SEEG study of a rare male temporal lobe epilepsy with orgasmic aura originating from the right amygdala.

As a primitive driving force for biological reproduction, sexual behavior (and its associated mechanisms) is extremely complex, and orgasm plays an essential role. The limbic system plays a very important role in regulating human sexual behavior. However, it is not clear which components of the limbic system are related to orgasm sensation. We studied a rare case of spontaneous orgasmic aura in a male patient with temporal lobe epilepsy. Stereoelectroencephalography (SEEG) revealed that the right amygdala was the origin of orgasmic aura. Surgical removal of the medial temporal lobe, including the right amygdala, completely eliminated the patient's seizures. This study demonstrates the critical role of the amygdala in human male orgasm.

Stereoelectroencephalography-based research on the value of drug-resistant temporal lobe epilepsy auras: A retrospective single-center study.

PURPOSE: To explore the localization value of drug-resistant temporal lobe epilepsy (TLE) aura for preoperative evaluation, based on stereoelectroencephalography (SEEG), and its prognostic value on the surgical outcome. METHODS: The data of patients with drug-resistant TLE who had SEEG electrodes implanted during preoperative evaluation at the First Affiliated Hospital of the University of Science and Technology of China (Hefei, China) were retrospectively analyzed. The patients were divided into aura-positive and aura-negative groups according to the presence of aura in seizures. To explore the clinical features of aura, we evaluated the localizing and prognostic values of aura for the outcome of anterior temporal lobectomy based on SEEG. RESULTS: Among forty patients, twenty-seven patients were in the aura-positive group and ten (25.0%) patients had multiple auras. The most common TLE aura was abdominal aura [thirteen (34.2%) patients]. The postoperative seizure frequency was significantly reduced in the preoperative aura-positive patients compared to the preoperative aura-negative patients (P = 0.011). Patients with abdominal (P = 0.029) and single (P = 0.036) auras had better surgical prognoses than aura-negative patients. In the preoperative evaluation, aura-positive patients had a better surgical outcome if the laterality of positron emission tomography-computed tomography (PET-CT) hypometabolism was concordant with the epileptogenic focus identified with SEEG (P = 0.031). A good postoperative epileptic outcome in aura-positive patients was observed among those with hippocampal sclerotic medial temporal lobe epilepsy (P = 0.025). CONCLUSION: Epileptic aura is valuable for the localization of the epileptogenic focus. Abdominal aura and single aura were good predictors of better surgical outcomes. Among patients with a preoperative diagnosis of hippocampal sclerosis or with laterality of PET-CT hypometabolism concordant with the epileptogenic focus identified using SEEG, those with aura are likely to benefit from surgery.

Resective epilepsy surgery in tuberous sclerosis complex: a nationwide multicentre retrospective study from China.

At least 50% of patients with tuberous sclerosis complex present with intractable epilepsy; for these patients, resective surgery is a treatment option. Here, we report a nationwide multicentre retrospective study and analyse the long-term seizure and neuropsychological outcomes of epilepsy surgery in patients with tuberous sclerosis complex. There were 364 patients who underwent epilepsy surgery in the study. Patients' clinical data, postoperative seizure outcomes at 1-, 4-, and 10-year follow-ups, preoperative and postoperative intelligence quotients, and quality of life at 1-year follow-up were collected. The patients' ages at surgery were 10.35 ± 7.70 years (range: 0.5-47). The percentage of postoperative seizure freedom was 71% (258/364) at 1-year, 60% (118/196) at 4-year, and 51% (36/71) at 10-year follow-up. Influence factors of postoperative seizure freedom were the total removal of epileptogenic tubers and the presence of outstanding tuber on MRI at 1- and 4-year follow-ups. Furthermore, monthly seizure (versus daily seizure) was also a positive influence factor for postoperative seizure freedom at 1-year follow-up. The presence of an outstanding tuber on MRI was the only factor influencing seizure freedom at 10-year follow-up. Postoperative quality of life and intelligence quotient improvements were found in 43% (112/262) and 28% (67/242) of patients, respectively. Influence factors of postoperative quality of life and intelligence quotient improvement were postoperative seizure freedom and preoperative low intelligence quotient. The percentage of seizure freedom in the tuberectomy group was significantly lower compared to the tuberectomy plus and lobectomy groups at 1- and 4-year follow-ups. In conclusion, this study, the largest nationwide multi-centre study on resective epilepsy surgery, resulted in improved seizure outcomes and quality of life and intelligence quotient improvements in patients with tuberous sclerosis complex. Seizure freedom was often achieved in patients with an outstanding tuber on MRI, total removal of epileptogenic tubers, and tuberectomy plus. Quality of life and intelligence quotient improvements were frequently observed in patients with postoperative seizure freedom and preoperative low intelligence quotient.

