Genetic architecture of brain morphology and overlap with neuropsychiatric traits.

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought.

Integration of Prior Expectations and Suppression of Prediction Errors During Expectancy-Induced Pain Modulation: The Influence of Anxiety and Pleasantness.

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating.

Diurnal Variation of Brain Activity in the Human Suprachiasmatic Nucleus.

The suprachiasmatic nucleus (SCN) is the central clock for circadian rhythms. Animal studies have revealed daily rhythms in the neuronal activity in the SCN. However, the circadian activity of the human SCN has remained elusive. In this study, to reveal the diurnal variation of the SCN activity in humans, we localized the SCN by employing an areal boundary mapping technique to resting-state functional images and investigated the SCN activity using perfusion imaging. In the first experiment (n = 27, including both sexes), we scanned each participant four times a day, every 6 h. Higher activity was observed at noon, while lower activity was recorded in the early morning. In the second experiment (n = 20, including both sexes), the SCN activity was measured every 30 min for 6 h from midnight to dawn. The results showed that the SCN activity gradually decreased and was not associated with the electroencephalography. Furthermore, the SCN activity was compatible with the rodent SCN activity after switching off the lights. These results suggest that the diurnal variation of the human SCN follows the zeitgeber cycles of nocturnal and diurnal mammals and is modulated by physical lights rather than the local time.

Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy.

BACKGROUND: Current cardiovascular magnetic resonance sequences cannot discriminate between different myocardial extracellular space (ECSs), including collagen, noncollagen, and inflammation. We sought to investigate whether cardiovascular magnetic resonance radiomics analysis can distinguish between noncollagen and inflammation from collagen in dilated cardiomyopathy. METHODS: We identified data from 132 patients with dilated cardiomyopathy scheduled for an invasive septal biopsy who underwent cardiovascular magnetic resonance at 3 T. Cardiovascular magnetic resonance imaging protocol included native and postcontrast T1 mapping and late gadolinium enhancement (LGE). Radiomic features were computed from the midseptal myocardium, near the biopsy region, on native T1, extracellular volume (ECV) map, and LGE images. Principal component analysis was used to reduce the number of radiomic features to 5 principal radiomics. Moreover, a correlation analysis was conducted to identify radiomic features exhibiting a strong correlation (r>0.9) with the 5 principal radiomics. Biopsy samples were used to quantify ECS, myocardial fibrosis, and inflammation. RESULTS: Four histopathological phenotypes were identified: low collagen (n=20), noncollagenous ECS expansion (n=49), mild to moderate collagenous ECS expansion (n=42), and severe collagenous ECS expansion (n=21). Noncollagenous expansion was associated with the highest risk of myocardial inflammation (65%). Although native T1 and ECV provided high diagnostic performance in differentiating severe fibrosis (C statistic, 0.90 and 0.90, respectively), their performance in differentiating between noncollagen and mild to moderate collagenous expansion decreased (C statistic: 0.59 and 0.55, respectively). Integration of ECV principal radiomics provided better discrimination and reclassification between noncollagen and mild to moderate collagen (C statistic, 0.79; net reclassification index, 0.83 [95% CI, 0.45-1.22]; P<0.001). There was a similar trend in the addition of native T1 principal radiomics (C statistic, 0.75; net reclassification index, 0.93 [95% CI, 0.56-1.29]; P<0.001) and LGE principal radiomics (C statistic, 0.74; net reclassification index, 0.59 [95% CI, 0.19-0.98]; P=0.004). Five radiomic features per sequence were identified with correlation analysis. They showed a similar improvement in performance for differentiating between noncollagen and mild to moderate collagen (native T1, ECV, LGE C statistic, 0.75, 0.77, and 0.71, respectively). These improvements remained significant when confined to a single radiomic feature (native T1, ECV, LGE C statistic, 0.71, 0.70, and 0.64, respectively). CONCLUSIONS: Radiomic features extracted from native T1, ECV, and LGE provide incremental information that improves our capability to discriminate noncollagenous expansion from mild to moderate collagen and could be useful for detecting subtle chronic inflammation in patients with dilated cardiomyopathy.

