Hz Band Research Articles

Network Hyperexcitability in Early Alzheimer’s Disease: Is Functional Connectivity a Potential Biomarker?

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer’s disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4–8 Hz and the 8–13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

Network controllability analysis of awake and asleep conditions in the brain.

The difference between sleep and wakefulness is critical for human health. Sleep takes up one third of our lives and remains one of the most mysterious conditions; it plays an important role in memory consolidation and health restoration. Distinct neural behaviors take place under awake and asleep conditions, according to neuroimaging studies. While disordered transitions between wakefulness and sleep accompany brain disease, further investigation of their specific characteristics is required. In this study, the difference is objectively quantified by means of network controllability. We propose a new pipeline using a public intracranial stereo-electroencephalography (stereo-EEG) dataset to unravel differences in the two conditions in terms of system neuroscience. Because intracranial stereo-EEG records neural oscillations covering large-scale cerebral areas, it offers the highest temporal resolution for recording neural behaviors. After EEG preprocessing, the EEG signals are band-passed into sub-slow (0.1‍-‍1 Hz), delta (1‍-‍4 Hz), theta (4‍-‍8 Hz), alpha (8‍-‍13 Hz), beta (13‍-‍30 Hz), and gamma (30‍-‍45 Hz) band oscillations. Then, dynamic functional connectivity is extracted from time-windowed EEG neural oscillations through phase-locking value (PLV) and non-overlapping sliding time windows. Next, average and modal network controllability are implemented on these time-varying brain networks. Based on this preliminary study, it appears that significant differences exist in the dorsolateral frontal-parietal network (FPN), salience network (SN), and default-mode network (DMN). The combination of network controllability and dynamic functional networks offers new insight for characterizing distinctions between awake and asleep stages in the brain. In other words, network controllability captures the underlying brain dynamics under both awake and asleep conditions.

Reducing control noise in gravitational wave detectors with interferometric local damping of suspended optics.

Control noise is a limiting factor in the low-frequency performance of the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO). In this paper, we model the effects of using new sensors called Homodyne Quadrature Interferometers (HoQIs) to control the suspension resonances. We show that if we were to use HoQIs, instead of the standard shadow sensors, we could suppress resonance peaks up to tenfold more while simultaneously reducing the noise injected by the damping system. Through a cascade of effects, this will reduce the resonant cross-coupling of the suspensions, allow for improved stability for feed-forward control, and result in improved sensitivity of the detectors in the 10-20 Hz band. This analysis shows that improved local sensors, such as HoQIs, should be used in current and future detectors to improve low-frequency performance.

The Response of Auroral-Oval Waves to CIR-Driven Recurrent Storms: FY-3E/ACMag Observations

Alfven-branch waves provide an efficient means for transporting energy into the auroral oval. Here, we report observations of these waves obtained by the Fengyun-3E (FY-3E)/ACMag instruments, which are designed to detect three-dimensional AC magnetic fields in the 0.05–25 Hz band. The observations suggest that broadband waves are a permanent feature of the auroral oval, although their amplitude and locations vary with the global state of the magnetosphere. We primarily focus on the data obtained from 10 July 2021 to 26 August 2021, during which a series of recurrent storms driven by solar wind corotating interaction regions (CIRs) occurred. Analysis of the observations shows that the auroral-oval waves grow in amplitude (1–3 orders of magnitude) and shift to lower latitude (∼10°) immediately following the decrease in the SYM-H index in each storm. Further investigation reveals the response of the auroral-oval waves has a time scale equal to or less than FY-3E’s effective revisiting time, which is about 45 min. The observations presented in this paper confirm that the FY-3E/ACMag instruments provide a high-resolution monitor of the auroral-oval waves and could further our understanding of auroral physics.