Application of multimodal MRI and radiologic features for stereotactic brain biopsy: insights from a series of 208 patients.

OBJECTIVES: We reviewed our institutional experience during a 10-year period for improvement of safety and efficacy of stereotactic biopsy procedures. METHODS: We performed a retrospective review of inpatient summaries, stereotactic worksheets and radiologic investigations of 208 consecutive patients, who underwent MRI-guided stereotactic biopsies between March 2010 and March 2020. RESULTS: The overall diagnostic yield was 96.2%. CT-confirmed intracranial hemorrhage occurred in 17 patients (8.2%), and the overall mortality rate was 0.5%. Combined MRS and PWI helped target selection in 27 cases (13.0%), the diagnostic yield was 100%. The results of the regression analysis revealed that non-diagnostic biopsy specimen significantly correlated with the cystic trait (p<.01) and edema of lesions (p<.05). Enhancement (p<.01) is shown to be an important factor for obtaining a diagnostic biopsy. Furthermore, the edema trait of lesions (p<.01) showed the important factors of hemorrhage. CONCLUSIONS: The radiological features of lesions and use of the most suitable MRI sequences during biopsy planning are recommended ways to improve the diagnostic yield and safety of this technique.

Spectroscopic optical coherence refraction tomography.

In optical coherence tomography (OCT), the axial resolution is often superior to the lateral resolution, which is sacrificed for long imaging depths. To address this anisotropy, we previously developed optical coherence refraction tomography (OCRT), which uses images from multiple angles to computationally reconstruct an image with isotropic resolution, given by the OCT axial resolution. On the other hand, spectroscopic OCT (SOCT), an extension of OCT, trades axial resolution for spectral resolution and hence often has superior lateral resolution. Here, we present spectroscopic OCRT (SOCRT), which uses SOCT images from multiple angles to reconstruct a spectroscopic image with isotropic spatial resolution limited by the OCT lateral resolution. We experimentally show that SOCRT can estimate bead size based on Mie theory at simultaneously high spectral and isotropic spatial resolution. We also applied SOCRT to a biological sample, achieving axial resolution enhancement limited by the lateral resolution.

Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration.

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. We demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This work shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT guided robotic ophthalmic surgery.

Altered attention networks and DMN in refractory epilepsy: A resting-state functional and causal connectivity study.

PURPOSE: Epilepsy is considered a disorder of neural networks. Patients diagnosed with refractory epilepsy frequently experience attention impairments. Seizure activity in epilepsy may disturb brain networks and damage the brain function of attention. The aims of this study were to assess functional and causal connectivities of the attention networks and default mode network using resting-state functional magnetic resonance imaging (fMRI). METHOD: Resting-state fMRI data were gathered from 19 patients with refractory epilepsy (mixed localization and aetiologies) and 21 healthy people. The fMRI data were analyzed by group independent component analysis (ICA) fMRI toolbox to extract dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). The components of the selected networks were compared between patients and healthy controls to explore the change in functional connectivity (FC). Granger causality analysis was performed by taking the aforementioned significant brain areas as regions of interest (ROIs) to calculate autoregression coefficients of each pair of ROIs. Comparisons were done to find the significantly different causal connectivity when FC was changed between patients and healthy controls. RESULTS: In DAN, the FC values of the bilateral frontal eye field (FEF) and left intraparietal sulcus (IPS) were decreased. In VAN, the FC values of the double-side ventral prefrontal cortex (vPFC) and the temporoparietal junction (TPJ) were reduced. As for DMN, the FC values of the bilateral medial prefrontal cortices (mPFC) were decreased whereas those for the bilateral precuneus (PCUN) were increased. Granger causal connectivity values were correlated: causal influence was decreased significantly from the left IPS (in DAN) to the double side of the vPFC but remained the same for the right FEF (in DAN) to the right TPJ. The value was decreased from the left PCUN (in DMN) to the right TPJ and FEF, and the causal flow from the right PCUN to the right TPJ and bilateral vPFC was also significantly inhibited (p < 0.05). CONCLUSION: Frequent seizures in patients with refractory epilepsy may damage the cortex and disturb DAN, VAN, and DMN, leading to functional and causal connectivity alteration. In addition, epileptic activity may disrupt network interactions and further influence information communication.

