Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study.

Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study.

The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.

Abnormal brain activities of cognitive processes in cerebral small vessel disease: A systematic review of task fMRI studies.

Cerebral small vessel disease (CSVD) is characterized by widespread functional changes in the brain, as evident from abnormal brain activations during cognitive tasks. However, the existing findings in this area are not yet conclusive. We systematically reviewed 25 studies reporting task-related fMRI in five cognitive domains in CSVD, namely executive function, working memory, processing speed, motor, and affective processing. The findings highlighted: (1) CSVD affects cognitive processes in a domain-specific manner; (2) Compensatory and regulatory effects were observed simultaneously in CSVD, which may reflect the interplay between the negative impact of brain lesion and the positive impact of cognitive reserve. Combined with behavioral and functional findings in CSVD, we proposed an integrated model to illustrate the relationship between altered activations and behavioral performance in different stages of CSVD: functional brain changes may precede and be more sensitive than behavioral impairments in the early pre-symptomatic stage; Meanwhile, compensatory and regulatory mechanisms often occur in the early stages of the disease, while dysfunction/decompensation and dysregulation often occur in the late stages. Overall, abnormal hyper-/hypo-activations are crucial for understanding the mechanisms of small vessel lesion-induced behavioral dysfunction, identifying potential neuromarker and developing interventions to mitigate the impact of CSVD on cognitive function.

Dobutamine-induced alternations in cerebral blood flow of healthy adults: a 3D pseudocontinuous arterial spin labeling study.

BACKGROUND: It is unclear whether dobutamine, commonly used clinically in echocardiography and short-term congestive heart failure treatment for promoting increased myocardial contractility, affects brain microcirculatory behavior. Cerebral microcirculation plays an important role in ensuring adequate oxygen transport. Therefore, we investigated the effects of dobutamine on cerebral hemodynamics. METHODS: Forty-eight healthy volunteers without cardiovascular or cerebrovascular disease underwent MRI to obtain cerebral blood flow (CBF) maps using 3D pseudocontinuous arterial spin labeling before and during the dobutamine stress test. Additionally, cerebrovascular morphology was obtained based on 3D-time-off-light (3D-TOF) magnetic resonance angiography (MRA). Electrocardiogram, heart rate (HR), respiration rate (RR), blood pressure, and blood oxygen were simultaneously recorded before and during dobutamine injection and during recovery (not during MRI). The anatomic features of the circle of Willis and the basilar artery (BA) diameter were assessed on MRA images by two radiologists with extensive neuroimaging experience. Binary logistic regression was used to test for the independent determinants of CBF changes. RESULTS: HR, RR, systolic (SBP), and diastolic blood pressure (DBP) significantly increased after dobutamine infusion. Blood oxygen levels remained similar. Compared to the CBF in the resting state, the CBF values exhibited significantly lower CBF levels in both grey matter and white matter. Furthermore, compared with the CBF in the resting state, that in the stress state was decreased in the anterior circulation, mainly in the frontal lobe (voxel level P < 0.001, pixel level P < 0.05). Logistic regression showed that body mass index (BMI; odds ratio [OR] 5.80, 95% confidence interval [CI] 1.60-21.01, P = 0.008], resting SBP (OR 0.64, 95% CI 0.45-0.92, P = 0.014), and BA diameter (OR 11.04, 95% CI 1.05-116.53, P = 0.046) were significantly associated with frontal lobe CBF changes. CONCLUSIONS: Dobutamine-induced stress significantly decreased CBF in the frontal lobe anterior circulation. Individuals with a high BMI and low SBP during the dobutamine stress test are more likely to have a stress-induced CBF decrease. Thus, attention should be paid to blood pressure, BMI, and cerebrovascular morphology of patients undergoing dobutamine stress echocardiography or those receiving intensive care or anesthesia.

A machine learning approach to predict cerebral perfusion status based on internal carotid artery blood flow.

