Gyrus rectus asymmetry predicts trait alexithymia, cognitive empathy, and social function in neurotypical adults.

Reduced empathy and elevated alexithymia are observed in autism spectrum disorder (ASD), which has been linked to altered asymmetry in brain morphology. Here, we investigated whether trait autism, empathy, and alexithymia in the general population is associated with brain morphological asymmetry. We determined left-right asymmetry indexes for cortical thickness and cortical surface area (CSA) and applied these features to a support-vector regression model that predicted trait autism, empathy, and alexithymia. Results showed that less leftward asymmetry of CSA in the gyrus rectus (a subregion of the orbitofrontal cortex) predicted more difficulties in social functioning, as well as reduced cognitive empathy and elevated trait alexithymia. Meta-analytic decoding of the left gyrus rectus annotated functional items related to social cognition. Furthermore, the link between gyrus rectus asymmetry and social difficulties was accounted by trait alexithymia and cognitive empathy. These results suggest that gyrus rectus asymmetry could be a shared neural correlate among trait alexithymia, cognitive empathy, and social functioning in neurotypical adults. Left-right asymmetry of gyrus rectus influenced social functioning by affecting the cognitive processes of emotions in the self and others. Interventions that increase leftward asymmetry of the gyrus rectus might improve social functioning for individuals with ASD.

Temporal dynamics of electroencephalographic microstates during sustained pain.

Brain dynamics can be modeled by a sequence of transient, nonoverlapping patterns of quasi-stable electrical potentials named "microstates." While electroencephalographic (EEG) microstates among patients with chronic pain remained inconsistent in the literature, this study characterizes the temporal dynamics of EEG microstates among healthy individuals during experimental sustained pain. We applied capsaicin (pain condition) or control (no-pain condition) cream to 58 healthy participants in different sessions and recorded resting-state EEG 15 min after application. We identified 4 canonical microstates (A-D) that are related to auditory, visual, salience, and attentional networks. Microstate C had less occurrence, as were bidirectional transitions between microstate C and microstates A and B during sustained pain. In contrast, sustained pain was associated with more frequent and longer duration of microsite D, as well as more bidirectional transitions between microstate D and microstates A and B. Microstate D duration positively correlated with intensity of ongoing pain. Sustained pain improved global integration within microstate C functional network, but weakened global integration and efficiency within microstate D functional network. These results suggest that sustained pain leads to an imbalance between processes that load on saliency (microstate C) and processes related to switching and reorientation of attention (microstate D).

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods.

Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF-SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM.

Robust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction.

BACKGROUND: Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). PURPOSE: To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. STUDY TYPE: Retrospective. SUBJECTS: Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). FIELD STRENGTH/SEQUENCE: 3T/gradient-echo echo planar imaging (EPI). ASSESSMENT: We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). STATISTICAL TESTS: Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. RESULTS: No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. DATA CONCLUSION: ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsfMRI in comparison with the other three methods. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1421-1431.

Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain.

ABSTRACT: Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.

Analgesia of noninvasive electrical stimulation of the dorsolateral prefrontal cortex: A systematic review and meta-analysis.

OBJECTIVE: The dorsolateral prefrontal cortex (DLPFC) is implicated in pain modulation, suggesting its potential as a therapeutic target for pain relief. However, studies on transcranial electrical stimulation (tES) over the DLPFC yielded diverse results, likely due to differences in stimulation protocols or pain assessment methods. This study aims to evaluate the analgesic effects of DLPFC-tES using a meta-analytical approach. METHODS: A meta-analysis of 29 studies involving 785 participants was conducted. The effects of genuine and sham DLPFC-tES on pain perception were examined in healthy individuals and patients with clinical pain. Subgroup analyses explored the impact of stimulation parameters and pain modalities. RESULTS: DLPFC-tES did not significantly affect pain outcomes in healthy populations but showed promise in reducing pain-intensity ratings in patients with clinical pain (Hedges' g = -0.78, 95% CI = [-1.33, -0.24], p = 0.005). Electrode placement significantly influenced the analgesic effect, with better results observed when the anode was at F3 and the cathode at F4. CONCLUSIONS: DLPFC-tES holds potential as a cost-effective pain management option, particularly for clinical populations. Optimizing electrode placement, especially with an symmetrical configuration, may enhance therapeutic efficacy. These findings underscore the promise of DLPFC-tES for alleviating perceived pain intensity in clinical settings, emphasizing the importance of electrode placement optimization.

Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. METHODS: Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. RESULTS: The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. CONCLUSIONS: Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification.

Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost of establishing high-quality medical datasets. Many FSL approaches have been proposed in natural image scenes. However, present FSL methods are rarely evaluated on medical images and the FSL technology applicable to medical scenarios need to be further developed. Meta-learning has supplied an optional framework to address the challenging FSL setting. In this paper, we propose a novel multi-learner based FSL method for multiple medical image classification tasks, combining meta-learning with transfer-learning and metric-learning. Our designed model is composed of three learners, including auto-encoder, metric-learner and task-learner. In transfer-learning, all the learners are trained on the base classes. In the ensuing meta-learning, we leverage multiple novel tasks to fine-tune the metric-learner and task-learner in order to fast adapt to unseen tasks. Moreover, to further boost the learning efficiency of our model, we devised real-time data augmentation and dynamic Gaussian disturbance soft label (GDSL) scheme as effective generalization strategies of few-shot classification tasks. We have conducted experiments for three-class few-shot classification tasks on three newly-built challenging medical benchmarks, BLOOD, PATH and CHEST. Extensive comparisons to related works validated that our method achieved top performance both on homogeneous medical datasets and cross-domain datasets.

Aftereffects of alpha transcranial alternating current stimulation over the primary sensorimotor cortex on cortical processing of pain.

ABSTRACT: Transcranial alternating current stimulation (tACS) is believed to modulate brain oscillations in a frequency-specific manner. Given the correlation between sensorimotor α-oscillations and pain perception, tACS that targets sensorimotor α-oscillations has the potential to reduce pain. Therefore, this study sought to determine the aftereffects of α-tACS over unilateral primary sensorimotor cortex (SM1) on the perceptual and neural responses to noxious painful stimulation of the contralateral hand. Using a double-blinded and sham-controlled design, 60 healthy participants were recruited to receive either α-tACS or sham stimulation of unilateral SM1 through an electrode montage in a 4 × 1 ring configuration. Neural responses to laser nociceptive stimuli were assessed using functional magnetic resonance imaging immediately before and after α-tACS intervention. Perceptual reports were recorded simultaneously. Compared with sham stimulation, α-tACS attenuated bilateral SM1 responses to painful stimuli delivered to the contralateral hand. Although α-tACS did not exert direct effect on subjective pain perception, it can indirectly decrease ratings of pain perception by reducing brain activity within the targeted SM1. Moreover, α-tACS decreased the functional connectivity between the targeted SM1 and a network of regions that are crucially involved in pain processing, including the middle cingulate cortex, contralateral somatosensory cortex, and dorsolateral prefrontal cortex. These results demonstrated that after α-tACS applied over the unilateral SM1 does attenuate subsequent neural processing of pain within bilateral sensorimotor regions as well as sensorimotor functional connectivity. The findings provide evidence that sensorimotor α-oscillations directly affect pain processing and support the application of sensorimotor α-tACS for inducing pain analgesia.

Neurocognitive Mechanisms Underlying Attention Bias Towards Pain: Evidence From a Drift-Diffusion Model and Event-Related Potentials.

Although combining computational modeling with event-related potentials (ERPs) can precisely characterize neurocognitive processes involved in attention bias, it has yet to be applied in the context of pain. Here, a hierarchical drift-diffusion model (DDM) along with ERPs was used to characterize the neurocognitive mechanisms underlying attention bias towards pain. A spatial cueing paradigm was adopted, in which the locations of targets were either validly or invalidly predicted by spatial cues related to pain or nonpain signals. DDM-derived nondecision time was shorter for targets validly cued by pain signals than by nonpain signals, thus indicating speeded attention engagement towards pain; drift rate was slower for targets invalidly cued by pain signals than by nonpain signals, reflecting slower attention disengagement from pain. The facilitated engagement towards pain was partially mediated by the enhanced lateralization of cue-evoked N1 amplitudes, which relate to the bottom-up, stimulus-driven processes of detecting threatening signals. On the other hand, the retarded disengagement from pain was partially mediated by the enhanced target-evoked anterior N2 amplitudes, which relate to the top-down, goal-driven processes of conflict monitoring and behavior regulating. These results demonstrated that engagement and disengagement components of pain-related attention bias are governed by distinct neurocognitive mechanisms. However, it remains possible that the findings are not pain-specific, but rather, are related to threat or aversiveness in general. This deserves to be further examined by adding a control stimulus modality. PERSPECTIVE: This study characterized the neurocognitive processes involved in attention bias towards pain through combining a hierarchical DDM and ERPs. Our results revealed distinctive neurocognitive mechanisms underlying engagement and disengagement components of attention bias. Future studies are warranted to examine whether our findings are pain-specific or not.

