Subcortical structural covariance predicts symptoms in children with different subtypes of ADHD.

Attention-deficit/hyperactivity disorder has increasingly been conceptualized as a disorder of abnormal brain connectivity. However, far less is known about the structural covariance in different subtypes of this disorder and how those differences may contribute to the symptomology of these subtypes. In this study, we used a combined volumetric-based methodology and structural covariance approach to investigate structural covariance of subcortical brain volume in attention-deficit/hyperactivity disorder-combined and attention-deficit/hyperactivity disorder-inattentive patients. In addition, a linear support vector machine was used to predict patient's attention-deficit/hyperactivity disorder symptoms. Results showed that compared with TD children, those with attention-deficit/hyperactivity disorder-combined exhibited decreased volume of both the left and right pallidum. Moreover, we found increased right hippocampal volume in attention-deficit/hyperactivity disorder-inattentive children. Furthermore and when compared with the TD group, both attention-deficit/hyperactivity disorder-combined and attention-deficit/hyperactivity disorder-inattentive groups showed greater nonhomologous inter-regional correlations. The abnormal structural covariance network in the attention-deficit/hyperactivity disorder-combined group was located in the left amygdala-left putamen/left pallidum/right pallidum and right pallidum-left pallidum; in the attention-deficit/hyperactivity disorder-inattentive group, this difference was noted in the left hippocampus-left amygdala/left putamen/right putamen and right hippocampus-left amygdala. Additionally, different combinations of abnormalities in subcortical structural covariance were predictive of symptom severity in different attention-deficit/hyperactivity disorder subtypes. Collectively, our findings demonstrated that structural covariance provided valuable diagnostic markers for attention-deficit/hyperactivity disorder subtypes.

A connectome-based neuromarker of nonverbal number acuity and arithmetic skills.

The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual's ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.

The brain network underlying attentional blink predicts symptoms of attention deficit hyperactivity disorder in children.

Attention deficit hyperactivity disorder (ADHD) is a chronic neuropsychiatric disease that can markedly impair educational, social, and occupational function throughout life. Behavioral deficits may provide clues to the underlying neurological impairments. Children with ADHD exhibit a larger attentional blink (AB) deficit in rapid serial visual presentation (RSVP) tasks than typically developing children, so we examined whether brain connectivity in the neural network associated with AB can predict ADHD symptoms and thus serve as potential biomarkers of the underlying neuropathology. We first employed a connectome-based predictive model analysis of adult resting-state functional magnetic resonance imaging data to identify a distributed brain network for AB. The summed functional connectivity (FC) strength within the AB network reliably predicted individual differences in AB magnitude measured by a classical dual-target RSVP task. Furthermore, the summed FC strength within the AB network predicted individual differences in ADHD Rating Scale scores from an independent dataset of pediatric patients. Our findings suggest that the individual AB network could serve as an applicable neuroimaging-based biomarker of AB deficit and ADHD symptoms.

Structural Brain Changes and Associated Symptoms of ADHD Subtypes in Children.

Attention-deficit/hyperactivity disorder (ADHD) is presumed to be heterogeneous, but the best way to characterize this heterogeneity remains unclear. Although considerable evidence suggests that the 2 different types of ADHD, inattention and combined, have different cognitive and behavioral profiles, and underlying neurobiologies, we currently lack information on whether these subtypes reflect separated brain structure changes. Structural magnetic resonance imaging scans (N = 234), diagnostic, and demographic information were obtained from the ADHD-200 database. Of this sample, 138 were Typically Developing people, 37 were ADHD-Combined, and 59 were ADHD-Inattentive patients. Freesurfer segmentation methods were used to measure cortical thickness, area, and volume, subcortical volume and hipposubfield volume. ADHD-Inattentive patients showed milder clinical symptoms but more serious cognitive injury than ADHD-Combined patients. In addition, dissociable structural brain changes were found in different subtypes of ADHD, particularly in terms of decreased subcortical volume in ADHD-Combined patients compared with Typically Developing people. Clinical symptoms were predominantly related to smaller rh_caudalanteriorcingulate thickness and left-Pallidum volume, whereas verbal IQ injury was correlated strongly with smaller rh_insula area. These findings indicate that there are significant differences in clinical symptoms and gray matter damage between ADHD-Combined and -Inattentive patients. This supports the growing evidence of heterogeneity in the ADHD-Inattentive subtype and the evidence of brain structure differences.

Modulation of the proteoglycan receptor PTPσ promotes white matter integrity and functional recovery after intracerebral hemorrhage stroke in mice.

