scholarly journals Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 239 ◽  
Author(s):  
Jie Sun ◽  
Bin Wang ◽  
Yan Niu ◽  
Yuan Tan ◽  
Chanjuan Fan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Time Series ◽  
Single Channel ◽  
Complexity Analysis ◽  
Future Research ◽  
Brain Signals ◽  
Current Review ◽  
Potential Biomarker ◽  
Signal Complexity

Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

scholarly journals A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mahshad Ouchani ◽  
Shahriar Gharibzadeh ◽  
Mahdieh Jamshidi ◽  
Morteza Amini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Future Research ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Classification Methods ◽  
Clinical Markers ◽  
Eeg Signals ◽  
Key Steps

This study will concentrate on recent research on EEG signals for Alzheimer’s diagnosis, identifying and comparing key steps of EEG-based Alzheimer’s disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article’s purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer’s disease, extreme Alzheimer’s disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer’s disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer’s disease science.

Neuropeptides in Alzheimer’s Disease: An Update

2019 ◽  
Vol 16 (6) ◽  
pp. 544-558 ◽  
Author(s):  
Carla Petrella ◽  
Maria Grazia Di Certo ◽  
Christian Barbato ◽  
Francesca Gabanella ◽  
Massimo Ralli ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Models ◽  
Potential Role ◽  
Brain Distribution ◽  
Brain Areas ◽  
Small Proteins ◽  
Current Review ◽  
The Central Nervous System ◽  
Available Information

Neuropeptides are small proteins broadly expressed throughout the central nervous system, which act as neurotransmitters, neuromodulators and neuroregulators. Growing evidence has demonstrated the involvement of many neuropeptides in both neurophysiological functions and neuropathological conditions, among which is Alzheimer’s disease (AD). The role exerted by neuropeptides in AD is endorsed by the evidence that they are mainly neuroprotective and widely distributed in brain areas responsible for learning and memory processes. Confirming this point, it has been demonstrated that numerous neuropeptide-containing neurons are pathologically altered in brain areas of both AD patients and AD animal models. Furthermore, the levels of various neuropeptides have been found altered in both Cerebrospinal Fluid (CSF) and blood of AD patients, getting insights into their potential role in the pathophysiology of AD and offering the possibility to identify novel additional biomarkers for this pathology. We summarized the available information about brain distribution, neuroprotective and cognitive functions of some neuropeptides involved in AD. The main focus of the current review was directed towards the description of clinical data reporting alterations in neuropeptides content in both AD patients and AD pre-clinical animal models. In particular, we explored the involvement in the AD of Thyrotropin-Releasing Hormone (TRH), Cocaine- and Amphetamine-Regulated Transcript (CART), Cholecystokinin (CCK), bradykinin and chromogranin/secretogranin family, discussing their potential role as a biomarker or therapeutic target, leaving the dissertation of other neuropeptides to previous reviews.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Alzheimer’s Disease Pathogenesis: Role of Autophagy and Mitophagy Focusing in Microglia

2021 ◽  
Vol 22 (7) ◽  
pp. 3330
Author(s):  
Mehdi Eshraghi ◽  
Aida Adlimoghaddam ◽  
Amir Mahmoodzadeh ◽  
Farzaneh Sharifzad ◽  
Hamed Yasavoli-Sharahi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Therapeutic Target ◽  
Neurological Disorder ◽  
Inflammatory Cells ◽  
Disease Pathogenesis ◽  
Current Review ◽  
The Brain ◽  
Comprehensive Discussion

Alzheimer’s disease (AD) is a debilitating neurological disorder, and currently, there is no cure for it. Several pathologic alterations have been described in the brain of AD patients, but the ultimate causative mechanisms of AD are still elusive. The classic hallmarks of AD, including am-yloid plaques (Aβ) and tau tangles (tau), are the most studied features of AD. Unfortunately, all the efforts targeting these pathologies have failed to show the desired efficacy in AD patients so far. Neuroinflammation and impaired autophagy are two other main known pathologies in AD. It has been reported that these pathologies exist in AD brain long before the emergence of any clinical manifestation of AD. Microglia are the main inflammatory cells in the brain and are considered by many researchers as the next hope for finding a viable therapeutic target in AD. Interestingly, it appears that the autophagy and mitophagy are also changed in these cells in AD. Inside the cells, autophagy and inflammation interact in a bidirectional manner. In the current review, we briefly discussed an overview on autophagy and mitophagy in AD and then provided a comprehensive discussion on the role of these pathways in microglia and their involvement in AD pathogenesis.