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

2024 AHA/ACC/AMSSM/HRS/PACES/SCMR Guideline for the Management of Hypertrophic Cardiomyopathy: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.

AIM: The "2024 AHA/ACC/AMSSM/HRS/PACES/SCMR Guideline for the Management of Hypertrophic Cardiomyopathy" provides recommendations to guide clinicians in the management of patients with hypertrophic cardiomyopathy. METHODS: A comprehensive literature search was conducted from September 14, 2022, to November 22, 2022, encompassing studies, reviews, and other evidence on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through May 23, 2023, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate. STRUCTURE: Hypertrophic cardiomyopathy remains a common genetic heart disease reported in populations globally. Recommendations from the "2020 AHA/ACC Guideline for the Diagnosis and Treatment of Patients With Hypertrophic Cardiomyopathy" have been updated with new evidence to guide clinicians.

Autologous bone marrow mononuclear cells to treat severe traumatic brain injury in children.

Autologous bone marrow mononuclear cells (BMMNCs) infused after severe traumatic brain injury have shown promise for treating the injury. We evaluated their impact in children, particularly their hypothesized ability to preserve the blood-brain barrier and diminish neuroinflammation, leading to structural CNS preservation with improved outcomes. We performed a randomized, double-blind, placebo-sham-controlled Bayesian dose-escalation clinical trial at two children's hospitals in Houston, TX and Phoenix, AZ, USA (NCT01851083). Patients 5-17 years of age with severe traumatic brain injury (Glasgow Coma Scale score ≤ 8) were randomized to BMMNC or placebo (3:2). Bone marrow harvest, cell isolation and infusion were completed by 48 h post-injury. A Bayesian continuous reassessment method was used with cohorts of size 3 in the BMMNC group to choose the safest between two doses. Primary end points were quantitative brain volumes using MRI and microstructural integrity of the corpus callosum (diffusivity and oedema measurements) at 6 months and 12 months. Long-term functional outcomes and ventilator days, intracranial pressure monitoring days, intensive care unit days and therapeutic intensity measures were compared between groups. Forty-seven patients were randomized, with 37 completing 1-year follow-up (23 BMMNC, 14 placebo). BMMNC treatment was associated with an almost 3-day (23%) reduction in ventilator days, 1-day (16%) reduction in intracranial pressure monitoring days and 3-day (14%) reduction in intensive care unit (ICU) days. White matter volume at 1 year in the BMMNC group was significantly preserved compared to placebo [decrease of 19 891 versus 40 491, respectively; mean difference of -20 600, 95% confidence interval (CI): -35 868 to -5332; P = 0.01], and the number of corpus callosum streamlines was reduced more in placebo than BMMNC, supporting evidence of preserved corpus callosum connectivity in the treated groups (-431 streamlines placebo versus -37 streamlines BMMNC; mean difference of -394, 95% CI: -803 to 15; P = 0.055), but this did not reach statistical significance due to high variability. We conclude that autologous BMMNC infusion in children within 48 h after severe traumatic brain injury is safe and feasible. Our data show that BMMNC infusion led to: (i) shorter intensive care duration and decreased ICU intensity; (ii) white matter structural preservation; and (iii) enhanced corpus callosum connectivity and improved microstructural metrics.

Where do we stand on fMRI in awake mice?

Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Longitudinal MRI of the developing ferret brain reveals regional variations in timing and rate of growth.

Normative ferret brain development was characterized using magnetic resonance imaging. Brain growth was longitudinally monitored in 10 ferrets (equal numbers of males and females) from postnatal day 8 (P8) through P38 in 6-d increments. Template T2-weighted images were constructed at each age, and these were manually segmented into 12 to 14 brain regions. A logistic growth model was used to fit data from whole brain volumes and 8 of the individual regions in both males and females. More protracted growth was found in males, which results in larger brains; however, sex differences were not apparent when results were corrected for body weight. Additionally, surface models of the developing cortical plate were registered to one another using the anatomically-constrained Multimodal Surface Matching algorithm. This, in turn, enabled local logistic growth parameters to be mapped across the cortical surface. A close similarity was observed between surface area expansion timing and previous reports of the transverse neurogenic gradient in ferrets. Regional variation in the extent of surface area expansion and the maximum expansion rate was also revealed. This characterization of normative brain growth over the period of cerebral cortex folding may serve as a reference for ferret studies of brain development.