BACKGROUND: Intracerebral hemorrhage (ICH) is associated with high morbidity and mortality rates. However, extant investigations have mainly focused on gray matter injury within the primary injury site after ICH rather than on white matter (WM) injury in the brain and spinal cord. This focus partly accounts for the diminished therapeutic discovery. Recent evidence suggests that chondroitin sulphate proteoglycans (CSPG), which can bind to the neural transmembrane protein tyrosine phosphatase-sigma (PTPσ), may facilitate axonal regrowth and remyelination by ameliorating neuroinflammation. METHODS: A clinically relevant ICH model was established using adult C57BL/6 mice. The mice were then treated systemically with intracellular sigma peptide (ISP), which specifically targets PTPσ. Sensorimotor function was assessed by various behavioral tests and electrophysiological assessment. Western blot was used to verify the expression levels of Iba-1 and different inflammatory cytokines. The morphology of white matter tracts of brain and spinal cord was evaluated by immunofluorescence staining and transmission electron microscopy (TEM). Adeno-associated virus (AAV) 2/9 injection was used to assess the ipsilateral axonal compensation after injury. Parallel in vitro studies on the effects of CSPG interference on oligodendrocyte-DRG neuron co-culture explored the molecular mechanism through which ISP treatment promoted myelination capability. RESULTS: ISP, by targeting PTPσ, improved WM integrity and sensorimotor recovery via immunomodulation. In addition, ISP administration significantly decreased WM injury in the peri-hematomal region as well as cervical spinal cord, enhanced axonal myelination and facilitated neurological restoration, including electrophysiologically assessed sensorimotor functions. Parallel in vitro studies showed that inhibition of PTPσ by ISP fosters myelination by modulating the Erk/CREB signaling pathway. CONCLUSIONS: Our findings revealed for the first time that manipulation of PTPσ signaling by ISP can promote prolonged neurological recovery by restoration of the integrity of neural circuits in the CNS through modulation of Erk/CREB signaling pathway.

Occupational functional plasticity revealed by brain entropy: A resting-state fMRI study of seafarers.

Recently, functional magnetic resonance imaging (fMRI) has been increasingly used to assess brain function. Brain entropy is an effective model for evaluating the alteration of brain complexity. Specifically, the sample entropy (SampEn) provides a feasible solution for revealing the brain's complexity. Occupation is one key factor affecting the brain's activity, but the neuropsychological mechanisms are still unclear. Thus, in this article, based on fMRI and a brain entropy model, we explored the functional complexity changes engendered by occupation factors, taking the seafarer as an example. The whole-brain entropy values of two groups (i.e., the seafarers and the nonseafarers) were first calculated by SampEn and followed by a two-sample t test with AlphaSim correction (p < .05). We found that the entropy of the orbital-frontal gyrus (OFG) and superior temporal gyrus (STG) in the seafarers was significantly higher than that of the nonseafarers. In addition, the entropy of the cerebellum in the seafarers was lower than that of the nonseafarers. We conclude that (1) the lower entropy in the cerebellum implies that the seafarers' cerebellum activity had strong regularity and consistency, suggesting that the seafarer's cerebellum was possibly more specialized by the long-term career training; (2) the higher entropy in the OFG and STG possibly demonstrated that the seafarers had a relatively decreased capability for emotion control and auditory information processing. The above results imply that the seafarer occupation indeed impacted the brain's complexity, and also provided new neuropsychological evidence of functional plasticity related to one's career.

Sparse Bayesian learning for DOA estimation with mutual coupling.

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

Different patterns and development characteristics of processing written logographic characters and alphabetic words: an ALE meta-analysis.

The neural systems for phonological processing of written language have been well identified now, while models based on these neural systems are different for different language systems or age groups. Although each of such models is mostly concordant across different experiments, the results are sensitive to the experiment design and intersubject variability. Activation likelihood estimation (ALE) meta-analysis can quantitatively synthesize the data from multiple studies and minimize the interstudy or intersubject differences. In this study, we performed two ALE meta-analysis experiments: one was to examine the neural activation patterns of the phonological processing of two different types of written languages and the other was to examine the development characteristics of such neural activation patterns based on both alphabetic language and logographic language data. The results of our first meta-analysis experiment were consistent with the meta-analysis which was based on the studies published before 2005. And there were new findings in our second meta-analysis experiment, where both adults and children groups showed great activation in the left frontal lobe, the left superior/middle temporal gyrus, and the bilateral middle/superior occipital gyrus. However, the activation of the left middle/inferior frontal gyrus was found increase with the development, and the activation was found decrease in the following areas: the right claustrum and inferior frontal gyrus, the left inferior/medial frontal gyrus, the left middle/superior temporal gyrus, the right cerebellum, and the bilateral fusiform gyrus. It seems that adults involve more phonological areas, whereas children involve more orthographic areas and semantic areas.