Long working distance OCT with a compact 2f retinal scanning configuration for pediatric imaging.

Young and/or autistic children cannot be imaged with tabletop or handheld optical coherence tomography (OCT) because of their lack of attention and fear of large objects close to their face. We demonstrate a prototype retinal swept-source OCT system with a long working distance (from the last optical element to the subject's eye) to facilitate pediatric imaging. To reduce the number of optical elements and axial length compared to the traditional 4f telescope, we employ a compact 2f retinal scanning configuration and achieve a working distance of 350 mm with a 16° OCT field of view. We test our prototype system on pediatric and adult subjects.

[Functional connectivity of amygdala in refractory epilepsy:a resting-state functional study of magnetic resonance imaging].

OBJECTIVE: To explore the functions of amygdala functional connectivity in the pathogenesis of refractory epilepsy with resting-state functional magnetic resonance imaging (RS-fMRI). METHODS: A total of 19 patients with refractory epilepsy were recruited from August 2013 to June 2014. And 19 healthy persons were selected as the controls.No obvious epileptogenic lesions of intracranial structures were found on multi-modal neuroimaging.Ictal and interictal epileptic activities on long-term video electroencephalogram (EEG) showed spine spread spike and wave in bilateral cerebral hemispheres. All fMRI data were preprocessed after RS-fMRI scanning. Then left and right amygdalas were selected as regions of interest (ROIs) for calculating the linear correlation between amygdala and whole brain. As relative to the control group, the changes of brain areas in functional connectivity were examined for the intractable epilepsy group. RESULTS: Compared with the controls, left amygdala in refractory epilepsy group showed increased functional connectivity with bilateral fusiform gyrus, bilateral calcarine gyrus and right lingual, on the contrary decreased functional connectivity with bilateral cuneus, bilateral precuneus, bilateral caudatas and left thalamus.However, right amygdala demonstrated increased functional connectivity with bilateral calcarine gyrus and bilateral linguals, but decreased functional connectivity with bilateral caudatas and left putamen (P < 0.05). CONCLUSION: Altered functional connectivity of amygdala reflects its dysfunction in refractory epilepsy patients. It suggested that amygdala is an important component of "epileptic network" participating in the occurrence and development of refractory epilepsy.

[Alterations of brain network efficiency in patients with post-concussion syndrome].

OBJECTIVE: To discuss the alterations of brain network efficiency in patients with post-concussion syndrome. METHODS: A total of 23 patients from Anhui Provincial Hospital in the period from 2013/6 to 2014/3 who have had the concussion for 3 months were enrolled and 23 volunteers paired in sex, age and education were also enrolled as healthy controls. Comparisons of selective attention of both groups were conducted using Stroop Word-Color Test. The data of resting-state functional magnetic resonance imaging (fMRI) in both groups were collected and the data were dealt with Network Construction which is a part of GRETNA software to obtain the Matrix of brain network. Network analysis was used to obtain Global and Nodal efficiency, then independent t-test was used for statistical analyses of the value of Global and Nodal efficiency. RESULTS: The difference in Global efficiency of two groups in every threshold value had no statistical significance. Compared with healthy controls, the Nodal efficiencies in patients with post-concussion syndrome were significantly different in the brain regions as below: left orbital middle frontal gyrus, left posterior cingulate, left lingual, left thalamus, left superior temporal gyrus, right anterior cingulate, right posterior cingulate, right supramarginalgyrus. CONCLUSIONS: Compared with healthy controls, there is no significant changes of Globe efficiency in patients with post-concussion syndrome, and the brain function deficits in these patients may be caused by changes of Nodal efficiency in their brain network.