Volumetric analysis of age- and sex-related changes in the corpus striatum and thalamus in the 1-18 age group: a retrospective magnetic resonance imaging study.

Studies of the development and asymmetry of the corpus striatum and thalamus in early childhood are rare. Studies investigating these structures across the lifespan have not presented their changes during childhood and adolescence in detail. For these reasons, this study investigated the effect of age and sex factors on the development and asymmetry of the corpus striatum and thalamus in the 1-18 age group. In this retrospective study, we included 652 individuals [362 (56%) males] aged 1-18 years with normal brain MRI between 2012 and 2021. Absolute and relative volumes of the corpus striatum and thalamus were obtained by segmentation of three-dimensional T1-weighted MRIs with volBrain1.0. We created age-specific volume data and month-based development models with the help of SPSS (ver.28). The corpus striatum and thalamus had cubic absolute volumetric developmental models. The relative volume of the caudate and thalamus (only males) is consistent with the decreasing "growth" model, the others with the decreasing cubic model. The absolute volumes of the males' bilateral corpus striatum and thalamus and the relative volumes of the caudate and thalamus of the females were significantly larger (P < 0.05). The caudate showed right > left lateralization; putamen, globus pallidus, and thalamus showed left > right lateralization.

The associations of peripheral interleukin alterations and hippocampal subfield volume deficits in schizophrenia.

The hippocampus is one of the brain regions most vulnerable to inflammatory insults, and the relationships between peripheral inflammation and hippocampal subfields in patients with schizophrenia remain unclear. In this study, forty-six stably medicated patients with schizophrenia and 48 demographically matched healthy controls (HCs) were recruited. The serum levels of IL - 1ß, IL-6, IL-10, and IL-12p70 were measured, and 3D high-resolution T1-weighted magnetic resonance imaging was performed. The IL levels and hippocampal subfield volumes were both compared between patients and HCs. The associations of altered IL levels with hippocampal subfield volumes were assessed in patients. Patients with schizophrenia demonstrated higher serum levels of IL-6 and IL-10 but lower levels of IL-12p70 than HCs. In patients, the levels of IL-6 were positively correlated with the volumes of the left granule cell layer of the dentate gyrus (GCL) and cornu Ammonis (CA) 4, while the levels of IL-10 were negatively correlated with the volumes of those subfields. IL-6 and IL-10 might have antagonistic roles in atrophy of the left GCL and CA4. This suggests a complexity of peripheral cytokine dysregulation and the potential for its selective effects on hippocampal substructures, which might be related to the pathophysiology of schizophrenia.

Structural and functional abnormalities and cognitive profiles in older adults with early-onset and late-onset focal epilepsy.

This study aimed to determine the patterns of changes in structure, function, and cognitive ability in early-onset and late-onset older adults with focal epilepsy (OFE). This study first utilized the deformation-based morphometry analysis to identify structural abnormalities, which were used as the seed region to investigate the functional connectivity with the whole brain. Next, a correlation analysis was performed between the altered imaging findings and neuropsychiatry assessments. Finally, the potential role of structural-functional abnormalities in the diagnosis of epilepsy was further explored by using mediation analysis. Compared with healthy controls (n = 28), the area of reduced structural volume was concentrated in the bilateral cerebellum, right thalamus, and right middle cingulate cortex, with frontal, temporal, and occipital lobes also affected in early-onset focal epilepsy (n = 26), while late-onset patients (n = 31) displayed cerebellar, thalamic, and cingulate atrophy. Furthermore, correlation analyses suggest an association between structural abnormalities and cognitive assessments. Dysfunctional connectivity in the cerebellum, cingulate cortex, and frontal gyrus partially mediates the relationship between structural abnormalities and the diagnosis of early-onset focal epilepsy. This study identified structural and functional abnormalities in early-onset and late-onset focal epilepsy and explored characters in cognitive performance. Structural-functional coupling may play a potential role in the diagnosis of epilepsy.

