|Year : 2022 | Volume
| Issue : 2 | Page : 105-114
Current Understanding of Alzheimer's Disease on Biomarkers, Magnetic Resonance Imaging Modalities, and Diagnosis, Prevention, and Treatment Approach
Chintan Revashnakar Varnagar1, Hitesh Shah2
1 Department of Computer Engineering, Government Engineering College, Rajkot; Department of Electronics and Communication, G. H. Patel College of Engineering and Technology, Anand, Gujarat, India
2 Department of Electronics and Communication, G. H. Patel College of Engineering and Technology, Anand, Gujarat, India
|Date of Submission||13-Apr-2022|
|Date of Decision||17-May-2022|
|Date of Acceptance||28-May-2022|
|Date of Web Publication||15-Jun-2022|
Prof. Chintan Revashnakar Varnagar
Computer Engineering Department, Government Engineering College Rajkot, Gujarat
Source of Support: None, Conflict of Interest: None
Alzheimer's disease (AD), a neurodegenerative disorder in which Activities of Daily Living (ADL) are hampered and steep decline in gross cognitive function is observed, in the early stage of life. AD is characterized by progressive loss and damage to the structure and/or function of neuronal cell, resulting in death of neurons, however, etiology and pathophysiology of the disease are not known in its entirety. The purpose of this article is to understand, analyze, evaluate, and synthesize information in order to provide conclusive, decisive, and actionable information on (1) microscopic features and known etiology, pathophysiology, genes involved, and protein misfolding observed in AD; (2) selection and use of prominent magnetic resonance imaging (MRI) modalities and allied biomarkers to detect and diagnose AD by application of AI techniques; (3) role of preventive intervention (diet and lifestyle) in reducing risk of developing AD, to act on modifiable and correctable risk factors of AD, to manage AD and treatment strategies of AD through the use of pharmacology and therapeutic drugs. Deep learning-based techniques have proven capabilities to learn features automatically to discriminate class effectively. We proposed a method that incorporates features (biomarkers) derived from the structural MRI modality, clinical assessment tools, and personal and demographic quantifiable parameters into a convolution neural network. and further boosted the ensemble-based learning algorithm to improve prediction accuracy. An ensemble-based learning algorithm is then used to integrate weights to improve prediction accuracy.
Keywords: Alzheimer's diseases, deep learning, diagnosis of Alzheimer's diseases, magnetic resonance imaging biomarkers, magnetic resonance imaging modalities, prevention and treatment
|How to cite this article:|
Varnagar CR, Shah H. Current Understanding of Alzheimer's Disease on Biomarkers, Magnetic Resonance Imaging Modalities, and Diagnosis, Prevention, and Treatment Approach. J Prev Diagn Treat Strategies Med 2022;1:105-14
|How to cite this URL:|
Varnagar CR, Shah H. Current Understanding of Alzheimer's Disease on Biomarkers, Magnetic Resonance Imaging Modalities, and Diagnosis, Prevention, and Treatment Approach. J Prev Diagn Treat Strategies Med [serial online] 2022 [cited 2022 Jun 26];1:105-14. Available from: http://www.jpdtsm.com/text.asp?2022/1/2/105/347546
| Introduction|| |
Alzheimer's disease (AD) is a neurodegenerative disorder in which one gradually or rapidly ends up losing one's capabilities to accomplish Activities of Daily Living (ADL), primarily (but not exclusively) in the cognitive domain or cognitive functions such as short term and long term memory, language, expression, judgment and problem solving, active attention, planning, time and space orientation and so on. This happens due to progressive and irrevocable neuronal damage gradually leading to abnormal death of neuronal cell. This disease is allied with progressive accumulation of extracellular neuritic abnormal proteins beta-amyloid protein (Aβ) and intraneuronal aggregates of hyperphosphorylated tau protein, known as neurofibrillary tangles (NFTs) in the brain, which leads to progressive synaptic, neuronal, and axonal damage, leading to macroscopic atrophy. Aside from dystrophic neuritis, neuropil threads associated with astrogliosis (astrocytosis) and cerebral amyloid angiopathy are commonly found in conjunction. This neurobiological progression of disruptive change begins years previously, progressively eroding the “brain reserve,” and symptomatic deterioration or loss in performing ADL is recognised in patients only when the clinical threshold is exceeded.