[Neuroimaging diagnosis and therapeutic efficacy of different surgical methods of gliomatosis cerebri].

OBJECTIVE: To explore the neuroimaging diagnosis and therapeutic efficacy of different surgical methods of gliomatosis cerebri. METHODS: 26 cases of gliomatosis cerebri at our department between September 2008 and September 2013 were retrospectively analyzed. Preoperative cranial computed tomography (CT), magnetic resonance imaging (MRI) and other multimodal imaging scans were performed. The procedures included stereotactic brain biopsy (n = 11) and large craniotomy lobotomy (n = 15). Whole brain radiotherapy and/or temozolomide therapy was performed postoperatively according to the malignancy of tumors. Follow-ups were conducted to analyze the survival differences between stereotactic brain biopsy and large craniotomy lobotomy groups. RESULTS: According to the different features of multimodal imaging, gliomatosis cerebri could be divided into two types: (1) type I(n = 19) showed a diffuse infiltrating lesion infringing multiple brain lobes or regions with central corpus callosum but without obvious enhancement; (2) type II (n = 7) appeared as sporadic or tuberous enhancement in addition to the features of type I. Pathological diagnosis: pilocytic astrocytoma (n = 2), diffuse astrocytoma (n = 13), oligodendroglial tumors (n = 3), oligoastrocytoma (n = 1), anaplastic astrocytoma (n = 5) and glioblastoma (n = 2). The degree of malignancy was a prognostic factor for postoperative survival in patients with gliomatosis cerebri. The mean survival time (MST) of large craniotomy lobotomy group (23 ± 7) was significantly longer than that of stereotactic brain biopsy group (13 ± 3) (P < 0.05). CONCLUSION: Gliomatosis cerebri is a primary brain glial tumor with diffuse infiltrative growth but retaining the general structure of central nervous system. Multimodal imaging studies plus pathological examination yield a definitive diagnosis. Comprehensive treatment of operation plus chemo- or radio-therapy can prolong postoperative MST.

[Functional connectivity of temporal parietal junction in online game addicts:a resting-state functional magnetic resonance imaging study].

OBJECTIVE: To explore the functions of temporal parietal junction (TPJ) as parts of attention networks in the pathogenesis of online game addiction using resting-state functional magnetic resonance imaging (fMRI). METHODS: A total of 17 online game addicts (OGA) were recruited as OGA group and 17 healthy controls during the same period were recruited as CON group. The neuropsychological tests were performed for all of them to compare the inter-group differences in the results of Internet Addiction Test (IAT) and attention functions. All fMRI data were preprocessed after resting-state fMRI scanning. Then left and right TPJ were selected as regions of interest (ROIs) to calculate the linear correlation between TPJ and entire brain to compare the inter-group differences. RESULTS: Obvious differences existed between OGA group (71 ± 5 scores) and CON group (19 ± 7 scores) in the IAT results and attention function (P < 0.05). Compared with the controls, right TPJ in online game addicts showed decreased functional connectivity with bilateral ventromedial prefrontal cortex (VMPFC), bilateral hippocampal gyrus and bilateral amygdaloid nucleus, but increased functional connectivity with right cuneus.However, left TPJ demonstrated decreased functional connectivity with bilateral superior frontal gyrus and bilateral middle frontal gyrus, but increased functional connectivity with bilateral cuneus (P < 0.05). CONCLUSION: Altered functional connectivity of TPJ reflected its dysfunction in online game addicts.It suggests that TPJ is an important component of attention networks participating in the generation of online game addiction.

Angular-domain scattering interferometry.

