Quantitative multimodal multiparametric imaging in Alzheimer's disease

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [68Ga]Ga-FAPI-04

PURPOSE

[⁶⁸Ga]Ga-FAPI PET/CT has been widely used in clinical diagnosis and radiopharmaceutical therapy. In this study, tumor-to-blood ratio (TBR) was evaluated as a powerful tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as an effective index for tumors with high FAP expression in theranostics.

METHODS

Nine patients with pancreatic cancer underwent a 60-min dynamic PET/CT scan by total-body PET/CT (with a long AFOV of 194 cm) after injection of [⁶⁸Ga]Ga-FAPI-04. After dynamic PET/CT scan, three patients received chemotherapy and underwent the second dynamic scan to evaluate treatment response. Time-activity curves (TACs) were obtained by drawing regions of interest for primary pancreatic lesions and metastatic lesions. The lesion TACs were fitted using four compartment models by the software PMOD PKIN kinetic modeling. The preferred pharmacokinetic model for [⁶⁸Ga]Ga-FAPI-04 was evaluated based on the Akaike information criterion. The correlations between simplified methods for quantification of [⁶⁸Ga]Ga-FAPI-04 (SUVs; tumor-to-blood ratios [TBRs]) and the total distribution volume (V_t) estimates obtained from pharmacokinetic analysis were calculated.

RESULTS

In total, 9 primary lesions and 25 metastatic lesions were evaluated. The reversible two-tissue compartment model (2TCM) was the most appropriate model among the four compartment models. The total distribution volume V_t values derived from 2TCM varied significantly in pathological lesions and background regions. A strong positive correlation was observed between TBR_mean and V_t from the 2TCM model in pathological lesions (R²=0.92, P<0.001). The relative difference range for TBR_mean was 2.1% compared to the reduction rate of V_t in the patients who were treated with chemotherapy.

CONCLUSIONS

A strong positive correlation was observed between TBR_mean and V_t for [⁶⁸Ga]Ga-FAPI-04. TBR_mean reflects FAP receptor density better than SUV_mean and SUV_max, and would be the preferred measurement tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as a means for evaluating treatment response.

The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review

Neurodegenerative diseases (NDs), including chronic disease such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis, and acute diseases like traumatic brain injury and ischemic stroke are characterized by progressive degeneration, brain tissue damage and loss of neurons, accompanied by behavioral and cognitive dysfunctions. So far, there are no complete cures for NDs; thus, early and timely diagnoses are essential and beneficial to patients' treatment. Magnetic resonance imaging (MRI) has become one of the advanced medical imaging techniques widely used in the clinical examination of NDs due to its non-invasive diagnostic value. In this review, research published in English in current decade from PubMed electronic database on the use of MRI to detect specific biomarkers of NDs was collected, summarized, and discussed, which provides valuable suggestions for the early diagnosis, prevention, and treatment of NDs in the clinic.

Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common causes of dementia and difficult to study as the pool of subjects is highly heterogeneous. Saturation transfer (ST) magnetic resonance imaging (MRI) methods are quantitative modalities with potential for non-invasive identification and tracking of various aspects of AD pathology. In this review we cover ST-MRI studies in both humans and animal models of AD over the past 20 years. A number of magnetization transfer (MT) studies have shown promising results in human brain. Increased computing power enables more quantitative MT studies, while access to higher magnetic fields improves the specificity of chemical exchange saturation transfer (CEST) techniques. While much work remains to be done, results so far are very encouraging. MT is sensitive to patterns of AD-related pathological changes, improving differential diagnosis, and CEST is sensitive to particular pathological processes which could greatly assist in the development and monitoring of therapeutic treatments of this currently incurable disease.

Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.

Natural Peptides in Drug Discovery Targeting Acetylcholinesterase

Acetylcholinesterase-inhibitory peptide has gained much importance since it can inhibit acetylcholinesterase (AChE) and increase the availability of acetylcholine in cholinergic synapses, enhancing cholinergic transmission in pharmacological treatment of Alzheimer’s disease (AD). Natural peptides have received considerable attention as biologically important substances as a source of AChE inhibitors. These natural peptides have high potential pharmaceutical and medicinal values due to their bioactivities as neuroprotective and neurodegenerative treatment activities. These peptides have attracted great interest in the pharmaceutical industries, in order to design potential peptides for use in the prophylactic and therapy purposes. Some natural peptides and their derivatives have high commercial values and have succeeded in reaching the pharmaceutical market. A large number of peptides are already in preclinical and clinical pipelines for treatment of various diseases. This review highlights the recent researches on the various natural peptides and future prospects for AD management.