Transcranial random noise stimulation over the left dorsolateral prefrontal cortex attenuates pain expectation and perception.

OBJECTIVE: The dorsolateral prefrontal cortex (DLPFC) has been increasingly used as a neuromodulatory target in pain management. Transcranial random noise stimulation (tRNS) was shown to effectively elevate cortical excitability. Hence, this study aimed to characterize how tRNS over the left DLPFC affects pain expectation and perception, as well as the efficacy of conditioned-pain modulation (CPM) that reflects the function of the endogenous pain-inhibitory pathway. METHODS: Using a randomized, double-blinded, and sham-controlled design, healthy participants were randomly recruited to receive tRNS with a direct current offset or sham stimulation. Their expectations and perceptions of painful electrocutaneous stimuli, as well as CPM efficacy were assessed before, immediately after, and 30 min after tRNS. RESULTS: Compared with sham stimulation, perceived-pain ratings to the painful stimuli, and expected-pain ratings before painful stimuli, attenuated immediately after tRNS, whereas this analgesic effect was ineffective 30 min after tRNS. Importantly, the immediate analgesia induced by tRNS could be accounted for by tRNS effect on attenuating expected-pain ratings before certain painful stimuli. However, CPM efficacy was not significantly affected by tRNS. CONCLUSIONS: These results demonstrate analgesia immediately after applying tRNS over the left DLPFC. SIGNIFICANCE: This study provides evidence for analgesia of DLPFC-tRNS on an experimental pain model.

Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review.

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.

Effects of High-Definition Transcranial Direct Current Stimulation Over the Primary Motor Cortex on Cold Pain Sensitivity Among Healthy Adults.

Some clinical studies have shown promising effects of transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) on pain relief. Nevertheless, a few studies reported no significant analgesic effects of tDCS, likely due to the complexity of clinical pain conditions. Human experimental pain models that utilize indices of pain in response to well-controlled noxious stimuli can avoid many confounds that are present in the clinical data. This study aimed to investigate the effects of high-definition tDCS (HD-tDCS) stimulation over M1 on sensitivity to experimental pain and assess whether these effects could be influenced by the pain-related cognitions and emotions. A randomized, double-blinded, crossover, and sham-controlled design was adopted. A total of 28 healthy participants received anodal, cathodal, or sham HD-tDCS over M1 (1 mA for 20 min) in different sessions, in which montage has the advantage of producing more focal stimulation. Using a cold pressor test, several indices reflecting the sensitivity to cold pain were measured immediately after HD-tDCS stimulation, such as cold pain threshold and tolerance and cold pain intensity and unpleasantness ratings. Results showed that only anodal HD-tDCS significantly increased cold pain threshold when compared with sham stimulation. Neither anodal nor cathodal HD-tDCS showed significant analgesic effects on cold pain tolerance, pain intensity, and unpleasantness ratings. Correlation analysis revealed that individuals that a had lower level of attentional bias to negative information benefited more from attenuating pain intensity rating induced by anodal HD-tDCS. Therefore, single-session anodal HD-tDCS modulates the sensory-discriminative aspect of pain perception as indexed by the increased pain threshold. In addition, the modulating effects of HD-tDCS on attenuating pain intensity to suprathreshold pain could be influenced by the participant's negative attentional bias, which deserves to be taken into consideration in the clinical applications.

Effect of segmentation from different diffusive metric maps on diffusion tensor imaging analysis of the cervical spinal cord.