We present an angular-scattering optical method that is capable of measuring the mean size of scatterers in static ensembles within a field of view less than 20 µm in diameter. Using interferometry, the method overcomes the inability of intensity-based models to tolerate the large speckle grains associated with such small illumination areas. By first estimating each scatterer's location, the method can model between-scatterer interference as well as traditional single-particle Mie scattering. Direct angular-domain measurements provide finer angular resolution than digitally transformed image-plane recordings. This increases sensitivity to size-dependent scattering features, enabling more robust size estimates. The sensitivity of these angular-scattering measurements to various sizes of polystyrene beads is demonstrated. Interferometry also allows recovery of the full complex scattered field, including a size-dependent phase profile in the angular-scattering pattern.

EEG-Based Seizure Prediction via Model Uncertainty Learning.

Deep neural networks (DNNs) have the powerful ability to automatically extract efficient features, which makes them prominent in electroencephalogram (EEG) based seizure prediction tasks. However, current research in this field cannot take the model uncertainty into account, causing the prediction less credible. To this end, we introduce a novel end-to-end patient-specific seizure prediction framework via model uncertainty learning. Specifically, we propose a reparameterized EEG-based lightweight CNN architecture and a modified Monte Carlo dropout (RepNet-MMCD) strategy to improve the reliability of the DNNs-based model. In RepNet, we obtain multi-scale feature representations by applying depthwise separable convolutions of different kernels. After training, depthwise convolutions with different scales are equivalently converted into a single convolution layer, which can greatly reduce computational budgets without losing model performance. In addition, we propose a modified Monte Carlo (MMCD) strategy, leveraging the samples-based temporal information in EEG signals to simulate the Monte Carlo dropout sampling. Sensitivity, false-positive rate (FPR), and area under curve (AUC) of the proposed RepNet-MMCD achieve 93.1%, 0.033/h, 0.950 and 81.6%, 0.056/h, 0.903 on two public datasets, respectively. We further extend the MMCD strategy to the other baseline methods, which can improve the performance of seizure prediction by a clear margin.

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising.

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. However, they still suffer from the following limitations. The existing structure designs have not fully taken into account the temporal characteristics of artifacts. Meanwhile, the existing training strategies usually ignore the holistic consistency between denoised EEG signals and authentic clean ones. To address these issues, we propose a GAN guided parallel CNN and transformer network, named GCTNet. The generator contains parallel CNN blocks and transformer blocks to respectively capture local and global temporal dependencies. Then, a discriminator is employed to detect and correct the holistic inconsistencies between clean and denoised EEG signals. We evaluate the proposed network on both semi-simulated and real data. Extensive experimental results demonstrate that GCTNet significantly outperforms state-of-the-art networks in various artifact removal tasks, as evidenced by its superior objective evaluation metrics. For example, in the task of removing electromyography artifacts, GCTNet achieves 11.15% reduction in RRMSE and 9.81% improvement in SNR over other methods, highlighting the potential of the proposed method as a promising solution for EEG signals in practical applications.

A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network.

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, the "black-box" nature of deep learning models makes the clinician mistrust the prediction results, severely limiting its clinical application. For this purpose, in this study, we propose a self-interpretable deep learning model for patient-specific epileptic seizure prediction: Multi-Scale Prototypical Part Network (MSPPNet). This model attempts to measure the similarity between the inputs and prototypes (learned during training) as evidence to make final predictions, which could provide a transparent reasoning process and decision basis (e.g., significant prototypes for inputs and corresponding similarity score). Furthermore, we assign different sizes to the prototypes in latent space to capture the multi-scale features of EEG signals. To the best of our knowledge, this is the first study that develops a self-interpretable deep learning model for seizure prediction, other than the existing post hoc interpretation studies. Our proposed model is evaluated on two public epileptic EEG datasets (CHB-MIT: 16 patients with a total of 85 seizures, Kaggle: 5 dogs with a total of 42 seizures), with a sensitivity of 93.8% and a false prediction rate of 0.054/h in the CHB-MIT dataset and a sensitivity of 88.6% and a false prediction rate of 0.146/h in the Kaggle dataset, achieving the current state-of-the-art performance with self-interpretable evidence.

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

[A voxel-based morphometric analysis of brain gray matter in online game addicts].