Dementia is considered to be the end result or final stage of this devastating erosion of neuronal cell within brain. In the year 2011, an international workgroup was formed under the aegis of “National institute on Aging-Alzheimer's Association” to revise the criteria as laid down by the same agency in 1984. This group reviewed, revised, and expanded its understanding of biomarkers, clinical understanding, pathology, pathophysiology, and neuropsychological processes, diagnosis methods, genetics of AD, distinguishing AD from other dementia types such as Lewy bodies, vascular dementia, behaviour variant frontotemporal dementia, primary progressive aphasia, and so on.
The revision is flexible enough so that it can be used by broad range of people, one with limited availability of resources such as advanced imaging modalities and pathological, pathophysiological assessments and tools and another set of people with relatively easy access of these resources, i.e., researchers in advanced medical facilities.
Rate of decline in performing activities of daily living (ADLs) is much more rapid than that observed in normal aging for AD. The symptoms get worse over months to years, not sudden over days to months, i.e., decline observed in ADL is slower not spontaneous.,
| Magnetic Resonance Imaging Modalities and Associated Biomarkers|| |
Researchers have used various in vivo biomarkers such as hippocampus and entorhinal cortex volumes, cortical thickness, defamation and voxel-based morphometry, and structural brain atrophy derived from structural magnetic resonance imaging (MRI). [Table 1] outlines different MRI modalities and their capacity to access physiology, pathology, pathophysiology, neuropathology, metabolic, and morphological alterations.
|Table 1: Summary of various magnetic resonance imaging modalities used by health care professionals in the clinical setting, its usefulness in accessing, imaging principle it uses and biomarkers derived from it|
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These imaging techniques would benefit doctors in disease detection, diagnosis, knowing and quantifying spread and severity, deciding, monitoring, accessing, and measuring medication response, and assisting in the novel drug discovery process.
To detect pathophysiological disruptive changes in AD, such as deposition of amyloid-β peptides into plaques, tau protein accumulation, functional changes with neuroreceptors, metabolic abnormalities, neuroinflammation - in vivo and noninvasively, to detect such changes as early as possible and diagnose disease at an early stage Positron Emission Tomography (PET) scanning is useful.
Biomarkers used in PET studies in the context of Alzheimer's disease (AD) for (1) amyloid depositions, (2) neuroinflammation, and (3) neurodegeneration are outlined, and a full comparison of the pros and disadvantages of different amyloid imaging agents is provided, taking into account various criteria and their clinical and research applications. However, the use of PET imaging in typical clinical settings is limited due to many factors: (1) its cost is very high, (2) time spent on MR machine is relatively higher as compared to scan of other modalities, and (3) injecting of radioactive material in the body and associated threat of ionizing radiation. PET studies are highly reliable in longitudinal examination and give the best accuracy in detecting such disruptive changes at the earliest.
| Nonmagnetic Resonance Imaging Biomarkers|| |
Examining Cerebrospinal Fluid (CSF) for Aβ42, hyperphosphorylated tau peptide (p tau), and total tau protein concentration is another less expensive approach and effective biomarker compared to PET imaging. It has demonstrated to be an effective biomarker for differentiating patients with MCI who are likely to proceed to AD. Although this technique is (1) more invasive and risky in nature, (2) inconvenient – as it requires lumbar puncture, (3) has slightly less diagnostic accuracy (85%–90%), and (4) takes longer times for report (over a couple of weeks).
CSF analysis performs equally well with appropriate and established processes and in practice, has the ability to include additional biomarkers, and so increases differential diagnosis capabilities. CSF analysis requires less advanced instruments, which makes it more accessible and available. Hence, in conclusion, best test among PET and CSF analysis for patients depends on multiple factors such as cost, time, and availability in vicinity and doctor/patient preference.