BACKGROUND AND OBJECTIVE: Cerebral blood flow (CBF), or perfusion, is a prerequisite for maintaining brain metabolism and normal physiological functions. Diagnosing and evaluating cerebral perfusion status is crucial to managing brain disease. However, cerebral perfusion imaging devices are complicated to operate, should be controlled by specialized technicians, are often large, and are usually installed in fixed places such as hospitals. It is significantly difficult for clinicians to obtain the cerebral perfusion status in time. Considering that CBF is mainly supplied by the internal carotid artery (ICA), this study proposes a cerebral perfusion status prediction model that can automatically quantify the level of cerebral perfusion in patients by modeling the association between ICA blood flow and cerebral perfusion. MATERIALS AND METHODS: Forty-eight participants were enrolled in the study after screening. We collected participants' ICA ultrasound and brain magnetic resonance imaging (MRI) data before and after dobutamine injection based on a rigorous experimental paradigm and built an ICA-cerebral perfusion datasetdd. Support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) were used for early prediction of cerebral perfusion status. The SHAP analysis was adopted to reveal the impact of interpretable predictions for each feature. RESULTS: The XGBOOST model demonstrated the best overall classification performance with an accuracy of 78.01%, sensitivity of 96.67%, specificity of 98.23%, F1 score of 74.57%, Matthews correlation coefficient (MCC) of 62.17%, and area under the receiver operating characteristic curve (AUC) of 87.08%. Accelerated speed, peak systolic flow velocity, and resistance index of ICA blood flow are important factors for cerebral perfusion prediction. CONCLUSIONS: The proposed method paves a new avenue for the study of predicting cerebral perfusion status automatically and providesv a noninvasive, real-time, and low-cost alternative to brain perfusion imaging. Moreover, this analysis identifies highly predictive features for the cerebral perfusion status and gives clinicians an intuitive understanding of the influence of key features. The prediction models can serve as an early warning tool that offers sufficient time for clinicians to take early intervention measures.

Integrating domain knowledge for biomedical text analysis into deep learning: A survey.

The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable performance in biomedical natural language processing, however, its development has been limited by well-annotated datasets and interpretability. To solve this, researchers have considered combining domain knowledge (such as biomedical knowledge graph) with biomedical data, which has become a promising means of introducing more information into biomedical datasets and following evidence-based medicine. This paper comprehensively reviews more than 150 recent literature studies on incorporating domain knowledge into deep learning models to facilitate typical biomedical text analysis tasks, including information extraction, text classification, and text generation. We eventually discuss various challenges and future directions.

Lesion Network Mapping for Neurological Deficit in Acute Ischemic Stroke.

OBJECTIVE: To create a comprehensive map of strategic lesion network localizations for neurological deficits, and identify prognostic neuroimaging biomarkers to facilitate the early detection of patients with a high risk of poor functional outcomes in acute ischemic stroke (AIS). METHODS: In a large-scale multicenter study of 7,807 patients with AIS, we performed voxel-based lesion-symptom mapping, functional disconnection mapping (FDC), and structural disconnection mapping (SDC) to identify distinct lesion and network localizations for National Institutes of Health Stroke Scale (NIHSS) score. Impact scores were calculated based on the odds ratios or t-values of voxels from voxel-based lesion-symptom mapping, FDC, and SDC results. Ordinal regression models were used to investigate the predictive value of the impact scores on functional outcome (defined as the modified Rankin score at 3 months). RESULTS: We constructed lesion, FDC, and SDC maps for each item of the NIHSS score, which provided insights into the neuroanatomical substrate and network localization of neurological function deficits after AIS. The lesion impact score of limb ataxia, the SDC impact score of limb deficit, and FDC impact score of sensation and dysarthria were significantly associated with modified Rankin Scale at 3 months. Adding the SDC impact score, FDC impact score, and lesion impact score to the NIHSS total score improved the performance in predicting functional outcomes, as compared with using the NIHSS score alone. INTERPRETATION: We constructed comprehensive maps of strategic lesion network localizations for neurological deficits that were predictive of functional outcomes in AIS. These results may provide specifically localized targets for future neuromodulation therapies. ANN NEUROL 2023;94:572-584.

D-A Structural Oligomers Containing Benzothiadiazole or Benzophenone as Novel Multifunctional Materials for Electrochromic and Photodetector Devices.

In this study, six conjugated oligomers containing D-A structures were synthesized using the Stille coupling reaction and named PHZ1-PHZ6. All the oligomers utilized demonstrated excellent solubilities in common solvents and notable color variations in the domain of electrochromic characteristics. By designing and synthesizing two electron-donating groups modified with alkyl side chains and a common aromatic electron-donating group, as well as cross-binding them with two electron-withdrawing groups with lower molecular weights, the six oligomers presented good color-rendering efficiencies, among which PHZ4 presented the best color-rendering efficiency (283 cm2·C-1). The products also demonstrated excellent electrochemical switching-response times. PHZ5 presented the fastest coloring time (0.7 s), PHZ3 and PHZ6 presented the fastest bleaching times (2.1 s). Following 400 s of cycling activity, all the oligomers under study showed good working stabilities. Moreover, three kinds of photodetectors based on conducting oligomers were prepared, and the experimental results show that the three photodetectors have better specific detection performances and gains. These characteristics indicate that oligomers containing D-A structures are suitable for use as electrochromic and photodetector materials in the research.

