Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.

BnaABF3 and BnaMYB44 regulate the transcription of zeaxanthin epoxidase genes in carotenoid and abscisic acid biosynthesis.

Zeaxanthin epoxidase (ZEP) is a key enzyme that catalyzes the conversion of zeaxanthin to violaxanthin in the carotenoid and abscisic acid (ABA) biosynthesis pathways. The rapeseed (Brassica napus) genome has 4 ZEP (BnaZEP) copies that are suspected to have undergone subfunctionalization, yet the 4 genes' underlying regulatory mechanisms remain unknown. Here, we genetically confirmed the functional divergence of the gene pairs BnaA09.ZEP/BnaC09.ZEP and BnaA07.ZEP/BnaC07.ZEP, which encode enzymes with tissue-specific roles in carotenoid and ABA biosynthesis in flowers and leaves, respectively. Molecular and transgenic experiments demonstrated that each BnaZEP pair is transcriptionally regulated via ABA-responsive element-binding factor 3 s (BnaABF3s) and BnaMYB44s as common and specific regulators, respectively. BnaABF3s directly bound to the promoters of all 4 BnaZEPs and activated their transcription, with overexpression of individual BnaABF3s inducing BnaZEP expression and ABA accumulation under drought stress. Conversely, loss of BnaABF3s function resulted in lower expression of several genes functioning in carotenoid and ABA metabolism and compromised drought tolerance. BnaMYB44s specifically targeted and repressed the expression of BnaA09.ZEP/BnaC09.ZEP but not BnaA07.ZEP/BnaC07.ZEP. Overexpression of BnaA07.MYB44 resulted in increased carotenoid content and an altered carotenoid profile in petals. Additionally, RNA-seq analysis indicated that BnaMYB44s functions as a repressor in phenylpropanoid and flavonoid biosynthesis. These findings provide clear evidence for the subfunctionalization of duplicated genes and contribute to our understanding of the complex regulatory network involved in carotenoid and ABA biosynthesis in B. napus.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis.

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia.

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis.

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Dynamic cortical connectivity alterations associated with Major Depressive Disorder: an EEG study.

There are various depressive subtypes identified in patients with major depressive disorder (MDD). Depression with psychotic symptoms is usually known to be a severe type of depression that includes symptoms such as delusions and/or hallucinations, and remains a common condition that is often underrecognized and inadequately treated in clinical practice. Electroencephalography (EEG) biomarkers have been implicated to classify healthy and psychopathological neural signals using machine learning algorithms. In this study, we sought to identify cortical functional connectivity metrics that differentiate network manifestation of different depressive subtypes and healthy controls. We first performed replication analyses to obtain the principal functional connectivity microstates across each independent group (healthy controls, psychotic depressions and nonpsychotic depressions). Next, we examined temporal functional connectivity dynamics in each group. The results show that fundamental dynamic functional connectivity microstates are highly reproducible, both within and across participants. Based on the temporal and sequential parameters (mean duration, fractional windows and transition number) derived from dynamic functional connectivity analysis, we found inter-group differences across healthy and MDD subgroups statistically significant. These results show that the principal FC microstates dynamics are essential neural biomarkers distinctly associated with depression clinical phenotypes.Clinical relevance-Our findings suggest that a network-level feature, that may reflect the neurobiological difference between different depression subtypes, and healthy controls, and in turn may contribute towards a scalable EEG-based assisted diagnostic tool.

Medical Image Processing based on Generative Adversarial Networks: A Systematic Review.

BACKGROUND: Generative adversarial networks (GANs) have demonstrated superior data generation capabilities compared to other methods, making them popular for use in medical image applications. These features have intrigued researchers in the medical imaging field, resulting in a swift implementation of these techniques in various conventional and novel applications such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. A comprehensive review of recent medical imaging breakthroughs will benefit researchers interested in this field. In this review, we aimed to introduce the origin, principle, and extended forms of GANs and summarize the state-of-the-art progress of GAN-based medical image processing methods. METHODS: We searched the literature for studies on Google Scholar and PubMed using the keywords "Segmentation," "Classification," "medical image," and "generative adversarial network." Specifically, the initial search revealed 5423 publications after the removal of duplicated and non-accessible fulltext publications. Then, after the title and abstract screening, 680 underwent full-text screening. Finally, 121 studies were included in our final analysis after full-text screening. RESULTS: The date range of the studies covered in this review is from January 1, 2017, to the present. After a thorough screening and qualification assessment, 121 studies involving GAN-based applications in seven areas of medical images were included in the final methodological review. These areas included synthesis, classification, segmentation, conversion, reconstruction, denoising, and lesion detection. We further classified and summarized these papers into clinical applications, classification methods, and imaging modalities. CONCLUSION: We thoroughly examined the latest research progress of GAN-based medical image augmentation. These techniques effectively alleviate the challenge of limited training samples for medical image diagnosis and treatment models. Furthermore, several critical issues associated with GANs, such as pattern collapse, instability, and lack of interpretability, require attention in future research.

