Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia.

INTRODUCTION: Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS: A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS: The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS: These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

Robust Kramers-Kronig holographic imaging with Hilbert-Huang transform.

Holography based on Kramers-Kronig relations (KKR) is a promising technique due to its high-space-bandwidth product. However, the absence of an iterative process limits its noise robustness, primarily stemming from the lack of a regularization constraint. This Letter reports a generalized framework aimed at enhancing the noise robustness of KKR holography. Our proposal involves employing the Hilbert-Huang transform to connect the real and imaginary parts of an analytic function. The real part is initially processed by bidimensional empirical mode decomposition into a series of intrinsic mode functions (IMFs) and a residual term. They are then selected to remove the noise and bias terms. Finally, the imaginary part can be obtained using the Hilbert transform. In this way, we efficiently suppress the noise in the synthetic complex function, facilitating high-fidelity wavefront reconstruction using ∼20% of the exposure time required by existing methods. Our work is expected to expand the applications of KKR holography, particularly in low phototoxicity biological imaging and other related scenarios.

Critical role of rhythms in prefrontal transcranial magnetic stimulation for depression: A randomized sham-controlled study.

Repetitive transcranial magnetic stimulation (rTMS) is an alternative treatment for depression, but the neural correlates of the treatment are currently inconclusive, which might be a limit of conventional analytical methods. The present study aimed to investigate the neurophysiological evidence and potential biomarkers for rTMS and intermittent theta burst stimulation (iTBS) treatment. A total of 61 treatment-resistant depression patients were randomly assigned to receive prolonged iTBS (piTBS; N = 19), 10 Hz rTMS (N = 20), or sham stimulation (N = 22). Each participant went through a treatment phase with resting state electroencephalography (EEG) recordings before and after the treatment phase. The aftereffects of stimulation showed that theta-alpha amplitude modulation frequency (fam ) was associated with piTBS_Responder, which involves repetitive bursts delivered in the theta frequency range, whereas alpha carrier frequency (fc ) was related to 10 Hz rTMS, which uses alpha rhythmic stimulation. In addition, theta-alpha amplitude modulation frequency was positively correlated with piTBS antidepressant efficacy, whereas the alpha frequency was not associated with the 10 Hz rTMS clinical outcome. The present study showed that TMS stimulation effects might be lasting, with changes of brain oscillations associated with the delivered frequency. Additionally, theta-alpha amplitude modulation frequency may be as a function of the degree of recovery in TRD with piTBS treatment and also a potential EEG-based predictor of antidepressant efficacy of piTBS in the early treatment stage, that is, first 2 weeks.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

The nonsinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single-cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time series using masked empirical mode decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency, and phase) with instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase grid space, makes it possible to compare cycles of different durations and shapes. "Normalized shapes" can then be constructed with high temporal detail while accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks nonsinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average yet exhibit high variability on a cycle-by-cycle basis. We show how principal component analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration, and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of inquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.NEW & NOTEWORTHY We propose a novel analysis approach quantifying nonsinusoidal waveform shape. The approach isolates oscillations with empirical mode decomposition before waveform shape is quantified using phase-aligned instantaneous frequency. This characterizes the full shape profile of individual cycles while accounting for between-cycle differences in duration, amplitude, and timing. We validated in simulations before applying to identify a range of data-driven nonsinusoidal shape motifs in hippocampal theta oscillations.

Human visual steady-state responses to amplitude-modulated flicker: Latency measurement.