Cross-Task Mental Workload Recognition Based on EEG Tensor Representation and Transfer Learning.

The accurate evaluation of mental workload of operators in human machine systems is of great significance in ensuring the safety of operators and the correct execution of tasks. However, the effectiveness of EEG based cross-task mental workload evaluation are still unsatisfactory because of the different EEG response patterns in different tasks, which hindered its generalization in real scenario severely. To solve this problem, this paper proposed a feature construction method based on EEG tensor representation and transfer learning, which was verified in various task conditions. Specifically, four working memory load tasks with different types of information were designed firstly. The EEG signals of participants were collected synchronously during task execution. Then, the wavelet transform method was used to perform time-frequency analysis of multi-channel EEG signals, and three-way EEG tensor (time-frequency-channel) features were constructed. EEG tensor features from different tasks were transferred based on the criteria of feature distribution alignment and class-wise discrimination criteria. Finally, the support vector machine was used to construct a 3-class mental workload recognition model. Results showed that compared with the classical feature extraction methods, the proposed method can achieve higher accuracy in both within-task and cross-task mental workload evaluation (91.1% for within-task and 81.3% for cross-task). These results demonstrated that the EEG tensor representation and transfer learning method is feasible and effective for cross-task mental workload evaluation, which can provide theoretical basis and application reference for future researches.

Diffusivity of Human Cartilage Endplates in Healthy and Degenerated Intervertebral Disks.

The cartilage endplates (CEPs) on the superior and inferior surfaces of the intervertebral disk (IVD), are the primary nutrient transport pathways between the disk and the vertebral body. Passive diffusion is responsible for transporting small nutrient and metabolite molecules through the avascular CEPs. The baseline solute diffusivities in healthy CEPs have been previously studied, however alterations in CEP diffusion associated with IVD degeneration remain unclear. This study aimed to quantitatively compare the solute diffusion in healthy and degenerated human CEPs using a fluorescence recovery after photobleaching (FRAP) approach. Seven healthy CEPs and 22 degenerated CEPs were collected from five fresh-frozen human cadaveric spines and 17 patients undergoing spine fusion surgery, respectively. The sodium fluorescein diffusivities in CEP radial and vertical directions were measured using the FRAP method. The CEP calcification level was evaluated by measuring the average X-ray attenuation. No difference was found in solute diffusivities between radial and axial directions in healthy and degenerated CEPs. Compared to healthy CEPs, the average solute diffusivity was 44% lower in degenerated CEPs (Healthy: 29.07 µm2/s (CI: 23.96-33.62 µm2/s); degenerated: 16.32 µm2/s (CI: 13.84-18.84 µm2/s), p < 0.001). The average solute diffusivity had an inverse relationship with the degree of CEP calcification as determined by the normalized X-ray attenuation values (ß = -22.19, R2 = 0.633; p < 0.001). This study suggests that solute diffusion through the disk and vertebral body interface is significantly hindered by CEP calcification, providing clues to help further understand the mechanism of IVD degeneration.

Increased Resting-State Functional Connectivity as a Compensatory Mechanism for Reduced Brain Volume in Prediabetes and Type 2 Diabetes.

OBJECTIVE: To investigate the contribution of alterations in brain structure and function to cognitive function and their interactions in individuals with diabetes and patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: This population-based study included 2,483 participants who underwent structural MRI (n = 569 with normal glucose metabolism [NGM], n = 1,353 with prediabetes, and n = 561 with T2DM) and cognitive testing. Of these, 2145 participants also underwent functional MRI (n = 496 NGM, n = 1,170 prediabetes, and n = 479 T2DM). Multivariate linear regression models were used to assess the association of brain volume and functional connectivity with cognition, as well as the association of brain volume and functional connectivity. RESULTS: Compared with NGM participants, those with T2DM had lower brain volume in a wide range of brain regions and stronger functional connectivity between the bilateral thalamus and brain functional network (visual network and default mode network), and those with prediabetes had lower brain volume in specific local regions (subcortical gray matter volume and subcortical subregions [bilateral thalamus, bilateral nucleus accumbens, and right putamen]) and stronger functional connectivity between the right thalamus and visual network. Cognition was associated with greater right thalamus volume and lower functional connectivity between the right thalamus and visual network. Functional connectivity between the right thalamus and visual network was associated with lower right thalamus volume. CONCLUSIONS: Cognition was associated with greater brain volume and lower functional connectivity in T2DM. Increased functional connectivity may indicate a compensatory mechanism for reduced brain volume that begins in the prediabetic phase.