Association of Subthalamic Beta Frequency Sub-bands to Symptom Severity in Patients with Parkinson’s Disease: A Systematic Review (P5-11.014)

<h3>Objective:</h3> To synthesize existing literature regarding the association of beta sub-band local field potential (LFP) characteristics to clinical ratings of motor symptoms in patients with Parkinson’s disease (PD). <h3>Background:</h3> Beta (13–30 Hz) frequency measures have been found to be associated with motor dysfunction in patients with PD undergoing deep brain stimulation (DBS). Low-beta may be more responsive to medication states and express stronger association to motor dysfunction in PD compared to High-beta. However, a consensus on beta sub-band relationships to clinical state has yet to be determined. <h3>Design/Methods:</h3> A systematic search of existing literature was completed using EMBASE. Articles which collected subthalamic nucleus (STN) LFPs using macroelectrodes in patients with PD, analyzed low- (13–20 Hz) and high-beta (21–35 Hz) bands, collected UPDRS-III measures, and reported correlational strength or predictive capacity of LFPs to UPDRS-III scores. Articles were screened by title and abstract prior to undergoing full-text review for inclusion and data extraction. <h3>Results:</h3> The initial search yielded 233 articles. After screening and full-text review, 10 articles, with a total of 300 patients, were included. Beta measures included power spectral density, peak characteristics, and burst characteristics. Eight (80%) articles found low-beta and six (60%) found high-beta to be significantly correlated with, or predictive of, UPDRS-III scores or response to therapy. Four (40%) articles found both low- and high-beta to be associated with, or predictive of, UPDRS-III sub-scores or response to therapy. However, only one of the four found that high-beta had a stronger association to symptom severity compared to low-beta. <h3>Conclusions:</h3> This systematic review suggests that low-beta measures represent a biomarker of Parkinsonian symptom severity in patients with STN-DBS to a greater capacity than high-beta measures. Interestingly, these associations were found across multiple LFP measures, suggesting multiple analytical approaches using LFP signals can still yield clinically meaningful biomarkers of patient clinical state. <b>Disclosure:</b> Rebekah Summers has received personal compensation for serving as an employee of Medtronic. Rebekah Summers has received research support from National Institute of Health. Dr. Morelli has received personal compensation for serving as an employee of Medtronic.

3-D geometry of the Lonar impact crater, India, imaged from cultural seismic noise

SUMMARY The Lonar impact crater in the Deccan Volcanic Province of India is an excellent analogue for impact-induced structures on the Moon and other terrestrial planets. We present a detailed architecture of the crater using a high-resolution 3-D seismic velocity image to a depth of 1.5 km through the inversion of high-frequency ambient noise data recorded over 20 broad-band seismographs operating around the crater. The ambient noise waveform is dominated by cultural noise in the 1–10 Hz band. The shear wave velocity (Vs) model is created from Rayleigh wave group velocity data with a horizontal resolution of 0.5–1 km in the period range of 0.1–1.2 s. A key feature of the model is a velocity reduction of 10–15 per cent below the crater compared to outside the ejecta zone. The low-velocity zone below the crater is nearly circular and extends to a depth of ∼500 m. This estimated crater's depth is consistent with global depth–diameter scaling relations for simple craters. The basement, with a Vs of more than 2.5 km s−1, lies beneath the Deccan basalt, which has a Vs of ∼2.4 km s−1. These results are consistent with laboratory-measured data from the Lonar crater and borehole data in the western Deccan trap. This study opens a new window for exploring impact craters and sub-basalt structures using high-frequency ambient noise tomography.

Detecting fluctuations in student engagement and retention during video lectures using electroencephalography

