Multimodal Covariance Network Reflects Individual Cognitive Flexibility.

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks.

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.

Frequency-specific directed interactions between whole-brain regions during sentence processing using multimodal stimulus.

Neural oscillations subserve a broad range of speech processing and language comprehension functions. Using an electroencephalogram (EEG), we investigated the frequency-specific directed interactions between whole-brain regions while the participants processed Chinese sentences using different modality stimuli (i.e., auditory, visual, and audio-visual). The results indicate that low-frequency responses correspond to the process of information flow aggregation in primary sensory cortices in different modalities. Information flow dominated by high-frequency responses exhibited characteristics of bottom-up flow from left posterior temporal to left frontal regions. The network pattern of top-down information flowing out of the left frontal lobe was presented by the joint dominance of low- and high-frequency rhythms. Overall, our results suggest that the brain may be modality-independent when processing higher-order language information.

The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS.

Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Auditory Dominance in Processing Chinese Semantic Abnormalities in Response to Competing Audio-visual Stimuli.

Language is a remarkable cognitive ability that can be expressed through visual (written language) or auditory (spoken language) modalities. When visual characters and auditory speech convey conflicting information, individuals may selectively attend to either one of them. However, the dominant modality in such a competing situation and the neural mechanism underlying it are still unclear. Here, we presented participants with Chinese sentences in which the visual characters and auditory speech convey conflicting information, while behavioral and electroencephalographic (EEG) responses were recorded. Results showed a prominent auditory dominance when audio-visual competition occurred. Specifically, higher accuracy (ACC), larger N400 amplitudes and more linkages in the posterior occipital-parietal areas were demonstrated in the auditory mismatch condition compared to that in the visual mismatch condition. Our research illustrates the superiority of the auditory speech over the visual characters, extending our understanding of the neural mechanisms of audio-visual competition in Chinese.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder.

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

Assessment of radiation doses and image quality of multiple low-dose CT exams in COVID-19 clinical management.

Purpose: The Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 from an outbreak of unexplained pneumonia in Wuhan (Hubei, China) that subsequently spread rapidly around the world. Because of the public health emergency, chest CT has been widely used for sensitive detection and diagnosis, monitoring the changes of lesions and also for treatment evaluation. The purpose of this study was to investigate radiation dose and image quality of chest CT scans received by COVID-19 patients and to evaluate the oncogenic risk of multiple chest CT examinations. Methods: A retrospective review of 33 patients with RT-PCR confirmed COVID-19 infection was performed from January 31, 2020 to February 19, 2020. The date of each CT exam and respective radiation dose for each exam was recorded for all patients. Multiple pulmonary CT scans were obtained during diagnosis and treatment procedure. Scan frequency, total scan times, radiation dose, and image quality were determined. Results: Thirty-three patients (15 males and 18 females, age 21-82 years) with confirmed COVID-19 pneumonia underwent a total of 143 chest CT scans. The number of CT scans per patient was 4 ± 1, with a range of 2-6. The time interval between two consecutive chest CT scans was 3 ± 1 days. The average effective dose from a single chest CT scan was 1.21 ± 0.10 mSv, with a range of 1.02-1.44 mSv. The average cumulative effective dose per patient was 5.25 ± 1.52 mSv, with a range of 2.24-7.48 mSv. The maximum cumulative effective dose was 7.48 mSv for six CT examinations during COVID-19 treatment. Based on subjective image quality analysis, the visual scoring of CT findings was 11.23 ± 1.35 points out of 15 points. Conclusions: The frequency, total number and image quality of chest CT scans should be reviewed carefully to guarantee minimally required CT scans during the COVID-19 management.

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

Development of Radiofrequency Saturation Amplitude-independent Quantitative Markers for Magnetization Transfer MRI of Prostate Cancer.

BACKGROUND: In the United States, prostate cancer has a relatively large impact on men's health. Magnetic resonance imaging (MRI) is useful for the diagnosis and treatment of prostate cancer. INTRODUCTION: The purpose of this study was to develop a quantitative marker for use in prostate cancer magnetization transfer (MT) magnetic resonance imaging (MRI) studies that is independent of radiofrequency (RF) saturation amplitude. METHODS: Eighteen patients with biopsy-proven prostate cancer were enrolled in this study. MTMRI images were acquired using four RF saturation amplitudes at 33 frequency offsets. ROIs were delineated for the peripheral zone (PZ), central gland (CG), and tumor. Z-spectral data were collected in each region and fit to a three-parameter equation. The three parameters are: the magnitude of the bulk water pool (Aw), the full width at half maximum of the water pool (Gw), and the magnitude of the bound pool (Ab), while, the slopes from the linear regressions of Gw and Ab on RF saturation amplitude (called kAb and kGw) were used as quantitative markers. RESULTS: A pairwise statistically significant difference was found between the PZ and tumor regions for the two saturation amplitude-independent quantitative markers. No pairwise statistically significant differences were found between the CG and tumor regions for any quantitative markers. CONCLUSION: The significant differences between the values of the two RF saturation amplitudeindependent quantitative markers in the PZ and tumor regions reveal that these markers may be capable of distinguishing healthy PZ tissue from prostate cancer.