BACKGROUND: Segmentation is a crucial and necessary step in diffusion tensor imaging (DTI) analysis of the cervical spinal cord. In existing studies, different diffusive metric maps [B0, fractional anisotropy (FA) and axial diffusivity (AD) maps] have been involved in the segmentation of tissues of the cervical spinal cord. The selection of a diffusive metric map for segmentation may affect the accuracy of segmentation and then affect the validity and effectiveness of the extracted diffusive features. However, there are few discussions on this problem. Therefore, this study would like to examine the effect of segmentation based on different diffusive metric maps for DTI analysis of the cervical spinal cord. METHODS: Twenty-nine healthy subjects and thirty patients with cervical spondylotic myelopathy (CSM) were finally included in this study. All subjects accepted DTI scanning at cervical levels from C2 to C7/T1. For healthy subjects, all cervical levels were included for analysis; while, for each patient, only one compressed cervical level was included. After DTI scanning, DTI metrics including B0, FA, AD, radial diffusivity (RD) and mean diffusivity (MD) were calculated. The evaluation was performed to B0, FA and AD maps from two aspects. First, the accuracy of segmentation was evaluated via a comparison between segmentation based on each diffusive metric map and segmentation based on an average image, which was acquired by averaging B0, FA, AD, RD and MD maps. The segmentation was achieved by a semi-automatic segmentation process, and the similarity between two segmentation results was denoted by the intersection of the union (IOU). Second, the diversity of extracted diffusive features was equalized as their performance in the classification of image pixels of different regions of interest (ROIs) and then was evaluated by mutual information (MI) and area under the curve (AUC). One-way ANOVA and Bonferroni's post hoc tests were applied to compare the evaluation results. RESULTS: One-way ANOVA suggested that there were differences (P<0.001) in IOU, MI and AUC values among the three diffusive metric maps for both healthy subjects and patients. The post-hoc tests further indicated that FA performed the best (P<0.001), i.e., the most substantial accuracy of segmentation and the highest diversity in extracted diffusive features. CONCLUSIONS: Different evaluation results had been observed for segmentation based on different diffusive metric maps, suggesting the necessity of selection of diffusive metric maps for segmentation in DTI analysis of the cervical spinal cord. Moreover, FA map is suggested for segmentation due to its best performance in the evaluation.

A computational measurement of cartilaginous endplate structure using ultrashort time-to-echo MRI scanning.

BACKGROUND AND OBJECTIVE: Ultrashort time-to-echo (UTE) MRI scanning has been applied to observe the cartilaginous endplate (CEP) in intervertebral disc. CEP plays a critical role in IVD health and disease. Nevertheless, current measurements of CEP based on UTE MRI technique are still by manual segmentation, and observation of signal abnormality was usually time-consuming and often disturbed by subjective bias. This study hence proposed an efficient way to harvest the global parameters of CEP after UTE MRI scanning. METHODS: Ex-vivo UTE-MRI scanning was performed using 12 goat lumbar spine specimens. After the UTE-MRI data were collected, the computational method for CEP segmentation and assessment was developed. Global view of CEP, e.g., surface morphology as well as distributions of thickness and signal intensity, were measured. Histological staining of the CEP as well as manual CEP segmentation was then conducted to validate the accuracy. RESULTS: Segmentation of CEP by the proposed method presented a good agreement with manual measurement, with mean Jaccard index of 0.7296 and mean Cohen's Kappa coefficient of 0.8286. The processing time for CEP segmentation and property measurements was 59.2s which was much shorter than the manual measurement. CONCLUSIONS: This newly-developed technique is able to qualitatively and quantitatively assess the CEP structure, which is very valuable for the clinicians and researchers to accurately evaluate the endplate health after UTE MRI scanning.

Amplitude of Low Frequency Fluctuation (ALFF) in the Cervical Spinal Cord with Stenosis: A Resting State fMRI Study.

Cervical spondylotic myelopathy (CSM) is a common spinal cord dysfunction disease with complex symptoms in clinical presentation. Resting state fMRI (rsfMRI) has been introduced to study the mechanism of neural development of CSM. However, most of those studies focused on intrinsic functional connectivity rather than intrinsic regional neural activity level which is also frequently analyzed in rsfMRI studies. Thus, this study aims to explore whether the level of neural activity changes on the myelopathic cervical cord and evaluate the possible relationship between this change and clinical symptoms through amplitude of low frequency fluctuation (ALFF). Eighteen CSM patients and twenty five healthy subjects participated in rsfMRI scanning. ALFF was investigated on each patient and subject. The results suggested that ALFF values were higher in the CSM patients at all cervical segments, compared to the healthy controls. The severity of myelopathy was associated with the increase of ALFF. This finding would enrich our understanding on the neural development mechanism of CSM.