AbstractVideo lectures are commonly used in online and flipped courses, but students often find it challenging to stay engaged and retain lecture content. The current study examined to what extent the power of electroencephalography (EEG) brain activity in the theta (4–7 Hz), alpha (8–12 Hz) and beta (13–20 Hz) bands can dynamically capture fluctuations of student engagement and retention throughout pre‐recorded lectures. EEG activity was recorded from 33 college students throughout four video‐based chemistry lectures. In‐video probes were used to assess both student engagement and content retention at random moments during the video. Our findings reveal that there are significant fluctuations in self‐reported engagement throughout pre‐recorded lectures. Further, among the three frequency bands that were tested, only alpha power closely tracked fluctuations in self‐reported engagement at the individual student level. In‐lecture fluctuations in engagement were associated with content retention, but content retention itself was not well captured by EEG activity in any of the frequency bands that were examined. These findings suggest that the design of video lectures should consider fluctuations in student engagement and potentially incorporate self‐reported and physiological indicators of engagement. Future research should further investigate how EEG and other physiological engagement indicators can be used in real time to personalize online instruction. Practitioner NotesWhat is already known about this topic Students often find it challenging to stay engaged during online lectures and retain lecture content. Measuring engagement and retention throughout an online lecture (rather than only at its end) is important but challenging because it requires the insertion of in‐lecture questions that interrupt the learning process. Electroencephalography (EEG) could potentially provide a continuous and implicit measure of engagement and retention throughout online lectures. What this paper adds Self‐reported engagement tends to gradually decrease throughout the duration of video lectures with substantial variation both within and between students. Fluctuations in student engagement are predictive of content retention throughout video lectures. EEG power in the alpha band (8–12 Hz) dynamically tracks fluctuations in student engagement. EEG power in the alpha band significantly predicts overall lecture engagement as well as learning confidence. However, EEG power might not be sensitive to variations in post‐lecture test performance. Implications for practice and/or policy The design of online lectures should take into consideration the dynamic and idiosyncratic nature of student engagement. In‐video self‐report probes and EEG power measures can be useful sources of information on students' level of engagement during online lectures. It should be further investigated whether EEG and other physiological indicators of engagement can be used in real time to personalize online instruction.

Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed “Spectral Events,” and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox (https://github.com/jonescompneurolab/SpectralEvents), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1–6 Hz), alpha (7–14 Hz), and beta (15–29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Aerobic exercise and action observation priming modulate functional connectivity.

Aerobic exercise and action observation are two clinic-ready modes of neural priming that have the potential to enhance subsequent motor learning. Prior work using transcranial magnetic stimulation to assess priming effects have shown changes in corticospinal excitability involving intra- and interhemispheric circuitry. The objective of this study was to determine outcomes exclusive to priming- how aerobic exercise and action observation priming influence functional connectivity within a sensorimotor neural network using electroencephalography. We hypothesized that both action observation and aerobic exercise priming would alter resting-state coherence measures between dominant primary motor cortex and motor-related areas in alpha (7-12 Hz) and beta (13-30 Hz) frequency bands with effects most apparent in the high beta (20-30 Hz) band. Nine unimpaired individuals (24.8 ± 3 years) completed a repeated-measures cross-over study where they received a single five-minute bout of action observation or moderate-intensity aerobic exercise priming in random order with a one-week washout period. Serial resting-state electroencephalography recordings acquired from 0 to 30 minutes following aerobic and action observation priming revealed increased alpha and beta coherence between leads overlying dominant primary motor cortex and supplementary motor area relative to pre- and immediate post-priming timepoints. Aerobic exercise priming also resulted in enhanced high beta coherence between leads overlying dominant primary motor and parietal cortices. These findings indicate that a brief bout of aerobic- or action observation-based priming modulates functional connectivity with effects most pronounced with aerobic priming. The gradual increases in coherence observed over a 10 to 30-minute post-priming window may guide the pairing of aerobic- or action observation-based priming with subsequent training to optimize learning-related outcomes.

Accurate Broadband Gradient Estimates Enable Local Sensitivity Analysis of Ocean Acoustic Models

Sensitivity analysis is a powerful tool for analyzing multi-parameter models. For example, the Fisher information matrix (FIM) and the Cramér–Rao bound (CRB) involve derivatives of a forward model with respect to parameters. However, these derivatives are difficult to estimate in ocean acoustic models. This work presents a frequency-agnostic methodology for accurately estimating numerical derivatives using physics-based parameter preconditioning and Richardson extrapolation. The methodology is validated on a case study of transmission loss in the 50–400[Formula: see text]Hz band from a range-independent normal mode model for parameters of the sediment. Results demonstrate the utility of this methodology for obtaining Cramér–Rao bound (CRB) related to both model sensitivities and parameter uncertainties, which reveal parameter correlation in the model. This methodology is a general tool that can inform model selection and experimental design for inverse problems in different applications.

