Childhood exposure to organophosphate pesticides: Functional connectivity and working memory in adolescents.

BACKGROUND: Early life exposure to organophosphate (OP) pesticides is linked with adverse neurodevelopment and brain function in children. However, we have limited knowledge of how these exposures affect functional connectivity, a measure of interaction between brain regions. To address this gap, we examined the association between early life OP pesticide exposure and functional connectivity in adolescents. METHODS: We administered functional near-infrared spectroscopy (fNIRS) to 291 young adults with measured prenatal or childhood dialkylphosphates (DAPs) in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study, a longitudinal study of women recruited during pregnancy and their offspring. We measured DAPs in urinary samples collected from mothers during pregnancy (13 and 26 weeks) and children in early life (ages 6 months, 1, 2, 3, and 5 years). Youth underwent fNIRS while they performed executive function and semantic language tasks during their 18-year-old visit. We used covariate-adjusted regression models to estimate the associations of prenatal and childhood DAPs with functional connectivity between the frontal, temporal, and parietal regions, and a mediation model to examine the role of functional connectivity in the relationship between DAPs and task performance. RESULTS: We observed null associations of prenatal and childhood DAP concentrations and functional connectivity for the entire sample. However, when we looked for sex differences, we observed an association between childhood DAPs and functional connectivity for the right interior frontal and premotor cortex after correcting for the false discovery rate, among males, but not females. In addition, functional connectivity appeared to mediate an inverse association between DAPs and working memory accuracy among males. CONCLUSION: In CHAMACOS, a secondary analysis showed that adolescent males with elevated childhood OP pesticide exposure may have altered brain regional connectivity. This altered neurofunctional pattern in males may partially mediate working memory impairment associated with childhood DAP exposure.

Longitudinal changes in functional neural activation and sensitization during face processing in fragile X syndrome.

BACKGROUND: Fragile X syndrome (FXS) is a genetic condition associated with increased risk for social anxiety and avoidance. Using functional near-infrared spectroscopy (fNIRS), we previously demonstrated aberrant neural activity responding to faces in young girls with FXS cross-sectionally. Here, we tested the hypothesis that abnormalities in neural activation and sensitization would increase with age in 65 girls with FXS, ages 5-16 years, relative to an age-matched control group of 52 girls who had comparable cognitive function and clinical symptoms. METHODS: Functional NIRS data were collected at two time points, 2.8±0.6 years apart during a face-processing task. Linear mixed-effects models examined longitudinal neural profiles in girls with FXS and control. Correlational analysis was performed to examine associations between neural sensitization (increasing neural response to repeated stimuli), and clinical ratings. RESULTS: In girls with FXS, 32 participants had one, and 24 had two fNIRS scans. In controls, 21 had one, and 29 had two fNIRS scans. Brain activations in the right middle and superior frontal gyri were higher in FXS than controls at both time points. Neural sensitization also increased in FXS at a higher rate than controls in the superior frontal gyrus when responding to upright faces. For the FXS group, sensitization in the superior frontal gyrus positively correlated with longitudinal increases in anxiety and social avoidance scores. CONCLUSION: Girls with FXS show increasingly abnormal neural activation and sensitization responding to faces over time. Aberrant neural sensitization in girls with FXS is associated with longitudinal changes in anxiety and social skills.

Associations between brain network, puberty, and behaviors in boys with Klinefelter syndrome.

BACKGROUND: Klinefelter syndrome (KS), also referred to as XXY syndrome, is a significant but inadequately studied risk factor for neuropsychiatric disability. Whether alterations in functional brain connectivity or pubertal delays are associated with aberrant cognitive-behavioral outcomes in individuals with KS is largely unknown. In this observational study, we investigated KS-related alterations in the resting-state brain network, testosterone level, and cognitive-behavioral impairment in adolescents with Klinefelter syndrome. METHODS: We recruited 46 boys with KS, ages 8 to 17 years, and 51 age-matched typically developing (TD) boys. All participants underwent resting-state functional magnetic resonance imaging scans, pubertal, and cognitive-behavioral assessments. Resting-state functional connectivity and regional brain activity of the participants were assessed. RESULTS: We found widespread alterations in global functional connectivity among the inferior frontal gyrus, temporal-parietal area, and hippocampus in boys with KS. Aberrant regional activities, including enhanced fALFF in the motor area and reduced ReHo in the caudate, were also found in the KS group compared to the TD children. Further, using machine learning methods, brain network alterations in these regions accurately differentiated boys with KS from TD controls. Finally, we showed that the alterations of brain network properties not only effectively predict cognitive-behavioral impairment in boys with KS, but also appear to mediate the association between total testosterone level and language ability, a cognitive domain at particular risk for dysfunction in this condition. CONCLUSION: Our results offer an informatic neurobiological foundation for understanding cognitive-behavioral impairments in individuals with KS and contribute to our understanding of the interplay between pubertal status, brain function, and cognitive-behavioral outcome in this population.

Effects of radial extracorporeal shock wave therapy on flexor spasticity of the upper limb in post-stroke patients: A randomized controlled trial.

OBJECTIVE: This study aimed to assess the efficacy of radial extracorporeal shock wave therapy in treating upper limb spasticity after a stroke. DESIGN: Randomized controlled trial. SETTING: Zhujiang Hospital of Southern Medical University. SUBJECTS: This study included 95 people with stroke. INTERVENTION: The active (n = 47) and sham-placebo (n = 48) radial extracorporeal shockwave therapy groups received three treatment sessions (every third day). MAIN MEASURES: The Modified Ashworth Scale, Hmax/Mmax ratio, root mean square, co-contraction ratio, mechanical parameters of the muscle and temperature were measured at baseline and days 2, 5 and 8. RESULTS: Among the 135 potential participants screened, 100 were enrolled and allocated randomly, with 95 participants ultimately being included in the intent-to-treat analysis dataset. The active group showed significantly better improvements in upper limb spasticity and muscle function than did the sham-placebo group. Greater improvements in the Modified Ashworth Scale were observed in the active group than in the sham-placebo group (difference, -0.45; 95% CI, -0.69 to -0.22; P < 0.001). Moreover, significant differences in root mean square, co-contraction ratio and Hmax/Mmax ratio were observed between the two groups (all P < 0.001). The mechanical parameters of the biceps muscle were significantly better in the active group than in the sham-placebo group (P < 0.001). The active group had a higher temperature than the sham-placebo group, although the difference was not significant (P = 0.070). CONCLUSIONS: This study revealed that the treatment with extracorporeal shockwave therapy can relieve upper limb spasticity in people with stroke.

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.

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.

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.

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.

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.

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.

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.

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.

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.