Xanthophyll esterases in association with fibrillins control the stable storage of carotenoids in yellow flowers of rapeseed (Brassica juncea).

Biosynthesis, stabilization, and storage of carotenoids are vital processes in plants that collectively contribute to the vibrant colors observed in flowers and fruits. Despite its importance, the carotenoid storage pathway remains poorly understood and lacks thorough characterization. We identified two homologous genes, BjA02.PC1 and BjB04.PC2, belonging to the esterase/lipase/thioesterase (ELT) family of acyltransferases. We showed that BjPCs in association with fibrillin gene BjFBN1b control the stable storage of carotenoids in yellow flowers of Brassica juncea. Through genetic, high-resolution mass spectrometry and transmission electron microscopy analyses, we demonstrated that both BjA02.PC1 and BjB04.PC2 can promote the accumulation of esterified xanthophylls, facilitating the formation of carotenoid-enriched plastoglobules (PGs) and ultimately producing yellow pigments in flowers. The elimination of BjPCs led to the redirection of metabolic flux from xanthophyll ester biosynthesis to lipid biosynthesis, resulting in white flowers for B. juncea. Moreover, we genetically verified the function of two fibrillin genes, BjA01.FBN1b and BjB05.FBN1b, in mediating PG formation and demonstrated that xanthophyll esters must be deposited in PGs for stable storage. These findings identified a previously unknown carotenoid storage pathway that is regulated by BjPCs and BjFBN1b, while offering unique opportunities for improving the stability, deposition, and bioavailability of carotenoids.

Test and re-test reliability of optimal stimulation targets and parameters for personalized neuromodulation.

Protocols have been proposed to optimize neuromodulation targets and parameters to increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal neuromodulation targets and parameters simultaneously via exploring the test-retest reliability of the optimal neuromodulation protocols. In this study, we employed a publicly available structural and resting-state functional magnetic resonance imaging (fMRI) dataset to investigate the temporal effects of the optimal neuromodulation targets and parameters inferred from our customized neuromodulation protocol and examine the test-retest reliability over scanning time. 57 healthy young subjects were included in this study. Each subject underwent a repeated structural and resting state fMRI scan in two visits with an interval of 6 weeks between two scanning visits. Brain controllability analysis was performed to determine the optimal neuromodulation targets and optimal control analysis was further applied to calculate the optimal neuromodulation parameters for specific brain states transition. Intra-class correlation (ICC) measure was utilized to examine the test-retest reliability. Our results demonstrated that the optimal neuromodulation targets and parameters had excellent test-retest reliability (both ICCs > 0.80). The test-retest reliability of model fitting accuracies between the actual final state and the simulated final state also showed a good test-retest reliability (ICC > 0.65). Our results indicated the validity of our customized neuromodulation protocol to reliably identify the optimal neuromodulation targets and parameters between visits, which may be reliably extended to optimize the neuromodulation protocols to efficiently treat different neuropsychiatric disorders.

Social gaze behavior and hyperarousal in young females with fragile X syndrome: A within-person approach.

Children with fragile X syndrome (FXS) often avoid eye contact, a behavior that is potentially related to hyperarousal. Prior studies, however, have focused on between-person associations rather than coupling of within-person changes in gaze behaviors and arousal. In addition, there is debate about whether prompts to maintain eye contact are beneficial for individuals with FXS. In a study of young females (ages 6-16), we used eye tracking to assess gaze behavior and pupil dilation during social interactions in a group with FXS (n = 32) and a developmentally similar comparison group (n = 23). Participants engaged in semi-structured conversations with a female examiner during blocks with and without verbal prompts to maintain eye contact. We identified a social-behavioral and psychophysiological profile that is specific to females with FXS; this group exhibited lower mean levels of eye contact, significantly increased mean pupil dilation during conversations that included prompts to maintain eye contact, and showed stronger positive coupling between eye contact and pupil dilation. Our findings strengthen support for the perspective that gaze aversion in FXS reflects negative reinforcement of social avoidance behavior. We also found that behavioral skills training may improve eye contact, but maintaining eye contact appears to be physiologically taxing for females with FXS.

Association of Intrinsic Functional Brain Network and Longitudinal Development of Cognitive Behavioral Symptoms in Young Girls With Fragile X Syndrome.