The response latency of steady-state visually evoked potentials (SSVEPs) is a sensitive measurement for investigating visual functioning of the human brain, specifically in visual development and for clinical evaluation. This latency can be measured from the slope of phase versus frequency of responses by using multiple frequencies of stimuli. In an attempt to provide an alternative measurement of this latency, this study utilized an envelope response of SSVEPs elicited by amplitude-modulated visual stimulation and then compared with the envelope of the generating signal, which was recorded simultaneously with the electroencephalography recordings. The advantage of this measurement is that it successfully estimates the response latency based on the physiological envelope in the entire waveform. Results showed the response latency at the occipital lobe (Oz channel) was approximately 104.55 ms for binocular stimulation, 97.14 ms for the dominant eye, and 104.75 ms for the nondominant eye with no significant difference between these stimulations. Importantly, the response latency at frontal channels (125.84 ms) was significantly longer than that at occipital channels (104.11 ms) during binocular stimulation. Together with strong activation of the source envelope at occipital cortex, these findings support the idea of a feedforward process, with the visual stimuli propagating originally from occipital cortex to anterior cortex. In sum, these findings offer a novel method for future studies in measuring visual response latencies and also potentially shed a new light on understanding of how long collective neural activities take to travel in the human brain.

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data.

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert-Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time-frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability.

The complex fluctuations in heart rate variability (HRV) reflect cardiac autonomic modulation and are an indicator of congestive heart failure (CHF). This paper proposes a novel nonlinear approach to HRV investigation, the multi dynamic trend analysis (MDTA) method, based on the empirical mode decomposition algorithm of the Hilbert-Huang transform combined with a variable-sized sliding-window method. Electrocardiographic signal data obtained from the PhysioNet database were used. These data were from subjects with CHF (mean age = 59.4 ± 8.4), an age-matched elderly healthy control group (59.3 ± 10.6), and a healthy young group (30.3 ± 4.8); the HRVs of these subjects were processed using the MDTA method, time domain analysis, and frequency domain analysis. Among all HRV parameters, the MDTA absolute value slope (MDTS) and MDTA deviation (MDTD) exhibited the greatest area under the curve (AUC) of the receiver operating characteristics in distinguishing between the CHF group and the healthy controls (AUC = 1.000) and between the healthy elderly subject group and the young subject group (AUC = 0.834 ± 0.067 for MDTS; 0.837 ± 0.066 for MDTD). The CHF subjects presented with lower MDTA indices than those of the healthy elderly subject group. Furthermore, the healthy elderly subjects exhibited lower MDTA indices than those of the young controls. The MDTA method can adaptively and automatically identify the intrinsic fluctuation on variable temporal and spatial scales when investigating complex fluctuations in the cardiac autonomic regulation effects of aging and CHF.

Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness.

Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia.

Patients with migraine are right about their perception of temperature as a trigger: time series analysis of headache diary data.

BACKGROUND: Researches to date on the association between headache and weather have yielded inconsistent results. Only a limited number of studies have examined the clinical significance of self-reported weather sensitivity. This study aimed to identify the difference in the association of headache with temperature between migraine patients with and without temperature sensitivity. METHODS: 66 migraine patients (75.8 % female; mean age 43.3 ± 12.9 years) provided their 1-year headache diaries from 2007 to a headache clinic in Taipei, Taiwan. 34 patients (51.5 %) reported sensitivity to temperature change but 32 (48.5 %) did not. Time series of daily headache incidence was modeled and stratified by temperature sensitivity. Empirical mode decomposition was used to identify temporal weather patterns that were correlated to headache incidence, and regression analysis was used to examine the amount of variance in headache incidence that could be explained by temperature in different seasons. RESULTS: Among all migraine patients, temperature change accounted for 16.5 % of variance in headache incidence in winter and 9.6 % in summer. In winter, the explained variance increased to 29.2 % among patients with temperature sensitivity, but was not significant among those without temperature sensitivity. Overall, temperature change explained 27.0 % of the variance of the mild headache incidence but only 4.8 % of the incidence of moderate to severe headache during winter. CONCLUSIONS: This diary-based study provides evidence to link the perception of temperature sensitivity and headache incidence in migraine patients. Those who reported temperature sensitivity are more likely to have headache increase during the winter, particular for mild headaches.

