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Transcranial electrical stimulation (tES) is a non-invasive technique widely used in modulating brain activity and behavior, but its effects differ across individuals and are influenced by head anatomy. In this study, we investigated how the electric field (EF) generated by high-definition tES varies across the lifespan among different demographic groups and its relationship with neural responses measured by functional magnetic resonance imaging (fMRI). We employed an MRI-guided finite element method to simulate the EF for the two most common tES montages (i.e., targeting the dorsolateral prefrontal cortex and motor cortex, respectively) in two large cohorts of white and Asian participants aged 12 to 100 years. We found that the EF intensity decreased with age, particularly in individuals under 25 years of age, and was influenced by gender and ethnicity. We identified skull thickness, scalp thickness, and epidural cerebrospinal fluid thickness, as the primary anatomical factors accounting for the inter-individual EF variability. Using a concurrent tES-fMRI approach, we observed a spatial consistency between the simulated EF and the brain activity changes induced by tES in the target region. Finally, we developed an open-source toolbox incorporating age-stratified head models to facilitate efficient EF calculations. These findings characterize and quantify the individual differences in tES-induced EF, offering a reference for implementing personalized neuromodulation strategies., (Copyright © 2024 Science China Press. Published by Elsevier B.V. All rights reserved.)

Pain perception varies widely among individuals due to the varying degrees of biological, psychological, and social factors. Notably, sex differences in pain sensitivity have been consistently observed in various experimental and clinical investigations. However, the neuropsychological mechanism underlying sex differences in pain sensitivity remains unclear. To address this issue, we quantified pain sensitivity (i.e., pain threshold and tolerance) using the cold pressure test and negative emotions (i.e., pain-related fear, pain-related anxiety, trait anxiety, and depression) using well-established questionnaires and collected magnetic resonance imaging (MRI) data (i.e., high-resolution T1 structural images and resting-state functional images) from 450 healthy subjects. We observed that, as compared to males, females exhibited lower pain threshold and tolerance. Notably, sex differences in pain sensitivity were mediated by pain-related fear and anxiety. Specifically, pain-related fear and anxiety were the complementary mediators of the relationship between sex and pain threshold, and they were the indirect-only mediators of the relationship between sex and pain tolerance. Besides, structural MRI data revealed that the amygdala subnuclei (i.e., the lateral and basal nuclei in the left hemisphere) volumes were the complementary mediators of the relationship between sex and pain-related fear, which further influenced pain sensitivity. Altogether, our results provided a comprehensive picture of how negative emotions (especially pain-related negative emotions) and related brain structures (especially the amygdala) contribute to sex differences in pain sensitivity. These results deepen our understanding of the neuropsychological underpinnings of sex differences in pain sensitivity, which is important to tailor a personalized method for treating pain according to sex and the level of pain-related negative emotions for patients with painful conditions., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The dynamic nature of resting-state functional magnetic resonance imaging (fMRI) brain activity and connectivity has drawn great interest in the past decade. Specific temporal properties of fMRI brain dynamics, including metrics such as occurrence rate and transitions, have been associated with cognition and behaviors, indicating the existence of mechanism distruption in neuropsychiatric disorders. The development of new methods to manipulate fMRI brain dynamics will advance our understanding of these pathophysiological mechanisms from native observation to experimental mechanistic manipulation. In the present study, we applied repeated transcranial direct current stimulation (tDCS) to the right dorsolateral prefrontal cortex (rDLPFC) and the left orbitofrontal cortex (lOFC), during multiple simultaneous tDCS-fMRI sessions from 81 healthy participants to assess the modulatory effects of stimulating target brain regions on fMRI brain dynamics. Using the rDLPFC and the lOFC as seeds, respectively, we first identified two reoccurring co-activation patterns (CAPs) and calculated their temporal properties (e.g., occurrence rate and transitions) before administering tDCS. The spatial maps of CAPs were associated with different cognitive and disease domains using meta-analytical decoding analysis. We then investigated how active tDCS compared to sham tDCS in the modulation of the occurrence rates of these different CAPs and perturbations of transitions between CAPs. We found that by enhancing neuronal excitability of the rDLPFC and the lOFC, the occurrence rate of one CAP was significantly decreased while that of another CAP was significantly increased during the first 6 min of stimulation. Furthermore, these tDCS-associated changes persisted over subsequent testing sessions (both during and before/after tDCS) across three consecutive days. Active tDCS could perturb transitions between CAPs and a non-CAP state (when the rDLPFC and the lOFC were not activated), but not the transitions within CAPs. These results demonstrate the feasibility of modulating fMRI brain dynamics, and open new possibilities for discovering stimulation targets and dynamic connectivity patterns that can ensure the propagation of tDCS-induced neuronal excitability, which may facilitate the development of new treatments for disorders with altered dynamics., Competing Interests: Declaration of Competing Interests JAC is a scientific advisor for Apex Neuroscience. J.K has a disclosure to report (holding equity in a startup company (MNT) and a patent to develop new peripheral neuromodulation tool), but declare no conflict of interest. All other authors declare no conflict of interest., (Copyright © 2021. Published by Elsevier Inc.)