BACKGROUND: Fragile X syndrome (FXS) is an X chromosome-linked genetic disorder characterized by increased risk for behavioral, social, and neurocognitive deficits. Because males express a more severe phenotype than females, research has focused largely on identifying neural abnormalities in all-male or both-sex populations with FXS. Therefore, very little is known about the neural alterations that contribute to cognitive behavioral symptoms in females with FXS. This cross-sectional study aimed to elucidate the large-scale resting-state brain networks associated with the multidomain cognitive behavioral phenotype in girls with FXS. METHODS: We recruited 38 girls with full-mutation FXS (11.58 ± 3.15 years) and 32 girls without FXS (11.66 ± 2.27 years). Both groups were matched on age, verbal IQ, and multidomain cognitive behavioral symptoms. Resting-state functional magnetic resonance imaging data were collected. RESULTS: Compared with the control group, girls with FXS showed significantly greater resting-state functional connectivity of the default mode network, lower nodal strength at the right middle temporal gyrus, stronger nodal strength at the left caudate, and higher global efficiency of the default mode network. These aberrant brain network characteristics map directly onto the cognitive behavioral symptoms commonly observed in girls with FXS. An exploratory analysis suggested that brain network patterns at a prior time point (time 1) were predictive of the longitudinal development of participants' multidomain cognitive behavioral symptoms. CONCLUSIONS: These findings represent the first examination of large-scale brain network alterations in a large sample of girls with FXS, expanding our knowledge of potential neural mechanisms underlying the development of cognitive behavioral symptoms in girls with FXS.

Virtual (Zoom) Interactions Alter Conversational Behavior and Interbrain Coherence.

A growing number of social interactions are taking place virtually on videoconferencing platforms. Here, we explore potential effects of virtual interactions on observed behavior, subjective experience, and neural "single-brain" and "interbrain" activity via functional near-infrared spectroscopy neuroimaging. We scanned a total of 36 human dyads (72 participants, 36 males, 36 females) who engaged in three naturalistic tasks (i.e., problem-solving, creative-innovation, socio-emotional task) in either an in-person or virtual (Zoom) condition. We also coded cooperative behavior from audio recordings. We observed reduced conversational turn-taking behavior during the virtual condition. Given that conversational turn-taking was associated with other metrics of positive social interaction (e.g., subjective cooperation and task performance), this measure may be an indicator of prosocial interaction. In addition, we observed altered patterns of averaged and dynamic interbrain coherence in virtual interactions. Interbrain coherence patterns that were characteristic of the virtual condition were associated with reduced conversational turn-taking. These insights can inform the design and engineering of the next generation of videoconferencing technology.SIGNIFICANCE STATEMENT Videoconferencing has become an integral part of our lives. Whether this technology impacts behavior and neurobiology is not well understood. We explored potential effects of virtual interaction on social behavior, brain activity, and interbrain coupling. We found that virtual interactions were characterized by patterns of interbrain coupling that were negatively implicated in cooperation. Our findings are consistent with the perspective that videoconferencing technology adversely affects individuals and dyads during social interaction. As virtual interactions become even more necessary, improving the design of videoconferencing technology will be crucial for supporting effective communication.

Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.

BACKGROUND: Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS: A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS: The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS: Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

miR319 and its target TCP4 involved in plant architecture regulation in Brassica napus.

Plant architecture is a collection of genetically controlled crop productivity and adaptation. MicroRNAs (miRNAs) have been proved to function in various biological processes, but little is known about how miRNA regulates plant architecture in rapeseed (Brassica napus L.). In this study, four small RNA libraries and two degradome libraries from shoot apex of normal and rod-like plants were sequenced. A total of 639 miRNA precursors and 16 differentially expressed miRNAs were identified in this study. In addition, 322 targets were identified through degradome sequencing. Among them, 14 targets were further validated via RNA ligase-mediated 5' rapid amplification of cDNA ends. Transgenic approach showed that increased TCP4 activity in Arabidopsis resulted in premature onset of maturation and reduced plant size along with early flowering and shortened flowering time. miR319-OE lines in Brassica napus exhibited serrated leaves and abnormal development of shoot apical meristem (SAM), which led to the deformed growth of stem and reduced plant height. In conclusion, our study lays the foundation for elucidating miRNA regulate plant architecture and provides new insight into the miR319/TCP4 module regulates plant architecture in rapeseed.

Kinase CIPK9 integrates glucose and abscisic acid signaling to regulate seed oil metabolism in rapeseed.