Tightly intercalated Ti3C2Tx/MoO3-x/PEDOT:PSS free-standing films with high volumetric/gravimetric performance for flexible solid-state supercapacitors.

Ti3C2Tx-MXenes have extremely promising applications in electrochemistry, but the development of Ti3C2Tx is limited due to severe self-stacking problem. Here, we introduced oxygen vacancy-enriched molybdenum trioxide (MoO3-x) with pseudocapacitive properties as the intercalator of Ti3C2Tx and PEDOT with high electronic conductivity as the co-intercalator and conductive binder of Ti3C2Tx to synthesize Ti3C2Tx/MoO3-x/PEDOT:PSS (TMP) free-standing films by vacuum-assisted filtration and H2SO4 soaking. The tightly intercalated free-standing film structure can effectively improve the self-stacking phenomenon of Ti3C2Tx, expose more active sites and facilitate electron/ion transport, thus making TMP show excellent electrochemical performance. The volumetric and gravimetric capacitance of optimized TMP-2 can reach 1898.5 F cm-3 and 523.0 F g-1 at 1 A g-1 with a rate performance of 90.5% at the current density from 1 A g-1 to 20 A g-1, which is significantly better than those of MXene-based composites reported in the literature. The directly-assembled TMP-2//TMP-2 flexible solid-state supercapacitor displays high energy/power output performances (25.1 W h L-1 at 6383.1 W L-1, 6.9 W h kg-1 at 1758.4 W kg-1) and there is no obvious change after 100 cycles at a bending angle of 180°. As a result, the tightly intercalated TMP-2 free-standing film with high volumetric/gravimetric capacitances is a promising material for flexible energy storage devices.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old.

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

The effect of dobutamine on ocular blood flow of healthy adults: A 3D pseudocontinuous aterial spin labelling study.

Purpose: Ocular blood flow (OBF) is an important risk factor for incidence, prevalence and progression of some ocular disorders. To date, there are very limited therapeutic options to increase OBF. This study investigated the effect of dobutamine on OBF of heathy adults using 3D pseudocontinuous arterial spin labelling (3D-pcASL), and explored the risk factors associated with OBF. Methods: Forty-three healthy participants (86 eyes) were given an intravenous injection of dobutamine. We measured OBF using 3D-pcASL with a 3.0T- MRI scanner, OBF values were independently obtained by two doctors from the OBF map. We also collected physiological parameters using a vital signs monitor. The OBF and physiological parameters in the in the period before and after dobutamine injection states were obtained. Results: OBF increased significantly after dobutamine injection using paired t test method (from 22.43 ± 9.87 to 47.73 ± 14.02 ml/min/100g, p < 0.001). Age, heart rate and systolic blood pressure were the main risk factors affecting OBF using logistic regression analysis (all p values < 0.05). Conclusion: To the best of our knowledge, this is the first study observing the effect of dobutamine on OBF. Our findings indicated that intravenously injected dobutamine increased OBF, making it a possible option to counteract ocular vascular ischaemia in the future.

Conjugated Polymers Containing EDOT Units as Novel Materials for Electrochromic and Resistance Memory Devices.

Four new multifunctional polymers (P1, P2, P3, and P4) containing EDOT units were synthesized by the Stille coupling reaction. A PL spectrum test found that the introduction of large conjugated groups led to strong fluorescence emission in all polymers. Among the electrochromic and switch properties, these polymer films exhibited reversible color changes, as well as good coloring efficiency. Electrochemical experiments found that the initial oxidation potentials of the polymers were 1.27 V, 1.67 V, 1.78 V, and 1.58 V, respectively. Among them, P3 showed a higher coloring efficiency (283.3 cm2·C-1), and P2 showed a shorter response time (tc = 2.2 s, tb = 2.8 s). These polymers all exhibited electrochromic and resistive switching storage characteristics. They have good solubility in many organic solvents. In the resistance switch memory characteristics, all polymers had obvious memristive properties, and P4 exhibited a larger switch current ratio (2527.42) and a smaller threshold voltage (0.9 V).

Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study.