Effects of multiple anesthetic exposures on rhesus macaque brain development: a longitudinal structural MRI analysis.

Concerns about the potential neurotoxic effects of anesthetics on developing brain exist. When making clinical decisions, the timing and dosage of anesthetic exposure are critical factors to consider due to their associated risks. In our study, we investigated the impact of repeated anesthetic exposures on the brain development trajectory of a cohort of rhesus monkeys (n = 26) over their first 2 yr of life, utilizing longitudinal magnetic resonance imaging data. We hypothesized that early or high-dose anesthesia exposure could negatively influence structural brain development. By employing the generalized additive mixed model, we traced the longitudinal trajectories of brain volume, cortical thickness, and white matter integrity. The interaction analysis revealed that age and cumulative anesthetic dose were variably linked to white matter integrity but not to morphometric measures. Early high-dose exposure was associated with increased mean, axial, and radial diffusivities across all white matter regions, compared to late-low-dose exposure. Our findings indicate that early or high-dose anesthesia exposure during infancy disrupts structural brain development in rhesus monkeys. Consequently, the timing of elective surgeries and procedures that require anesthesia for children and pregnant women should be strategically planned to account for the cumulative dose of volatile anesthetics, aiming to minimize the potential risks to brain development.

GABAergic inhibition shapes behavior and neural dynamics in human visual working memory.

Neuronal inhibition, primarily mediated by GABAergic neurotransmission, is crucial for brain development and healthy cognition. Gamma-aminobutyric acid concentration levels in sensory areas have been shown to correlate with hemodynamic and oscillatory neuronal responses. How these measures relate to one another during working memory, a higher-order cognitive process, is still poorly understood. We address this gap by collecting magnetoencephalography, functional magnetic resonance imaging, and Flumazenil positron emission tomography data within the same subject cohort using an n-back working-memory paradigm. By probing the relationship between GABAA receptor distribution, neural oscillations, and Blood Oxygen Level Dependent (BOLD) modulations, we found that GABAA receptor density in higher-order cortical areas predicted the reaction times on the working-memory task and correlated positively with the peak frequency of gamma power modulations and negatively with BOLD amplitude. These findings support and extend theories linking gamma oscillations and hemodynamic responses to gamma-aminobutyric acid neurotransmission and to the excitation-inhibition balance and cognitive performance in humans. Considering the small sample size of the study, future studies should test whether these findings also hold for other, larger cohorts as well as to examine in detail how the GABAergic system and neural fluctuations jointly support working-memory task performance.

The relationship between gray/white matter contrast and cognitive performance in first-episode schizophrenia.

Previous postmortem brain studies have revealed disturbed myelination in the intracortical regions in patients with schizophrenia, possibly reflecting anomalous brain maturational processes. However, it currently remains unclear whether this anomalous myelination is already present in early illness stages and/or progresses during the course of the illness. In this magnetic resonance imaging study, we examined gray/white matter contrast (GWC) as a potential marker of intracortical myelination in 63 first-episode schizophrenia (FESz) patients and 77 healthy controls (HC). Furthermore, we investigated the relationships between GWC findings and clinical/cognitive variables in FESz patients. GWC in the bilateral temporal, parietal, occipital, and insular regions was significantly higher in FESz patients than in HC, which was partly associated with the durations of illness and medication, the onset age, and lower executive and verbal learning performances. Because higher GWC implicates lower myelin in the deeper layers of the cortex, these results suggest that schizophrenia patients have less intracortical myelin at the time of their first psychotic episode, which underlies lower cognitive performance in early illness stages.

Alterations in the brain functional network of abstinent male individuals with methamphetamine use disorder.