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

Acupuncture and imagery interventions for pain management have a long history. The present study comparatively investigated whether acupuncture and video-guided acupuncture imagery treatment (VGAIT, watching a video of acupuncture on the participant's own body while imagining it being applied) could modulate brain regional connectivity to produce analgesic effects. The study also examined whether pre-intervention brain functional and structural features could be used to predict the magnitude of analgesic effects. Twenty-four healthy participants were recruited and received four different interventions (real acupuncture, sham acupuncture, VGAIT, and VGAIT control) in random order using a cross-over design. Pain thresholds and magnetic resonance imaging (MRI) data were collected before and after each intervention. We first compared the modulatory effects of real acupuncture and VGAIT on intra- and inter-regional intrinsic brain connectivity and found that real acupuncture decreased regional homogeneity (ReHo) and functional connectivity (FC) in sensorimotor areas, whereas VGAIT increased ReHo in basal ganglia (BG) (i.e., putamen) and FC between the BG subcortical network and default mode network. The altered ReHo and FC were associated with changes in pain threshold after real acupuncture and VGAIT, respectively. A multimodality fusion approach with pre-intervention ReHo and gray matter volume (GMV) as features was used to explore the brain profiles underlying individual variability of pain threshold changes by real acupuncture and VGAIT. Variability in acupuncture responses was associated with ReHo and GMV in BG, whereas VGAIT responses were associated with ReHo and GMV in the anterior insula. These results suggest that, through different pathways, both real acupuncture and VGAIT can modulate brain systems to produce analgesic effects., Competing Interests: Declaration of Competing Interest Jian Kong has a disclosure to report (holding equity in a startup company, MNT, and pending patents to develop new neuromodulation tools) but declares no conflict of interest. All other authors declare no conflicts of interest., (Copyright © 2020. Published by Elsevier Inc.)

Background: Previous studies have found widespread pain processing alterations in the brain in chronic low back pain (cLBP) patients. We aimed to (1) identify brain regions showing altered amplitude of low-frequency fluctuations (ALFF) using MRI and use these regions to discriminate cLBP patients from healthy controls (HCs) and (2) identify brain regions that are sensitive to cLBP pain intensity changes., Methods: We compared ALFF differences by MRI between cLBP subjects (90) and HCs (74), conducted a discriminative analysis to validate the results, and explored structural changes in key brain regions of cLBP. We also compared ALFF changes in cLBP patients after pain-exacerbating manoeuvres., Results: ALFF was increased in the post-/precentral gyrus (PoG/PrG), paracentral lobule (PCL)/supplementary motor area (SMA), and anterior cingulate cortex (ACC), and grey matter volume was increased in the left ACC in cLBP patients. PCL/SMA ALFF reliably discriminated cLBP patients from HCs in an independent cohort. cLBP patients showed increased ALFF in the insula, amygdala, hippocampal/parahippocampal gyrus, and thalamus and decreased ALFF in the default mode network (DMN) when their spontaneous low back pain intensity increased after the pain-exacerbating manoeuvre., Conclusions: Brain low-frequency oscillations in the PCL, SMA, PoG, PrG, and ACC may be associated with the neuropathology of cLBP. Low-frequency oscillations in the insula, amygdala, hippocampal/parahippocampal gyrus, thalamus, and DMN are sensitive to manoeuvre-induced spontaneous back pain intensity changes., (Copyright © 2019 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Simultaneous collection of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has become increasingly popular in neuroscientific studies, because it can provide neural information with both high spatial and temporal resolution. In order to maximally utilize the information contained in simultaneous EEG-fMRI recording, many sophisticated multimodal data-mining methods, such as joint ICA, have been developed. However, these methods normally deal with data recorded in one experimental condition, and they cannot effectively extract information on activities that are distinct in two conditions. In this paper, a new data decomposition method called joint common spatial pattern (jCSP) is proposed. Compared with previous methods, the jCSP method exploits inter-conditional difference in the strength of brain source activities to achieve source separation, and is able to uncover the source activities with the strongest discriminative power. A group analysis based on clustering is further proposed to reveal distinctive jCSP patterns at group level. We applied joint CSP to a simultaneous EEG-fMRI dataset collected from 21 subjects under two different resting-state conditions (eyes-closed and eyes-open). Results show a distinct dynamic pattern shared by EEG alpha power and fMRI signal during eyes-open resting-state.

Background: Both attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental disorders with a high prevalence. They are often comorbid and both exhibit abnormalities in sustained attention, yet common and distinct neural patterns of ASD and ADHD remain unidentified.AimsTo investigate shared and distinct functional connectivity patterns in a relatively large sample of boys (7- to 15-year-olds) with ADHD, ASD and typical development matched by age, gender and IQ.Method: We applied machine learning techniques to investigate patterns of surface-based brain resting-state connectivity in 86 boys with ASD, 83 boys with ADHD and 125 boys with typical development.Results: We observed increased functional connectivity within the limbic and somatomotor networks in boys with ASD compared with boys with typical development. We also observed increased functional connectivity within the limbic, visual, default mode, somatomotor, dorsal attention, frontoparietal and ventral attention networks in boys with ADHD compared with boys with ASD. In addition, using a machine learning approach, we were able to discriminate typical development from ASD, typical development from ADHD and ASD from ADHD with accuracy rates of 76.3%, 84.1%, and 79.3%, respectively.Conclusions: Our results may shed new light on the underlying mechanisms of ASD and ADHD and facilitate the development of new diagnostic methods for these disorders.Declaration of interestJ.K. holds equity in a startup company, MNT. [ABSTRACT FROM AUTHOR]