Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals

Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features.

Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment

Background

Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.

Method

We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.

Results

The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.

Conclusions

This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.

Use of electroencephalogram, gait, and their combined signals for classifying cognitive impairment and normal cognition

Background

Early identification of people at risk for cognitive decline is an important step in delaying the occurrence of cognitive impairment. This study investigated whether multimodal signals assessed using electroencephalogram (EEG) and gait kinematic parameters could be used to identify individuals at risk of cognitive impairment.

Methods

The survey was conducted at the Veterans Medical Research Institute in the Veterans Health Service Medical Center. A total of 220 individuals volunteered for this study and provided informed consent at enrollment. A cap-type wireless EEG device was used for EEG recording, with a linked-ear references based on a standard international 10/20 system. Three-dimensional motion capture equipment was used to collect kinematic gait parameters. Mild cognitive impairment (MCI) was evaluated by Seoul Neuropsychological Screening Battery-Core (SNSB-C).

Results

The mean age of the study participants was 73.5 years, and 54.7% were male. We found that specific EEG and gait parameters were significantly associated with cognitive status. Individuals with decreases in high-frequency EEG activity in high beta (25-30 Hz) and gamma (30-40 Hz) bands increased the odds ratio of MCI. There was an association between the pelvic obliquity angle and cognitive status, assessed by MCI or SNSB-C scores. Results from the ROC analysis revealed that multimodal signals combining high beta or gamma and pelvic obliquity improved the ability to discriminate MCI individuals from normal controls.

Conclusion

These findings support prior work on the association between cognitive status and EEG or gait, and offer new insights into the applicability of multimodal signals to distinguish cognitive impairment.

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis

In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

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.

A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms

A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.

A Novel Detection Tool for Mild Cognitive Impairment Patients Based on Eye Movement and Electroencephalogram

BACKGROUND

Detecting subtle changes in visual attention from electroencephalography (EEG) and the perspective of eye movement in mild cognitive impairment (MCI) patients can be of great significance in screening early Alzheimer's disease (AD) in a large population at primary care.

OBJECTIVE

We proposed an automatic, non-invasive, and quick MCI detection approach based on multimodal physiological signals for clinical decision-marking.

METHODS

The proposed model recruited 152 patients with MCI and 184 healthy elderly controls (HC) who underwent EEG and eye movement signal recording under a visual stimuli task, as well as other neuropsychological assessments. Forty features were extracted from EEG and eye movement signals by linear and nonlinear analysis. The features related to MCI were selected by logistic regression analysis. To evaluate the efficacy of this MCI detection approach, we applied the same procedures to achieve the Clinical model, EEG model, Eye movement model, EEG+ Clinical model, Eye movement+ Clinical model, and Combined model, and compared the classification accuracy between the MCI and HC groups with the above six models.

RESULTS

After the penalization of logistic regression analysis, five features from EEG and eye movement features exhibited significant differences (p < 0.05). In the classification experiment, the combined model resulted in the best accuracy. The average accuracy for the Clinical/EEG/Eye movement/EEG+ Clinical/Eye movement+ Clinical/Combined model was 68.69%, 61.79%, 73.13%, 69.46%, 75.61%, and 81.51%, respectively.

CONCLUSION

These results suggest that the proposed MCI detection tool has the potential to screen MCI patients from HCs and may be a powerful tool for personalized precision MCI screening in the large-scale population under primary care condition.

A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease

(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.

Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection.

Spatiotemporal Oscillatory Patterns During Working Memory Maintenance in Mild Cognitive Impairment and Subjective Cognitive Decline

Working memory (WM) is a crucial cognitive process and its disruption is among the earliest symptoms of Alzheimer’s disease. While alterations of the neuronal processes underlying WM have been evidenced in mild cognitive impairment (MCI), scarce literature is available in subjective cognitive decline (SCD). We used magnetoencephalography during a WM task performed by MCI [Formula: see text], SCD [Formula: see text] and healthy elders [Formula: see text] to examine group differences during the maintenance period (0–4000[Formula: see text]ms). Data were analyzed using time–frequency analysis and significant oscillatory differences were localized at the source level. Our results indicated significant differences between groups, mainly during the early maintenance (250–1250[Formula: see text]ms) in the theta, alpha and beta bands and in the late maintenance (2750–3750[Formula: see text]ms) in the theta band. MCI showed lower local synchronization in fronto-temporal cortical regions in the early theta–alpha window relative to controls [Formula: see text] and SCD [Formula: see text], and in the late theta window relative to controls [Formula: see text] and SCD [Formula: see text]. Early theta–alpha power was significantly correlated with memory scores [Formula: see text] and late theta power was correlated with task performance [Formula: see text] and functional activity scores [Formula: see text]. In the early beta window, MCI showed reduced power in temporo-posterior regions relative to controls [Formula: see text] and SCD [Formula: see text]. Our results may suggest that these alterations would reflect that memory-related networks are damaged.

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer’s Disease

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

This study addresses the problem of Alzheimer’s disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer’s disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects

In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (p < 0.05), i.e., a reduced overall coupling strength, specifically in delta and θ bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, θ, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.

Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment

Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Graph Theory and Brain Connectivity in Alzheimer's Disease

This article presents a review of recent advances in neuroscience research in the specific area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the application of graph theory. The review will begin with a brief overview of connectivity and graph theory. Then resent advances in connectivity as a biomarker for Alzheimer's disease will be presented and analyzed.