Less invasive as compared to lumbar puncture, commonly accepted and comfortable technique is serum analysis, which aims to detect or quantify the amount of certain protein – beta-secretase 1 (BACE1) encoded by BACE1 gene, either an increased expression of the BACE1 gene or an abnormal function of β-secretase is one of the earliest processes of the pathogenesis of AD. By quantification of plasmaBACE1 activity it was possible to discriminate and distinguish among healthy controls, Mild Cognitive Impairment (MCI), and AD dementia caused by (AD) with sensitivities and specificities of 84 percent and 88 percent, respectively. There are many other serum-based biomarkers which are based on detection of metabolic changes, immune system, and transcriptome which alone or in addition to BACE1 can be used.
Researchers have explored possibility to use serum micro-RNA (miRNA) profile as a biomarker for AD and concluded that value of four different miRNA (miR-31, miR-93, miR-143, and miR-146a) was decreased marginally in AD patients as compared to controls and further value of miR-93 and miR-146a was considerably elevated in MCI as compared to controls. This panel of four serum miRNA proves to be novel and potential biomarkers for diagnosis of AD.
Fifteen different CSF and blood-based biomarkers were selected and evaluated systematically, with an objective of identifying the most prominent ones among them, and concluded that due to consistency of T-tau, P-tau, Aβ42, and NFL in CSF, they should be used in clinical practice. Hence, in general, there are many CSF and serum-based novel biomarkers under consideration and further research and study is going on.
| Current Understanding of Alzheimer's Disease|| |
The neuropathological changes, macroscopic and microscopic features, its composition, characteristics, topographical distribution, and clinicopathological correlations for the neuropathological hallmarks of Alzheimer's disease, particularly regarding an amyloid plaque, cerebral amyloid angiopathy, NFTs, and glial responses have been thoroughly observed, discussed and outlined.
Due to multifaceted, rapid, and progressive advancements with IT technology and its application, advancements in neuroimaging methods and protocols, availability of computing power and resources, fulfillment and strict adherence in storing (protocol development and adherence to the standards), retrieving and transmission of data, effective dissemination of information and knowledge derived, and advancements in novel drug discovery, there has been unprecedented availability and access of various treatments to patients. The following subsections aim to review available treatment options specific to AD, which is summarized in [Figure 1].
|Figure 1: Treatment and management strategies for Alzheimer's disease (1) by reducing or slowing degenerative damage, (2) By increasing cognitive reserve, (3) By medicinal intervention|
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| Prevention and Treatment Strategies|| |
The efficacy, effectiveness, and results of drugs in each AD patient vary significantly due to a variety of factors in general. As a result, most practitioners recommend varying levels of exercise depending on the current state of the patient's health, and it is sometimes considered as part of the treatment throughout preclinical AD, later stages of AD, in prevention, delaying the onset of disease, and limiting the progression of disruptive changes in AD pathology. Exercise has been shown, to improve brain blood flow, resulting in good vascular health, improving brain and whole body metabolism, increases hippocampus volume and promotes neurogenesis in AD patients.,
Exercise promotes neurogenesis through increase in exercise-induced metabolic factors and muscle-derived myokines, which stimulates the production of family of proteins that induce the survival, development, and function of neurons known as “Neurotrophins;” it signals neuronal cell to survive, differentiate, and grow. Exercise boosts anti-inflammatory effects and improves the redox condition of the brain, lowering amyloid-β accumulation, a pathophysiological hallmark of AD. Recently in 2020, they reviewed existing knowledge on benefits of exercise in AD and explained potential biological mechanisms underlying its benefits of it.
Role of diet
Strategies and treatments for the prevention of AD through nonpharmacological interventions include lifestyle modification approaches and interventions in addition to exercise. Approaches found to be effective are (1) healthy diet patterns and habits (2) restricting and controlling high-calorie food consumption (3) mental challenges and activities such as (i) learning or playing musical instruments (ii) solving puzzles (iii) learning new language etc. (4) socialization in any form (5) to reduce or quit smoking etc.
Researchers have evaluated a hypothesis whether a low carbohydrate, high-fat ketogenic diet (KD) may reduce the damage associated with typical AD pathology and can be seen as an effective treatment and prevention for AD. However, this KD in the elderly may lead to a reduced appetite, may introduce gastrointestinal side effects, may reduce food consumption and end up with further decline in the nutrition absorbed from diet, and warrants further research to decide suitability of KD as therapy in treatment of AD.