Breathe–squeeze: pharmacodynamics of a stimulus-free behavioural paradigm to track conscious states during sedation☆

BackgroundConscious states are typically inferred through responses to auditory tasks and noxious stimulation. We report the use of a stimulus-free behavioural paradigm to track state transitions in responsiveness during dexmedetomidine sedation. We hypothesised that estimated dexmedetomidine effect-site (Ce) concentrations would be higher at loss of responsiveness (LOR) compared with return of responsiveness (ROR), and both would be lower than comparable studies that used stimulus-based assessments. MethodsClosed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine data were analysed for secondary analysis. Fourteen healthy volunteers were asked to perform the breathe–squeeze task of gripping a dynamometer when inspiring and releasing it when expiring. LOR was defined as five inspirations without accompanied squeezes; ROR was defined as the return of five inspirations accompanied by squeezes. Brain states were monitored using 64-channel EEG. Dexmedetomidine was administered as a target-controlled infusion, with Ce estimated from a pharmacokinetic model. ResultsCounter to our hypothesis, mean estimated dexmedetomidine Ce was lower at LOR (0.92 ng ml−1; 95% confidence interval: 0.69–1.15) than at ROR (1.43 ng ml−1; 95% confidence interval: 1.27–1.58) (paired t-test; P=0.002). LOR was characterised by progressively increasing fronto-occipital EEG power in the 0.5–8 Hz band and loss of occipital alpha (8–12 Hz) and global beta (16–30 Hz) power. These EEG changes reverted at ROR. ConclusionsThe breathe–squeeze task can effectively track changes in responsiveness during sedation without external stimuli and might be more sensitive to state changes than stimulus-based tasks. It should be considered when perturbation of brain states is undesirable. Clinical trial registrationNCT04206059.

High-Resolution Optical Fiber Strain Sensor Array Based on Pound-Drever-Hall Technique With Fast Phase Regulation Method

Sensors that use Pound-Drever-Hall (PDH) technology benefit from the good performance and high resolution but are in shortage of large-scale multiplexing. This paper proposed a fast phase regulation method for the hybrid PDH multiplexing scheme based on space division multiplexing and time division multiplexing. Using both the information provided by an I/Q demodulator, the points from the error signal that the close-loop control requires can be rebuilt in the software with a phase rotation algorithm within a time period of less than 20 us. The relationship between the multiplexing scale and parameters selection is also given in this paper, showing that the system can have a larger multiplexing scale up to several tens sensors with appropriate parameters selection. A multi-channel strain array of up to 10 channels with a resolution better than 10 pϵ/√Hz@1 Hz over 50 Hz band per channel has been realized in this paper, and the system works with good channel crosstalk isolation. The experiment results show that the developed instrument has potential usage in the field of large-scale quasi-static strain measurement and earthquake observation.

Patterns of neural activity and clinical outcomes in a juvenile Huntington's Disease patient undergoing Deep Brain Stimulation of the subthalamic nucleus

BackgroundHuntington's disease (HD) is a hereditary neurodegenerative disease leading to cognitive and motor impairment. HD depends on basal ganglia dysfunctions, but the role of subthalamic nucleus (STN) neurons is not completely known. Drug-resistant motor symptoms of HD can be alleviated by neuromodulation of the basal ganglia through Deep Brain Stimulation (DBS) of STN. DBS target selection is supported by intra-operative microelectrode recordings (MER). MER have been previously used to characterize neural dynamics of STN in several movement disorders and can provide information on firing patterns underlying HD. MethodsWe analyzed MER data acquired during bilateral DBS of STN in a juvenile HD female patient with hypokinetic motor symptoms (generalized dystonia, stiffness, and severe gait impairment). Firing patterns of STN in HD were characterized by isolating single neuron activities (n = 23) and measuring their regularity, bursting, and oscillatory behavior. Multi-unit activity recordings spectrum was used to estimate the presence of network oscillations. ResultsSTN neurons displayed irregular dynamics and intense and sparse bursting. Only 3/23 neurons presented oscillatory activity. However, network oscillations were detected, in particular in the beta (12–30 Hz) band. After bilateral STN-DBS surgery, the Unified Huntington's Disease Rating Scale decreased from 60 to 54. ConclusionsThe most salient difference between HD and other movement disorders in STN activity is the presence of a weakly synchronized oscillatory mode, in which oscillations are evident at the network level but not at the single neuron level.