OBJECTIVE: To explore the possible brain mechanism of online game addiction (OGA) in terms of brain morphology through voxel-based morphometric (VBM) analysis. METHODS: Seventeen subjects with OGA and 17 age- and gender-matched healthy controls (HC group) were recruited from Department of Psychology at our hospital during February-December 2011. The internet addiction scale (IAS) was used to measure the degree of OGA tendency. Magnetic resonance imaging (MRI) scans were performed to acquire 3-dimensional T1-weighted images. And FSL 4.1 software was employed to confirm regional gray matter volume changes. For the regions where OGA subjects showed significantly different gray matter volumes from the controls, the gray matter volumes of these areas were extracted, averaged and regressed against the scores of IAS. RESULTS: The OGA group had lower gray matter volume in left orbitofrontal cortex (OFC), left medial prefrontal cortex (mPFC), bilateral insula (INS), left posterior cingulate cortex (PCC) and left supplementary motor area (SMA). Gray matter volumes of left OFC and bilateral INS showed a negative correlation with the scores of IAS (r = -0.65, r = -0.78, P < 0.05). CONCLUSION: Gray matter volume changes are present in online game addicts and they may be correlated with the occurrence and maintenance of OGA.

[Impulsive decision-making behaviors in heroin addicts: a study of functional magnetic resonance imaging].

OBJECTIVE: To explore the brain regions associated with impulsive decision-making behaviors and interpret the nervous mechanism for addiction and relapse in heroin abusers. METHODS: Using the paradigms of psychological experiment, the subjects in both heroin addiction group (HA group) and normal control group (HC group) performed Iowa gambling task (IGT) and simultaneously underwent functional magnetic resonance imaging (fMRI) scan. All the above data were gathered and then analyzed by SPM5 software to explore both the brain regions and their functional changes correlated with impulsive decision-making. RESULTS: Evidence by IGT behavioral consequences demonstrated that the net scores in HC group increased with numbers of decision-making whereas no increment (fluctuating between-1 and 0) was observed in HA group. Based on the results of fMRI analysis, right orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), left ventromedial prefrontal cortex (MPFC) and anterior cingulate cortex (ACC) were activated in both groups. But the right OFC was more active while the right DLPFC and left MPFC were weaker in HA group versus the HC group. Meanwhile, activation of right lenticular nucleus, right thalamus, right insula, hippocampus and left caudate nucleus were observed in HA group. CONCLUSION: Heroin abusers are incapable of impulsive decision-making in behavioral studies. Such a brain region as prefrontal cortex participates in the decision-making performance and control of impulsiveness. Functionally abnormal brain regions correlated with impulsive decision-making may be one cause of genesis, maintenance and relapse of heroin addiction.

A general sample-weighted framework for epileptic seizure prediction.

OBJECTIVE: Effective epileptic seizure prediction can make the patients know the onset of the seizure in advance to take timely preventive measures. Many studies based on machine learning methods have been proposed to tackle this problem and achieve significant progress in recent years. However, most studies treat each EEG training sample's contribution to the model as equal, while different samples have different predictive effects on epileptic seizures (e.g., preictal samples from different times). To this end, in this paper, we propose a general sample-weighted framework for patient-specific epileptic seizure prediction. METHODS: Specifically, we define the mapping from the sample weights of training sets to the performance of the validation sets as the fitness function to be optimized. Then, the genetic algorithm is employed to optimize this fitness function and obtain the optimal sample weights. Finally, we obtain the final model by using the training sets with optimized sample weights. RESULTS: To evaluate the effectiveness of our framework, we conduct extensive experiments on both traditional machine learning methods and prevalent deep learning methods. Our framework can significantly improve performance based on these methods. Among them, our framework based on Transformer achieves an average sensitivity of 94.6%, an average false prediction rate of 0.06/h, and an average AUC of 0.939 in 12 pediatric patients from the CHB-MIT database with the leave-one-out method, which outperforms the state-of-the-art methods. CONCLUSION: This study provides new insights into the field of epileptic seizure prediction by considering the discrepancies between EEG samples. Moreover, we develop a general sample-weighted framework, which applies to almost all classical classification methods and can significantly improve performance based on these methods.