Brain health in diverse settings: How age, demographics and cognition shape brain function.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1,298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Daily Pain Severity but Not Vertebral Fractures Is Associated With Lower Physical Activity in Postmenopausal Women With Back Pain.

Back pain lifetime incidence is 60%-70%, while 12%-20% of older women have vertebral fractures (VFs), often with back pain. We aimed to provide objective evidence, currently lacking, regarding whether back pain and VFs affect physical activity (PA). We recruited 69 women with recent back pain (age 74.5 ± 5.4 years). Low- (0.5 < g < 1.0), medium- (1.0 ≤ g < 1.5), and high-impact (g ≥ 1.5) PA and walking time were measured (100 Hz for 7 days, hip-worn accelerometer). Linear mixed-effects models assessed associations between self-reported pain and PA, and group differences (VFs from spine radiographs/no-VF) in PA. Higher daily pain was associated with reduced low (ß = -0.12, 95% confidence interval, [-0.22, -0.03], p = .013) and medium-impact PA (ß = -0.11, 95% confidence interval, [-0.21, -0.01], p = .041), but not high-impact PA or walking time (p > .11). VFs were not associated with PA (all p > .2). Higher daily pain levels but not VFs were associated with reduced low- and medium-impact PA, which could increase sarcopenia and falls risk in older women with back pain.

Assessment and Comparison of Nonlinear Measures in Resting-State Magnetoencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

BACKGROUND: Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD). OBJECTIVE: To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG. METHODS: We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.06±6.95 years), 18 MCI (mean age = 74.89±5.57 years), and 26 HC subjects (mean age = 71.77±6.38 years) in different brain regions and also evaluated the effect of the length of MEG epochs on their performance. We also studied the correlation between these measures and cognitive performance based on the Mini-Mental State Examination (MMSE). RESULTS: The results obtained by LZC, zero-crossing rate (ZCR), FD, and dispersion entropy (DispEn) measures showed significant differences among the three groups. There was no significant difference between HC and MCI. The highest Hedge's g effect sizes for HC versus AD and MCI versus AD were respectively obtained by Higuchi's FD (HFD) and fuzzy DispEn (FuzDispEn) in the whole brain and was most prominent in left lateral. The results obtained by HFD and FuzDispEn had a significant correlation with the MMSE scores. DispEn-based techniques, LZC, and ZCR, compared with HFD, were less sensitive to epoch length in distinguishing HC form AD. CONCLUSIONS: FuzDispEn was the most consistent technique to distinguish MEG dynamical patterns in AD compared with HC and MCI.

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women.

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

Brain signal complexity in adults with Down syndrome: Potential application in the detection of mild cognitive impairment.

Background: Down syndrome (DS) is considered the most frequent cause of early-onset Alzheimer's disease (AD), and the typical pathophysiological signs are present in almost all individuals with DS by the age of 40. Despite of this evidence, the investigation on the pre-dementia stages in DS is scarce. In the present study we analyzed the complexity of brain oscillatory patterns and neuropsychological performance for the characterization of mild cognitive impairment (MCI) in DS. Materials and methods: Lempel-Ziv complexity (LZC) values from resting-state magnetoencephalography recordings and the neuropsychological performance in 28 patients with DS [control DS group (CN-DS) (n = 14), MCI group (MCI-DS) (n = 14)] and 14 individuals with typical neurodevelopment (CN-no-DS) were analyzed. Results: Lempel-Ziv complexity was lowest in the frontal region within the MCI-DS group, while the CN-DS group showed reduced values in parietal areas when compared with the CN-no-DS group. Also, the CN-no-DS group exhibited the expected pattern of significant increase of LZC as a function of age, while MCI-DS cases showed a decrease. The combination of reduced LZC values and a divergent trajectory of complexity evolution with age, allowed the discrimination of CN-DS vs. MCI-DS patients with a 92.9% of sensitivity and 85.7% of specificity. Finally, a pattern of mnestic and praxic impairment was significantly associated in MCI-DS cases with the significant reduction of LZC values in frontal and parietal regions (p = 0.01). Conclusion: Brain signal complexity measured with LZC is reduced in DS and its development with age is also disrupted. The combination of both features might assist in the detection of MCI within this population.

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance- The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100.