Healthy dietary patterns that include a high amount of plant-based and whole (unprocessed) foods like soy proteins, nuts, legumes, probiotics, and omega-3 polyunsaturated fatty acids while reducing or eliminating saturated fat, trans fat, dairy products, and refined sugar have been shown to reduce the risk of neurocognitive impairments, slows the rate of cognitive decline, and eventually reduce the probability of the onset of AD significantly. Researchers reviewed preclinical and clinical evidence regarding the role of nutrition and some diet protocols or dietary patterns such as Dietary Approaches to Stop Hypertension (DASH), KD, Mediterranean Diet, and Mediterranean-DASH diet Intervention for Neurological Delay Diet. They suggests the overall composition of diet for prevention, delaying progression of cognitive impairment and AD.,,
Gut microbiota, gut flora, or microbiome is the name given to collective (group of) microorganisms including bacteria, Archaea, and fungi that live in the digestive tracts, especially in the intestine of humans. Research in this field is currently going through great development. Overall, it is proven that this gut microbiota has a great influence on various activities performed by different organs of the body and so it is applicable equally to brain and contributes toward well-functioning or dysfunctions of brain. Researchers reviewed and analyzed the role of (1) gut microbiota in brain, (2) possible roles of antibiotics in AD pathophysiology, and (3) alteration of gut microbiota for possible therapeutic application in AD.
Researchers evaluated the effect of intermittent fasting in rat model for assessments of cognitive functions and metabolic disturbances and found to be preventive in development of metabolic pathologies and memory loss.
Researchers invented a clinical methodology, which is (1) a multimodal interventional, i.e., takes into account many risk factors simultaneously; (2) based on principles of clinical precision medicine; (3) shown application of unique patient-to-patient recommendation; and (4) follows patients over the period of time for possible augmentation and refinements in intervention to be done or coarse to be decided thereupon.
Nonpharmacological treatment-acting upon modifiable risk factor
Development of AD typically occurs over long preclinical stages, which may last for several decades in some individuals. The US National Institute of Health has associated following with increased risk of cognitive decline.
Elevated value of serum and plasma cholesterol, the condition known as hypercholesterolemia, is found to be a risk factor. This increased level hinders various activities of amyloid precursor protein (APP) such as metabolism, trafficking, secretases, and synthesis of various elements.
Presence of hypertension is found to be accelerating brain atrophy and generation of NFTs. Those who developed AD had a lower baseline systolic blood pressure level, and a steep increase in systolic blood pressure was observed when compared to normal, and it was emphasized that monitoring an increase in blood pressure in midlife and keeping it under control was beneficial.
Homocysteine, is a nonproteinogenic α-amino acid, increased levels of it are known as Hyperhomocysteinemia. There are multiple factors such as age, lifestyle, genetics, deficiency of certain nutrition elements responsible for such increase and pharmacological evidence suggest that it is accountable for lipid accumulation, inflammatory processes.
Increased body weight characterized by body mass index (BMI) >30 is considered to be precursor condition for several chronic disorders including AD. There are studies which link obesity for decrease in cognitive decline and AD risk. Adiposity is a condition in which the total body mass is raised as a result of changes in adipose tissue. The role of regulating molecules released by adipose tissue, including adipokines, leptin, and adiponectin, is summarized. How its abnormal levels disturb the metabolic functions, its underlying biochemistry, and biochemical effects on pathological and pathophysiological processes, which enhances or exaggerates the development of AD.
Type 2 diabetes mellitus
Glucose is the only required source of energy for executing various functions for neuron and any disruptive changes in glucose metabolism for longer duration may lead to compromised neuronal functions as a whole. Obesity and type 2 diabetes mellitus (T2DM) are linked to aging, impact millions of individuals throughout the world and it is a risk factor for AD. T2DM is characterized by hyperglycemia, impairment in insulin production, and insulin resistance. Evidence from experimental studies suggests, therapeutic interventions that focus on correcting this disruptive energy metabolism caused by T2DM is found effective in worsening the cognitive dysfunction., Also (1) the role of insulin (2) how maintaining an optimal level of it ensures good structural and functional aspects at the synapse (3) how an unregulated positive energy balance causes insulin resistance, allied triggering biochemical events, and the outcome of it and how it develops, contributes, or exaggerates the typical AD pathology of plaques and tangles is summarized.
