Altered Neurovascular Coupling for Multidisciplinary Intensive Rehabilitation in Parkinson's Disease.

Effective rehabilitation in Parkinson's disease (PD) is related to brain reorganization with restoration of cortico-subcortical networks and compensation of frontoparietal networks; however, further neural rehabilitation evidence from a multidimensional perspective is needed. To investigate how multidisciplinary intensive rehabilitation treatment affects neurovascular coupling, 31 PD patients (20 female) before and after treatment and 30 healthy controls (17 female) underwent blood oxygenation level-dependent functional magnetic resonance imaging and arterial spin labeling scans. Cerebral blood flow (CBF) was used to measure perfusion, and fractional amplitude of low-frequency fluctuation (fALFF) was used to measure neural activity. The global CBF-fALFF correlation and regional CBF/fALFF ratio were calculated as neurovascular coupling. Dynamic causal modeling (DCM) was used to evaluate treatment-related alterations in the strength and directionality of information flow. Treatment reduced CBF-fALFF correlations. The altered CBF/fALFF exhibited increases in the left angular gyrus and the right inferior parietal gyrus and decreases in the bilateral thalamus and the right superior frontal gyrus. The CBF/fALFF alteration in right superior frontal gyrus showed correlations with motor improvement. Further, DCM indicated increases in connectivity from the superior frontal gyrus and decreases from the thalamus to the inferior parietal gyrus. The benefits of rehabilitation were reflected in the dual mechanism, with restoration of executive control occurring in the initial phase of motor learning and compensation of information integration occurring in the latter phase. These findings may yield multimodal insights into the role of rehabilitation in disease modification and identify the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD.SIGNIFICANCE STATEMENT Although rehabilitation has been proposed as a promising supplemental treatment for PD as it results in brain reorganization, restoring cortico-subcortical networks and eliciting compensatory activation of frontoparietal networks, further multimodal evidence of the neural mechanisms underlying rehabilitation is needed. We measured the ratio of perfusion and neural activity derived from arterial spin labeling and blood oxygenation level-dependent fMRI data and found that benefits of rehabilitation seem to be related to the dual mechanism, restoring executive control in the initial phase of motor learning and compensating for information integration in the latter phase. We also identified the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD patients.

Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability.

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

Functional and structural gradients reveal atypical hierarchical organization of Parkinson's disease.

Parkinson's disease (PD) patients exhibit deficits in primary sensorimotor and higher-order executive functions. The gradient reflects the functional spectrum in sensorimotor-associated areas of the brain. We aimed to determine whether the gradient is disrupted in PD patients and how this disruption is associated with treatment outcome. Seventy-six patients (mean age, 59.2 ± 12.4 years [standard deviation], 44 women) and 34 controls participants (mean age, 58.1 ± 10.0 years [standard deviation], 19 women) were evaluated. We explored functional and structural gradients in PD patients and control participants. Patients were followed during 2 weeks of multidisciplinary intensive rehabilitation therapy (MIRT). The Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) was administered to patients before and after treatment. We investigated PD-related alterations in the principal functional and structural gradients. We further used a support vector machine (SVM) and correlation analysis to assess the classification ability and treatment outcomes related to PD gradient alterations, respectively. The gradients showed significant differences between patients and control participants, mainly in somatosensory and visual networks involved in primary function, and higher-level association networks (dorsal attentional network (DAN) and default mode network (DMN)) related to motor control and execution. On the basis of the combined functional and structural gradient features of these networks, the SVM achieved an accuracy of 91.2% in discriminating patients from control participants. Treatment reduced the gradient difference. The altered gradient exhibited a significant correlation with motor improvement and was mainly distributed across the visual network, DAN and DMN. This study revealed damage to gradients in the brain characterized by sensorimotor and executive control deficits in PD patients. The application of gradient features to neurological disorders could lead to the development of potential diagnostic and treatment markers for PD.

Evidence of a large current of transcranial alternating current stimulation directly to deep brain regions.