Electroencephalogram Connectivity for the Diagnosis of Psychogenic Non-epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) are attacks that resemble epilepsy but are not associated with epileptic brain activity and are regularly misdiagnosed. The current gold standard method of diagnosis is expensive and complex. Electroencephalogram (EEG) analysis with machine learning could improve this. A k-nearest neighbours (kNN) and support vector machine (SVM) were used to classify EEG connectivity measures from 48 patients with PNES and 29 patients with epilepsy. The synchronisation method - correlation or coherence - and the binarisation threshold were defined through experimentation. Ten network parameters were extracted from the synchronisation matrix. The broad, delta, theta, alpha, beta, gamma, and combined 'all' frequency bands were compared along with three feature selection methods: the full feature set (no selection), light gradient boosting machine (LGBM) and k-Best. Coherence was the highest performing synchronisation method and 0.6 was the best coherence threshold. The highest balanced accuracy was 89.74%, produced by combining all six frequency bands and selecting features with LGBM, classified by the SVM. This method returned a comparatively high accuracy but at a high computation cost. Future research should focus on identifying specific frequency bands and network parameters to reduce this cost. Clinical relevance - This study found that EEG connectivity and machine learning methods can be used to differentiate PNES from epilepsy using interictal recordings to a high accuracy. Thus, this method could be an effective tool in assisting clinicians in PNES diagnosis without a video- EEG recording of a habitual seizure.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

Objective.This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals.Approach.The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size.Main results.The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced.Significance.These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study.

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.

Time course of cortical response complexity during extended wakefulness and its differential association with vigilance in young and older individuals.

Characterization of the complexity of electroencephalogram (EEG) responses has provided important insights in cognitive function as well as in the brain bases of consciousness and vigilance. Whether brain response complexity changes during prolonged wakefulness and sleep deprivation -when vigilance level considerably varies- is not fully elucidated yet. In the present study, we repeatedly assessed EEG responses to transcranial magnetic stimulation (TMS) over 34 h of sleep deprivation under constant routine conditions in healthy younger (N = 13; 5 women; 18-30 y) and older (N = 12; 6 women; 50-70 y) individuals, while they were performing a vigilance task. Response complexity was computed both at the global (all scalp sensors) and local (sensors surrounding TMS hotspot) levels using the Lempel-Ziv algorithm. Response complexity was significantly higher in the older compared to the young volunteers over the entire protocol. Global complexity response significantly changed with time spent awake, with an increasing trend from the beginning to the middle of the biological night, followed by a decreasing trend from the middle of the biological night to the following afternoon. An unexpected different link between vigilance performance and brain response complexity was detected across age groups: higher response complexity was associated with lower performance in the older group, particularly in the morning sessions. These findings show that cortical activity complexity changes with vigilance variation, as experienced during sleep deprivation and circadian misalignment, in two age groups, with no evident time course difference across age-groups. Aside from classical linear EEG analyses, computation of Lempel-Ziv complexity provides additional insights on the neurophysiology of the processes associated with vigilance and their modifications throughout ageing.

Complexity changes in preclinical Alzheimer's disease: An MEG study of subjective cognitive decline and mild cognitive impairment.

OBJECTIVE: To analyse magnetoencephalogram (MEG) signals with Lempel-Ziv Complexity (LZC) to identify the regions of the brain showing changes related to cognitive decline and Alzheimers Disease (AD). METHODS: LZC was used to study MEG signals in the source space from 99 participants (36 male, 63 female, average age: 71.82 ± 4.06) in three groups (33 subjects per group): healthy (control) older adults, older adults with subjective cognitive decline (SCD), and adults with mild cognitive impairment (MCI). Analyses were performed in broadband (2-45 Hz) and in classic narrow bands (theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz), and, gamma (30-45 Hz)). RESULTS: LZC was significantly lower in subjects with MCI than in those with SCD. Moreover, subjects with MCI had significantly lower MEG complexity than controls and SCD subjects in the beta frequency band. Lower complexity was correlated with smaller hippocampal volumes. CONCLUSIONS: Brain complexity - measured with LZC - decreases in MCI patients when compared to SCD and healthy controls. This decrease is associated with a decrease in hippocampal volume, a key feature in AD progression. SIGNIFICANCE: This is the first study to date characterising the changes of brain activity complexity showing the specific spatial pattern of the alterations as well as the morphological correlations throughout preclinical stages of AD.