The APOE ɛ4 allele affects complexity and functional connectivity of resting brain activity in healthy adults.

The apolipoprotein E (APOE) gene is associated with structural and functional brain changes. We have used multiscale entropy (MSE) analysis to detect changes in the complexity of resting blood oxygen level-dependent (BOLD) signals associated with aging and cognitive function. In this study, we further hypothesized that the APOE genotype may affect the complexity of spontaneous BOLD activity in younger and older adults, and such altered complexity may be associated with certain changes in functional connectivity. We conducted a resting-state functional magnetic resonance imaging experiment in a cohort of 100 younger adults (aged 20-39 years; mean 27.2 ± 4.3 years; male/female: 53/47) and 112 older adults (aged 60-79 years; mean 68.4 ± 6.5 years; male/female: 54/58), and applied voxelwise MSE analysis to assess the main effect of APOE genotype on resting-state BOLD complexity and connectivity. Although the main effect of APOE genotype on BOLD complexity was not observed in younger group, we observed that older APOE ɛ4 allele carriers had significant reductions in BOLD complexity in precuneus and posterior cingulate regions, relative to noncarriers. We also observed that reduced BOLD complexity in precuneus and posterior cingulate regions was associated with increased functional connectivity to the superior and inferior frontal gyrus in the older group. These results support the compensatory recruitment hypothesis in older APOE ɛ4 carriers, and confer the impact of the APOE genotype on the temporal dynamics of brain activity in older adults.

The use of signal analyses of ventricular tachycardia electrograms to predict the response of antitachycardia pacing in patients with implantable cardioverter-defibrillators.

INTRODUCTION: Antitachycardia pacing (ATP), a quick, painless, and effective therapy available in implantable cardioverter-defibrillators (ICDs), can terminate most, but not all, sustained ventricular tachycardias (VTs). This study investigated the possible ventricular electrogram (EGM) factors for predicting the effectiveness of ATP therapy from ICD recordings. METHODS AND RESULTS: In this study, we analyzed 113 EGMs of VT episodes acquired from 20 patients who received ATP or shock to terminate tachyarrhythmias during follow-up after ICD implantations. The relationship between the outcome of ATP and VT EGM features (such as voltage, width, cycle length, and beat-to-beat morphologic variation) was investigated. The divergence (beat-to-beat morphologic variation) of the VT EGMs was determined by calculating the total deviation of all EGMs away from the average template after all VT EMGs were aligned. In total, 72 (63.7%) successful (Group I) and 41 (36.3%) unsuccessful (Group II) ATP therapy episodes were analyzed. The mean amplitude, cycle length, and EGM width were similar between these 2 groups (P > 0.05). A multivariate analysis demonstrated that the only predictor of successful ATP was the divergence among the VT EGMs (0.56 ± 0.32 vs 1.07 ± 0.64, P < 0.001, for Groups 1 and 2, respectively). The optimal cutoff value for determining a successful ATP therapy was 0.73 (with an area under the curve of 0.769, sensitivity of 81.9% [95% CI = 71.1-90.0], and specificity of 65.9% [95% CI = 49.4-79.9], P < 0.0001). CONCLUSION: Signal analyses from stored EGMs of VT can predict the response of ATP therapy in patients with ICD implantations. A lesser ventricular beat-to-beat morphologic variation in the intracardiac recordings from ICDs correlated with a higher probability of a successful ATP.

Nonlinear analysis of fibrillatory electrogram similarity to optimize the detection of complex fractionated electrograms during persistent atrial fibrillation.