Deep brain regions such as hippocampus, insula, and amygdala are involved in neuropsychiatric disorders, including chronic insomnia and depression. Our recent reports showed that transcranial alternating current stimulation (tACS) with a current of 15 mA and a frequency of 77.5 Hz, delivered through a montage of the forehead and both mastoids was safe and effective in intervening chronic insomnia and depression over 8 weeks. However, there is no physical evidence to support whether a large alternating current of 15 mA in tACS can send electrical currents to deep brain tissue in awake humans. Here, we directly recorded local field potentials (LFPs) in the hippocampus, insula and amygdala at different current strengths (1 to 15 mA) in 11 adult patients with drug-resistant epilepsy implanted with stereoelectroencephalography (SEEG) electrodes who received tACS at 77.5 Hz from 1 mA to 15 mA at 77.5 Hz for five minutes at each current for a total of 40 min. For the current of 15 mA at 77.5 Hz, additional 55 min were applied to add up a total of 60 min. Linear regression analysis revealed that the average LFPs for the remaining contacts on both sides of the hippocampus, insula, and amygdala of each patient were statistically associated with the given currents in each patient (p < 0.05-0.01), except for the left insula of one subject (p = 0.053). Alternating currents greater than 7 mA were required to produce significant differences in LFPs in the three brain regions compared to LFPs at 0 mA (p < 0.05). The differences remained significant after adjusting for multiple comparisons (p < 0.05). Our study provides direct evidence that the specific tACS procedures are capable of delivering electrical currents to deep brain tissues, opening a realistic avenue for modulating or treating neuropsychiatric disorders associated with hippocampus, insula, and amygdala.

Individual prefrontal neurons contribute to sensory-to-motor information transformation by rotating reference frames during spatial working memory performance.

Performing working memory tasks correctly requires not only the temporary maintenance of information but also the visual-to-motor transformation of information. Although sustained delay-period activity is known to be a mechanism for temporarily maintaining information, the mechanism for information transformation is not well known. An analysis using a population of delay-period activities recorded from prefrontal neurons visualized a gradual change of maintained information from sensory to motor as the delay period progressed. However, the contributions of individual prefrontal neurons to this process are not known. In the present study, we used a version of the delayed-response task, in which monkeys needed to make a saccade 90o clockwise from a visual cue after a 3-s delay, and examined the temporal change in the preferred directions of delay-period activity during the delay period for individual neurons. One group of prefrontal neurons encoded the cue direction by a retinotopic reference frame and either maintained it throughout the delay period or rotated it 90o counterclockwise to adjust visual information to saccade information, whereas other groups of neurons encoded the cue direction by a saccade-based reference frame and rotated it 90o clockwise. The results indicate that visual-to-motor information transformation is achieved by manipulating the reference frame to adjust visual coordinates to motor coordinates.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease.

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Neurovascular coupling dysfunction of visual network organization in Parkinson's disease.

Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing.

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

Spatiotemporal and sensory modality attention processing with domain-specific representations in frontoparietal areas.

The frontoparietal network (FPN), including bilateral frontal eye field, inferior parietal sulcus, and supplementary motor area, has been linked to attention processing, including spatiotemporal and sensory modality domains. However, it is unclear whether FPN encodes representations of these domains that are generalizable across subdomains. We decomposed multivariate patterns of functional magnetic resonance imaging activity from 20 participants into domain-specific components and identified latent multivariate representations that generalized across subdomains. The 30 experimental conditions were organized into unimodal-bimodal and spatial-temporal models. We found that brain areas in the FPN, form the primary network that modulated during attention across domains. However, the activation patterns of areas within the FPN were reorganized according to the specific attentional demand, especially when pay attention to different sensory, suggesting distinct regional neural representations associated with specific attentional processes within FPN. In addition, there were also other domain-specific areas outside the FPN, such as the dorsolateral prefrontal cortex. Our conclusion is that, according to the results of the analysis of representation similarity, 2 types of activated brain regions, related to attention domain detailed information processing and general information processing, can be revealed.

Episodic memory in aspects of brain information transfer by resting-state network topology.

Cognitive functionality emerges due to neural interactions. The interregional signal interactions underlying episodic memory are a complex process. Thus, we need to quantify this process more accurately to understand how brain regions receive information from other regions. Studies suggest that resting-state functional connectivity (FC) conveys cognitive information; additionally, activity flow estimates the contribution of the source region to the activation pattern of the target region, thus decoding the cognitive information transfer. Therefore, we performed a combined analysis of task-evoked activation and resting-state FC voxel-wise by activity flow mapping to estimate the information transfer pattern of episodic memory. We found that the cinguloopercular (CON), frontoparietal (FPN) and default mode networks (DMNs) were the most recruited structures in information transfer. The patterns and functions of information transfer differed between encoding and retrieval. Furthermore, we found that information transfer was a better predictor of memory ability than previous methods. Additional analysis indicated that structural connectivity (SC) had a transportive role in information transfer. Finally, we present the information transfer mechanism of episodic memory from multiple neural perspectives. These findings suggest that information transfer is a better biological indicator that accurately describes signal communication in the brain and strongly influences the function of episodic memory.