Iterative Reconstruction Algorithms in Magneto-Acousto-Electrical Computed Tomography (MAE-CT) for Image Quality Improvement.

Magneto-acousto-electrical computed tomography (MAE-CT) is a recently developed rotational magneto-acousto-electrical tomography (MAET) method, which can map the conductivity parameter of tissues with high spatial resolution. Since the imaging mode of MAE-CT is similar to that of CT, the reconstruction algorithms for CT are possible to be adopted for MAE-CT. Previous studies have demonstrated that the filtered back-projection (FBP) algorithm, which is one of the most common CT reconstruction algorithms, can be used for MAE-CT reconstruction. However, FBP has some inherent shortcomings of being sensitive to noise and non-uniform distribution of views. In this study, we introduced iterative reconstruction (IR) method in MAE-CT reconstruction and compared its performance with that of the FBP. The numerical simulation, the phantom, and in vitro experiments were performed, and several IR algorithms (ART, SART, SIRT) were used for reconstruction. The results show that the images reconstructed by the FBP and IR are similar when the data is noise-free in the simulation. As the noise level increases, the images reconstructed by SART and SIRT are more robust to the noise than FBP. In the phantom experiment, noise and some stripe artifacts caused by the FBP are removed by SART and SIRT algorithms. In conclusion, the IR method used in CT is applicable in MAE-CT, and it performs better than FBP, which indicates that the state-of-the-art achievements in the CT algorithm can also be adopted for the MAE-CT reconstruction in the future.

Advances in Antioxidant Nanomedicines for Imaging and Therapy of Alzheimer's Disease.

Significance: Reactive oxygen species (ROS) are crucial signaling molecules in the regulation of numerous physiological activities including the formation and function of the central nervous system (CNS). So far, many functional antioxidant nanomedicines with ROS scavenging capability to reduce oxidative stress in Alzheimer's disease (AD) have been developed for both imaging and therapy of AD. Recent Advances: This review focuses on the most recent advances in antioxidant nanomedicines such as ROS-scavenging nanoparticles (NPs), NPs with intrinsic antioxidant activity, and drug-loaded antioxidant NPs for AD theranostics. In addition to antioxidant nanomedicines, the emerging phototherapy treatment paradigms and the promising preclinic drug carriers, such as exosomes and liposomes, are also introduced. Critical Issues: In general, excessive generation of ROS can cause lipid peroxidation, oxidative DNA, as well as protein damage, aggravating pathogenic alterations, accumulation of amyloid-beta plaques and neurofibrillary tangles in the brain. These negative factors further cause cell death, which is the beginning of AD. Future Directions: We anticipate that this review will help researchers in the area of preclinical research and clinical translation of antioxidant nanomedicines for AD imaging and therapy.

Intracerebral Hemorrhage Models and Behavioral Tests in Rodents.

Intracerebral hemorrhage (ICH) is one of the common types of stroke, which can cause neurological dysfunction. In preclinical ICH studies, researchers often established rodent models by donor/autologous whole blood or a collagenase injection. White matter injury (WMI) can result from primary and secondary injuries after ICH. WMI can lead to short- and long-term neurological impairment, and functional recovery can assess the effect of drug therapy after ICH. Therefore, researchers have devised various behavioral tests to assess dysfunction. This review compares the two ICH modeling methods in rodents and summarizes the pathological mechanisms underlying dysfunction after ICH. We also summarize the functions and characteristics of various behavioral methods, including sensation, motion, emotion, and cognition, to assist researchers in selecting the appropriate tests for preclinical ICH research.

Accelerate wound healing by microscale gel array patch encapsulating defined SDF-1α gradient.

Recruiting endogenous stem cells to deliver signaling molecules is an attractive therapeutic strategy for the treatment of skin injuries. Although various signaling molecule delivery techniques have been developed, they are limited in their ability to accurately mimic the natural physiological process in which stem cells are recruited via signaling molecule concentration gradients. Hence, herein, we developed an approach to generate persistent signaling molecule concentration gradients in microscale gel arrays. Signaling molecule concentration gradients were established in each microscale gel via chemical conjugation and were maintained for >12 days. Moreover, the microscale gel provided a suitable environment for bone mesenchymal stem cells (BMSCs) growth, with many BMSCs migrating toward the stromal cell-derived factor-1 alpha (SDF-1α) gradient in vitro. Subsequently, a patch was formulated by mounting a microscale gel array on an adhesive layer and designated as the SDF-1α gradient microscale gel array patch. In a murine full-thickness skin defect model, this patch effectively increased the recruitment of endogenous BMSCs, accelerated wound healing, and enhanced neovascularization. Moreover, the regenerated tissue was more similar to normal skin tissue, as evidenced by histological analysis. The SDF-1α gradient microscale gel array patch also proved its efficacy in a diabetic animal model. Taken together, our findings indicate that the microscale gel array system developed in this study provides an innovative strategy for accelerating wound healing by creating well-defined and localized SDF-1α gradients in vivo. Furthermore, the patch-like design will facilitate on-demand use, thereby further aiding with wound healing.