Magnetic resonance elastography of the brain: A study of feasibility and reproducibility using an ergonomic pillow-like passive driver.

Magnetic resonance elastography (MRE) can be used to noninvasively resolve the displacement pattern of induced mechanical waves propagating in tissue. The goal of this study is to establish an ergonomically flexible passive-driver design for brain MRE, to evaluate the reproducibility of MRE tissue-stiffness measurements, and to investigate the relationship between tissue-stiffness measurements and driver frequencies. An ergonomically flexible passive pillow-like driver was designed to induce mechanical waves in the brain. Two-dimensional finite-element simulation was used to evaluate mechanical wave propagation patterns in brain tissues. MRE scans were performed on 10 healthy volunteers at mechanical frequencies of 60, 50, and 40 Hz. An axial mid-brain slice was acquired using an echo-planar imaging sequence to map the displacement pattern with the motion-encoding gradient along the through-plane (z) direction. All subjects were scanned and rescanned within 1 h. The Wilcoxon signed-rank test was used to test for differences between white matter and gray matter shear-stiffness values. One-way analysis of variance (ANOVA) was used to test for differences between shear-stiffness measurements made at different frequencies. Scan-rescan reproducibility was evaluated by calculating the within-subject coefficient of variation (CV) for each subject. The finite-element simulation showed that a pillow-like passive driver is capable of efficient shear-wave propagation through brain tissue. No subjects complained about discomfort during MRE acquisitions using the ergonomically designed driver. The white-matter elastic modulus (mean ±â€¯standard deviation) across all subjects was 3.85 ±â€¯0.12 kPa, 3.78 ±â€¯0.15 kPa, and 3.36 ±â€¯0.11 kPa at frequencies of 60, 50, and 40 Hz, respectively. The gray-matter elastic modulus across all subjects was 3.33 ±â€¯0.14 kPa, 2.82 ±â€¯0.16 kPa, and 2.24 ±â€¯0.14 kPa at frequencies of 60, 50, and 40 Hz, respectively. The Wilcoxon signed-rank test confirmed that the shear stiffness was significantly higher in white matter than gray matter at all three frequencies. The ranges of within-subject coefficients of variation for white matter, gray matter, and whole-brain shear-stiffness measurements for the three frequencies were 1.8-3.5% (60 Hz), 4.7-6.0% (50 Hz), and 3.7-4.1% (40 Hz). An ergonomic pneumatic pillow-like driver is feasible for highly reproducible in vivo evaluation of brain-tissue shear stiffness. Brain-tissue shear-stiffness values were frequency-dependent, thus emphasizing the importance of standardizing MRE acquisition protocols in multi-center studies.

Deficits of congenital amusia beyond pitch: Evidence from impaired categorical perception of vowels in Cantonese-speaking congenital amusics.

Congenital amusia is a lifelong disorder of fine-grained pitch processing in music and speech. However, it remains unclear whether amusia is a pitch-specific deficit, or whether it affects frequency/spectral processing more broadly, such as the perception of formant frequency in vowels, apart from pitch. In this study, in order to illuminate the scope of the deficits, we compared the performance of 15 Cantonese-speaking amusics and 15 matched controls on the categorical perception of sound continua in four stimulus contexts: lexical tone, pure tone, vowel, and voice onset time (VOT). Whereas lexical tone, pure tone and vowel continua rely on frequency/spectral processing, the VOT continuum depends on duration/temporal processing. We found that the amusic participants performed similarly to controls in all stimulus contexts in the identification, in terms of the across-category boundary location and boundary width. However, the amusic participants performed systematically worse than controls in discriminating stimuli in those three contexts that depended on frequency/spectral processing (lexical tone, pure tone and vowel), whereas they performed normally when discriminating duration differences (VOT). These findings suggest that the deficit of amusia is probably not pitch specific, but affects frequency/spectral processing more broadly. Furthermore, there appeared to be differences in the impairment of frequency/spectral discrimination in speech and nonspeech contexts. The amusic participants exhibited less benefit in between-category discriminations than controls in speech contexts (lexical tone and vowel), suggesting reduced categorical perception; on the other hand, they performed inferiorly compared to controls across the board regardless of between- and within-category discriminations in nonspeech contexts (pure tone), suggesting impaired general auditory processing. These differences imply that the frequency/spectral-processing deficit might be manifested differentially in speech and nonspeech contexts in amusics-it is manifested as a deficit of higher-level phonological processing in speech sounds, and as a deficit of lower-level auditory processing in nonspeech sounds.