INTRODUCTION: Currently, the identification of complex fractionated atrial electrograms (CFEs) in the substrate modification is mostly based on cycle length-derived algorithms. The characteristics of the fibrillation electrogram morphology and their consistency over time are not clear. The aim of this study was to optimize the detection algorithm of crucial CFEs by using nonlinear measure electrogram similarity. METHODS AND RESULTS: One hundred persistent atrial fibrillation patients that underwent catheter ablation were included. In patients who required CFE ablation (79%), the time-domain fibrillation signals (6 seconds) were acquired for a linear analysis (mean fractionation interval and dominant frequency [DF]) and nonlinear-based waveform similarity analysis of the local electrograms, termed the similarity index (SI). Continuous CFEs were targeted with an endpoint of termination. Predictors of the various signal characteristics on the termination and clinical outcome were investigated. Procedural termination was observed in 39% and long-term sinus rhythm maintenance in 67% of the patients. The targeted CFEs didn't differ based on the linear analysis modalities between the patients who responded and did not respond to CFE ablation. In contrast, the average SI of the targeted CFEs was higher in termination patients, and they had a better outcome. Multivariate regression analysis showed that a higher SI independently predicted sites of termination (≥ 0.57; OR = 4.9; 95% CI = 1.33-18.0; P = 0.017). CONCLUSIONS: In persistent AF patients, a cycle length-based linear analysis could not differentiate culprit CFEs from bystanders. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFE sites were important for AF maintenance.

Suicide and media reporting: a longitudinal and spatial analysis.

PURPOSE: The impact of media reporting on copycat suicides has been well established in various cases of celebrity suicide. However, knowledge is limited about the spatial and temporal relationship between suicide death and media reporting over a long period of time. This study investigated the association of suicide deaths with suicide news in longitudinal and spatial dimensions. METHODS: All suicides during 2003-2010 (n = 31,364) were included. Suicide news in the study period was retrieved from Google News, and included all available news media in Taiwan. Empirical mode decomposition was used to identify the main intrinsic oscillation, reflecting both major and minor suicide events, and time-dependent intrinsic correlation was used to quantify the temporal correlation between suicide deaths and suicide news. RESULTS: The media reporting of suicide was synchronized with increased suicide deaths during major suicide events such as celebrity death, and slightly lagged behind the suicide deaths for 1 month in other periods without notable celebrity deaths. The means of suicide reported in the media diversely affected the suicide models. Reports of charcoal burning suicide exhibited an exclusive copycat effect on actual charcoal burning deaths, whereas media reports of jumping had a wide association with various suicide models. Media reports of suicide had a higher association with suicide deaths in urban than in rural areas. CONCLUSIONS: This report suggested that a delayed effect of copycat suicide may exist in media reports of minor suicide events. The competitive reporting of minor suicide events must be avoided and addressed by media professionals.

Resting State Dynamics in People with Varying Degrees of Anxiety and Mindfulness: A Nonlinear and Nonstationary Perspective.

Anxiety and mindfulness are two inversely linked traits shown to be involved in various physiological domains. The current study used resting state electroencephalography (EEG) to explore differences between people with low mindfulness-high anxiety (LMHA) (n = 29) and high mindfulness-low anxiety (HMLA) (n = 27). The resting EEG was collected for a total of 6 min, with a randomized sequence of eyes closed and eyes opened conditions. Two advanced EEG analysis methods, Holo-Hilbert Spectral Analysis and Holo-Hilbert cross-frequency phase clustering (HHCFPC) were employed to estimate the power-based amplitude modulation of carrier frequencies, and cross-frequency coupling between low and high frequencies, respectively. The presence of higher oscillation power across the delta and theta frequencies in the LMHA group than the HMLA group might have been due to the similarity between the resting state and situations of uncertainty, which reportedly triggers motivational and emotional arousal. Although these two groups were formed based on their trait anxiety and trait mindfulness scores, it was anxiety that was found to be significant predictor of the EEG power, not mindfulness. It led us to conclude that it might be anxiety, not mindfulness, which might have contributed to higher electrophysiological arousal. Additionally, a higher δ-ß and δ-γ CFC in LMHA suggested greater local-global neural integration, consequently a greater functional association between cortex and limbic system than in the HMLA group. The present cross-sectional study may guide future longitudinal studies on anxiety aiming with interventions such as mindfulness to characterize the individuals based on their resting state physiology.