Smoking or nicotine exposure in individuals results in cerebral oxidative stress and is found accountable in promoting AD pathology and increase in risk of AD.
By keeping track and check on these risk factors through adoption of good lifestyle choices, there is a multifold chance of preventing AD.
ADs being irreversible and incurable neurodegenerative condition needs to be diagnosed precisely as early as possible. Therapeutic treatment in general aims to achieve the following objective: (1) to provide symptomatic relief and (2) to reduce the rate of progression or damage by targeting the known etiology of the disease so far. [Figure 2] summarizes various pharmacological treatment approach and strategies.
|Figure 2: Known idiopathy of AD and effect of medicinal treatment. Gross cognitive functions (f) in AD– is on Y-axis. Rate of decline in executing various cognitive functions over time (t) in years (aging) is on X-axis. (1) Normal aging – very slow rate of decline triggered in later years of life. (2) AD – typically characterized by rapid cognitive decline and usually occurs in early stage of life. (3) Current therapeutic approach enhances cognition only and does not change the rate of decline. (4) Anticipated effect of novel drug discovery aiming to enhance cognition and to reduce the rate of decline. AD: Alzheimer's disease|
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Symptomatic treatment (pharmacological treatment) –The therapeutic treatments falling under this group aim to ameliorate symptoms and reduce the rate of progression and damage to the neuronal cell. The majority of therapies relies on cholinesterase inhibitors and a glutamate antagonist and provides only symptomatic relief.
Acetylcholinesterase is a neurotransmitter, which plays a crucial role in memory and learning. These groups of drugs promote higher ACh levels and improve the brain's cholinergic function by inhibiting the enzyme acetylcholinesterase which degrades the neurotransmitter. It is also considered to be safe and does not have any adverse effect.
N-Methyl-D-Aspartate Receptor Antagonist
Glutamate-mediated excitotoxicity is known to result in calcium overload and mitochondrial dysfunction, with increased nitric oxide generation, which can be detrimental to cells, forming high levels of oxidants and eliciting neuronal apoptosis.
Efforts on the etiology and pathology-based treatment (disease-modifying treatment) are currently under clinical trials and among NFTs (composed of p-tau) and senile plaques (Aβ) which should be targeted to slow or halt the neurologic decline, at what time or stage it should be used and for how long this treatment should last is also under debate.
The deposition of Aβ and inefficient removal of peptides may be involved in the pathology. Preventing the formation of Aβ extracellular neuritic (senile) plaques is one of the objectives for disease-modifying treatment in AD. Researchers have used (1) monoclonal antibodies, which target abnormal Aβ and facilitate its removal from the brain; however, this approach failed to improve cognitive score for any category of patients., (2) To reduce Aβ plaque is to produce the Aβ peptide from its precursor, APP.
Preventing aggregates of paired, helically twisted filaments of hyperphosphorylated tau in NFTs is one of the targets of disease-modifying treatment in AD. This type of drugs aims in reducing the devastating effects of p-tau protein accumulation, these have been studied widely, and they are in development at different clinical stages.
Vaccines (AADvac1 and ACI-35) for this purpose have shown good efficacy and safety profile and stimulated positive immune response in animal and human trial.
Different vaccines, AD pathology it targets, and their immunological mechanisms to prevent disease through different modes of vaccine administration i.e. oral, intravenous etc., plant-based vaccines, as well as their effectiveness in reducing Aβ aggregation and restoring the cognitive function of the brain, are discussed, and a multidimensional approach to deal with AD is suggested.
Deposition of amyloid-beta (Aβ) protein in amyloid plaques in the brain is considered to be the hallmark pathology in AD. Researchers have engineered a novel pseudo-β-hairpin structure in the N-terminal region of Aβ and tested N-Truncated Amyloid Peptide AntibodieS; In a mouse model, the “TAPAS” vaccination significantly reduced amyloid plaque formation, stabilized neuronal loss, and helped improve memory deficits.