Cryopreservable Through-Hole Arrays for the High-Throughput Three-Dimensional Smartphone-Based Cell Colorimetric Assay.

In vitro assays are an important platform for cancer research as they allow high-throughput experimentation that is not possible using in vivo animals. Although various in vitro assays are developed to study cell viability or migration, many of these assays are often limited to two dimensions, involving complex procedures or relying specialized equipment, etc. Here, we designed a simple colorimetric assay that accommodates automatic liquid samples loading, high-throughput generation of chemical concentration gradient, three-dimensional (3D) cell culture establishment, and smartphone-based colorimetric readouts. This assay is based on through-hole arrays in the poly(methyl methacrylate) (PMMA) layers. Liquid samples can be automatically loaded into through-hole arrays in PMMA layers by capillary force. Different drug concentrations can be generated by aligning and stacking to mix the contents of the corresponding through-holes with different volumes. 3D culture of cancer cells can be established by the rapid absorption of cell suspensions into the macroporous gels. After exposing the 3D cultured cells to different drug concentrations, the number of viable cells and migrated cells was reflected by the color change of Alamar blue, which enable on-site readout by a smartphone. This assay can study cell viability as well as cell migration, the two main characteristics of cancer cells, using one device. Interestingly, HeLa cells remained with high viability after cryopreservation at -80 °C, which allows for storage and distribution using dry ice. The simple protocol, along with the cryopreservability at -80 °C facilitates its ease of use to study cell viability together with cell migration in common laboratories or clinical settings.

Recent progress in nanomedicines for imaging and therapy of brain tumors.

Nowadays, a malignant brain tumor is one of the most life-threatening diseases with poor prognosis, high risk of recurrence, and low survival rate for patients because of the existence of the blood-brain barrier (BBB) and the lack of efficient diagnostic and therapeutic paradigms. So far, many researchers have devoted their efforts to innovating advanced drugs to efficiently cross the BBB and selectively target brain tumors for optimal imaging and therapy outcomes. Herein, we update the most recent developments in nanomedicines for the diagnosis and treatment of brain tumors in preclinical mouse models. The special focus is on burgeoning drug delivery carriers to improve the specificity of visualization and to enhance the efficacy of brain tumor treatment. Also, we highlight the challenges and perspectives for the future development of brain tumor theranostics. This review is expected to receive wide attention from researchers, professors, and students in various fields to participate in future advancements in preclinical research and clinical translation of brain tumor nanomedicines.

Habit-DisHabit Design with a Quadratic Equation: A Better Model of the Hemodynamic Changes in Preschoolers during the Dimension Change Card Sorting Task.

General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quadratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task.

The neural correlates of apathy in the context of aging and brain disorders: a meta-analysis of neuroimaging studies.

Introduction: Apathy is a prevalent mood disturbance that occurs in a wide range of populations, including those with normal cognitive aging, mental disorders, neurodegenerative disorders and traumatic brain injuries. Recently, neuroimaging technologies have been employed to elucidate the neural substrates underlying brain disorders accompanying apathy. However, the consistent neural correlates of apathy across normal aging and brain disorders are still unclear. Methods: This paper first provides a brief review of the neural mechanism of apathy in healthy elderly individuals, those with mental disorders, neurodegenerative disorders, and traumatic brain injuries. Further, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the structural and functional neuroimaging meta-analysis using activation likelihood estimation method is performed on the apathy group with brain disorders and the healthy elderly, aiming at exploring the neural correlates of apathy. Results: The structural neuroimaging meta-analysis showed that gray matter atrophy is associated with apathy in the bilateral precentral gyrus (BA 13/6), bilateral insula (BA 47), bilateral medial frontal gyrus (BA 11), bilateral inferior frontal gyrus, left caudate (putamen) and right anterior cingulate, while the functional neuroimaging meta-analysis suggested that the functional connectivity in putamen and lateral globus pallidus is correlated with apathy. Discussion: Through the neuroimaging meta-analysis, this study has identified the potential neural locations of apathy in terms of brain structure and function, which may offer valuable pathophysiological insights for developing more effective therapeutic interventions for affected patients.