Resting-State Functional MRI of Healthy Adults: Temporal Dynamic Brain Coactivation Patterns.

Background The aging brain is typically associated with aberrant interactions of large-scale intrinsic networks. However, the dynamic variation of these networks' coactivation or deactivation across the adult lifespan remains unclear. Purpose To promote the interpretation of dynamic brain network variations underlying the complex aging process by quantifying activation levels and obtaining a clear definition of coactivation patterns (CAPs) with resting-state functional MRI (rsfMRI). Materials and Methods In a retrospective study (October 2010 to September 2013), rsfMRI data from healthy participants in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository were used to generate CAPs by applying single-volume temporal clustering analysis. Spatial clustering analysis was then performed to capture dynamic coactivation and deactivation within or between primary sensory networks and high-order cognitive networks (including the default mode network [DMN], attentional network [AN], and frontoparietal network [FPN]). Linear relationships between dynamic metrics and age were revealed with Spearman partial correlations. Results A total of 614 participants (mean age, 54 years ± 18 [SD]; 311 women) ranging in age from 18 to 88 years were evaluated. There was a negative correlation of the CAPs (Spearman correlations: r = -0.98, P < .001) with loss of coactivation (partial correlations: r = -0.17, P < .001) and deactivation (partial correlations: r = 0.216, P < .001) with aging. The CAPs, characterized by negative correlation patterns between the DMN and AN, occurred (partial correlations: r = 0.14, P = .003) and dwelled (partial correlations: r = 0.10, P = .04) more with aging. Moreover, the AN and DMN CAP transitioned more to the AN and FPN CAP with aging (partial correlations: r = 0.17, P < .001). Conclusion The dynamics of the healthy aging brain are characterized mainly by more flexibility of the high-order cognitive networks while maintaining primary sensory functions (networks). Online supplemental material is available for this article. © RSNA, 2022 See also the editorial by Holodny in this issue.

Development of a Piezoelectric Actuated Tactile Stimulation Device for Population Receptive Field Mapping in Human Somatosensory Cortex With fMRI.

BACKGROUND: Multichannel tactile stimulation devices is need to investigate human finger population receptive field (pRF) characteristics in the primary somatosensory cortex during functional magnetic resonance imaging (fMRI). PURPOSE: To accurately characterize right-hand somatosensory representation based on the Bayesian pRF model. STUDY TYPE: Prospective. POPULATION: A water phantom and six healthy participants (four males, mean 23.8 years old). FIELD STRENGTH/SEQUENCE: T1-weighted magnetization-prepared rapid gradient-echo, T2*-weighted echo planar imaging at 3 T. ASSESSMENT: The piezoelectric actuated tactile stimulation device consisted of execution unit and control unit. The output performance of the device was measured by a laser displacement sensor. The effect of the device on images' signal-to-noise ratio (SNR) was measured by phantom experiments. The activation representation arrangement order, relative volumes, and receptive field size of the right hand were assessed during the along-digits and cross-digits paradigms. STATISTICAL TESTS: The normality of the data was tested by the Shapiro-Wilk method. A paired-sample t test was performed to test pRF characteristics for all digit pairings. The significance level was set to P = 0.05 (false discovery rate [FDR] correct). RESULTS: Percussive stimulation provided by the piezoelectric actuated tactile stimulator had a stable displacement (2.64 mm) over a wide range of vibration frequencies (0-30 Hz). The output delay of the device was 1 millisecond. The device did not affect the image's SNR (without the device: SNR = 138.24 ± 7.87, temporal SNR [TSNR] = 440.03 ± 52.08. With the device: SNR = 138.06 ± 8.44, TSNR = 438.52 ± 56.38. PSNR  = 0.88, PTSNR  = 0.46). Representations of right-hand fingers showed the same arrangement order in both experiments (D1-D5 arranged along the central sulcus). However, the relative volumes of D3 showed significant differences in S1 (P = 0.003). Among four subareas, the relative volumes of D3 were significantly different in area 1 (P = 0.047). DATA CONCLUSION: This developed stimulator, through experimental verification, could play a role in pRF mapping exploration. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Population Receptive Field Characteristics in the between- and Within-Digit Dimensions of the Undominant Hand in the Primary Somatosensory Cortex.