Patients with AD have clinical symptoms due to global neural network dysfunction at a large. A recent study found that induction of gamma-frequency oscillations by noninvasive 40 Hz photic stimulator results in reduction of Aβ deposition and improves cognitive outcomes in a mouse model.
Anti-Aβ aggregation compounds
In recent decades, researchers have explored therapies targeting prevention of Aβ peptide formation; these drugs achieved stabilization of the Aβ monomers and with some unwanted side effects as well.
A human brain Atlas More Details – approach and application
A “brain atlas” is a volumetric or surface-based description of the geometry of the brain, where in each anatomical coordinate is labeled according to some scheme keeping in the mind objective to be fulfilled. It helps to gather, present, and discover knowledge about human brain further it facilitates standardization or “stereotaxic” 3D coordinate frame for the purpose of data analysis, reporting, finding, and sharing of the results from neuroimaging experiments. Researcher proposed a brain atlas architecture consisting of four functional units, which determines the major component of the atlas, their mutual relationship, and their functional role and provides a base to design and develop a new, extensible, and multipurpose platform which can be used for varied purposes such as prevention, diagnosis, treatment, assessment and monitoring, and predication. [Table 2] provides the summary of various atlas in terms of its approach/working, advantage, or application.
|Table 2: Summary of human brain atlas – its approach, application, and advantage|
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| Future Scope of Work|| |
Deep learning is a machine learning approach in which an algorithm, stimulates and mimics the learning induced by the structure and function of the human brain. It aims to learn important and discriminative features from a very large number of examples, known as training data.
Recent advancements in deep learning due to easy availability of (1) computing resources, (2) massive data set, and (3) open source frameworks in deep learning have showcased many breakthrough in the domain of medical image analysis by providing more robust, consistent, and reliable techniques and tools for the detection, classification, and quantification of patterns from the medical images or volume as a whole. These findings enabled, assisted, and augmented doctors and practitioners in a variety of capacities, including detection, diagnosis, differential diagnosis, automated classification and segmentation, grade/severity labeling, and tracking and monitoring treatment response for the disease in question.
Structural MRI of the brain, a biomarker which is used to access structural changes in the brain, is used to access and quantify brain atrophy of medial temporal structure and hippocampal and whole brain and it has proven its significance in differential diagnosis and as a one of the measurement criteria to evaluate, track, access, and monitor the effectiveness of disease-modifying therapies and helped in changing, revising and alternating treatment of this diseases.
Considering ability to learn and detect micro and small changes with best accuracy through deep learning techniques, we propose to use convolutional neural network (CNN)-based model as depicted in [Figure 3], a deep learning-based approach for the automatic classification of AD.
|Figure 3: Block diagram depicting consideration of clinical assessment score, personal, demographic, SMRI-derived biomarkers fed to CNN ensemble learning approach. CNN: Convolutional neural network, SMRI: Structural magnetic resonance imaging|
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The proposed approach includes integration of various clinical assessment tests like Mini-Mental State Examination; Clinical Dementia Rating; certain personal information such as age, weight, education, and BMI; and demographic information such as race and ethnicity. Second, it uses CNN which learns useful patterns from the dataset itself to classify among three different classes, namely AD, MCI, and healthy/normal controls. Finally, the quantified results are fed to Boosting-based Ensemble Learning algorithm, here the idea is to improve the prediction accuracy by correcting predictions of the previous model.
| Summary|| |
In this paper, we have done explorative to cover breadth of the available knowledge yet concise by carefully eliminating details (depth) to make it precise and short. The article aims to provide all necessary and important information under a common umbrella. We have reviewed the current understanding of the AD from multiple perspectives, and then discussed widely used MRI modalities, most prominent biomarkers derived from it to detect presence of disease. Different “brain atlas” have been critically reviewed, which would be useful, to reduce search space by marking region of interest in the proposed framework. The article enlists and discusses criteria's for preventive care, modifiable risk factors of the disease and method, and mechanism of therapeutic treatment groups. We coined hypothesis backed up by evidence, previous work, and logical reasoning, to use structural MRI modality coupled with CNN and ensemble-based learning algorithm to improve prediction accuracy.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Weller J, Budson A. Current understanding of Alzheimer's disease diagnosis and treatment. F1000Res 2018;7:1-9.
Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med 2011;1:a006189.
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, et al.
The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:263-9.
Hampel H, Bürger K, Teipel SJ, Bokde AL, Zetterberg H, Blennow K. “Core candidate neurochemical and imaging biomarkers of Alzheimer's disease – This paper was presented in part by the 1st
author at the 10th
International Conference of Alzheimer's Disease and Related Disorders (ICAD), Madrid, Spain, July 2006, as an invite.” Alzheimer's Dement 2008;4:38-48.
Schilling LP, Zimmer ER, Shin M, Leuzy A, Pascoal TA, Benedet AL, et al.
Imaging Alzheimer's disease pathophysiology with PET. Dement Neuropsychol 2016;10:79-90.
Palmqvist S, Zetterberg H, Mattsson N, Johansson P, Alzheimer's Disease Neuroimaging Initiative; Minthon L, Blennow K, et al.
Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology 2015;85:1240-9.
Shen Y, Wang H, Sun Q, Yao H, Keegan AP, Mullan M, et al.
Increased plasma beta-secretase 1 may predict conversion to Alzheimer's disease dementia in individuals with mild cognitive impairment. Biol Psychiatry 2018;83:447-55.
Dong H, Li J, Huang L, Chen X, Li D, Wang T, et al.
Serum MicroRNA profiles serve as novel biomarkers for the diagnosis of Alzheimer's disease. Dis Markers 2015;2015:625659.
Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, et al.
CSF and blood biomarkers for the diagnosis of Alzheimer's disease: A systematic review and meta-analysis. Lancet Neurol 2016;15:673-84.
Maliszewska-Cyna E, Lynch M, Oore JJ, Nagy PM, Aubert I. The benefits of exercise and metabolic interventions for the prevention and early treatment of Alzheimer's disease. Curr Alzheimer Res 2017;14:47-60.
Cass SP. Alzheimer's disease and exercise: A literature review. Curr Sports Med Rep 2017;16:19-22.
Valenzuela PL, Castillo-García A, Morales JS, de la Villa P, Hampel H, Emanuele E, et al.
Exercise benefits on Alzheimer's disease: State-of-the-science. Ageing Res Rev 2020;62:101108.
Mendiola-Precoma J, Berumen LC, Padilla K, Garcia-Alcocer G. Therapies for prevention and treatment of Alzheimer's disease. Biomed Res Int 2016;2016:2589276.
Broom GM, Shaw IC, Rucklidge JJ. The ketogenic diet as a potential treatment and prevention strategy for Alzheimer's disease. Nutrition 2019;60:118-21.
Włodarek D. Role of ketogenic diets in neurodegenerative diseases (Alzheimer's Disease and Parkinson's Disease). Nutrients 2019;11:169.
Omar SH. Mediterranean and MIND diets containing olive biophenols reduces the prevalence of Alzheimer's disease. Int J Mol Sci 2019;20:2797.
Pistollato F, Iglesias RC, Ruiz R, Aparicio S, Crespo J, Lopez LD, et al.
Nutritional patterns associated with the maintenance of neurocognitive functions and the risk of dementia and Alzheimer's disease: A focus on human studies. Pharmacol Res 2018;131:32-43.
Cremonini AL, Caffa I, Cea M, Nencioni A, Odetti P, Monacelli F. Nutrients in the prevention of Alzheimer's disease. Oxid Med Cell Longev 2019;2019:9874159.
Angelucci F, Cechova K, Amlerova J, Hort J. Antibiotics, gut microbiota, and Alzheimer's disease. J Neuroinflammation 2019;16:108.
Shin BK, Kang S, Kim DS, Park S. Intermittent fasting protects against the deterioration of cognitive function, energy metabolism and dyslipidemia in Alzheimer's disease-induced estrogen deficient rats. Exp Biol Med (Maywood) 2018;243:334-43.