The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity.

Electrophysiological working memory (WM) research shows brain areas communicate via macroscopic oscillations across frequency bands, generating nonlinear amplitude modulation (AM) in the signal. Traditionally, AM is expressed as the coupling strength between the signal and a prespecified modulator at a lower frequency. Therefore, the idea of AM and coupling cannot be studied separately. In this study, 33 participants completed a color recall task while their brain activity was recorded through EEG. The AM of the EEG data was extracted using the Holo-Hilbert spectral analysis (HHSA), an adaptive method based on the Hilbert-Huang transforms. The results showed that WM load modulated parieto-occipital alpha/beta power suppression. Furthermore, individuals with higher frontal theta power and lower parieto-occipital alpha/beta power exhibited superior WM precision. In addition, the AM of parieto-occipital alpha/beta power predicted WM precision after presenting a target-defining probe array. The phase-amplitude coupling (PAC) between the frontal theta phase and parieto-occipital alpha/beta AM increased with WM load while processing incoming stimuli, but the PAC itself did not predict the subsequent recall performance. These results suggest frontal and parieto-occipital regions communicate through theta-alpha/beta PAC. However, the overall recall precision depends on the alpha/beta AM following the onset of the retro cue.

Causality Network of Infectious Disease Revealed With Causal Decomposition.

Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer's disease.

Aims: Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms. Methods: A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms. Results: (a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier. Conclusion: Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.

Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis.

Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of ß bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of ß bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and ß bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.

Can Daytime Transcranial Direct Current Stimulation Treatment Change the Sleep Electroencephalogram Complexity of REM Sleep in Depressed Patients? A Double-Blinded, Randomized, Placebo-Controlled Trial.

Study Objectives: The purpose of this study was to determine the effects of daytime transcranial direct current stimulation (tDCS) on sleep electroencephalogram (EEG) in patients with depression. Methods: The study was a double-blinded, randomized, controlled clinical trial. A total of 37 patients diagnosed with a major depression were recruited; 19 patients (13 females and 6 males mean age 44.79 ± 15.25 years) received tDCS active stimulation and 18 patients (9 females and 9 males; mean age 43.61 ± 11.89 years) received sham stimulation. Ten sessions of daytime tDCS were administered with the anode over F3 and the cathode over F4. Each session delivered a 2 mA current for 30 min per 10 working days. Hamilton-24 and Montgomery scales were used to assess the severity of depression, and polysomnography (PSG) was used to assess sleep structure and EEG complexity. Eight intrinsic mode functions (IMFs) were computed from each EEG signal in a channel. The sample entropy of the cumulative sum of the IMFs were computed to acquire high-dimensional multi-scale complexity information of EEG signals. Results: The complexity of Rapid Eye Movement (REM) EEG signals significantly decreased intrinsic multi-scale entropy (iMSE) (1.732 ± 0.057 vs. 1.605 ± 0.046, P = 0.0004 in the case of the C4 channel, IMF 1:4 and scale 7) after tDCS active stimulation. The complexity of the REM EEG signals significantly increased iMSE (1.464 ± 0.101 vs. 1.611 ± 0.085, P = 0.001 for C4 channel, IMF 1:4 and scale 7) after tDCS sham stimulation. There was no significant difference in the Hamilton-24 (P = 0.988), Montgomery scale score (P = 0.726), and sleep structure (N1% P = 0.383; N2% P = 0.716; N3% P = 0.772) between the two groups after treatment. Conclusion: Daytime tDCS changed the complexity of sleep in the REM stage, and presented as decreased intrinsic multi-scale entropy, while no changes in sleep structure occurred. This finding indicated that daytime tDCS may be an effective method to improve sleep quality in depressed patients. Trial registration This trial has been registered at the ClinicalTrials.gov (protocol ID: TCHIRB-10409114, in progress).