/mg, respectively, P <  0.05).

CONCLUSION

High cholesterol diet can induce the brain metabolic and structural changes of the rabbit including lowered level of NAA and Glu and the atrophy of the brain which were similar to those of human AD.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.