Occupation-modulated language networks and its lateralization: A resting-state fMRI study of seafarers.

Introduction: Studies have revealed that the language network of Broca's area and Wernicke's area is modulated by factors such as disease, gender, aging, and handedness. However, how occupational factors modulate the language network remains unclear. Methods: In this study, taking professional seafarers as an example, we explored the resting-state functional connectivity (RSFC) of the language network with seeds (the original and flipped Broca's area and Wernicke's area). Results: The results showed seafarers had weakened RSFC of Broca's area with the left superior/middle frontal gyrus and left precentral gyrus, and enhanced RSFC of Wernicke's area with the cingulate and precuneus. Further, seafarers had a less right-lateralized RSFC with Broca's area in the left inferior frontal gyrus, while the controls showed a left-lateralized RSFC pattern in Broca's area and a right-lateralized one in Wernicke's area. Moreover, seafarers displayed stronger RSFC with the left seeds of Broca's area and Wernicke's area. Discussion: These findings suggest that years of working experience significantly modulates the RSFC of language networks and their lateralization, providing rich insights into language networks and occupational neuroplasticity.

A state-of-the-art liposome technology for glioblastoma treatment.

Glioblastoma (GBM) is a challenging problem due to the poor BBB permeability of cancer drugs, its recurrence after the treatment, and high malignancy and is difficult to treat with the currently available therapeutic strategies. Furthermore, the prognosis and survival rate of GBM are still poor after surgical removal via conventional combination therapy. Owing to the existence of the formidable blood-brain barrier (BBB) and the aggressive, infiltrating nature of GBM growth, the diagnosis and treatment of GBM are quite challenging. Recently, liposomes and their derivatives have emerged as super cargos for the delivery of both hydrophobic and hydrophilic drugs for the treatment of glioblastoma because of their advantages, such as biocompatibility, long circulation, and ease of physical and chemical modification, which facilitate the capability of targeting specific sites, circumvention of BBB transport restrictions, and amplification of the therapeutic efficacy. Herein, we provide a timely update on the burgeoning liposome-based drug delivery systems and potential challenges in these fields for the diagnosis and treatment of brain tumors. Furthermore, we focus on the most recent liposome-based drug delivery cargos, including pH-sensitive, temperature-sensitive, and biomimetic liposomes, to enhance the multimodality in imaging and therapeutics of glioblastoma. Furthermore, we highlight the future difficulties and directions for the research and clinical translation of liposome-based drug delivery. Hopefully, this review will trigger the interest of researchers to expedite the development of liposome cargos and even their clinical translation for improving the prognosis of glioblastoma.

Long-term practice of intuitive inquiry meditation modulates EEG dynamics during self-schema processing.

Objective: Intuitive inquiry meditation is a unique form of Buddhist Zen/Chan practice in which individuals actively and intuitively utilize the cognitive functions to cultivate doubt and explore the concept of the self. This event-related potential (ERP) study aimed to investigate the neural correlates by which long-term practice of intuitive inquiry meditation induces flexibility in self-schema processing, highlighting the role of doubt and belief processes in this exploration. Methods: Twenty experienced and eighteen beginner meditators in intuitive inquiry meditation were recruited for this ERP study. The interactions of doubt and belief processes with concepts of the self and Buddha were investigated. A 128-channel electroencephalography (EEG) system was used to collect EEG data. The ERP data were processed and analyzed using EEGLAB. Results: The data showed a double dissociation between beginners and experienced meditators (monks) in the concepts of the self and Buddha: intuitive inquiry meditation reduced the brain activity of beginners when viewing Buddha image but not when viewing a picture of themselves. However, in experienced meditators, intuitive inquiry meditation reduced brain activity when they viewed images of themselves but not when they viewed Buddha image. Further event-related spectral perturbation (ERSP) analysis revealed that experienced meditators had a greater theta spectral power and higher intertrial coherence (ITC), indicating that they could more flexibly modulate ongoing cognitive processes than beginner meditators. Conclusion: Intuitive inquiry meditation could help beginner meditators detach from the concept of Buddha but not from that of the self. However, in experienced meditators, the opposite was true. ERSP analysis showed that only experienced meditators exhibited significant alterations in brain activity dynamics during intuitive inquiry meditation, which might enable these practitioners to become spontaneously detached from the concept of the self. These findings revealed the neural mechanism by which long-term practice of intuitive inquiry meditation can influence the doubting process and its effect on self-schema processing.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.

Introduction: This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods: We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results: For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion: Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.