BACKGROUND: White matter (WM) microstructural abnormalities have been observed in diabetes. However, evidence of prediabetes is currently lacking. This study aims to investigate the WM integrity in prediabetes and diabetes. We also assess the association of WM abnormalities with glucose metabolism status and continuous glucose measures. METHODS: The WM integrity was analyzed using cross-sectional baseline data from a population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. The cohort, including a total of 2218 cases with the mean age of 61.3 ± 6.6 years and 54.1% female, consisted of 1205 prediabetes which are categorized into two subgroups (a group of 254 prediabetes with combined impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) and the other group of 951 prediabetes without combined IFG/IGT), 504 diabetes, and 509 normal control subjects. Alterations of WM integrity were determined by diffusion tensor imaging along with tract-based spatial statistics analysis to compare diffusion metrics on WM skeletons between groups. The mixed-effects multivariate linear regression models were used to assess the association between WM microstructural alterations and glucose status. FINDINGS: Microstructural abnormalities distributed in local WM tracts in prediabetes with combined IFG/IGT and spread widely in diabetes. These WM abnormalities are associated with higher glucose measures. INTERPRETATION: Our findings suggest that WM microstructural abnormalities are already present at the prediabetes with combined IFG/IGT stage. Preventative strategies should begin early to maintain normal glucose metabolism and avert further destruction of WM integrity. FUNDING: Partially supported by National Key R&D Program of China (2016YFC0901002).

EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks With Different Types of Information.

The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.

[Evaluation of Mental Workload in Working Memory Tasks with Different Information Types Based on EEG].

OBJECTIVE: Exploring the effectiveness of using EEG linear and nonlinear features for accessing mental workload in different tasks. METHODS: Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. Linear and nonlinear features of EEGs were then extracted. Indices that can effectively reflect mental workload levels were selected by using multivariate analysis of variance statistical approach. RESULTS: With the increment of task load, power of frontal Theta, Theta/Alpha ratio, and sample entropies (scales>10) in parietal regions increased significantly first and decreased slightly then, while the power of central-parietal Alpha decreased significantly first and increased slightly then. No difference in power of frontal Theta, central-parietal Alpha, and sample entropies (scales>10) of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks. CONCLUSIONS: The results can provide evidence for the mental workload evaluation in tasks with different information types.

Inverse Problem in Resonant Ultrasound Spectroscopy With Sampling and Optimization: A Comparative Study on Human Cortical Bone.

The Bayesian inference with prior knowledge has been proposed recently to solve the inverse problem in resonant ultrasound spectroscopy. It allows inferring the elastic properties of high damping materials, such as cortical bone with less dependence on the initial guessed values. In this method, the estimation of the stiffness coefficients is expressed as a probabilistic solution to the inverse problem, which can be achieved by sampling or optimization methods. However, the detailed performance comparison of these two strategies applied to high damping materials has not been fully studied. In this work, the full stiffness tensor of 52 transversely isotropic cortical bone specimens was obtained using Markov chain Monte Carlo (MCMC) sampling and particle swarm optimization (PSO), respectively. Results showed that the local probability distributions of stiffness coefficients estimated by the two methods are consistent. Compared with MCMC, the average calculation speed of PSO is ten times faster [614 s ± 59 s (MCMC) versus 53 s ± 22 s (PSO)]. The mean standard error between theoretical and experimental resonant frequencies was slightly smaller for PSO compared with MCMC. In conclusion, PSO, a global optimization strategy, is suitable to solve the inverse problem for high damping materials.

Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network.

Objective.The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learning method for fast reconstruction of synthetic MRI.Approach.A total of 44 healthy subjects were recruited and random divided into a training set (30 subjects) and a testing set (14 subjects). A multiple-dynamic, multiple-echo (MDME) sequence was used to acquire synthetic MRI images. Quantification maps (T1, T2, and proton density (PD) maps) and weighted (T1W, T2W, and T2W FLAIR) images were created with MAGiC software and then used as the ground truth images in the deep learning (DL) model. An improved multichannel U-Net structure network was trained to generate quantification maps and weighted images from raw synthetic MRI imaging data (8 module images). Quantitative evaluation was performed on quantification maps. Quantitative evaluation metrics, as well as qualitative evaluation were used in weighted image evaluation. Nonparametric Wilcoxon signed-rank tests were performed in this study.Main results.The results of quantitative evaluation show that the error between the generated quantification images and the reference images is small. For weighted images, no significant difference in overall image quality or signal-to-noise ratio was identified between DL images and synthetic images. Notably, the DL images achieved improved image contrast with T2W images, and fewer artifacts were present on DL images than synthetic images acquired by T2W FLAIR.Significance.The DL algorithm provides a promising method for image generation in synthetic MRI techniques, in which every step of the calculation can be optimized and faster, thereby simplifying the workflow of synthetic MRI techniques.