MicroRNA-4422-5p as a Negative Regulator of Amyloidogenic Secretases: A Potential Biomarker for Alzheimer’s disease

Neuroscience ◽  
2021 ◽  
Author(s):  
Seyedeh Nazanin Hajjari ◽  
Saeed Sadigh-Eteghad ◽  
Dariush Shanehbandi ◽  
Shahram Teimourian ◽  
Ali Shahbazi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Negative Regulator ◽  
Potential Biomarker

scholarly journals Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer’s disease

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Aidan Kenny ◽  
Eva M. Jiménez-Mateos ◽  
María Ascensión Zea-Sevilla ◽  
Alberto Rábano ◽  
Pablo Gili-Manzanaro ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Unmet Need ◽  
Cost Effective ◽  
Specific Protein ◽  
Tear Fluid ◽  
Initiation Factor ◽  
Non Invasive ◽  
A Genome ◽  
Potential Biomarker

Abstract Alzheimer’s disease (AD) is characterized by a progressive loss of neurons and cognitive functions. Therefore, early diagnosis of AD is critical. The development of practical and non-invasive diagnostic tests for AD remains, however, an unmet need. In the present proof-of-concept study we investigated tear fluid as a novel source of disease-specific protein and microRNA-based biomarkers for AD development using samples from patients with mild cognitive impairment (MCI) and AD. Tear protein content was evaluated via liquid chromatography-mass spectrometry and microRNA content was profiled using a genome-wide high-throughput PCR-based platform. These complementary approaches identified enrichment of specific proteins and microRNAs in tear fluid of AD patients. In particular, we identified elongation initiation factor 4E (eIF4E) as a unique protein present only in AD samples. Total microRNA abundance was found to be higher in tears from AD patients. Among individual microRNAs, microRNA-200b-5p was identified as a potential biomarker for AD with elevated levels present in AD tear fluid samples compared to controls. Our study suggests that tears may be a useful novel source of biomarkers for AD and that the identification and verification of biomarkers within tears may allow for the development of a non-invasive and cost-effective diagnostic test for AD.

scholarly journals Exploratory human PET study of the effectiveness of 11C-ketoprofen methyl ester, a potential biomarker of neuroinflammatory processes in Alzheimer's disease

2016 ◽  
Vol 43 (7) ◽  
pp. 438-444 ◽  
Author(s):  
Akihito Ohnishi ◽  
Michio Senda ◽  
Tomohiko Yamane ◽  
Tomoko Mikami ◽  
Hiroyuki Nishida ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Methyl Ester ◽  
Potential Biomarker

scholarly journals Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

2016 ◽  
Vol 113 (42) ◽  
pp. E6535-E6544 ◽  
Author(s):  
Xiuming Zhang ◽  
Elizabeth C. Mormino ◽  
Nanbo Sun ◽  
Reisa A. Sperling ◽  
Mert R. Sabuncu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Executive Function ◽  
Bayesian Model ◽  
Late Onset ◽  
Temporal Cortex ◽  
Cortical Atrophy ◽  
Future Research ◽  
Dementia Patients ◽  
With Memory

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Anticipatory and Reactive Grip Force Control in Patients with Alzheimer’s Disease: A Pilot Study

2021 ◽  
pp. 1-15
Author(s):  
Anna Gabriel ◽  
Carolin T. Lehner ◽  
Chiara Höhler ◽  
Thomas Schneider ◽  
Tessa P.T. Pfeiffer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pilot Study ◽  
Force Control ◽  
Grip Force ◽  
Object Manipulation ◽  
Step Test ◽  
Grip Force Control ◽  
Potential Biomarker ◽  
Neurological Conditions

Background: Alzheimer’s disease (AD) affects several cognitive functions and causes altered motor function. Fine motor deficits during object manipulation are evident in other neurological conditions, but have not been assessed in dementia patients yet. Objective: Investigate reactive and anticipatory grip force control in response to unexpected and expected load force perturbation in AD. Methods: Reactive and anticipatory grip force was investigated using a grip-device with force sensors. In this pilot study, fifteen AD patients and fourteen healthy controls performed a catching task. They held the device with one hand while a sandbag was dropped into an attached receptacle either by the experimenter or by the participant. Results: In contrast to studies of other neurological conditions, the majority of AD patients exerted lower static grip force levels than controls. Interestingly, patients who were slow in the Luria’s three-step test produced normal grip forces. The timing and magnitude of reactive grip force control were largely preserved in patients. In contrast, timing and extent of anticipatory grip forces were impaired in patients, although anticipatory control was generally preserved. These deficits were correlated with decreasing Mini-Mental State Examination scores. Apraxia scores, assessed by pantomime of tool-use, did not correlate with performance in the catching task. Conclusion: We interpreted the decreased grip force in AD in the context of loss of strength and lethargy, typical for patients with AD. The lower static grip force during object manipulation may emerge as a potential biomarker for early stages of AD, but more studies with larger sample sizes are necessary.

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