Out-of-plane arrivals recorded by surface drifters during the Northern Ocean Rapid Surface Evolution experiment

The Northern Ocean Rapid Surface Evolution (NORSE) focuses on characterizing the key physical processes that govern the predictability of upper-ocean rapid evolution events. The principal experimental site is Jan Mayen Channel, which connects the Greenland and Norwegian Seas. During the fall 2022 process cruise, signals from a moored source transmitting a 135-second-long LFM upsweep in the 500–1500 Hz band every four hours were recorded by three SVP Drifters equipped with hydrophone arrays. Over a three-day period, the drifters moved north across Jan Mayen Channel toward the moored source. The individual recordings are subject to variable levels of ambient sound caused by changing wind conditions and platform noise. In recordings with positive SNR, an in-plane arrival is observed. In a subset of these recordings, a second arrival is observed having travel time consistent with a propagation path from the moored source, reflecting off the ridge on the south side of the channel, and arriving at the drifters. A third arrival is also observed having travel time consistent with reflection from face of the bathymetric rise on the east end of the channel which forms Jan Mayen Island. This talk will present the measurements and explain the data though forward modeling. [Work supported by ONR.]

Neural activity during monkey vehicular wayfinding

Navigation gets us from place to place, creating a path to arrive at a goal. We trained a monkey to steer a motorized cart in a large room, beginning at its trial-by-trial start location and ending at a trial-by-trial cued goal location. While the monkey steered its autonomously chosen path to its goal, we recorded neural activity simultaneously in both the hippocampus (HPC) and medial superior temporal (MST) cortex.Local field potentials (LFPs) in these sites show similar patterns of activity with the 15–30 Hz band highlighting specific room locations. In contrast, 30–100 Hz LFPs support a unified map of the behaviorally relevant start and goal locations. The single neuron responses (SNRs) do not substantially contribute to room or start-goal maps. Rather, the SNRs form a continuum from neurons that are most active when the monkey is moving on a path toward the goal, versus other neurons that are most active when the monkey deviates from paths toward the goal.Granger analyses suggest that HPC firing precedes MST firing during cueing at the trial start location, mainly mediated by off-path neurons. In contrast, MST precedes HPC firing during steering, mainly mediated by on-path neurons. Interactions between MST and HPC are mediated by the parallel activation of on-path and off-path neurons, selectively activated across stages of this wayfinding task.

Statistical analysis of electric field perturbations in ELF based on the CSES observation data before the earthquake

To explore the correlation between earthquakes and the pre-earthquake ionospheric shallow frequency (ELF) electric field perturbations phenomenon, the paper investigated the pre-earthquake ionospheric perturbations phenomenon, and then the Spatio-temporal evolution characteristics of the electromagnetic field before and after the global Ms ≥6.0 strong earthquakes from 2019 to 2021 were statistically analyzed. In this paper, the power spectrum data of the ELF (19.5–250 Hz) band of ionospheric electric field observed by the China Seismo-Electromagnetic Satellite (CSES) electric field detector are preclinically processed by the C-value method. A stable background field observation model was constructed using the data from 75 to 45 days before the earthquake observed by the CSES in the range of 15° above the epicenter. Then, the amplitude of the spatial electric field disturbance over the epicenter relative to the background field is extracted. Finally, the superposition analysis method statistically analyzes the spatial and temporal evolution of the spatial electric field before and after the earthquake with different characteristics. The statistical results show that the anomalies first appear in the fourth period (15–19 days before the earthquake) and the third period (10–14 days before the earthquake) and then reach the most vital and most evident during the pro-earthquake period (4 days before the earthquake and the day of the earthquake); In terms of the intensity of the anomalies caused, the magnitude seven earthquakes are stronger than the magnitude 6.0–7.0 earthquakes, and marine earthquakes are stronger than land earthquakes; in terms of the ease of observing the anomalies, the magnitude 7.0 and above are more accessible to observe than the magnitude 6.0–7.0 earthquakes, and marine earthquakes are more accessible to observe than land earthquakes.