Rapeseed (Brassica napus), an important oil crop worldwide, provides large amounts of lipids for human requirements. Calcineurin B-like (CBL)-interacting protein kinase 9 (CIPK9) was reported to regulate seed oil content in the plant. Here, we generated gene-silenced lines through RNA interference biotechnology and loss-of-function mutant bnacipk9 using CRISPR/Cas9 to further study BnaCIPK9 functions in the seed oil metabolism of rapeseeds. We discovered that compared with wild-type (WT) lines, gene-silenced and bnacipk9 lines had substantially different oil contents and fatty acid compositions: seed oil content was improved by 3%-5% and 1%-6% in bnacipk9 lines and gene-silenced lines, respectively; both lines were with increased levels of monounsaturated fatty acids and decreased levels of polyunsaturated fatty acids. Additionally, hormone and glucose content analyses revealed that compared with WT lines the bnacipk9 lines showed significant differences: in bnacipk9 seeds, indoleacetic acid and abscisic acid (ABA) levels were higher; glucose and sucrose contents were higher with a higher hexose-to-sucrose ratio in bnacipk9 mid-to-late maturation development seeds. Furthermore, the bnacipk9 was less sensitive to glucose and ABA than the WT according to stomatal aperture regulation assays and the expression levels of genes involved in glucose and ABA regulating pathways in rapeseeds. Notably, in Arabidopsis (Arabidopsis thaliana), exogenous ABA and glucose imposed on developing seeds revealed the effects of ABA and glucose signaling on seed oil accumulation. Altogether, our results strongly suggest a role of CIPK9 in mediating the interaction between glucose flux and ABA hormone signaling to regulate seed oil metabolism in rapeseed.

Aberrant Neural Response During Face Processing in Girls With Fragile X Syndrome: Defining Potential Brain Biomarkers for Treatment Studies.

BACKGROUND: Children and adolescents with fragile X syndrome (FXS) manifest significant symptoms of anxiety, particularly in response to face-to-face social interaction. In this study, we used functional near-infrared spectroscopy to reveal a specific pattern of brain activation and habituation in response to face stimuli in young girls with FXS, an important but understudied clinical population. METHODS: Participants were 32 girls with FXS (age: 11.8 ± 2.9 years) and a control group of 28 girls without FXS (age: 10.5 ± 2.3 years) matched for age, general cognitive function, and autism symptoms. Functional near-infrared spectroscopy was used to assess brain activation during a face habituation task including repeated upright/inverted faces and greeble (nonface) objects. RESULTS: Compared with the control group, girls with FXS showed significant hyperactivation in the frontopolar and dorsal lateral prefrontal cortices in response to all face stimuli (upright + inverted). Lack of neural habituation (and significant sensitization) was also observed in the FXS group in the frontopolar cortex in response to upright face stimuli. Finally, aberrant frontopolar sensitization in response to upright faces in girls with FXS was significantly correlated with notable cognitive-behavioral and social-emotional outcomes relevant to this condition, including executive function, autism symptoms, depression, and anxiety. CONCLUSIONS: These findings strongly support a hypothesis of neural hyperactivation and accentuated sensitization during face processing in FXS, a phenomenon that could be developed as a biomarker end point for improving treatment trial evaluation in girls with this condition.

Cortical activation predicts posttraumatic improvement in youth treated with TF-CBT or CCT.

BACKGROUND: Identifying neural activation patterns that predict youths' treatment response may aid in the development of imaging-based assessment of emotion dysregulation following trauma and foster tailored intervention. Changes in cortical hemodynamic activity measured with functional near-infrared spectroscopy (fNIRS) may provide a time and cost-effective option for such work. We examined youths' PTSD symptom change following treatment and tested if previously identified activation patterns would predict treatment response. METHODS: Youth (N = 73, mean age = 12.97, SD = 3.09 years) were randomly assigned to trauma-focused cognitive behavioral therapy (TF-CBT), cue-centered therapy (CCT), or treatment as usual (TAU). Parents and youth reported on youth's PTSD symptoms at pre-intervention, post-intervention, and follow-up. Neuroimaging data (N = 31) assessed at pre-intervention were obtained while youth engaged in an emotion expression task. Treatment response slopes were calculated for youth's PTSD symptoms. RESULTS: Overall, PTSD symptoms decreased from pre-intervention through follow-up across conditions, with some evidence of relative benefit of TF-CBT and CCT over TAU but significant individual variation in treatment response. Cortical activation patterns were correlated with PTSD symptom improvement slopes (r = 0.53). In particular, cortical responses to fearful and neutral facial stimuli in six fNIRS channels in the bilateral dlPFC were important predictors of PTSD symptom improvement. CONCLUSIONS: The use of fNIRS provides a method of monitoring and assessing cortical activation patterns in a relatively inexpensive and portable manner. Associations between functional activation and youths' PTSD symptoms improvement may be a promising avenue for understanding emotion dysregulation in clinical populations.

Recognition of Cervical Precancerous Lesions Based on Probability Distribution Feature Guidance.

INTRODUCTION: Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate. METHODS: In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the Acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance. RESULTS: Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively. CONCLUSION: Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.