Somatotopy is an important guiding principle for sensory fiber organization in the primary somatosensory cortex (S1), which reflects tactile information processing and is associated with disease-related reorganization. However, it is difficult to measure the neuronal encoding scheme in S1 in vivo in normal participants. Here, we investigated the somatotopic map of the undominant hand using a Bayesian population receptive field (pRF) model. The model was established in hand space with between- and within-digit dimensions. In the between-digit dimension, orderly representation was found, which had low variability across participants. The pRF shape tended to be elliptical for digits with high spatial acuity, for which the long axis was along the within-digit dimension. In addition, the pRF width showed different change trends in the 2 dimensions across digits. These results provide new insights into the neural mechanisms in S1, allowing for in-depth investigation of somatosensory information processing and disease-related reorganization.

Development and validation of a prognostic nomogram among patients with acute exacerbation of chronic obstructive pulmonary disease in intensive care unit.

BACKGROUND: Acute exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) contributes significantly to mortality among patients with COPD in Intensive care unit (ICU). This study aimed to develop a nomogram to predict 30-day mortality among AECOPD patients in ICU. METHODS: In this retrospective cohort study, we extracted AECOPD patients from Medical Information Mart for Intensive Care III (MIMIC-III) database. Multivariate logistic regression based on Akaike information criterion (AIC) was used to establish the nomogram. Internal validation was performed by a bootstrap resampling approach with 1000 replications. The discrimination and calibration of the nomogram were evaluated by Harrell's concordance index (C-index) and Hosmer-Lemeshow (HL) goodness-of-fit test. Decision curve analysis (DCA) was performed to evaluate its clinical application. RESULTS: A total of 494 patients were finally included in the study with a mean age of 70.8 years old. 417 (84.4%) patients were in the survivor group and 77 (15.6%) patients were in the non-survivor group. Multivariate logistic regression analysis based on AIC included age, pO2, neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI), invasive mechanical ventilation and vasopressor use to construct the nomogram. The adjusted C-index was 0.745 (0.712, 0.778) with good calibration (HL test, P = 0.147). The Kaplan-Meier survival curves revealed a significantly lower survival probability in the high-risk group than that in the low-risk group (P < 0.001). DCA showed that nomogram was clinically useful. CONCLUSION: The nomogram developed in this study could help clinicians to stratify AECOPD patients and provide appropriate care in clinical setting.

Advanced cutting techniques for solid food: Mechanisms, applications, modeling approaches, and future perspectives.

Cutting is an imperative operation in the food-manufacturing factory, separating food into a predefined geometry. A broad range of solid foods, with various components, textures, and structures, pose enormous challenges to conventional cutting strategies. Additionally, the cutting performance is significantly impacted by the processing parameters, wherein trial-and-error or empirical methods are often used to select the parameters in source-wasting and time-consuming ways. Hence, there is a need to accelerate the development of advanced cutting techniques and novel modeling approaches in the food-manufacturing industry. Recently, advanced cutting techniques (ultrasonic vibration-assisted [UVA], laser, and waterjet cutting) are seen to be superior in processing foods of various textures, with the advantages of high cutting quality, low contamination, and easy operation. Compared with conventional cutting, advanced cutting techniques can dramatically reduce cutting force and energy consumption, resulting in high efficiency, energy-and-source saving, and low carbon footprint. Additionally, the finite element (FE) model does simulate the cutting process well, and artificial intelligence (AI) technology is competent to optimize the cutting parameters. This review is perhaps the first one focusing on the advanced cutting techniques applied in the food industry, serving as a summary of the cutting mechanisms, critical influence factors, and applications of conventional and advanced cutting techniques including UVA, laser, and waterjet cutting. In addition, the modeling approaches with respect to FE and AI models are emphasized. Finally, the challenges and future perspectives of advanced cutting techniques combined with modeling approaches are highlighted, and those approaches are promising in the future intelligent food-manufacturing industry. PRACTICAL APPLICATION: The review clearly demonstrates that advanced cutting techniques as having advantages such as high efficiency, energy-and-source saving, and low damages, thus exhibiting great potential in processing food of various textures with high cutting quality, low contamination, and easy operation. Additionally, the FE model does simulate the cutting process well and AI is competent in optimizing the cutting parameters, which possesses great potential in providing comprehensive cutting information and selecting the optimal combination of cutting parameters.

Hemodynamic response varies across tactile stimuli with different temporal structures.