Paroxysmal slow-wave discharges in a model of absence seizure are coupled to gamma oscillations in the thalamocortical and limbic systems

ObjectiveUsing the gamma-butyrolactone (GBL) model of absence seizures in Long-Evans rats, this study investigated if gamma (30–160 Hz) activity were cross-frequency modulated by the 2–6 Hz slow-wave discharges induced by GBL in the limbic system. We hypothesized that inactivation of the nucleus reuniens (RE), which projects to frontal cortex (FC) and hippocampus, would affect the cross-frequency coupling of gamma (γ) in different brain regions. MethodsLocal field potentials were recorded by electrodes implanted in the FC, ventrolateral thalamus (TH), basolateral amygdala (BLA), nucleus accumbens (NAC), and dorsal hippocampus (CA1) of behaving rats. At each electrode, the coupling between the γ amplitude envelope to the phase of the 2–6 Hz slow-waves (SW) was measured by modulation index (MI) or cross-frequency coherence (CFC) of γ amplitude with SW. In separate experiments, the RE was infused with saline or GABAA receptor agonist, muscimol, before the injection of GBL. ResultsFollowing GBL injection, an increase in MI and CFC of SW to γ1 (30–58 Hz), γ2 (62–100 Hz) and γ3 (100–160 Hz) bands was observed at the FC, hippocampus and BLA, with significant increase in SW-γ1 and SW-γ3 coupling at TH, and increase in peak SW-γ1 CFC at NAC. Strong SW-γ modulation was also found during baseline immobility high-voltage spindles. Muscimol inactivation of RE, as compared to saline infusion, significantly decreased SW-γ1 CFC in the FC, and peak frequency of the SW-γ1 CFC in the thalamus, but did not significantly alter SW-γ CFCs in the hippocampus, BLA or NAC. SignificanceThe paroxysmal 2–6 Hz SW discharges, a hallmark of absence seizure, significantly modulate γ activity in the hippocampus, BLA and NAC, suggesting a modulation of limbic functions. RE inactivation disrupted the SW modulation of FC and TH, partly supporting our hypothesis that RE participates in the modulation of SW discharges.

Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits.

Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors. Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability. Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important. Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends.

Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young's internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5-73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10-20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.

Altered functional connectivity density and structural covariance networks in women with premenstrual syndrome.

Premenstrual syndrome (PMS) is a menstrual-related disorder, characterized by physical, emotional, behavioral and cognitive symptoms. However, the neuropathological mechanisms of PMS remain unclear. This study aimed to investigate the frequency-specific functional connectivity density (FCD) and structural covariance in PMS. Functional and T1-weighted structural data were obtained from 35 PMS patients and 36 healthy controls (HCs). This study was a cross-sectional and prospective design. The local/long-range FCD (LFCD/LRFCD) across slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands were computed, and two-way analysis of variance (ANOVA) was performed to ascertain the main effects of group and interaction effects between group and frequency band. Receiver operating characteristic (ROC) curve was performed to investigate reliable biomarkers for identifying PMS from HCs. Based on the ROC results, characterized the changes of whole-brain structural covariance patterns of striatum subregions in two groups. Correlation analysis was applied to examine relationships between the clinical symptoms and abnormal brain regions. Compared with HCs, PMS patients exhibited: (I) aberrant functional communication in the middle cingulate cortex and precentral gyrus; (II) significant frequency band-by-group interaction effects of the striatum, thalamus and orbitofrontal cortex; (III) the better classification ability of the LFCD in the striatum in ROC analysis (slow-5); (IV) decreased gray matter volumes in the caudate subregions and decreased structural associations of between the caudate subregions and frontal cortex; (V) the LFCD value in thalamus were significantly negatively correlated with the sleep problems (slow-5). Based on multi-modal magnetic resonance imaging (MRI) analysis, this study might imply the aberrant emotional regulation and cognitive function related to menstrual cycle in PMS and improve our understanding of the pathophysiologic mechanism in PMS from novel perspective.