Methamphetamine is a highly addictive psychostimulant drug that is abused globally and is a serious threat to health worldwide. Unfortunately, the specific mechanism underlying addiction remains unclear. Thus, this study aimed to investigate the characteristics of functional connectivity in the brain network and the factors influencing methamphetamine use disorder in patients using magnetic resonance imaging. We included 96 abstinent male participants with methamphetamine use disorder and 46 age- and sex-matched healthy controls for magnetic resonance imaging. Compared with healthy controls, participants with methamphetamine use disorder had greater impulsivity, fewer small-world attributes of the resting-state network, more nodal topological attributes in the cerebellum, greater functional connectivity strength within the cerebellum and between the cerebellum and brain, and decreased frontoparietal functional connectivity strength. In addition, after controlling for covariates, the partial correlation analysis showed that small-world properties were significantly associated with methamphetamine use frequency, psychological craving, and impulsivity. Furthermore, we revealed that the small-word attribute significantly mediated the effect of methamphetamine use frequency on motor impulsivity in the methamphetamine use disorder group. These findings may further improve our understanding of the neural mechanism of impulse control dysfunction underlying methamphetamine addiction and assist in exploring the neuropathological mechanism underlying methamphetamine use disorder-related dysfunction and rehabilitation.

Structural and functional abnormalities in first-episode drug-naïve pediatric idiopathic generalized epilepsy.

The aim of this study was to investigate brain structure and corresponding static and dynamic functional connectivity (sFC & dFC) abnormalities in untreated, first-episode pediatric idiopathic generalized epilepsy (IGE), with the goal of better understanding the underlying pathological mechanisms of IGE. Thirty-one children with IGE and 31 age-matched healthy controls (HC) were recruited. Structural magnetic resonance imaging (sMRI) data were acquired, and voxel-based morphometry (VBM) analysis were performed to reveal abnormal gray matter volume (GMV). Moreover, sFC and dFC analyses were conducted using the brain areas exhibiting abnormal GMV as seed regions to explore abnormal functional couplings. Compared to HC, the IGE group exhibited increased GMV in left middle cingulate cortex (MCC) and right parahippocampus (ParaHipp). In addition, the analyses of dFC and sFC with MCC and ParaHipp as seeds revealed more extensive functional connectivity (FC) changes in dFC. Notably, the structurally and functionally abnormal brain areas were primarily localized in the default mode network (DMN). However, our study did not find any significant associations between these altered neuroimaging measurements and clinical outcomes. This study uncovered microstructural changes as well as corresponding sFC and dFC changes in patients with new-onset, untreated pediatric IGE. The affected brain regions were primarily located within the DMN, highlighting the DMN's crucial role in the development of pediatric IGE.

The effective connectivity analysis of fMRI based on asymmetric detection of transfer brain entropy.

It is important to explore causal relationships in functional magnetic resonance imaging study. However, the traditional effective connectivity analysis method is easy to produce false causality, and the detection accuracy needs to be improved. In this paper, we introduce a novel functional magnetic resonance imaging effective connectivity method based on the asymmetry detection of transfer entropy, which quantifies the disparity in predictive information between forward and backward time, subsequently normalizing this disparity to establish a more precise criterion for detecting causal relationships while concurrently reducing computational complexity. Then, we evaluate the effectiveness of this method on the simulated data with different level of nonlinearity, and the results demonstrated that the proposed method outperforms others methods on the detection of both linear and nonlinear causal relationships, including Granger Causality, Partial Granger Causality, Kernel Granger Causality, Copula Granger Causality, and traditional transfer entropy. Furthermore, we applied it to study the effective connectivity of brain functional activities in seafarers. The results showed that there are significantly different causal relationships between different brain regions in seafarers compared with non-seafarers, such as Temporal lobe related to sound and auditory information processing, Hippocampus related to spatial navigation, Precuneus related to emotion processing as well as Supp_Motor_Area associated with motor control and coordination, which reflects the occupational specificity of brain function of seafarers.