Building Imaging Institutes of Patient Care Outcomes: Imaging as a Nidus for Innovation in Clinical Care, Research, and Education

Traditionally, radiologists have been responsible for the protocol of imaging studies, imaging acquisition, supervision of imaging technologists, and interpretation and reporting of imaging findings. In this article, we outline how radiology needs to change and adapt to a role of providing value-based, integrated health-care delivery. We believe that the way to best serve our specialty and our patients is to undertake a fundamental paradigm shift in how we practice. We describe the need for imaging institutes centered on disease entities (eg, lung cancer, multiple sclerosis) to not only optimize clinical care and patient outcomes, but also spur the development of a new educational focus, which will increase opportunities for medical trainees and other health professionals. These institutes will also serve as unique environments for testing and implementing new technologies and for generating new ideas for research and health-care delivery. We propose that the imaging institutes focus on how imaging practices-including new innovations-improve patient care outcomes within a specific disease framework. These institutes will allow our specialty to lead patient care, provide the necessary infrastructure for state-of-the art-education of trainees, and stimulate innovative and clinically relevant research.

γH2AX is increased in peripheral blood lymphocytes of Alzheimer's disease patients in the South Australian Neurodegeneration, Nutrition and DNA Damage (SAND) study of aging

An early cellular response to DNA double-strand breaks is the phosphorylation of histone H2AX to form γH2AX. Although increased levels of γH2AX have been reported in neuronal nuclei of Alzheimer's disease (AD) patients, γH2AX responses in the lymphocytes of individuals with mild cognitive impairment (MCI) and AD remain unexplored. In this study, the endogenous γH2AX level was measured, using laser scanning cytometry (LSC) and visual scoring, in lymphocyte nuclei from MCI (n = 18), or AD (n = 20) patients and healthy controls (n = 40). Levels were significantly elevated in nuclei of the AD group compared to the MCI and control groups, and there was a concomitant increase, with a significant trend, from the control group through MCI to the AD group. A significant negative correlation was seen between γH2AX and the mini mental state examination (MMSE) score, when the analysis included all subjects. Receiver Operation Characteristic curves were carried out for different γH2AX parameters; visually scored percent cells containing overlapping γH2AX foci displayed the best area under the curve value of 0.9081 with 85% sensitivity and 92% specificity for the identification of AD patients versus control. Plasma homocysteine, creatinine, and chitinase-3-like protein 1 (CHI3L1) were positively correlated with lymphocyte γH2AX signals, while glomerular filtration rate (GFR) was negatively correlated. Finally, there was a diminished γH2AX response to X-rays in lymphocytes of the MCI and AD groups compared to the control group. Our results indicate that lymphocyte γH2AX levels are a potential marker for identifying individuals at increased risk of developing AD. Prospective studies with normal healthy individuals are needed to test whether there is indeed a link between γH2AX levels and AD risk.

Advanced bioanalytics for precision medicine

Precision medicine is a new paradigm that combines diagnostic, imaging, and analytical tools to produce accurate diagnoses and therapeutic interventions tailored to the individual patient. This approach stands in contrast to the traditional "one size fits all" concept, according to which researchers develop disease treatments and preventions for an "average" patient without considering individual differences. The "one size fits all" concept has led to many ineffective or inappropriate treatments, especially for pathologies such as Alzheimer's disease and cancer. Now, precision medicine is receiving massive funding in many countries, thanks to its social and economic potential in terms of improved disease prevention, diagnosis, and therapy. Bioanalytical chemistry is critical to precision medicine. This is because identifying an appropriate tailored therapy requires researchers to collect and analyze information on each patient's specific molecular biomarkers (e.g., proteins, nucleic acids, and metabolites). In other words, precision diagnostics is not possible without precise bioanalytical chemistry. This Trend article highlights some of the most recent advances, including massive analysis of multilayer omics, and new imaging technique applications suitable for implementing precision medicine. Graphical abstract Precision medicine combines bioanalytical chemistry, molecular diagnostics, and imaging tools for performing accurate diagnoses and selecting optimal therapies for each patient.

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment

BACKGROUND AND OBJECTIVE

The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention.

METHODS

The current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC. The proposed new algorithm was based on weighted combination and named as multi-modality SCDDL (mSCDDL). Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET data of 113 AD patients, 110 MCI patients and 117 NC subjects from the Alzheimer's disease Neuroimaging Initiative database were adopted for classification between MCI and NC, as well as between AD and NC.

RESULTS

Adopting mSCDDL, the classification accuracy achieved 98.5% for AD vs. NC and 82.8% for MCI vs. NC, which were superior to or comparable with the results of some other state-of-the-art approaches as reported in recent multi-modality publications.

CONCLUSIONS

The mSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.