Isaacson RS, Ganzer CA, Hristov H, Hackett K, Caesar E, Cohen R, et al.
The clinical practice of risk reduction for Alzheimer's disease: A precision medicine approach. Alzheimers Dement 2018;14:1663-73.
Wiesmann M, Capone C, Zerbi V, Mellendijk L, Heerschap A, Claassen JA, et al.
Hypertension impairs cerebral blood flow in a mouse model for Alzheimer's disease. Curr Alzheimer Res 2015;12:914-22.
Joas E, Bäckman K, Gustafson D, Ostling S, Waern M, Guo X, et al.
Blood pressure trajectories from midlife to late life in relation to dementia in women followed for 37 years. Hypertension 2012;59:796-801.
Talbot K, Wang HY, Kazi H, Han LY, Bakshi KP, Stucky A, et al.
Demonstrated brain insulin resistance in Alzheimer's disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest 2012;122:1316-38.
Kapogiannis D, Mattson MP. Disrupted energy metabolism and neuronal circuit dysfunction in cognitive impairment and Alzheimer's disease. Lancet Neurol 2011;10:187-98.
Jonathan Posner JA, Peterson BS. Smoking and increased Alzheimer's disease risk: A review of potential mechanisms. Bone 2008;23:1-7.
Mann DM, Hardy J. Amyloid or tau: The chicken or the egg? Acta Neuropathol 2013;126:609-13.
Doody RS, Thomas RG, Farlow M, Iwatsubo T, Vellas B, Joffe S, et al.
Phase 3 trials of solanezumab for mild-to-moderate Alzheimer's disease. N Engl J Med 2014;370:311-21.
Salloway S, Sperling R, Fox NC, Blennow K, Klunk W, Raskind M, et al.
Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N Engl J Med 2014;370:322-33.
Novak P, Schmidt R, Kontsekova E, Zilka N, Kovacech B, Skrabana R, et al.
Safety and immunogenicity of the tau vaccine AADvac1 in patients with Alzheimer's disease: A randomised, double-blind, placebo-controlled, phase 1 trial. Lancet Neurol 2017;16:123-34.
Ishiura S, Yoshida T. Plant-based vaccines for Alzheimer's disease. Proc Jpn Acad Ser B Phys Biol Sci 2019;95:290-4.
Bakrania P, Hall G, Bouter Y, et al
. Discovery of a novel pseudo β-hairpin structure of N-truncated amyloid-β for use as a vaccine against Alzheimer's disease. Mol Psychiatry 2022;27:840-8.
Awasthi M, Singh S, Pandey VP, Dwivedi UN. Alzheimer's disease: An overview of amyloid beta dependent pathogenesis and its therapeutic implications along with in silico approaches emphasizing the role of natural products. J Neurol Sci 2016;361:256-71.
Nowinski WL. Towards an architecture of a multi-purpose, user-extendable reference human brain atlas. Neuroinformatics 2021. doi: 10.1007/s12021-021-09555-2. Epub ahead of print. PMID: 34825350.
Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J. A probabilistic atlas of the human brain: Theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). Neuroimage 1995;2:89-101.
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al.
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273-89.
Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al.
The human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb Cortex 2016;26:3508-26.
Tian Y, Margulies DS, Breakspear M, Zalesky A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci 2020;23:1421-32.
Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, et al.
A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005;25:1325-35.
Yeh FC, Panesar S, Fernandes D, Meola A, Yoshino M, Fernandez-Miranda JC, et al.
Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 2018;178:57-68.
Brown CA, Johnson NF, Anderson-Mooney AJ, Jicha GA, Shaw LM, Trojanowski JQ, et al.
Development, validation and application of a new fornix template for studies of aging and preclinical Alzheimer's disease. Neuroimage Clin 2017;13:106-15.
Jung KH, Park H, Hwang W. Deep learning for medical image analysis: Applications to computed tomography and magnetic resonance imaging. Hanyang Med Rev 2017;37:61.
Frisoni GB, Fox NC, Jack CR Jr., Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 2010;6:67-77.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]