Tactile stimuli can be distinguished based on their temporal features (e.g., duration, local frequency, and number of pulses), which are fundamental for vibrotactile frequency perception. Characterizing how the hemodynamic response changes in shape across experimental conditions is important for designing and interpreting fMRI studies on tactile information processing. In this study, we focused on periodic tactile stimuli with different temporal structures and explored the hemodynamic response function (HRF) induced by these stimuli. We found that HRFs were stimulus-dependent in tactile-related brain areas. Continuous stimuli induced a greater area of activation and a stronger and narrower hemodynamic response than intermittent stimuli with the same duration. The magnitude of the HRF increased with increasing stimulus duration. By normalizing the characteristics into topographic matrix, nonlinearity was obvious. These results suggested that stimulation patterns and duration within a cycle may be key characters for distinguishing different stimuli. We conclude that different temporal structures of tactile stimuli induced different HRFs, which are essential for vibrotactile perception and should be considered in fMRI experimental designs and analyses.

Influence of layered skull modeling on the frequency sensitivity and target accuracy in simulations of transcranial current stimulation.

With the development of electrical stimulation technology, especially the emergence of temporally interfering (TI) stimulation, it is necessary to discuss the influence of current frequency on stimulation intensity. Accurate skull modeling is important for transcranial current stimulation (tCS) simulation prediction because of its large role in dispersing current. In this study, we simulated different frequencies of transcranial alternating current stimulation (tACS) and TI stimulation in single-layer and layered skull model, compared the electric field via error parameters such as the relative difference measure and relative magnification factor. Pearson correlation analysis and t-test were used to measure the differences in envelope amplitude. The results showed that the intensity of electric field in the brain generated by per unit of stimulation current will increase with current frequency, and the layered skull model had a better response to frequency. An obvious pattern difference was found between the electric fields of the layered and single-layer skull individualized models. For TI stimulation, the Pearson correlation coefficient between the envelope distribution of the layered skull model and the single-layer skull was only 0.746 in the individualized model, which is clearly lower than the correlation coefficient of 0.999 determined from the spherical model. Higher carrier frequencies seemed to be easier to generate a large enough brain electric field envelope in TI stimulation. In conclusion, we recommend using layered skull models instead of single-layer skull models in tCS (particularly TI stimulation) simulation studies in order to improve the accuracy of the prediction of stimulus intensity and stimulus target.

Pneumatical-Mechanical Tactile Stimulation Device for Somatotopic Mapping of Body Surface During fMRI.

BACKGROUND: There is a need for devices that allow reproducible stimulation of skin areas of humans for investigating somatosensory mapping of the whole-body surface. However, their design is not simple, due to the magnetic field of MRI scanners. PURPOSE: To accurately characterize the mapping of somatosensory presentation of the whole-body surface of subjects during functional (f)MRI scans. STUDY TYPE: Prospective. POPULATION: A water phantom and six healthy participants (age 23-27 years; two males) were recruited for the fMRI experiment. FIELD STRENGTH/SEQUENCE: T1 -weighted magnetization-prepared rapid acquisition gradient-echo, T2 *-weighted gradient echo sequence at 3T. ASSESSMENT: The stimulation device for somatotopic mapping was composed of three units: an air-generating unit, a control unit, and an execution unit. The fMRI in response to tactile stimulation was measured to characterize somatotopic mapping of the right-side body consisting of hand, arm, and leg in six healthy subjects. STATISTICAL TESTS: Pared-samples t-test for the conditions in SII. RESULTS: The pneumatical-mechanical tactile stimulation offered a wide range of stimulation intensities (0-400 g) in each channel. The predetermined physical pressure was successfully reached within ~5 msec and returned to baseline within 5 msec after the end of stimulation. With this tactile device, the digressive rate of the signal-to-noise ratio (SNR) (271.44 without the device, 269.68 with the device) was 0.65% in the magnetic field environment. For the fMRI experiment, the primary somatosensory activation contralateral to the stimulation site was detected in response to spatial task and attentive task. DATA CONCLUSION: This stimulation device characterized the mapping of somatosensory representation of the whole-body surface in individual participants during fMRI scans. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1. J. Magn. Reson. Imaging 2020;52:1093-1101.

Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China.

OBJECTIVE: We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak. METHODS: An online survey was run from February 19 to March 6, 2020; a total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2). RESULTS: Compared with nonmedical health workers (n = 1,255), medical health workers (n = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, p < 0.01), anxiety (13.0 vs. 8.5%, p < 0.01), depression (12.2 vs. 9.5%; p< 0.04), somatization (1.6 vs. 0.4%; p < 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; p < 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (p ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (p < 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (p < 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (p < 0.01 or 0.05). CONCLUSIONS: During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.