Sex Differences in the Complexity of Healthy Older Adults' Magnetoencephalograms.

The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2-45 Hz), as well as in narrow bands (i.e., theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz), and gamma (30-45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65-80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods.

Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer's Disease: Is the Method Superior to Sample Entropy?

Alzheimer's disease (AD) is the most prevalent form of dementia in the world, which is characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy, differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG) is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear signal processing methods have shown changes in the EEG due to AD, which is characterised reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn) algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p < 0.01) at electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn, reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the insight into brain dysfunction in AD, providing potentially useful diagnostic information. However, results depend heavily on the input parameters that are used to compute FuzzyEn.

Complexity Changes in Brain Activity in Healthy Ageing: A Permutation Lempel-Ziv Complexity Study of Magnetoencephalograms.

Maturation and ageing, which can be characterised by the dynamic changes in brain morphology, can have an impact on the physiology of the brain. As such, it is possible that these changes can have an impact on the magnetic activity of the brain recorded using magnetoencephalography. In this study changes in the resting state brain (magnetic) activity due to healthy ageing were investigated by estimating the complexity of magnetoencephalogram (MEG) signals. The main aim of this study was to identify if the complexity of background MEG signals changed significantly across the human lifespan for both males and females. A sample of 177 healthy participants (79 males and 98 females aged between 21 and 80 and grouped into 3 categories i.e., early-, mid- and late-adulthood) was used in this investigation. This investigation also extended to evaluating if complexity values remained relatively stable during the 5 min recording. Complexity was estimated using permutation Lempel-Ziv complexity, a recently introduced complexity metric, with a motif length of 5 and a lag of 1. Effects of age and gender were investigated in the MEG channels over 5 brain regions, i.e., anterior, central, left lateral, posterior, and, right lateral, with highest complexity values observed in the signals recorded by the channels over the anterior and central regions of the brain. Results showed that while changes due to age had a significant effect on the complexity of the MEG signals recorded over 5 brain regions, gender did not have a significant effect on complexity values in all age groups investigated. Moreover, although some changes in complexity were observed between the different minutes of recording, due to the small magnitude of the changes it was concluded that practical significance might outweigh statistical significance in this instance. The results from this study can contribute to form a fingerprint of the characteristics of healthy ageing in MEGs that could be useful when investigating changes to the resting state activity due to pathology.

Accounting for the complex hierarchical topology of EEG phase-based functional connectivity in network binarisation.

Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks. We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology.

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals.

OBJECTIVE: We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications. METHODS: We introduce multiscale dispersion entropy (DisEn-MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N2) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. RESULTS: We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE. CONCLUSION: For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. SIGNIFICANCE: MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/1982.

Characterisation of the complexity of intracranial pressure signals measured from idiopathic and secondary normal pressure hydrocephalus patients.

Hydrocephalus is a condition characterised by enlarged cerebral ventricles, which in turn affects intracranial pressure (ICP); however, the mechanisms regulating ICP are not fully understood. A nonlinear signal processing approach was applied to ICP signals measured during infusion studies from patients with two forms of hydrocephalus, in a bid to compare the differences. This is the first study of its kind. The two forms of hydrocephalus were idiopathic normal pressure hydrocephalus (iNPH) and secondary normal pressure hydrocephalus (SH). Following infusion tests, the Lempel-Ziv (LZ) complexity was calculated from the iNPH and SH ICP signals. The LZ complexity values were averaged for the baseline, infusion, plateau and recovery stages of the tests. It was found that as the ICP increased from basal levels, the LZ complexities decreased, reaching their lowest during the plateau stage. However, the complexities computed from the SH ICP signals decreased to a lesser extent when compared with the iNPH ICP signals. Furthermore, statistically significant differences were found between the plateau and recovery stage complexities when comparing the iNPH and SH results (p = 0.05). This Letter suggests that advanced signal processing of ICP signals with LZ complexity can help characterise different types of hydrocephalus in more detail.

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease.

Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (Cloc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of Cloc and K. Although not significant when considered alone, the skewness of Cloc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.